From b0c14f79597a50a8a181e6fbe204cf59a6ac1316 Mon Sep 17 00:00:00 2001 From: zalgo3 Date: Fri, 8 Nov 2024 02:05:19 +0900 Subject: [PATCH 1/2] fix broken codes --- benchmarks/benchmark.py | 1 + examples/PGM_experiment.ipynb | 6820 ----------------- .../PGM_experiment_with_various_a_b.ipynb | 92 +- examples/cameraman.ipynb | 139 +- pyproject.toml | 14 + 5 files changed, 169 insertions(+), 6897 deletions(-) delete mode 100644 examples/PGM_experiment.ipynb diff --git a/benchmarks/benchmark.py b/benchmarks/benchmark.py index 9a0d19d..7a4b423 100644 --- a/benchmarks/benchmark.py +++ b/benchmarks/benchmark.py @@ -12,6 +12,7 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +import scienceplots # noqa: F401 from joblib import Parallel, delayed from mpl_toolkits.mplot3d import Axes3D from tqdm.auto import tqdm diff --git a/examples/PGM_experiment.ipynb b/examples/PGM_experiment.ipynb deleted file mode 100644 index 2088e2a..0000000 --- a/examples/PGM_experiment.ipynb +++ /dev/null @@ -1,6820 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "09856ef5-7f36-4140-b02f-dcd51bf36e9a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-12T15:51:08.025057Z", - "iopub.status.busy": "2023-04-12T15:51:08.024821Z", - "iopub.status.idle": "2023-04-12T15:51:08.064293Z", - "shell.execute_reply": "2023-04-12T15:51:08.063184Z", - "shell.execute_reply.started": "2023-04-12T15:51:08.025035Z" - } - }, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "%load_ext autoreload\n", - "%load_ext line_profiler\n", - "%autoreload 2\n", - "\n", - "\n", - "sys.path.append(\"..\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "global-parcel", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T10:04:29.604016Z", - "start_time": "2022-05-25T10:04:28.158022Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T15:51:08.066752Z", - "iopub.status.busy": "2023-04-12T15:51:08.066403Z", - "iopub.status.idle": "2023-04-12T15:51:09.325023Z", - "shell.execute_reply": "2023-04-12T15:51:09.324234Z", - "shell.execute_reply.started": "2023-04-12T15:51:08.066729Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from joblib import Parallel, delayed\n", - "from matplotlib import rc\n", - "from tqdm.notebook import tqdm\n", - "\n", - "from zfista.problems import FDS, FDS_CONSTRAINED, JOS1, JOS1_L1, SD" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "27ff20ce-909e-4357-9ef0-d73c47060f58", - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-12T15:51:09.326748Z", - "iopub.status.busy": "2023-04-12T15:51:09.326287Z", - "iopub.status.idle": "2023-04-12T15:51:09.361729Z", - "shell.execute_reply": "2023-04-12T15:51:09.360778Z", - "shell.execute_reply.started": "2023-04-12T15:51:09.326725Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "fig_path = os.path.abspath(os.path.join(\"./figs\"))\n", - "data_path = os.path.abspath(os.path.join(\"./data\"))\n", - "os.makedirs(fig_path, exist_ok=True)\n", - "os.makedirs(data_path, exist_ok=True)\n", - "rc(\"text\", usetex=True)\n", - "plt.style.use([\"science\", \"bright\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "outside-recommendation", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T10:04:30.113483Z", - "start_time": "2022-05-25T10:04:30.075204Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T15:51:09.363251Z", - "iopub.status.busy": "2023-04-12T15:51:09.362931Z", - "iopub.status.idle": "2023-04-12T15:51:09.408194Z", - "shell.execute_reply": "2023-04-12T15:51:09.407233Z", - "shell.execute_reply.started": "2023-04-12T15:51:09.363226Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def generate_start_points(low, high, n_dims, n_samples=1000):\n", - " return [\n", - " np.random.uniform(low=low, high=high, size=n_dims) for _ in range(n_samples)\n", - " ]\n", - "\n", - "\n", - "def run(\n", - " problem,\n", - " start_points,\n", - " tol=1e-5,\n", - " nesterov=False,\n", - " nesterov_ratio=(0, 0.25),\n", - " n_jobs=-1,\n", - " verbose=False,\n", - "):\n", - " results = Parallel(n_jobs=n_jobs, verbose=10)(\n", - " delayed(problem.minimize_proximal_gradient)(\n", - " x0,\n", - " tol=tol,\n", - " nesterov=nesterov,\n", - " nesterov_ratio=nesterov_ratio,\n", - " return_all=True,\n", - " verbose=verbose,\n", - " )\n", - " for x0 in start_points\n", - " )\n", - " return results\n", - "\n", - "\n", - "def show_Pareto_front(\n", - " problem,\n", - " results,\n", - " results_nesterov,\n", - " step=None,\n", - " s=15,\n", - " alpha=0.75,\n", - " fname=None,\n", - " elev=15,\n", - " azim=130,\n", - " linewidths=0.1,\n", - "):\n", - " labels = [\n", - " \"Starting points\",\n", - " f\"PGM ($k={step}$)\",\n", - " f\"Acc-PGM ($k={step}$)\",\n", - " \"PGM (Solutions)\",\n", - " \"Acc-PGM (Solutions)\",\n", - " ]\n", - " fig = plt.figure(figsize=(7.5, 7.5), dpi=100)\n", - " if problem.m_dims == 2:\n", - " ax = fig.add_subplot(111)\n", - " fig.subplots_adjust(left=0, right=1, bottom=0, top=1)\n", - " if problem.m_dims == 3:\n", - " ax = fig.add_subplot(111, projection=\"3d\", clip_on=True)\n", - " ax.view_init(elev=elev, azim=azim)\n", - " fig.subplots_adjust(left=0, right=1, bottom=0, top=0.6)\n", - " for _i, (result, result_acc) in tqdm(enumerate(zip(results, results_nesterov))):\n", - " allvecs = result.allvecs\n", - " allvecs_acc = result_acc.allvecs\n", - " x0 = allvecs[0]\n", - " F_of_x0 = problem.f(x0) + problem.g(x0)\n", - " if step is not None:\n", - " xk = allvecs[step]\n", - " xk_acc = allvecs_acc[step]\n", - " F_of_xk = problem.f(xk) + problem.g(xk)\n", - " F_of_xk_acc = problem.f(xk_acc) + problem.g(xk_acc)\n", - " F_pareto = result.fun\n", - " F_pareto_acc = result_acc.fun\n", - " if problem.m_dims == 2:\n", - " ax.scatter(\n", - " *F_of_x0,\n", - " color=\"#8e44ad\",\n", - " marker=\"x\",\n", - " label=labels[0],\n", - " s=s,\n", - " alpha=alpha,\n", - " linewidths=linewidths,\n", - " )\n", - " if step is not None:\n", - " ax.scatter(\n", - " *F_of_xk,\n", - " color=\"#2980b9\",\n", - " marker=\"<\",\n", - " label=labels[1],\n", - " s=s,\n", - " alpha=alpha,\n", - " linewidths=linewidths,\n", - " )\n", - " ax.scatter(\n", - " *F_of_xk_acc,\n", - " facecolors=\"none\",\n", - " edgecolor=\"#e74c3c\",\n", - " marker=\"*\",\n", - " label=labels[2],\n", - " s=s,\n", - " alpha=alpha,\n", - " linewidths=linewidths,\n", - " )\n", - " ax.scatter(\n", - " *F_pareto,\n", - " color=\"#2980b9\",\n", - " marker=\".\",\n", - " label=labels[3],\n", - " s=s,\n", - " alpha=alpha,\n", - " linewidths=linewidths,\n", - " )\n", - " ax.scatter(\n", - " *F_pareto_acc,\n", - " facecolors=\"none\",\n", - " edgecolors=\"#e74c3c\",\n", - " marker=\"D\",\n", - " label=labels[4],\n", - " s=s,\n", - " alpha=alpha,\n", - " linewidths=linewidths,\n", - " )\n", - " ax.set_xlabel(r\"$F_1$\", fontsize=15)\n", - " ax.set_ylabel(r\"$F_2$\", fontsize=15)\n", - " if problem.m_dims == 3:\n", - " ax.set_zlabel(r\"$F_3$\", fontsize=15)\n", - " ax.legend(labels[-2:], bbox_transform=ax.transData)\n", - " elif step is None:\n", - " ax.legend([labels[0]] + labels[-2:])\n", - " else:\n", - " ax.legend(labels)\n", - " if fname is not None:\n", - " plt.savefig(fig_path + \"/\" + fname, bbox_inches=\"tight\")\n", - "\n", - "\n", - "def get_stats(results):\n", - " nits = [result.nit for result in results]\n", - " nit_internals = [result.nit_internal for result in results]\n", - " execution_times = [result.execution_time for result in results]\n", - " stats = {\n", - " \"nit\": {\"mean\": np.mean(nits), \"std\": np.std(nits), \"max\": np.max(nits)},\n", - " \"nit_internal\": {\n", - " \"mean\": np.mean(nit_internals),\n", - " \"std\": np.std(nit_internals),\n", - " \"max\": np.max(nit_internals),\n", - " },\n", - " \"execusion_time\": {\n", - " \"mean\": np.mean(execution_times),\n", - " \"std\": np.std(execution_times),\n", - " \"max\": np.max(execution_times),\n", - " },\n", - " }\n", - " return stats" - ] - }, - { - "cell_type": "markdown", - "id": "legal-louisiana", - "metadata": {}, - "source": [ - "## JOS1\n", - "Minimize\n", - "$$\n", - "f_1(x) = \\frac{1}{n} \\| x \\|_2^2, \\quad f_2(x) = \\frac{1}{n} \\| x - 2\\|_2^2\n", - "$$\n", - "subject to $x \\in \\mathbf{R^n}$." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "running-indicator", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T10:04:30.147882Z", - "start_time": "2022-05-25T10:04:30.116810Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T15:51:09.410011Z", - "iopub.status.busy": "2023-04-12T15:51:09.409494Z", - "iopub.status.idle": "2023-04-12T15:51:09.450198Z", - "shell.execute_reply": "2023-04-12T15:51:09.449669Z", - "shell.execute_reply.started": "2023-04-12T15:51:09.409987Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "n_dims = 50\n", - "problem_JOS1 = JOS1(n_dims=n_dims)\n", - "start_points_JOS1 = generate_start_points(low=-2, high=4, n_dims=n_dims)" - ] - }, - { - "cell_type": "markdown", - "id": "brown-interval", - "metadata": {}, - "source": [ - "### Proximal Gradient Method" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "intellectual-latter", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T10:36:59.451401Z", - "start_time": "2022-05-25T10:04:31.488101Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T15:51:09.451626Z", - "iopub.status.busy": "2023-04-12T15:51:09.451157Z", - "iopub.status.idle": "2023-04-12T16:06:48.806503Z", - "shell.execute_reply": "2023-04-12T16:06:48.805536Z", - "shell.execute_reply.started": "2023-04-12T15:51:09.451601Z" - }, - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 9.2s\n", - "[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 17.0s\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 18.9s\n", - "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 31.8s\n", - "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 39.7s\n", - 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Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. 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Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 632 tasks | elapsed: 9.9min\n", - "[Parallel(n_jobs=-1)]: Done 669 tasks | elapsed: 10.5min\n", - "[Parallel(n_jobs=-1)]: Done 706 tasks | elapsed: 11.1min\n", - "[Parallel(n_jobs=-1)]: Done 745 tasks | elapsed: 11.7min\n", - "[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 12.3min\n", - "[Parallel(n_jobs=-1)]: Done 825 tasks | elapsed: 13.0min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 866 tasks | elapsed: 13.6min\n", - "[Parallel(n_jobs=-1)]: Done 909 tasks | elapsed: 14.2min\n", - "[Parallel(n_jobs=-1)]: Done 952 tasks | elapsed: 14.9min\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 15.7min finished\n" - ] - } - ], - "source": [ - "results_JOS1 = run(problem_JOS1, start_points_JOS1)" - ] - }, - { - "cell_type": "markdown", - "id": "patient-vector", - "metadata": {}, - "source": [ - "### Accelerated Proximal Gradient Method" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "miniature-indianapolis", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T10:44:27.233638Z", - "start_time": "2022-05-25T10:36:59.460904Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:06:48.810440Z", - "iopub.status.busy": "2023-04-12T16:06:48.810164Z", - "iopub.status.idle": "2023-04-12T16:11:11.268333Z", - "shell.execute_reply": "2023-04-12T16:11:11.267622Z", - "shell.execute_reply.started": "2023-04-12T16:06:48.810421Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 2.0s\n", - "[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 4.0s\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 4.6s\n", - "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 8.1s\n", - "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 10.3s\n", - "[Parallel(n_jobs=-1)]: Done 45 tasks | elapsed: 12.7s\n", - "[Parallel(n_jobs=-1)]: Done 56 tasks | elapsed: 15.2s\n", - "[Parallel(n_jobs=-1)]: Done 69 tasks | elapsed: 19.1s\n", - "[Parallel(n_jobs=-1)]: Done 82 tasks | elapsed: 23.0s\n", - "[Parallel(n_jobs=-1)]: Done 97 tasks | elapsed: 26.8s\n", - "[Parallel(n_jobs=-1)]: Done 112 tasks | elapsed: 30.3s\n", - "[Parallel(n_jobs=-1)]: Done 129 tasks | elapsed: 35.2s\n", - "[Parallel(n_jobs=-1)]: Done 146 tasks 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tasks | elapsed: 2.5min\n", - "[Parallel(n_jobs=-1)]: Done 597 tasks | elapsed: 2.6min\n", - "[Parallel(n_jobs=-1)]: Done 632 tasks | elapsed: 2.8min\n", - "[Parallel(n_jobs=-1)]: Done 669 tasks | elapsed: 2.9min\n", - "[Parallel(n_jobs=-1)]: Done 706 tasks | elapsed: 3.1min\n", - "[Parallel(n_jobs=-1)]: Done 745 tasks | elapsed: 3.3min\n", - "[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 3.4min\n", - "[Parallel(n_jobs=-1)]: Done 825 tasks | elapsed: 3.6min\n", - "[Parallel(n_jobs=-1)]: Done 866 tasks | elapsed: 3.8min\n", - "[Parallel(n_jobs=-1)]: Done 909 tasks | elapsed: 4.0min\n", - "[Parallel(n_jobs=-1)]: Done 952 tasks | elapsed: 4.2min\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 4.4min finished\n" - ] - } - ], - "source": [ - "results_acc_JOS1 = run(problem_JOS1, start_points_JOS1, nesterov=True)" - ] - }, - { - "cell_type": "markdown", - "id": "lined-lancaster", - "metadata": {}, - "source": [ - "### Complexity" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "former-bishop", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T10:44:27.385223Z", - "start_time": "2022-05-25T10:44:27.237434Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:11:11.270075Z", - "iopub.status.busy": "2023-04-12T16:11:11.269331Z", - "iopub.status.idle": "2023-04-12T16:11:11.314918Z", - "shell.execute_reply": "2023-04-12T16:11:11.314249Z", - "shell.execute_reply.started": "2023-04-12T16:11:11.270048Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Acc-PGM': {'execusion_time': {'max': 2.7815802097320557,\n", - " 'mean': 2.0926460132598876,\n", - " 'std': 0.19141540397360826},\n", - " 'nit': {'max': 65, 'mean': 65.0, 'std': 0.0},\n", - " 'nit_internal': {'max': 1170,\n", - " 'mean': 952.28,\n", - " 'std': 64.73085508472757}},\n", - " 'PGM': {'execusion_time': {'max': 9.976227045059204,\n", - " 'mean': 7.481149582386017,\n", - " 'std': 0.6977661604192146},\n", - " 'nit': {'max': 236, 'mean': 231.746, 'std': 1.0542694152824503},\n", - " 'nit_internal': {'max': 4242,\n", - " 'mean': 3401.806,\n", - " 'std': 237.59770277508997}}}\n" - ] - } - ], - "source": [ - "import pprint\n", - "\n", - "stats_JOS1 = {\"PGM\": get_stats(results_JOS1), \"Acc-PGM\": get_stats(results_acc_JOS1)}\n", - "pprint.pprint(stats_JOS1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "organizational-option", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T10:45:54.340219Z", - "start_time": "2022-05-25T10:44:27.390608Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:11:11.316689Z", - "iopub.status.busy": "2023-04-12T16:11:11.316324Z", - "iopub.status.idle": "2023-04-12T16:12:01.167927Z", - "shell.execute_reply": "2023-04-12T16:12:01.167029Z", - "shell.execute_reply.started": "2023-04-12T16:11:11.316667Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": 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", 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c/1IdjtsvRbpJhMScuA+sFosFFosFZWVlYS1zNhRF9hgAgPPc2Qi3xDO1ksWPi6bj5e8W4mxLJ1Zs3IM3D5yKdLMIiSlxHVitVit4npcmBYeq6rk/2ORkMKlpcJ47E+mmeFUwJROvPbQYt84bC663NCEhxDdRs/LKZrPBaDSivr6+33Ge52GxWKDVasHz/JCFGTzR6/XSCg+e51FQUCB3swOiyB4T9YEVAFISlHjytjkA3IsLSl87gBtmjKaKWYQMIyoCqxg4B64nBtxFa8Vgy/M8jEZjQAV2zWYzKioqgm6rHBTZY+DyEliF7i50ffIRBGcPEvIXglFE/m3q6HGh2yngx5ZP8O4XZ/HEzbOkItuEkP4i/xsL9KtQ09fAggparbZfntRisXgsumAwGPrVbqysrMT69euDrucoF0X2GHQd3O/xe0JnJ+w/+Xd0f3EQAKDMyYPmF78Cm54RxhYOlqRS4DnDFXjr09N46i+f4/aju/HU8jm4Om9URNtFSDSKisA6FKvVCo1G0++YRqORCtUOFZAHXkMsHGGxWHw6J9TY7NFDDl41V/8a3UcakPn0r8Go1XD8/DE0/fwxaDY8D0ahCHNL+2MYBrfOG4eCKZl44o2DOHS6hQIrIR6EJLBu27YNmZmZ0vYJV155ZUDXEUt9DeStnFhfPM+juLgYWq0WDocDer0+KgKrInuMey5rWyvYlFTpuLPJjvZdf0HavUYkzFsAAOB+8jTspQ+jfeebSL55eaSa3M/Y9ERU36ODOMv1lb3HMH9iBuZP5CLZLEKihuyBddOmTViz5vI2E7t27cL27dtxxx13yHaPoQLuQFqtFk1NTT69VizCIgrV3ldAnylX58/2C6ztO98EWBZJRbdKx9Sz5iHxhpvQ8odqJF3/Lb/3KAoVhmHAwL1K6+2Dp1G58wsYr8nF2m/mQa2I68kmZIQT97oShbwIy/bt21FcXNzv2NKlS6X6iv7iOG5Q79Rut/s8KyAQYmGFUBZjUWSNBgC4Lpzrd7zj71YkXnMD2LT0fsdTVz8AoaUZ7R/sClmbAqVSsHjlgUJ8/7qp+N0/j+KuTR/i8FnaJZbEL19ihKyBVRAEj/sqBboL4lDFcEMxbSoc1a1ETJp72xRXy+UA5GptQc/xI0iYP3iPc+XY8UjIX4hLb22PmhoDfSlZFg9dq8Wf1ixEt9OFF3dHvsgMIaEWtupWer1+0FYJzz77LHJzc32+Rt+P+QNH8cW5qKHssYYDk5gEKJVwtTZLx7oPuWcBqGbN83hO0k3L0dP4JXqOHwlLGwMxe1w6LCWL8PjN7h1pdzdewIkmWhJLRh5ZA2tGRgZyc3Px7LPPAgAee+wxlJeXY9Mm7ztQWq1WaVVUeXk5LBaL9L2amhqUlZXBYrHAbDYHNIfVF+EodC1iGAZsajqElsuBtevQp2C5TCiG2LIlYUEhmKQkdO79R8jbF4wEpQKpCUq4BAH/bf0St2/cA4vtRFT2tAkJhrdC1yHZmmXbtm3S3jH19fXIycmR+xayC8fWLH2de/geJOgWIn3NIwAA+0/+HUxKKjIf/8WQ5zg2/Cd6zpzCqOeqw9XMoLR29qDinS+w7aOTuG76KDx52xxkp9JWMCS+hGVrliNHjqCsrAz5+fkwGo0B51fDLZw9VgBgU9Ph6tNj7Tl+BKrcqV7PSVj0DfQ0HILzwvlQN08WqQlK/HzZHDx/15U4cLIZ61/7NNJNIkQ23nqssk+3Ki4uhsFgwIYNGwAA1dXu3tUNN9wg961kJQ5ehQublg6h1T14JXR1wuVoAts7DWso6t6Bra6D+5F0bej2RJfbDTNG48qJHFo73XtrHb3QBk2KGumJtCSWxC5xSmbfaZoiWXusu3btglarlYIqABiNxpDv4R2LmNQ0qcfqPO+edqXIHu31HEWmBorxE9H92YGQt09umhQ1JmuSAQBPvHEQt7+wG7v5CxFuFSGhIWtg5Xked91116DjA5elRqPwpwLSpFkBzvPu5a2KYXqsAKCeMx9dBz8OadtC7ZmVVyAnKwVrXq3HL/56CO3dzkg3iRC/hW2XVr1eHzM51YHCOY8V6E0F9M5jdYmBNct7jxUAVLPnoecYD1dr7E7CH5eRiE335uPxm2bCYjsB46v1NGuAxBxv81hlzbHm5uYiNzcXmzZtgl6vR1NTE+rr66UZAuQyJi0drtYWCIIA57kzYNIzwCQMP2KunjEXEAR0N36BhPnRUV82ECzD4J6Fk7EkLwtnmjvAMAxaOrqRoFLQklgS82T/CV65ciXWrFkDQRCg1WqxZs0aZGREtuSdLyIxKwA93RA6O+A8dxaKUcP3VgFAMX4ioFKj51j0LhTwh3ZUChZrswAAP33zM9z9uw9x+GxrhFtFyPDClgroKzc3NyYCqij8qQD3slahpRnO82d9yq8CAKNQQDlpCnqONoayeRHx4JJcdHS7UFy1Fy/tOQoXpQdIFAvbklbiOya1t15Aawtc588OOyOgL2VOXlQvbQ3UnPHuJbGrCyfhmZ1fYs2r9bQFN4lJXnOsR44cQUVFhcfCKoHgOA7l5eWyXCvWsanuClaulmY47RfAZmb5fK5qSi469/wNgssFhvX8t9HV0oy27f+H7iONSChYhORb7pDtfQylRJUCZd+agetnZOPLM61QsAycLgEsg5hoPyHAMIE1NzcXJpNJtptFc/EUMccayjqsfbGpl1MB6Op0F2bxkXKyFkJ7O5xnT0PpobaA0NODpidL0XOch2rGHLSYf4ke/jDSHymLmeB0VY4GV+W4p+mZP+Bx8FQznrx1NkbRklgSJcS6rAGtvFqwYEFIGhVtwr3ySixYLXR3QejuAqPyfRWScrK7WpjzxDGPgbWt5lV0NxyCpuJ/oZ4xB+3vvo2Lz/0C6it0SLruRnkeIIxmjU3D5rqvsPyF3Xjy1tnQz/ItH01IKIVt5RXxg9IdSIX2dsDlAlRqn09ls0YBrMLjFtpCdxfa3tyG5FvugHqGe+vqpBtuQuI3bkDLpt/AdSn2yvhdP2M03li7BPmTM/GDrR9j/esH0NXjinSzCBnSsDlWs9ks28fHrKwsPProo7JcK9YxLAsoVXBdck8tYvwIrIxCAXZUtsfA2rH3AwjNF5F8U/+/omkPPIxza+5E+3tvI+UW+bbJCRdNihq/WjUfOz45hb1HLkCliI2UBhmZhs2xFhUVyXazWFjaGk6MWg2hrU36tz8U2WM8B9Z334Fq1lwoJ+UMen3i4mtx6c/bkHzz7UMOekUzhmGwfP54LJ/vTn/sOnQWe4/Y8SP9NCSpIruDLSF9DZtjXbp0aTjaEXHhHrwCAEalgnCpTfq3PxQettAWXC50fX4AKbff6fGc5FtWwP74D9B96FOoZ18RWKOjiONSFyy2E9jdeB4bVszDvAmxM2+axD5vg1ex120JkXAvEAAAqNRwtbVK//aHpx5rz/EjENpah9zeRTVnPlhOg469HwTU3GizUjcRlpJFSElQ4u7f/Qu/ea8B3U7KvZLwoAUCUYpRq4PosY6B6/w5CM7LlaG6Pz8AsAqoZsz2fD+WRcLCq9G59x9xU/QkLzsVf/zeVXjoWi3e/eIsLSggUWHYwHr06FHs378/DE0ZeRj15R6rP4NXQG+JQZcTrqbLNU27PjsApXYqWC9zYhMWXgPnqRNwnoifnVRVChb/dl0ethgXIVGlQMO5Vryy9xgtiSURM2xgrampQXFxsfT1xYsXUV1djXfffTekDRsRlGoIYirAz8ErcbeBvumAnqMNUE2b6fU89bwFAKtA16exXdPVE7Eq1l7ejg3vfIHvvbIPJx3tEW4VGYmGDawcx+Hw4cPS1wzDwGg09tummgSGUavhCjQVkJUN4PLuA4IgwHnG80qsvtjEJCi1U9H1eeztQuCrexZOxov3FeCrpnbc/sJuvLb/ZNykPkhsGDawGo1Gafvq6upq6HQ6TJs2DXa7PeSNi3fu6VaBpQKYlFSAZS/vm9XaAqH9EhSjxw17rnrWPHQfiu+N/RbmavD6Q4tRNHsMNrz9BeyXuiLdJDKCeJxuxbIs8vPzodfrUVRUhFWrVmH79u3geR4NDQ3hbmNYRGS6lfLydCt/ZwUwDAMmOUXK0TrPnAIAKMYMH1hVs+bi0p8tcDbZociM37nFaYkqPL18Ls5c34GslAQ0d3TDdtyB66ZnR7ppJA74Pd2qoqICW7duhVarxcaNG5GTk4OysjLwPI/33nsv5A2OhIhMt1Krgd6PqP4uEAAANiUVQu/KLefZ0wAAxeixw992xlwAQPfhz72+TnC50PlRHdpe34Ker7/yu33RYky6uy7DGx9/jYc3f4TH3/gULR3dEW4ViXV+T7dat24dcnNzYTQasXXrVtjtdmzduhUFBQUoLy+HRqPBt771rZA3PN71Dab+5lgBdzrA1Xq5x8okJoFJH36SPJs9GkxSMnqOHx3yNYIgoPn5SjT954/Q8tILOP/wvej45/t+tzGa3HPVZPxi+RzUfnYGt2/cgw+PUDqLhIbP81gXLFiAdevWYefOnbDb7di4cWMo2zUi9MurKv0PrGxKqpSjdZ49BcWYcT7VdWAYBsrJOej56uiQr2nf+We0176F9EfKMGbLO0i8+jo4nvkZuo/EbiqIYRisuHICXl+7BJMyk7Dm1XqcaIq9ojQk+gW8QKC4uBh33nknNm3ahKNHj8rYpJFDCqwqdUCFbpi+qYAzp31KA4iUE6fA+ZXnuayCswetW15B4nVFSL7xVjAJCcj44U+gGDsBzVW/ivkR9glcEn5/XwFevD8fEzOT4RIE2meLyCrgwLphwwYIgoDS0lLk5eUhKyuLAq2/elMBgaQBAHePVRq8OudfYFVMmoKeE8c8BsnOPR/Ade4MUm6/SzrGqFRIX/MIuj/dj66P6wNqbzRhGQYFU9wDd298/DVWmvfgt39rpCWxRBYBB1a9Xi/lXxsaGlBeXj4o0D7++ONobm6Ws71+s1gssFqt0uBbNBF7rIEMXAG9PdbeHKurtVXaR8sXykk5ENovwXXh3KDvtb/nrpClypve77g6fyGUk3Jw6e03AmpvtLp13jgYr8nFC3/jcc/v/wX+fFukm0RinCy1AnJzc1FSUiIF2vLycixduhT79u1Dbm5uxGYSOBwO1NXVQa/Xo7CwEBUVFRFpx1CknmoA+VWgd/Cqt8cqdHaASfJje5eJUwBg0ACW4OxB14GPkJC/ePD9GAZJNy1H594P4OyzlDbWqRQsHrl+Kv744FVo7ezBSvMefHGmJdLNIjEsJEVYSktLUVhYiJ07d6K2thbr1q0bNj1gs9mQn58/6DjP86isrITFYkFlZaVfK744jpOCaW1traz7d8lCHVyP1T3dyt27Etrb/do3SzFmHMCy0vxXUffhQxDaL0E9f/B7AQBJ198ICAI6//XPgNocza6YkAGLaTHWFU3H9NGpAEDTskhAAg6su3btQmFhIdauXeuxRyoOxuh0OlitVpjN5iGvZbFYALiD60DFxcUoLS2FwWCAwWCA0Wj0u61WqxUcx0XdZoaMKqH3v4GmAlIgdLRD6OgAerq9Fl8ZdK5CAZbTwGk/3+9418c2MMkpUE2b4fE8Ni0dqplz0Vm3J6A2R7sklQJ3XzUZDMNg75EL0P/yA7zx8dcxP2BHwmvYQtdDqa2txWOPPYYtW7Zg6dKlyMzMlD5yA0BdXZ30Wo7jvO4eYDAYPB4fmBPVarWwWq3S1xaLxWPe1GAwQKvVSl/r9XpoNBqYTCbU1tb69oBhIKUCAgysbIo7pyoGR396rADAakbBNTCwfvYJ1LPngVEM/aORULAIbVtf7d0EMbC2x4LZ49Jx/YxsrH/9U+w6dBY/u3U2NCnx+7xEPgEH1ry8PKxcuRIrV67ExYsXsWXLFlitVmzcuBH5+fnSR/C1a9fCZDIhLy/P73tYrdZBAVmj0cBms0Gn0w0ZkEVVVVVwOBwoLS0Fx3HRN3gV5KwAJsX9cVUcgPI3sCqyBgdW58njSLzmeq/nJRQsRusrVej67BMkzC/w656xJD1RhQ0r5mHpzNH42ZufYfkLu2H+jg6zx6VHumkkygWcCli1ahWeeeYZ7N+/HxkZGdLgVUNDA7Zs2YKcnBwAQENDQ0Af3wEMmU/1tQDMqlWrpF6u2WxGTU1NQO0ImWBzrMkpAABngIGV1WTBab88CCV0dcJ59jQU4yd5PU85RQsmJRXdhz7zs8WxqWjWGLy+dgmWzhyNKVnJAECpAeJVwD3WjIwMrFu3Drt27fL6ulB89PZ1AIvjOKlXq9frvb5WLMIiCkcxFqZ3NkDAPdbe6VVi6UB/ZgUAgEIzCp19eqw9p04CggDlhMne78uyUE2dMWytgXiSnZqAn93q3pnhq6ZL+P6f9uOJm2eiMCd+i9gQz8TiKyJPRVgCDqyiUG42yHHcoN6p3W4PySCUWIQlnBi1e/Aq8BzrwFRAon/na0bB5WiC0NMDRqmE86S70IpyovceKwCops1E+7tv+9ni+MAyDDKSVPjuy/tw36Ip+OHSqUhQ0i6xI8XATlffDpkoqve8GqqXWVAgf15P7LH2/UsUclKONcBZAcnuj6WBpgIUmlEAIG3v0nPyOJiUVDDp3LDnqqbPhst+Qbr3SDKBS8JL9xfg0aLp+L+64zBU7cWXNO91xNm8eTOWLVsWG7u09v2Y33dkH3DPEigoKAhpjzWcZQOllVeBpgIUSjApqdL2LExisl/ns1nuwCrOKug5cRzKiZN9qlsgbgHT/eXISQf0xTIMHliSA0vJYnBJKiSoqMc60kT9Lq3iklMAKC8vl+a1Au49t8rKymCxWEI6ABWJHqsUUAMcvAIANj0DzjPuWqxMQoJf5yo0WQAAV+8AlvP011CMG/xD4vG+WdlgUlLRc+K4X/eMN9NGp+LVB67CFE0yLnX14NFtn+AILYkdEbz1WIPOscpBr9dDr9d7XHKq1Wql48NNrwpGZHKswaUCAIBN5+A8dRJQJ4BR+NdrYtIyAIVCCqyulovDbkYoncswUIybCOepE363OV6dbenEZ6easdK8Bz/ST8fdV00CG0DVMhIbxFxrzOVYwykiOVaVDIE1g3P/188ZAYB7dJ9JSoarw12TVGhpAZvm+xxN5fgJ6PmaAqsoJysF20yLcceCCXj67UNY82o9Tl3siHSzSIiEPMe6f/9+OS4TUZHMsSLAHCtwObAyCf4HVvd5iRA6OiAIAlytLX5VyHL3WAf/UI1kSSoFnvj2LGy6Jx9HL7Sh4RzVeY1XIc+xFhcXy3GZEYcJcoEA4E4FAP7PYZXakNgbWDt76w2k+tNjnQiX/TxcHe0B3TueLcnLwl8fuQbfmDoKgiCg+h9H0EQ7xY4YsgTWeFiFEpnBq+BmBQDuwSvA/6lWUhsSEiF0tkNocdfNZdP86bG6/1JTr9UzcW7rCUc7Xtx9FMt+uxvvfznypqfFq5CnAgLZViTaRGSX1iCLsAB9UgF+Lg4QMYlJEDo64Gp1z8P0t8cKAE7Ks3o1KTMZb6xdgrnj0/Hw5o/wnzsOoq2zJ9LNIkGK+ulWIxWjUCBJ/22oZ80L+BqXA2ugPdYECJ0dcPX2WP3JsTLpHJikpEE1Xclg2WkJ+O3qBfiv22bjLwdP442Pv450k0gIUWDtFZFZAQAy/n09VNppAZ8ffI7V3WMVxB6rH7MCGIYBm5EJV7MjoHuPNAzDwKCbiB0PL8GdBe5lw387fA6dPc4It4wEIurnsUaDSMxjlQMj9Vj9W3UlnZ+QCFfzRXePlWHA9FbM8hWbwcF10RHQvUeq8RnuP4LnWzvxH1s/xsTMJGxYMY/KEcYYmscaxy4PXgWTY22Hq7XZXSfAz0UGbDoF1kCNSk3AFuNCKFgWd236EOYPePS4aJfYeECBNcYxiUmAWh14jlWcbtXSAtaP/KqIzeDgar4Y0L0JMG10GrYYF+J7S3Lwm/casPHv0VWMnQSGAmuvSOVYg8UwDJK/vQIJQ2z+N+z5CYnuwavWFjB+zAgQuQOrI6B7Eze1gsUPl07DHx64CvcudO+ee/RCG1xxMI0xnlGO1QexmmMFgPQHvx/wuZcDa7Nfc1hFlAqQz5WTOABAa2cP7tr0IeaOz8BTy+dgbHpgaR4SWpRjJUO6nApo9mtGgIjN4CBcaoPQTauK5JKaoMSzK69Aw7lWLP/tbvz5k1NxsQhnJJElsGZkZMhxGRIBTGIS0N0FV7MDTEpgOVYAlGeV2TVTR+GNtUtw7fRRKHvtAH79XkOkm0T8IEsqYLh9r0j0Emu4Os+cRsLib/p9vjiP1nXRAUVWtpxNG/EyklR45o4rcMOM0Zia7Z4Gd6mrB8lqyuBFO1neIeqxxi5xNoHQfgmKTP83xhPn0VKeNXRunjMWANDV48Lq3/0LV07MQOmNM5CSQAE2WlGOtVeszgoIFpNweWCEzczy+3yWAmvYqBQM7rlqMt48cBorNu7BvmNNkW7SiBZTe15FSkSKsESBvgsLWE0AgTUxCVAn0JSrMGAYBsX5E/HaQ4sxOi0B979UB/MHNO81UrwVYaHPEiNc3x6rgvM/sALu+gJiERcSepM1yXj5u4V4ac9RTNa4lzILghAXVebiBQXWEa7vii02gBwr4N4tFk4qJBJOCpbBg1fnAnAH1dLtBzB1dCoevDoHSpY+iEYavQMjnNhjZdIyAi+4rVAALgqskeISgPFcEn7zXgPue7EORy/QLrGRNmxgPXr0aFzsaUU8E3OsgcwIkCgUEKjHGjEKlsF/LJ2GV75biAttXVhp3oMt+76KdLNGtGEDa01NTb89rS5evIjq6mq8++67IW0YCQ9G7Z7HGsjAlXQNhQKgqkwRp5ucie0PLcayK8bjbEtnpJszog0bWDmOw+HDh6WvGYaB0WiEw+EIZbvCbsROt1IoALU6oKlWEoWCcqxRIkWtxE9vnY3vX5cHAHh5zzG8dYB2eAiFoKZbGY1GbNq0CQBQXV0NnU6HadOmwW63y9/SCBqp060A99bZQaUCWJZSAVGGYRgIgoDPTzdj3fYD+LHlEzhol1hZ+T3dimVZ5OfnQ6/Xo6ioCKtWrcL27dvB8zwaGmjNcrxJu68EqplzAj6fUSgAJ22OF20YhsGGFfNw7bRR+K+3PsfyF/bg58tm49pptPQ41Dz2WCsqKrB161ZotVps3LgROTk5KCsrA8/zeO+998LdRhJiyTctgyonL/ALsJRjjWbfnjsOOx5eghljU7G1nnbUDQePPdZ169YBcKcBjEYjAOCjjz6C1WpFeXk5Vq5cicLCQrzzzjvhaymJXjQrIOqNTkuE+W4d2rvd79M/G88jSaWAbnJmhFsWn3xeILBgwQIsWLBACrpHjhwJWaNCobi4GDU1NZFuRlxiWJbmscYAhmGkylh/2ncC7x46iwevzsH3r5sKtZKmtMsp4P+bubm5crYjpKxWK2w2W6SbEb+oxxpzflk8Hz9cOg0v7TmGVdV7ceh0S6SbFFcCDqwfffQR1q5di6NHj8rSEJvNhvz8wfs28TyPyspKWCwWVFZW+j3NS3y9VquVoZXEE4amW8UcBcvAeE0uthoXAQDWv/4p7VIgo4AD65YtW1BXV4eKigrp2JEjR7B+/Xq/g63FYgEAj73K4uJilJaWwmAwwGAwSDlfX1mtVuj1er/OIX5SKGnwKkbNHJuGrcZFeP6uK8EwDI5eaMMx+6VINyvmBVyEJS8vDxs2bOh3LDc3F+Xl5aiurkZhYSGuvPJKn65lMBg8Huf5/iXRtFotrFar9LXFYhn0GvF6Wq0WNpuNgmo4sCyELpojGavUShYTOHcxnl+924C/Hz6P0hunY1X+RKqYFaCAA6tWq8WmTZuwZs2aQd8TFxX4GliHYrVaodH0n7iu0Whgs9mg0+mGDMgDrwG4g3RVVRVKSkqCahMZjGEpxxovnlo+B8/s/BJPvvU5dn1xFk8tm4PRabRLrL8CTgUsXboU+/btQ1ZWFtauXTtofqscS16Huoavq77E4MtxXNBtIV5Qdau4kaJW4me3zsYLdy/AodMtuOf3deh2UprHXwH3WB966CEA7t6pzWbD0qVLwTAM9Ho97HZ7SD+C+xu09Xo9Ghsbvb5GrBUgEvcMJz6gWQFx55vTsrFj7RJ8ebYVKgWLlo5uOAWASwqwtGQc2bx5c7+aIp5qBQQcWPPz8/sNJF28eBG1tbWoqqpCfn4+ysvLA720hOO4Qb1Tu90ekh6oWCuA+I+hHmtc4pLVuCrHnYp7btdh7Dp0Dk8tn4NvTB0V4ZZF1sBOV98OmSjgVMDAXmNGRgYMBgN27tyJsrIyqXBLMIbq9RYUFAR97YFGanUrWbAsTbeKc6ZvaDF9TCpMf7ThyTc/w6Uuqg0Rks0ES0pK8Nhjjw2aWtXc3BzUSGLfgD1w7inP8ygoKAhpj5U+/geAUgFxb0x6Iqq+o8P/+/ZMvPHx1zBU7ZWWx45U3qpbBRxYMzIysGHDBtTW1vbbYeDpp59GXl4e6uvrfb6W1WpFWVkZAKC8vFya1wq4C22XlZXBYrHAbDaHbFkq9VgDRwsERgaGYbC6cDK2P7QY9y2cgiSVAi5BQNcIHdzy1mNlhBAst9i2bRv0ej0yMjLkvnTILFu2jHKsAWqu/jW69u/DqP99JdJNIWH28p5jeP3jr1GxYi6mj0mLdHMiwlPsCEnlhZUrV8ZUUCVBonmsI9ZVuZlwCQKKq/fid/88AqeLlsUCtEurhFIBgaNZASPXrLHp2GpciHsXTsH/WA/j/pfrcLG9O9LNCgtvqQCv062OHDmCiooK2Za1cRwnyzSsUKDpVkGgwasRLUGpwKNF03Hd9Gy88fHXSEsMeBZnTBGnXXmabuX1/0Bubi5MJpNsDYnmFVBij5UWBgSAdmklAAqmZKJgirtw9m7+Av7w4TE8eescZKclRLhloSEuFAhogcCCBQtC0qhoQz3WwDEsS3tekX4EQcCBk81Y9sJu/PSWWbhpzthIN0l23nqslGMlwaNUABng6rxR2PHwEizMycSPLJ9g3fZP0NIxMnKvgA85VrPZLFuONSsrC48++qgs1yLRg+axEk8yk9V4rng+3jxwGi/vPQp2BJUgHDbHWlRUJNvNBpYAjCaUYw0CS7MCiGcMw+C2K8bhlnljwTIMTjRdwpZ9J/D96/OQoFREunlBCSrHunTp0pA0KtpQjjUICgWEEbr6hvhG7K1++nUzXv3wOP52+Bwq7piHWWPTI9yywFGOlYQUQz1W4qOb5oxFTclCKFgWd1Z/iE3/iM9FBRRYSfAox0r8MG10GrasWYj7F02B+QMep5s7It0k2VFg7UUrr4KgUACCAIHmshIfqZUsflw0He/84BuYwCXhUlcP/vrp6ZjaKTYkZQPjDZUNDByj6B2EoF4r8ZMmRQ0AqP38LH687RP8sOZjNF2KjY0pQ1I2kBAJ2/tjRD1WEqDl88fjl8Xz8a+jTVj+wm78/fC5SDcpKBRYSfB6e6y0SIAE48bZY7Bj7RLMHJuGh/7vIxw+2xrpJgVsZFRLICHFsL2pAJoZQIKUnZYA89067DvWhGmjUyEIAvjzbcjLTo100/xCPdZeNHgVBCnHSvUCSPAYhkFh7yaGbx88g9tf2IP/fb8RPVGWaqLBKx/Q4FUQxFRAlP3gk9innzUaJd/Ixca/8/jO7/+FoxfaIt0kCQ1ekZCSUgGUYyUyUylYPHL9VPzhe4W42N6DleY9+OxUc6SbNSzKsZLg0eAVCbH5EzlsMy3Cn+q+wozevbU6up1IVEVnvQHqsZLg0TxWEgYpaiUevDoXCpaB7XgTin71AayHzka6WR5RYCVBo1QACbecrBTMn5iBH2zZjyfeOIi2zugaOKXASoKncP8Y0eAVCRdNihq/ufNK/HzZHLz92Wms2LgHh8+2RLpZEgqsvWi6VeBoHiuJBIZhsHLBBLz20GLMHp+O7LTEsN6fplv5gKZbBYFyrCSCJmUm45fF88ElqXCmuQMPvLIvLKu2aLoVCS1pVkB05bnIyNPS2YNzLZ0ortqLV/cegytC1bIosJKgMVSEhUSJqdmpsJQsQnH+RJS/8wWMf6jHmQjUe6XASoJHqQASRRJVCvzk5pmovkeHE03tuNge/t1hKbCS4Cnc60xogQCJJlfnjcJb378a08ekobPHieffb0BbV3jSVXEfWHmeBwA4HA44HI7INiZO0TxWEq2UvWmqQ6db8OLuY1hp3otPTl4M+X3jPrCaTCbk5+ejvLw80k2JXwqabkWim7gkNiNJie/87l/Y+Hc+pJsYRk2tAJvNBqPRiPr6+n7HeZ6HxWKBVqsFz/MoKSkBx3E+X9dkMkGr1UKr1fp1HvEDSwsESPTLyUrBHx64Ci/8jcfz7zdg3oR0XJ03KiT3iorAKgZOm8026HvFxcVSsOV5HkajETU1NT5fm+d56PV6VFVVQafTQa/Xy9Zu4kZ7XpFYoVKw+MENU7Fs/jjkZKVAEATUH29CwRSNrPeJisBqMBg8HhfzoyKtVgur1Sp9bbFYBr1GvJ5WqwUAlJaWSsf6BmkiIwqsJMbkZKUAAOqPO3DfS/twy9yx+H+3zEJ6okqW60dFYB2K1WqFRtP/L4lGo4HNZoNOpxsyIPc9X0wfDLwOkRGVDSQxqmBKJp65Yx7+663PsWLjHlSsmCtL7zWqB6+GGsW32+0+na/X66Vebnl5Oaqrq2VsHRFJCwRo5RWJQbfMG4ftDy3G+IxE3P/SPvyz8XzQ14zqHutQ/Jk2JeZUh8utikVYRKtXr6a6Ab6irVlIjJvAJeGl+wvx2v6TuCrHe4918+bN/Yo1eSrCEtWBleO4Qb1Tu90ektF9sQgLCQDNYyVxQMEyMOgmDvu6gZ2uvh0yUVSnAobqZRYUFMh+LyobGDiGYdzBleaxkhHEW9nAqOuxOhwOqUcqjuyLeJ5HQUEB9VijEcvS4BUZUcSea9T2WK1WK8rKygAA5eXlsFgs0vdqampQVlYGi8UCs9ns1xxWf1CPNTiMQkHVrciI4q3HyghChAoWRplly5ZRjzUIZ+68Cal3fRcpK+6KdFMICStPsSMqeqzRgHqsQVIoKBVARpSYyrFGCuVYg8MoFDQrgIwoUZ9jJXGAZWlWACG9KLD2olRAkFhKBZCRhVIBPqBUQHDcswIosJKRg1IBJPQox0qIhAJrL0oFBIlSAWSEoVSADygVEBxaIEBGGm+pAAqsRBZpDzwMNoOLdDMIiQoUWIksEvIXRroJhEQNyrH2ohwrIcQfVCvAB1QrgBASCKoVQAghYUCBNQjxmDagZ4od8fhc8fJMFFiDEC8/BH3RM8WOeHyueHkmCqy9xMGrRx55RPZr+/PDEorXhuqHNRT3D9VrI3n/eHwmf14brz9/jzzyyJCDVxRYe4kLBI4dOyb7tekHmwJrKO4fimtG+vn9Een//8eOHcOOHTswYcKEQd+jWQG95syZg7y8PJw8edLj/yhPfH1tKK5J96f70/2j4/6NjY04ePBgv+9RYCWEEJlRKoAQQmRGgZUQQmRGgZUQQmRGRVj8xPM8LBYLtFoteJ5HSUkJOI6LdLP8ZrPZAAA6nQ48z8PhcECn0wGIrWe02WwwGo2or6/vd9zbM0T78w31TLH+ntlsNlitVgBAXV0dqqurfXpPYuHZBhGIX3Q6nfTvxsZGwWAwRLA1gSspKREACAAEvV4vNDU1Sd+LlWesqakR6uvrBU8/xt6eIZqfz9szxfp7VlFR0e/ffdscq+/XUCiw+qGxsbHfmywIgsBxXIRaExyz2Sw0NTX1++UUhNh8xoFByNszxMrzeQqssfye1dfX92tTY2OjAEBobGyMi/drIMqx+sFqtUKj0fQ7ptFopI9osYbjuEEfqeLhGb09Q6w/X6y+ZzqdDtXV1dLXDocDgLud8fh+UY7VD+IPw0B2uz28DZGBw+GAxWIB4M53mUwmaLXauHhGb88Qy88X6++ZwWCQ/r1lyxbo9XpwHBeX7xcFVhkM9eZHs74DAFqtFkVFRWhsbBzy9bH4jAN5e4ZYeL54ec/EPxADB+c8vS6Q70UDSgX4geO4QX8p7XZ79I9QesDzvPRvcbSV5/m4eEZvzxDLzxcv71lZWRlqa2ul9sXj+0WB1Q96vd7j8YKCgjC3JDg2mw1Lly4ddFyj0cTFM3p7hlh9vnh5zyorK1FWVialMBwOR1y+XxRY/aDVavt9zfM8CgoKov6v50BarRYVFRXS11arFQaDARzHxewz9v1o6O0ZYun5Bj5TrL9nFosFOp1OCqpbt24dtv2x8mwDUREWP/E8D7PZjMLCQtTV1WH9+vVR/yZ7Io64chyHxsbGfr+0sfKMVqsVtbW1qKysRGlpKQoLC6UBEm/PEM3P5+2ZYvk943keeXl5/Y5xHIempibp+7H4fg2FAishhMiMUgGEECIzCqyEECIzCqyEECIzCqyEECIzCqyEECIzCqyEECIzCqyEECIzCqyEDKG4uBiZmZnIz8+PdFNIjKHASsgQampqoNVqo35dOok+FFgJ8cJms6GoqCjSzSAxhgIrIUMQN74bqsISIUOhwErIEGpra6HVaqO+4AeJPrSDACFDsFqt/XqrVqsVPM8PqixFyEDUYyXEA4fD0S+/arFYpKLSVVVVUpqAEE8osBLiQd/8qlgDVafTAQBWrVpFeVfiFdVjJcQDk8kEq9WKiooKaLVaKagS4gvKsRLigdhLraurQ11dncdtQggZCgVWQgZwOBzgeR61tbXQ6/WwWCzIy8tDU1MTzRAgPqEcKyEDbN26FcDl+atiGkDchrmysjIyDSMxgwIrIQOIPdWBxN1FqddKhkOpAEIGcDgcMJlM0tdarRYlJSWorKwEx3EoKSmJYOtILKBZAYQQIjNKBRBCiMwosBJCiMwosBJCiMwosBJCiMwosBJCiMwosBJCiMwosBJCiMwosBJCiMz+P5OprKGY0CK7AAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.yscale(\"log\")\n", - "plt.xlabel(r\"$k$\", fontsize=15)\n", - "plt.ylabel(r\"$\\|x^k - y^k\\|_\\infty$\", fontsize=15)\n", - "plt.plot(\n", - " results_JOS1[0].all_error_criteria, color=\"#2980b9\", label=\"PGM\", linestyle=\"dashed\"\n", - ")\n", - "plt.plot(results_acc_JOS1[0].all_error_criteria, color=\"#e74c3c\", label=\"Acc-PGM\")\n", - "plt.legend()\n", - "plt.savefig(fig_path + \"/JOS1_error.pdf\", bbox_inches=\"tight\")" - ] - }, - { - "cell_type": "markdown", - "id": "broken-nickname", - "metadata": {}, - "source": [ - "## JOS1 + $\\ell_1$ penalty\n", - "Minimize\n", - "$$\n", - "F_1(x) = \\frac{1}{n} \\| x \\|_2^2 + \\frac{1}{n} \\|x\\|_1, \\quad F_2(x) = \\frac{1}{n} \\| x - 2\\|_2^2 + \\frac{1}{2n} \\|x - 1\\|_1\n", - "$$\n", - "subject to $x \\in \\mathbf{R}^n$." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "racial-uruguay", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T11:16:18.696313Z", - "start_time": "2022-05-25T11:16:18.628662Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:12:02.031681Z", - "iopub.status.busy": "2023-04-12T16:12:02.031206Z", - "iopub.status.idle": "2023-04-12T16:12:02.065797Z", - "shell.execute_reply": "2023-04-12T16:12:02.064430Z", - "shell.execute_reply.started": "2023-04-12T16:12:02.031659Z" - } - }, - "outputs": [], - "source": [ - "n_dims = 50\n", - "problem_JOS1_L1 = JOS1_L1(n_dims=n_dims, l1_ratios=(1 / n_dims, 1 / n_dims / 2))\n", - "start_points_JOS1_L1 = generate_start_points(low=-2, high=4, n_dims=n_dims)" - ] - }, - { - "cell_type": "markdown", - "id": "commercial-terrorist", - "metadata": {}, - "source": [ - "### Proximal Gradient Method" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "effective-death", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T11:43:52.682919Z", - "start_time": "2022-05-25T11:16:19.885604Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:12:02.067557Z", - "iopub.status.busy": "2023-04-12T16:12:02.066806Z", - "iopub.status.idle": "2023-04-12T16:35:00.830597Z", - "shell.execute_reply": "2023-04-12T16:35:00.829890Z", - "shell.execute_reply.started": "2023-04-12T16:12:02.067536Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 9.1s\n", - "[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 17.9s\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 26.0s\n", - "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 36.2s\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 47.7s\n", - "[Parallel(n_jobs=-1)]: Done 45 tasks | elapsed: 1.0min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 56 tasks | elapsed: 1.2min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 69 tasks | elapsed: 1.5min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 82 tasks | elapsed: 1.8min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 97 tasks | elapsed: 2.2min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 112 tasks | elapsed: 2.5min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 129 tasks | elapsed: 2.9min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 3.3min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 165 tasks | elapsed: 3.7min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 4.1min\n", - "[Parallel(n_jobs=-1)]: Done 205 tasks | elapsed: 4.6min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 226 tasks | elapsed: 5.1min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 249 tasks | elapsed: 5.5min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 272 tasks | elapsed: 6.1min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 297 tasks | elapsed: 6.7min\n", - "[Parallel(n_jobs=-1)]: Done 322 tasks | elapsed: 7.2min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 7.8min\n", - "[Parallel(n_jobs=-1)]: Done 376 tasks | elapsed: 8.5min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 405 tasks | elapsed: 9.1min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 9.7min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 465 tasks | elapsed: 10.4min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 496 tasks | elapsed: 11.1min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 529 tasks | elapsed: 11.8min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 562 tasks | elapsed: 12.6min\n", - "[Parallel(n_jobs=-1)]: Done 597 tasks | elapsed: 13.4min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 632 tasks | elapsed: 14.2min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 669 tasks | elapsed: 15.0min\n", - "[Parallel(n_jobs=-1)]: Done 706 tasks | elapsed: 15.8min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 745 tasks | elapsed: 16.7min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 17.5min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 825 tasks | elapsed: 18.5min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 866 tasks | elapsed: 19.4min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 909 tasks | elapsed: 20.3min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 952 tasks | elapsed: 21.3min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 23.0min finished\n" - ] - } - ], - "source": [ - "results_JOS1_L1 = run(problem_JOS1_L1, start_points_JOS1_L1)" - ] - }, - { - "cell_type": "markdown", - "id": "first-dayton", - "metadata": {}, - "source": [ - "### Accelerated Proximal Gradient Method" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "wound-happiness", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T12:01:45.069561Z", - "start_time": "2022-05-25T11:43:52.688065Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:35:00.831909Z", - "iopub.status.busy": "2023-04-12T16:35:00.831507Z", - "iopub.status.idle": "2023-04-12T16:51:20.696020Z", - "shell.execute_reply": "2023-04-12T16:51:20.695301Z", - "shell.execute_reply.started": "2023-04-12T16:35:00.831886Z" - }, - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 6.8s\n", - "[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 13.3s\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 17.3s\n", - "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 26.2s\n", - "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 36.2s\n", - "[Parallel(n_jobs=-1)]: Done 45 tasks | elapsed: 45.0s\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 56 tasks | elapsed: 55.8s\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 69 tasks | elapsed: 1.1min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 82 tasks | elapsed: 1.3min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 97 tasks | elapsed: 1.6min\n", - "[Parallel(n_jobs=-1)]: Done 112 tasks | elapsed: 1.8min\n", - "[Parallel(n_jobs=-1)]: Done 129 tasks | elapsed: 2.1min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 2.4min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 165 tasks | elapsed: 2.7min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 3.0min\n", - "[Parallel(n_jobs=-1)]: Done 205 tasks | elapsed: 3.3min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 226 tasks | elapsed: 3.7min\n", - "[Parallel(n_jobs=-1)]: Done 249 tasks | elapsed: 4.1min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 272 tasks | elapsed: 4.4min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 297 tasks | elapsed: 4.9min\n", - "[Parallel(n_jobs=-1)]: Done 322 tasks | elapsed: 5.2min\n", - "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 5.7min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 376 tasks | elapsed: 6.2min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 405 tasks | elapsed: 6.6min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 7.1min\n", - "[Parallel(n_jobs=-1)]: Done 465 tasks | elapsed: 7.6min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 496 tasks | elapsed: 8.1min\n", - "[Parallel(n_jobs=-1)]: Done 529 tasks | elapsed: 8.7min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 562 tasks | elapsed: 9.2min\n", - "[Parallel(n_jobs=-1)]: Done 597 tasks | elapsed: 9.8min\n", - "[Parallel(n_jobs=-1)]: Done 632 tasks | elapsed: 10.3min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 669 tasks | elapsed: 10.9min\n", - "[Parallel(n_jobs=-1)]: Done 706 tasks | elapsed: 11.5min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 745 tasks | elapsed: 12.2min\n", - "[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 12.8min\n", - "[Parallel(n_jobs=-1)]: Done 825 tasks | elapsed: 13.5min\n", - "[Parallel(n_jobs=-1)]: Done 866 tasks | elapsed: 14.2min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 909 tasks | elapsed: 14.9min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 952 tasks | elapsed: 15.6min\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "/Users/zalgo/.anyenv/envs/pyenv/versions/3.10.10/lib/python3.10/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", - " warn('delta_grad == 0.0. Check if the approximated '\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 16.3min finished\n" - ] - } - ], - "source": [ - "results_acc_JOS1_L1 = run(problem_JOS1_L1, start_points_JOS1_L1, nesterov=True)" - ] - }, - { - "cell_type": "markdown", - "id": "cardiac-bikini", - "metadata": {}, - "source": [ - "### Complexity" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "north-melbourne", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T12:01:45.118579Z", - "start_time": "2022-05-25T12:01:45.072785Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:51:20.697415Z", - "iopub.status.busy": "2023-04-12T16:51:20.697030Z", - "iopub.status.idle": "2023-04-12T16:51:20.769779Z", - "shell.execute_reply": "2023-04-12T16:51:20.768844Z", - "shell.execute_reply.started": "2023-04-12T16:51:20.697393Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Acc-PGM': {'execusion_time': {'max': 12.22089409828186,\n", - " 'mean': 7.800320901155472,\n", - " 'std': 1.1757027460106988},\n", - " 'nit': {'max': 187, 'mean': 161.216, 'std': 16.46059974606029},\n", - " 'nit_internal': {'max': 3631,\n", - " 'mean': 2434.029,\n", - " 'std': 318.6689946621729}},\n", - " 'PGM': {'execusion_time': {'max': 54.171611070632935,\n", - " 'mean': 10.701421920776367,\n", - " 'std': 2.173705017024851},\n", - " 'nit': {'max': 320, 'mean': 219.759, 'std': 7.796211836526763},\n", - " 'nit_internal': {'max': 26144,\n", - " 'mean': 3342.193,\n", - " 'std': 816.4361320709661}}}\n" - ] - } - ], - "source": [ - "stats_JOS1_L1 = {\n", - " \"PGM\": get_stats(results_JOS1_L1),\n", - " \"Acc-PGM\": get_stats(results_acc_JOS1_L1),\n", - "}\n", - "pprint.pprint(stats_JOS1_L1)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "bibliographic-lighter", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T12:03:09.727235Z", - "start_time": "2022-05-25T12:01:45.125592Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:51:20.771810Z", - "iopub.status.busy": "2023-04-12T16:51:20.771125Z", - "iopub.status.idle": "2023-04-12T16:52:10.362270Z", - "shell.execute_reply": "2023-04-12T16:52:10.361408Z", - "shell.execute_reply.started": "2023-04-12T16:51:20.771786Z" - } - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8fd7128dd0f74db3b684390045055aa6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.yscale(\"log\")\n", - "plt.xlabel(r\"$k$\", fontsize=15)\n", - "plt.ylabel(r\"$\\|x^k - y^k\\|_\\infty$\", fontsize=15)\n", - "plt.plot(\n", - " results_JOS1_L1[0].all_error_criteria,\n", - " color=\"#2980b9\",\n", - " label=\"PGM\",\n", - " linestyle=\"dashed\",\n", - ")\n", - "plt.plot(results_acc_JOS1_L1[0].all_error_criteria, color=\"#e74c3c\", label=\"Acc-PGM\")\n", - "plt.legend()\n", - "plt.savefig(fig_path + \"/JOS1_L1_error.pdf\", bbox_inches=\"tight\")" - ] - }, - { - "cell_type": "markdown", - "id": "large-duration", - "metadata": {}, - "source": [ - "## SD\n", - "Minimize\n", - "$$F_1(x) = 2 x_1 + \\sqrt{2} x_2 + \\sqrt{2} x_3 + x_4, \\quad F_2(x) = \\frac{2}{x_1} + \\frac{2 \\sqrt{2}}{x_2} + \\frac{2 \\sqrt{2}}{x_3} + \\frac{2}{x_4}$$\n", - "subject to $(1, \\sqrt{2}, \\sqrt{2}, 1)^\\top \\le x \\le (3, 3, 3, 3)^\\top$." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "speaking-nurse", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T12:03:10.667910Z", - "start_time": "2022-05-25T12:03:10.623221Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:52:11.249007Z", - "iopub.status.busy": "2023-04-12T16:52:11.248612Z", - "iopub.status.idle": "2023-04-12T16:52:11.289492Z", - "shell.execute_reply": "2023-04-12T16:52:11.288751Z", - "shell.execute_reply.started": "2023-04-12T16:52:11.248980Z" - } - }, - "outputs": [], - "source": [ - "problem_SD = SD()\n", - "start_points_SD = generate_start_points(\n", - " low=problem_SD.lb, high=problem_SD.ub, n_dims=problem_SD.n_dims\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "upper-florida", - "metadata": {}, - "source": [ - "### Proximal Gradient Method" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "compressed-round", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T12:09:15.697504Z", - "start_time": "2022-05-25T12:03:10.671015Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:52:11.290906Z", - "iopub.status.busy": "2023-04-12T16:52:11.290597Z", - "iopub.status.idle": "2023-04-12T16:56:57.586322Z", - "shell.execute_reply": "2023-04-12T16:56:57.585695Z", - "shell.execute_reply.started": "2023-04-12T16:52:11.290876Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 1.5s\n", - "[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 3.0s\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 5.4s\n", - "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 7.1s\n", - "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 9.8s\n", - "[Parallel(n_jobs=-1)]: Done 45 tasks | elapsed: 12.4s\n", - "[Parallel(n_jobs=-1)]: Done 56 tasks | elapsed: 15.6s\n", - "[Parallel(n_jobs=-1)]: Done 69 tasks | elapsed: 19.3s\n", - "[Parallel(n_jobs=-1)]: Done 82 tasks | elapsed: 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"results_SD = run(problem_SD, start_points_SD)" - ] - }, - { - "cell_type": "markdown", - "id": "entire-strategy", - "metadata": {}, - "source": [ - "### Accelerated Proximal Gradient Method" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "affiliated-klein", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T12:15:30.605647Z", - "start_time": "2022-05-25T12:09:15.700728Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T16:56:57.588229Z", - "iopub.status.busy": "2023-04-12T16:56:57.587570Z", - "iopub.status.idle": "2023-04-12T17:01:21.623177Z", - "shell.execute_reply": "2023-04-12T17:01:21.622403Z", - "shell.execute_reply.started": "2023-04-12T16:56:57.588203Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 1.5s\n", - "[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 3.0s\n", 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3.3min\n", - "[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 3.5min\n", - "[Parallel(n_jobs=-1)]: Done 825 tasks | elapsed: 3.7min\n", - "[Parallel(n_jobs=-1)]: Done 866 tasks | elapsed: 3.8min\n", - "[Parallel(n_jobs=-1)]: Done 909 tasks | elapsed: 4.0min\n", - "[Parallel(n_jobs=-1)]: Done 952 tasks | elapsed: 4.2min\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 4.4min finished\n" - ] - } - ], - "source": [ - "results_acc_SD = run(problem_SD, start_points_SD, nesterov=True)" - ] - }, - { - "cell_type": "markdown", - "id": "engaged-kinase", - "metadata": {}, - "source": [ - "### Complexity" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "vocal-gasoline", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T12:15:30.654952Z", - "start_time": "2022-05-25T12:15:30.609498Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T17:01:21.624843Z", - "iopub.status.busy": "2023-04-12T17:01:21.624485Z", - "iopub.status.idle": "2023-04-12T17:01:21.672242Z", - "shell.execute_reply": "2023-04-12T17:01:21.671567Z", - "shell.execute_reply.started": "2023-04-12T17:01:21.624816Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Acc-PGM': {'execusion_time': {'max': 4.748649835586548,\n", - " 'mean': 2.1035816931724547,\n", - " 'std': 0.5833580472889623},\n", - " 'nit': {'max': 70, 'mean': 33.021, 'std': 8.791618679173933},\n", - " 'nit_internal': {'max': 1132,\n", - " 'mean': 485.989,\n", - " 'std': 137.77775175622514}},\n", - " 'PGM': {'execusion_time': {'max': 6.352782964706421,\n", - " 'mean': 2.280814779281616,\n", - " 'std': 0.8458761188684208},\n", - " 'nit': {'max': 95, 'mean': 36.481, 'std': 13.054104297116673},\n", - " 'nit_internal': {'max': 1532,\n", - " 'mean': 536.707,\n", - " 'std': 202.62950710841693}}}\n" - ] - } - ], - "source": [ - "stats_SD = {\"PGM\": get_stats(results_SD), \"Acc-PGM\": get_stats(results_acc_SD)}\n", - "pprint.pprint(stats_SD)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "great-travel", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T12:16:18.834073Z", - "start_time": "2022-05-25T12:15:30.669957Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T17:01:21.673759Z", - "iopub.status.busy": "2023-04-12T17:01:21.673340Z", - "iopub.status.idle": "2023-04-12T17:01:55.584736Z", - "shell.execute_reply": "2023-04-12T17:01:55.583709Z", - "shell.execute_reply.started": "2023-04-12T17:01:21.673735Z" - } - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "20dd54a7210a4b7680ec3e6be93354aa", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.yscale(\"log\")\n", - "plt.xlabel(r\"$k$\", fontsize=15)\n", - "plt.ylabel(r\"$\\|x^k - y^k\\|_\\infty$\", fontsize=15)\n", - "plt.plot(\n", - " results_SD[0].all_error_criteria, color=\"#2980b9\", label=\"PGM\", linestyle=\"dashed\"\n", - ")\n", - "plt.plot(results_acc_SD[0].all_error_criteria, color=\"#e74c3c\", label=\"Acc-PGM\")\n", - "plt.legend()\n", - "plt.savefig(fig_path + \"/SD_error.pdf\", bbox_inches=\"tight\")" - ] - }, - { - "cell_type": "markdown", - "id": "convenient-walnut", - "metadata": {}, - "source": [ - "## FDS\n", - "Minimize\n", - "$$F_1(x) = \\frac{1}{n^2} \\sum_{i = 1}^n i (x_i - i)^4, \\quad F_2(x) = \\exp \\left( \\sum_{i = 1}^n \\frac{x_i}{n} \\right) + \\|x\\|_2^2, \\quad F_3(x) = \\frac{1}{n(n + 1)} \\sum_{i = 1}^n i (n - i + 1) \\exp (- x_i)$$\n", - "subject to $x \\in \\mathbf{R}^n$." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "material-insight", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T12:16:20.699519Z", - "start_time": "2022-05-25T12:16:20.662316Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T17:01:57.471570Z", - "iopub.status.busy": "2023-04-12T17:01:57.471175Z", - "iopub.status.idle": "2023-04-12T17:01:57.513681Z", - "shell.execute_reply": "2023-04-12T17:01:57.512716Z", - "shell.execute_reply.started": "2023-04-12T17:01:57.471539Z" - } - }, - "outputs": [], - "source": [ - "n_dims = 10\n", - "problem_FDS = FDS(n_dims=n_dims)\n", - "start_points_FDS = generate_start_points(low=-2, high=2, n_dims=n_dims)" - ] - }, - { - "cell_type": "markdown", - "id": "rural-prague", - "metadata": {}, - "source": [ - "### Proximal Gradient Method" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "provincial-sodium", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T15:43:35.762929Z", - "start_time": "2022-05-25T12:16:20.702464Z" - }, - "execution": { - 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finished\n" - ] - } - ], - "source": [ - "results_FDS = run(problem_FDS, start_points_FDS)" - ] - }, - { - "cell_type": "markdown", - "id": "incredible-ratio", - "metadata": {}, - "source": [ - "### Accelerated Proximal Gradient Method" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "naked-episode", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T16:55:02.794466Z", - "start_time": "2022-05-25T15:43:35.766394Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T18:54:07.905713Z", - "iopub.status.busy": "2023-04-12T18:54:07.905320Z", - "iopub.status.idle": "2023-04-12T19:34:33.947365Z", - "shell.execute_reply": "2023-04-12T19:34:33.946592Z", - "shell.execute_reply.started": "2023-04-12T18:54:07.905688Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 12.9s\n", - 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"iopub.status.busy": "2023-04-12T19:34:33.948583Z", - "iopub.status.idle": "2023-04-12T19:34:33.994734Z", - "shell.execute_reply": "2023-04-12T19:34:33.993798Z", - "shell.execute_reply.started": "2023-04-12T19:34:33.949027Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Acc-PGM': {'execusion_time': {'max': 39.55773687362671,\n", - " 'mean': 19.32521717119217,\n", - " 'std': 5.735134424591513},\n", - " 'nit': {'max': 458, 'mean': 216.824, 'std': 67.05155497078349},\n", - " 'nit_internal': {'max': 15778,\n", - " 'mean': 7617.684,\n", - " 'std': 2285.9676896544274}},\n", - " 'PGM': {'execusion_time': {'max': 207.85198521614075,\n", - " 'mean': 53.482315192461016,\n", - " 'std': 33.66924945504039},\n", - " 'nit': {'max': 2640, 'mean': 598.637, 'std': 413.5841307775239},\n", - " 'nit_internal': {'max': 82537,\n", - " 'mean': 21056.817,\n", - " 'std': 13537.800721517178}}}\n" - ] - } - ], - "source": [ - "stats_FDS = {\"PGM\": get_stats(results_FDS), \"Acc-PGM\": get_stats(results_acc_FDS)}\n", - "pprint.pprint(stats_FDS)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "indian-experiment", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T16:55:39.918481Z", - "start_time": "2022-05-25T16:55:02.897077Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T19:34:33.996407Z", - "iopub.status.busy": "2023-04-12T19:34:33.995917Z", - "iopub.status.idle": "2023-04-12T19:35:03.447208Z", - "shell.execute_reply": "2023-04-12T19:35:03.446194Z", - "shell.execute_reply.started": "2023-04-12T19:34:33.996384Z" - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "02d591797d1f4f948a9f024b6ab56be5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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OTofQRVhHpw0IISiVSgghOr0UHR2dLsLY6QXo6HwTEUKgKIr2v8VikVwuhyRJGAwG7T9ZlrX/JEnq9LJ1dHTajC7COjoNolq3iqJU/ae+JkmS9jeDwUCxWKRQKGjnS5KkCbEuzjo63y50EdbR2SBCCO0/VVwDgQAulwuTyaQdp4qreo7670pRrRxruTjH43GcTidWq1UTaF2cdXS+WegirKNTg+WCq7qZ1dckSSIUCmG327FYLBsaW5KkVcVUCEE4HMZsNmMwGKpiyaoAq6Ksi7OOztZFF2EdnWWsJbp3795l3759VcKpWrprielmUceTZRmj8cXPtNJyzmQy3L9/n4MHD1ads9ylbTAYmr4+HR2d5qCLsM63nsokqnw+r4mVauVCWeDS6TQGg6GTS60SUyGEJtKV4lwoFDS3djgc1lzaqyWE6eKso9NZdBHW+VaxWjxXFWBJkvjqq6949dVXNXHaiEB1cvvRamsVQpBIJLBYLFgsFgqFAvl8vup4NWlMFWldnHV02osuwjrfaJYLbiaToVAo4HA4NOFdLriqi3kjdKNgVbq0Ky34yni2EIKbN29y+PDhVc9ZLd7cje9VR2eroouwzjeKWklUsViMbDaL0+nclNh+E6h0sUP5ocNkMlVdJ0VRtOIiqVSKfD6Pz+fTxVlHp8noIqyzpVleFENRFGZnZxkYGFiRkKQKBnSn5dpplouzSmWhkUpxVo+VJIlCoYDVasVkMq1ICNPR0VkbXYR1tgz1FMUAiEQi9Pf3r5pEpYvCxql0UVdSaTk/fvyYkZERLBZLlZhXWs3LY846Ojq6COt0MZVJVKr1pQrv3bt3q7bmVApELdfot6l+cyvfa6XYSpKE0WjU3NrLC5BIksSTJ0/YsWOHXrpTR6cCXYR1uobKeG5lFq/6WmUCVT6f77qYbreKezuEbfl2rtXmTCaTyLK8ojpY5bGFQgGn06mLs863Bl2EdTrGWklUqnvT7Xbj9XqB6oxl9Ua+FpIkrfl6q27m32SRaNbDxWoubXV8dX/zgwcPOHDgwIpz9LraOt9UdBHWaRvLk6hCoRBut3tVK6qVGbedtFhFLouIhJEGhpC6zJJfj1aKXWWlLzVTG1avq51Op0kmkwwMDFTFmXVx1tmq6CKs0xJqFcUAePToEePj4xsW2/Us3cr5uw2RyyGyGeTBYcTCHGwxIV6PZlzvyu8GrO3WVkMRqvW8vK52sVhEluUVBUh0cdbpRnQR1mkKqwluMBjE4XBoGbOrVaFa66ZYj9CuxXo32k7dhEUuC6kEcv9geQ3fQCFu17Wt5daORqMUCgUGBgZWeFj0uto63YYuwjqbop7OQtFoFLPZjN1u78j6NvPaZlk3GzuXRSQTSF7/i7/FIggEYm4WeXikq4W4nV6FRsSw8iHPYDCs6tZW62pHIhFkWcbtdq+Zra2Ls0470EVYpy5WK4qRy+WYn59ndHR0zc5Cm2UrWsJrrVdkM0hWm/ZvJRpGMluR3V6K9+8g0ikkZ0/L1tUM2pVhXc8xG13LamJaKBQwGo3a92y1utrFYhGj0YjFYtHFWadl6CKss4K1imJUVklSSaVSa3YWakRIG6WbYsKyy0MpHEJks/z2XpDP5jK8vn+ItwezGLaNIhIxhM2O1OEOTY3QrutdjwjXK9S1CpAEg0FkWcbv92vH63W1dZqNLsI6axbFALhx4wZHjhwBWJHYUs8NpxFrthWWMDQmGJuxxABklxvx4C6Tj0PM0cNXtx7zzo7DSHY7kt2OsjgPff1I8tYV4kYFaLPXdrVxNjvXegVI1PMqS3cmEgkURcHj8ejirLMpdBH+FlJvUQxAyzRdjVpC2Y03nkZd5A3N7fbglgIoc88w7x1Geh4rF0KAooC0fly4WSK1lan3AarZYr5WXe1CoUCpVFq3rrb6/y0Wi15XW2cFugh/C9hsUYxa1CPCnbCEoTPu6FpzSs5eggYbTq+BiDBr54iFOaTB4fWTuzJpRDQCdgeSy920NTVrjGYW9Gj0GCFEze/xRlzWtahsDFI5vvq/0WiUaDTK9u3btTFVt7bRaKwSZn0b1bcPXYS/gVQmUeVyOXK5HDabbc2iGOq/N0or9+u2ymJtVZy63vWOHxhh6s4s3x00bUyAs1nkoW2IVBIRi25IiJtxU2+WYK1HN8Xxof7482pU/s5Uga10a6tWc7FY1KzopaUlBgcH9aYX3zJ0Ed7i1CqKEY/HCYfD7N69e9XYVCtvnJ28Kbfqht6oS/jdg35+eqgPkYjXJcBkMwhZRvaUPRWSw6kJMQ7nptfRrTQj6aqZyVu12KjVrf7v8mTGfD5PNBplcHCwqumFirqNarn1rIvz1kcX4S3GcsEtFouEQiH8fv+KJgfqDxfaXxSjcr2bHbvbErOaebOTenqRenrXPyaXg2wGedv26r+rQhyPNm093UA7LeFmbodq1vdNtXyXn1tZuvPmzZscPnxYm3etPc66OG8ddBHucmoVxRBC8OzZM/r6+lZ9Im80bqu+vlYmaatiwo1Sy024FRBmM+Rzq15/kcuCxdqhla1kq25R6va5lnuv1Nray8VZ7UgFEI1GcblcWiKYLs7djS7CXcZqRTFmZ2cZHh4Gqn+Uav3c9W4ErRThWjQa1+0Gi7Wjc0oS9A0iFudhYIjf3AlyZTrCG/1GfnpkGMnYnp9vszKS68kfaNdn1w3boZazkfWste1JCEEoFMLhcKzoNqYKsN70orvQRbiD1FsUIxAIMDIysuaPpJHkqFZnMHdbsg20LtbckvcqSUgDQ4jFeT6dThKKpblasvCz161QLDZ/vjWX0R036HbHe9tlUSuK0rQEOiEERqMRY8VDWqXlXCgUiEajZLNZBgcHV7i19bra7UUX4TZS+UOoLIrx6NEjdu3apR232lPpepbuejQqlOudX48VvVla9XDQqqzr1VAURUuMi0QiVd19nE4nTqcTh8NRdbNcc96BIY5773O1ZOH7e8sVnLrxAWc9uulm3s4Er3Z/TqutabmYViaIVYpzpVtbkiSSySRut1uvq91CdBFuIbXiueoXORqNbmh/7kZoVChbbSl3glatSVEUotEo4XCYaDSKEAKXy4XX62VkZIRCoYDBYCCXy5FKpYhEIjx9+pRSqYQsyzgcDk2Ylze9kCSJn72xj58tm7NbboTN2o/cTdnR7U7eatZnuZH3v5qYVt6jHj9+zOHDh7WWkcuTPnVxbhxdhJvIWqJ79+5d9u3btyKeq9LKL22zYsKbpREBV0tntnPejYxbaenOzc0RDAbx+Xx4vV527NhRlelaKpW0bSc2mw2bzabVJFZfVxvWz83NkclkSCaTZDIZXC6XZjmbzeaWvK/1aFZMeCvSLrd2s0W4kTWpf1fvV6qnZnlCqNr0IhgMYrfbcTqdmjivto3qm/j9aAa6CDfA8iSqUqmk/b3yi5xOp9dsctBqWu2ubmTuVrHevI2sSQhBLBYjFotplm5vby9er5dSqYTf78fj8WxqbIPBQE9PDz09L7opPXjwAL/fj6IoJJNJFhYWVgh5oVBAUZSWeVLaSTut0614zEZoxXyV97RK1GJA6r1ktY5UapJYZelOXZzL6CJcJ5Xx3OWW7u3bt1fs3Vst/rLe2K2iHkt3PYuzG+O67aLS0o1Go8RiMex2O36/f4WlG4vFWrIGk8mEw+HQyoqq68pkMsRiMbLZLLdu3UJRFAwGg2YxO51OLBZL065hN90ou2kt3SjCnZivVulORVG4cePGivvkWk0vvgkPlfWii/Aa1BvPlSSJfD7ftV+aTrqjGz13s9Sad63XFEWhUCgwPT29wtLdsWMHN2/eZPfu3TUTqRpFKAokExhCAfC4weGoel2NH5vNZqLRKIcOHQLKzTaSySTJZJJQKEQ2myWVSpHP53G73XUngm1qzW3aXrRVLeFm1bKuh24R/eWWc+UeZ/W85U0vksmk9uD5bamrrYvwc9YT3a+++opXXnll1XhuM+hkTBhat4WpUVoxb+W1Xm7pCiHI5/O4XK4Vlm67EOkUytws2GyUfP2QzSIyaSSbvea5RqMRt9uN2+3W/nbv3j38fj+lUmnNRLBisbjuTbmdAtuuJKdmJl3VM067acaDQb1s9Hqv5dYWotwa0m63Y7Vatbraled9E+tqf2tFuFJwK2O56pel0qXczC9su+m0JbzZ5KpWJIQpikIikSAajfLll1+iKEqVpWswGJicnMTn821o3GYh0ilEOIg8OAyyATlwBzE4iMhmAeoSYgBRLECxiGQtx+msVit2u52+vj7tmMpEsGw2y40bNxBCYDabNYu5MhGsW25yzfwttivpqp65mvXgUC/NnK9Zlrf6uprYVXmu+p9aHezBgwfs3btXO28rl+78Voiw+mVarShGPp/nwYMHHD58eM2U/a1MPSJbi60W11Xfkyq66j5dIQQ2mw2z2czRo0c3bOnWSvhq6KEhnUJEQkhuL5LzeXKW2QyxKPK2EZRIuDxPDSEWxQIiEgajCdZZT2Ui2NLSEkePHgXKjQRUl7aaCJbL5bBarZRKJU2g1yqR2ijdlI28FY+pl3aLcL3FSFZLMlztnqxu91suzurx6v8+ePAAl8ulCXY38o0U4conJzV7dLUvgNqRpB2Wbq0vaitdfo1awp1gM6JWKbpLS0sYjUbN0h0dHcVgMJBOp3ny5EnHstVXQ2TSiHwOydePSCU1ERZWG5jNKNEw5HPgcq0/znMBlvsGAFAiYaRcdkNrMZvNeL3eqkSwubk5rdBIIBAgnU6jKApGo1GzmNW4XqPZ593EVszWrpdmxbKhPoGtd6yNvMe1DKbK+/9nn33G6OioLsKtZr14biAQIJfLMTo6CqwUlUZcpvWiCspaX65arzfC8vqxa8292dfXo9WWcKXoRqNRzb1stVoZGBiocr9W0mw3d8NYbRCN8Ju5In+6v8j3B0O89d395ddSKYRRRh4eQZLXfnBYLsAAsseL9Ggakc2AvT5X9mqoLu3+/n4GBl6MXygUSKVS2n7m69evl9/OBiuCbYRuFLRuEup6aeZ8zUw8a0b5zkpxLpVKmEymhsZrNVtShCtFN5vNar7/1eK5RqORXC635gdbS6SaQSuFrh46lZjV7HOFEFoiVSwW48svv1xh6QI8evRozZtCN4YaJEmCwWFu/81V5mUXny7k+VEoiBwJQW8P8ui+dQUYQCwFkIe2rfi70uOCWBS8a8e5a7HWZ2gymbREsHA4zNGjRxFCkM1mSSaThMPhqkSwVCpFIBDA6XRq+0qXz9NNn08zhapd49RLs0W4nYK+EQqFQkcK3GyELSHCquCq/1uZSHX//n1GR0dxOByruidqWbrtiFt2UoQ7GRNuFCHKxTGWW7per5fe3l6OHTu26nmtupG3UiAkSaLgH0C5PY3l0Bgilyn3Ex48UFOAASSPDyUcRPb6+fXXS1yZjvDmmIe9uSQ878DV6PrqPU4tJLI8EezatWsUi0WePXtGJpOpSgRzOp11eaS6LQbbbnd0s6inwEszRbjegjLNfhDL5/O6CG+USn++oiik02nMZvOKD0cV3FpN6xt1x9ZDve7mza6hEXd1PRmJrYoJb/TaVlq6gUCAfD5PoVDA6/Wyffv2Krfm48eP27KmjdDouMm8gjw4jCm4iOx/nVImD3VaBZK13FtYCZfbHQZTBT69G2DPLulFolcHUTNehyseCNTEyFQqpYUVstks8Xgcu91eVUu72Tkb7XZHtyvhrJljdcoSbqYIFwoF3R1di/XiuUIIbt26xfj4+JpB+E67eivn6EYRrkUnY8KrWbo9PT14vV6cTifJZLKqu9RGaLf13ozP5s0xD1emIxwa24e0iad3VYjf6I9z9VmBN3f0Iuy1E7Paca1Wm0OSJCwWCxaLRfNuxGIxtm/frtXOXp4IViqV6Onp0SqCrZWY0y7R67Zj6qWZ89WbmFXvWM184NIt4VWoJbqVYqt+GOt9eLXczbIstyXxar0vT601dLu7ullzV1q60WiUeDzO4uLiqpauuqVoM7TKrdeMa7Hezejdg328e3D1ZLJ6kaxWfnpkmJ8eLCA5e7h3715D47UbSZK0QiIOh2NFItijR4/I5/M8fvyYXC4HgM1mq7Ka6+Gb6rKul3bHcetNuGqFJayLcAVqJxlViIFVRXcjtMPd3Oo1dPI91DN2I3NnMhkePXq0wtLdvn07165dY9++fZseez3WWnM3Jf60irJFbH3x73Xec7tikfUK0XqYTCZNcNUtVKslgsXjcZLJJL29vVq8eXkiWDMfLNuVmNWtIgzNc8k3+/uoi/AqVFq7zfhC1ZN41a4tSI283qnksWZawkKIquIYuVwOWZbp6+tbYek2uq5a53Yb3bimjdCs32qzWS0R7P79+wwODiKEIJlM8uzZM9LpNICWCJbL5SgWi+veoLeiW7uZa2rGdqHK+erdc9zMOtv5fF6PCa9GM3+MtazIrbIFqZPW/GYTs4QoF0N5/PgxkUikytIdGRkhkUgQiUSq+uYuH3utH1QrRauRa1krxv1tpVnWcrNcu0ajEZvNRm9vb9Xf1YpgaqOOUqmEJElaP1yn04ndbtfuG+10WXdjk4d21peu97iNxI1rPWh1Ax1PzGqUdlmRrUy86rQ7utbrlTH7SktXURRyuRxOp5ORkZEVlm6jCWetEMutaJGKVBLJ4ez0Mhqimd/fzX6GlYlgCwsL7N+/H6PRqO3CSCaTLC4ukkqlNCt6ZmZm3UQwPTGrPpoZE96oJayL8DLUeHCzPtx6LOFG3dH1CFUrE6865Y5W421qqUdFUXA6nfh8Pk10Jycna1q6zV5XPee2gloJgi0hHASXGyUSRvZ4ax/fIZoRF20WG72Ry7KsWcGVXLt2DZfLRTqdJhgMrpoIptYsqDVXPXyTRXgjVnWzRVh3R7eYdljC6hxr1Rtutci2S4RXs3RLpRI+n0+zGlo1d7P5JriN5VgEdo0hudyQSbdEiJuRMNUsmikMzYphu93uqjrayxPB4vE4t27dwmAwaMK8WiJYuzKo66UbLe960d3RXUY7LOFG486trt9cy627FkIIUqmUVgJStXTVmK7RaOTJkyfY7fZNCXA3WsKN3gjaJUhKaAnF7gTT81aCz7sntUKI2yFq7cxGbqagLT9meSJYKpXi4MGDAFod7dnZWTKZDFBOBEun0/T09GAymda0ytr5cFHvfN0qwro7ugPUise2wxLuZPZzs0R4uaVbKpWw2WwYjUaOHDmyqtA28sPppIXfinGbcfOvByUcLFe5isar57fZoVhEJBNdUQVro7Trpt8soYb6P3ODwUBvb++qiWB37twhlUoRDocpFAqrJoI1c8310K0x4XrHqtcSLhQKTffgNZu2rk6SNr4tqVaWoizL68ZlukWEO/G6mlySTqfXtHSz2SwPHjxY84vaSqFcj1Zawp0Q6A2N5fEhAgvw/MFNrQV9fLSXdwalqk5JOpunWZ/ZegaCmgg2MjKCxWIBWDURLJ1OY7FYSCQSmjibzeaqsZtZTaqbY8L1jFXvujajOe2mux8RqF2Nqp0x4UZeb3VMWE14SyaThEIhotEoxWKRnp6ehi3dVrmUOyXw3Y4kSdA/iGHmjzAwUK4Fncxz5X6Ad186gjL/DMnnB6m1PbCbSbvd0e1y7W7Ggl0tEezJkyc4HA4MBgOpVIqlpaWqRDC1wUU994Fmrnuri/BWoOtFuBnx1FavoV0x3+VUWrrXr19fsU9XFd3JyclNW7qNCuVmbxqtjCe38v00C0mSKHn8EAxwfNTJlftBvn9gELE4jzQ4jAgHEVZbW9bSrPfczpt+O2mG4AshMJlMuFwuPB5P1d8zmYwmzMlkkmg0qgm5mgy2WmvIWvPVOr7dlne9NLu+dKfZEiJcqzZ0q2+M3eJuVkW3MqbrdDoxGo0cOHAA+yYat7fyIabWue2MgdU7Z1chSdDXzzuuLO8cOoxy9xbSwJGyi83XBwtzSIXCukO0S0DbRbstuGbQyJrV+LFaQKS3t5eRkRFKpdKaiWDZbJZIJILT6Vx3e85WjQl32+fbKF0vwrVEop0NGtZ7vRWWsiq6aqchWZY1S3fbtm2adXvjxo01t0/VopWWcCNjd0s2biWbeWgQQmj7TMPhMHa7nZ6eHnp6elbE/NaYFNxelNvXkfcfRgQDSM9jwpKvD/nRY0Q+h2S2bGrd7bLsm5mxvNVoRcLZWolgmUyGr7/+mng8ztzcHMVicdVEsHauWx2rWdarbgm3mUY7ENVDreziekS0kZixOv5yS1eN6ZpMJkZHR6uapK+2/s1Qb7x5s7Ry7LWoJe6tFp5sNks4HCYUCpHL5bDb7Xi9Xnbs2EGhUNBci/l8vuoG2dPTg91ur/4eKgqf/v5LrgYVXjHGeGePF2VpUUvOUiwWyGRgHRGuRbuEr50x2E54WRqZqxnvS5IkzGYzVquVHTt2aH+vTARbWFggnU4Ti8W4deuWJsyrJYJ1Io77bXxY63oRblTg6p1jvQ+21THdUChEJpNhZmZGq0g1PDysuZIePXq0rqXbamu11vVdz5VWa+7NrqtTrLamYrFIMplkaWmJmZkZzGYzPp+PvXv3Yn3e41dRFDKZDHa7vSrmV3mDnJ+fJ51OI4TAYrGQzaRxppP8JukknU/y5a2ZcqtDd1mIhdmCVCyWi3m0+T3rbIxmPThs5pjVEsGuX7/Ovn37SCaTayaCZTKZFVXENksnmkFsFdq+RWkz5zTi6q13jloZ2M1wN69m6ao1adVSkJuZv5WC1UjMuNXu6PXEv5UhCkVRiEQihMNhYrGYFhZQu0Vt5Hu+2g1SCEEul2N68nOiLje7rGm+ipfYISV4cuM6zuFtODIZpFwWpU37hdsRHmilEC2nnZZUO9/XRqxEk8mEx+NZNxEsGo2ysLBQ9T11OBybSgSrRzjrGbOZgt4NdL0lXI87utUVsTYbE1ZFNxgMkkwmmZub0/bpVlq68/PzHcsAb3TsWl6EVrmyG3nPG12TEOUiJ/F4nNu3byPLMm63m76+Pvbs2YMkSTx9+nTVAv+bQZIkrFYrlsFhBowG/i/7hvnrrx5z9akR37MkR5JfE87nSVntFIJLPDSbcT5vMrDCnd0m6rmm3XTjbLc7uhtFeDUqE8FyuRw2mw2fz7dmIpjFYiGTydRMBGt2fLmbvkuNsiVEuB2WcKst3d7e3qo4zfLzG91nvNkHkXpFdj02a802ErvdrPjXa2FnMhktmUqNzZvNZsbGxnC5XDXHaBo+P8rTR3z2NMWzkhVDUvDWnh4GTCaKqRQP7HYGZEiUSszNzZHJZBBCYLVacTqd5PP5upoMdAPdIkSdoN731al2h2slguVyOW7evFkzEazZmdabTUTtRrpehFspQPWOsZ6lm0qlCIVCWvPw1SzdQCCgxVs2Mv5GXm8lrXJH1xp7s2wmKSeXyxEKhUgkEkxOTmK32/H5fBw+fFj7HO/cubPpJh6bJhpG3jbK8aUn/HE2wes7PEiSQOSyMLYX0xd/wn7oMLbAAgNju5ANRoR40WQgm81y//59JEnCaDRqCWBqIk6z2GrC2O61dFNSWr3UcvuqiWAWi6VmIlgymSSZTOJ2u9dNBKuHbvsuNUpHRHgjF7Ed+4TrdUerohsOhzULSf0yDQ8Pr2vptiKxq97XG6HRuVsp4OtRyxIuFotaBnMqldKSqex2O6+99tqG19sKpHQK7H4kq40ff+8AP0ynEEuLSM4eJGcPpUiYkseLcutauXDH08dw4CXkXpfWZCAcDrNjxw6sViuFQkG7GS4sLFB4vsc4k8kQCATWLPrQLtdtMxNuttpNuhu9APV8HqsJ9Wp5Dvfu3WNgYABFUUgmkwQCAfL5PPAiEUy1mJuxrq3ElrCEG40J1/pga1m6kUiEhYUFnjx5olm6lRZSLUu30W1WnRbhWq+3wh1dz7o2Mq+iKESjUUKhELFYjBs3buD1etm+fTsOh0M7Z2FhYVPraQXC7oB8DpHPI5nN/P6rx3z+JMIPbCle/+5LCI8P062bsH07UjZTrqQVCSEkCannhdtQfW+rJeLk83lu375NsVisivWpN0a1XGK3iNo3zQpSabcINzNRrh5BVDP+rVbrqolg6i6RZDLJtWvXqlpDLk8E66bvYzNouwhv9MbbrphwLUu3r6+Pbdu2rXl+qy3dZoj4Zr64jcaEWyGyteaF8g81Ho8TCoWIRCIIIbRkqlgsxquvvrqpdbUdXx8iFgGXh385OYc1OM//Yrfz6sgMIhZFGE3IPj/KwhyS1Yrk6EFEIwBlIS4WEYEFxMjoqp+/LMuYTCaGh4e1v6nu7EQiQTAYZGlpiVAohM1m01zZ6v71evmmimezaNb1aXcC1EZEf7XjKhPBvF4v2WyWw4cPUywWSaVSpFKpFYlgqgVd6ztYKpW2hMW8JbYotSo7WhXdRCLBnTt3AHA4HCtigc+ePau5T7eTlm4rRRgay1JulQW/2rzpdJpQKMTc3Bz5fJ5sNovP52N0dLTq82vkJrVZ13sjyH0DFJ/NYI2HSMgWohYPJGIoiwtgdaBEwhj27IdSqVw9y+1BRCOIYhE5EYNdu8pdmfoH63rvkvSiZ25/fz8GgwGXy4XD4SCRSGh7movFIrIs43A4yOVyZDIZrFZrQ9+zrRZbbhbttoQbEc7NzFeP9Vp5jNFoxOVyVSVBClFuDfnw4UPS6TT37t3TEsEcDodmOaslPguFQtf3EoYt4I5upiW8mqXrcDi0ilRe7+rN0rtBZBtt19gqi7QRd3Qj80LZlapaadlsVttOMTo6SqFQYHR0dNPzdxNKLoeIRdm/a4hPlszsT87yPyR283PxAPvNKdi9m18/THD1UYQ3+gy8PWyCUgEpb6Tk9iIZDEj+/g0JcSXq52AymfB6vVW/E3XrSjAYZGZmhmw2C7xwZ/f09OBwOJp3Mb6hdGtMuJmWcD2Z3esdI0nl1pDq71zN1F6eCJZKpfhP/9P/FKvVSjQa5Te/+Q1Hjx5lcHDj3/12sCVEeLMCpIpuJpPh2rVrFAqFVS3dBw8erPvh11pDM7oo1RL59WhlzLiV7uiNJnUVi0UikQihUIhwOMy9e/fw+/3s3r0bm+1FRyG1JGSzqZVI1iqUuVkkg4H/6N/7Ab/+n77kvmGUgZtfw8lxiokkSnCJG7efEjR5uZIs8PaAG8lqxzAwBJFYeX0GAzQgxGsdr25dsVqt7N+/H6iO8wUCAVKpFMVikXw+z9OnT7VY83JXom4Jbz0RrreOc7Ot6so5V0sE+zf/5t9w584d/uk//afcvXuXCxcuMD8/z+nTp/n7f//v15xjLaampvj5z3/O5ORkzWMvX77M9PQ0Y2NjAExMTKx6XNeL8EYEpNLSjUQimugaDAYOHDigNdVeTqtbETajFWIrLfH1qOcBoBFqCbgqurFYDFmW8Xg8bNu2jVwux8GDB1d1N32TbtJCCGS3G2UpgIhFGHFbefxojhvO7Xx2b4ldbg+SwcBLIy7EvVmOHNmF5OxFdqyspCUZDAizBbIZRD6HyGYRXn/T11wZ5+vv7wfKoYKZmRkcDkfVntLKG+hWcR92im4U4UZjwpXUm3BV75wul4tt27bxj//xP655bD1cvHiRsbExpqamah57+fJlLly4wLlz55ienubEiRM8fPhw1WO7PjFrPQFay9L1er1VN+gvv/xyzX669aypGSLYaXd2p9zRG4knC/GilrZaEjIUCuHz+RgbG1vx9LserYrPtmrc1VCvj+zxgSJQlpbwS3kCssJ8ycS/ms3ynwwLDHsP8PbsE07s3AOZDAZfH0o4qI1z6W6IPz5NcnzQwrsHfJDPgcmM5OxFLMy17f0YDIY13dmJRIJwOMzS0hJzc3Mrmlqon3czE5i6iW61hJtVHKTZmdb1zFkoFNY0vDbDyZMn6z729OnTmrU8NjbGpUuX1jy26y3hyu09a1m6siyvaRWpY2xlkWzG643QTJfy8nPVLNxQKEShUKhqYHHr1i327Nmz4fW2yhLupIUt+8oW63e9z/hqWsafj5A02cntOYCkKBj2H0YszsPITq3LkhIOIuVzXH0SI5LMc6VY5N09vWUBtj+P0/r6MD1+3PL1r3XjrKzEJITAZrPh9Xq1GN/i4iKpVAohBGazmVQqRSwWw+PxrPtgvdXoRhGuxzLdyHytckevRT6f31AGf7OYnp4mHA7jdruZmppibGxMc0mvRld/i4Uol0WLRCLEYrE1Ld3Jycl13Vi1RLbb3cXdbAnDxkpE5vN5rUhGKBTC4XAwPDy84iGq1pyNiH8raOQa14vs8/Puy/A/PsjTs/QUZ1HCGA4i9u2DxXkt1itKpbJV4fUjP3jAG14zf1RM/HjYDKbyf8r8MyS7A8nuoOhyoyzOIw8MrTpvJ66lmu06MDCg/T2Xy3H79m0SiQSLi4uUSiVtP6m6dapey6cbQxbdJsLdKPobmbNQKHREhKempvB6vVy8eJGJiQnOnz/P2NjYmpZ0R9zRayFEuQG6mr1cKBQwGAxaTHez8aJWx3y3+uvr0WhMWAhBMBjUSkKqGba7du3CZrOtKB6x/FydamSfH49jlmjWzwERQDF5UO7eQtp3CCmwQCm0hLzvoPa5lHrdnNi+nb/ncKBEQoiSAqkwfxk18+mf5nhjyMIuhww1tvnV8zm3GovFgsViYfv27drNtVQqkUwmtT3NuVyORCLBgwcPqmoXb4X9orVoprXczvnqpV53dL2WcKdyDMLhMNPT00xMTOB2uzl16hQej2fN695RS3g10V1u6cZiMZaWlhq6mM2I2TZiSXdaZNsZE64skhGNRonH4yQSCYaGhti3b1/VD7ZWPHmz62qG+32tMbrhweAfHXTzp0cKR/ftxRC8CUePIu7eKidZDQ1T+upzOHgU2dlTfi/P3baSs5fSk4cYRnZy5U8zhHKCK88y7HfMIB061NI1t8rdqu5hrtxPeu3aNYaGhkgkElrtYtWd3dPTQ09PT1d8jhulGy3hegWxWfNt5Lh8Pt/UmHC9jI2N4Xa7cbvdANr/Tk1NMT4+vuL4totwqVRidna2Kqa73L1cSTPcfO1wR3cyu7qVIgy1C1SoTSzC4TCKotDb24vP52PXrl18+eWX7Nq1q+nzturc9X7c3eDCFMkEP9njYWJ8Z7lwgWEbPJkGjxeRiCMlJQy7D6A8vA979gHPP/9CASW0hOz2ItJJjo84uDKb4g1XiYKtH+IxcLk3va5uuDYqle5sFSHKhR4SiQSRSIREIqGVR1ze1KKb3kslzRLhZrp9u90d3QlLeL3472p0xB1tMpnWTaSqpFbd5Xpoh4i2egtRq19fi9XWls1mtZhuJBIhn88zODjIkSNHNpQs0yoLvVM30bZYV0JAxfszhoPIL7+Cks0g4rMYXn4N2WwGWUZ5cA9JebE2EVpC2nMAEjFEPgfZNGJ7P1IuDut8bu2yGlt505ckSXNn+/1+4vE4R48epVgsak0t1KYCkiRp/XRTqVTHejSvRjvjxt8Ed3SrYsLRaFSzcKFs5brdbi0J67XXXtOOUfcKr2YFQwdEWJZlBgYG6nZh1BK4ZowhSbUrUjVqSTci0vWM32hFrfXOLZVKLC4uEgqFSKfTWK1WvF4v+/fvZ2ZmhoGBgao+oxuhVTf4Rq3obrnpLkfq6UUk4ohEnEuPktz+Ksjx+Fe8LkeQDx3VxFQyW5A8HuQnMyiJGJRKGA4eKRfr8Pi4+nSBUNHIZ/cD7BkzITmc68/bQhd/JzEajVWuQ3hRgSkcDlf1aLZYLJrF7HQ6297Ttpku/Y3cfxudTz2unmOalWkNZXd0My3hy5cva1uNPvroI15//XUt2Ur994cffgjAhQsXOHPmDMeOHWNycrL7tiht5AbZjlaFnXY3t9oS3qgIl0olotEowWCQaDRKLpejt7eXnTt3NrXVXSM35VZZwptNamlHdrQ2V08vSjzG7S/v8sDSR//9Gb7zk0NQLPG733zGJ/MGciYL7782wt54BJFIIO987iLrH0QEFji+28ftL27zyg4Xwmzf0PwiHgODoaZwd4pm3C+cTidWq5W9e/dqY+ZyOZLJJOFwmJmZGa0tn9pdraenp6Xuz3q3DDVrb289dMIdXS/NdkdPTEwwMTHB2bNnV7x24cKFqn+73W7OnTtX17hdlR291vHNsIS7OSbb6PiNni+EIBaLaaIrSRIej4ehoSFGR0eZnp5esw5zq4S0G+kWS04IAakkUk8PycchAofGEaFnEA5xOebmSTKHZMjxtzdm2eM2I/e92OYjyTKib4C3l67z9om9FN1eZm5eR5RK5dKWteaOx0CWy92ZUsmmCnG73Z8bQZIkrFYrVqsVv/9FlbGvvvqK3t7eFe5stZlAu5PAujWOW6+13Mws9k7tE94oXb1PGJrbqrCR1zsZU67nGmxk/spkKnUPdiAQ0JKpKn8I2Wy2pUlfjdANVmknEIvzSANDiOw0Rq8Xc3ABw8QhRDzKW0vPmHf7MKUTvDnopNC3C2IRhNmMZDIhhOD3f/iaywETb+408JNBKyW3FxEMgL9/XSFWBVhylktiili0biHuNnd0s74fainVym12iqKQSqVIJpM8e/aMeDzOtWvXsFqtVU0tNurOblWGeSM0Uzib/R3ZKmVQu16Em2EJb/ViHNCYu1mSJPL5PM+ePSMcDpPL5bRGFtu2bePatWua2221c9ejVclV9ZzbKmp5DTqN5O9HBAO8PNbP3N9c4w9mN9mbGf7Jd7bxQ0Xh+5Ewcq8b486d3Lt9G2n3bkQ0DM/F9nIAQlmFq3MZfnIYkCSk/kHEwhzS0Mqe2UIISMahp1cTYADJ5daEuBl0kyXcyOcsy7K2FQogk8lw5MgRstksyWSSYDDIkydPUBQFo9FIT08P+Xy+Zgzzmy7C6vVoFvl8XvsMupmuF+F6LeH1vliNimijIq0e08r5l79eKBS0ylTBYJBYLMbQ0BB79+7FarWuu9aNrK1TVmc9LvbNjrvZ8xp9WKx7LqMRvH7+bHGe/6+pl/50mOkbJcQRB7jcyLksGE2UwkHE85u63DeAsrSIKOT54YCR30dknBYD/+w30+yx5TgwmnpRynK1OdPpVQWaXldb60/XQ7Pco7XYyHdMkl70aO7r69P+XigUtP7MDx48oFAoIEmSlvzldDq1PIxuFeFuFHTQLeE1kSRpQx9avQK33pehHZZuozRq6ZZKJW3bUDwex2g04vV62bFjBxaLBa/Xu2ZlqvVoRtLYZsduRBAbodu2Pq2GZDTC2D4Gi/d5KmzsJE8pGoJ8HtnmQMTCYLUhDC9+4nLfACKXZcKdY0KW+c9/N084XeBaLAvFItJ6+4S9fq0mNcCvv17i0+kIx33w0+/shqXQuuttpji0Q0DbJWYmkwmPx4PNZuPQ84Ipqjs7kUgwOztLJpMBIJVKsbCwQG9vr1Yzv1Pr7vaxoNz6VI8JN4F6RKAdiU2ttHJqPZistn4hhFaZKhAIUCwWkSSJgYEB9u7dWzVeI3H1Vj+ANGJFd7vbWEV9QAoGg9jtdnp7e5tSSlHKZXhq8WKRC2RKeZSnTyCXA68Xw+4DSEoJw+wsoliE5xaBZLGCxYqIxzg+bOP3j/Ps76FKgEUmjYhGkDw+JNVrYjIhOZ2aEH86HSEUSXIVBz+T27tVpx3UK2atYLk7W53rq6++wmg0srS0xOPHj1EUBZPJVFVspFsTs+qh2SLc7C1KrWJLiHA9xzRq6TZiCbcadX2VlamKxaJWmcrlchGNRtes1NLo+ltpCW+WWg8trRi3XoQQWuwvEokA5S0LHo+HQqFQVUrRarVqN9y1rJtV50jEAdizzc3sYpKfFZ5C1I58+ChSoYhYeIa0YwyEKO8N3ln93ZB6XbwzJviRNcoT5UVilcikEdks8tC2qnaIAJLJBG4vytIix31wFQffH/OyFWnWzb5d3hFJkjAYDFUNLaAsNGrt7Lm5Oe3B3O12a8JstVqr1tmtLuRmlsAEXYTXpdmCVqsYRqst5VaRy+UIhUIsLS0RDod58uQJPp+Pw4cPV7lZotFozbFaJZSdTMxqtyW83pzFYpFYLEY4HCabzeJ0OvH7/YyOjmIwGFAUhUwmg8FgYHBwUFuj2soxEAhoLftUYc7lcmt/rw0GyCr8hzsVAm4rizcM/L7vJX6cSoPTieTtQ5l7CkoJspkVpwshIJtB2rkbw7WvEMUCFAplAfaUhVX2+stCnMu+uAbPhfin3/FvyAJuNK+j3TQz/tpKzGZzVY/mR48e4fF4kGWZZDLJzMwM2Wz587PZbPT09DT1GjfzGrQiO1p3R7eJRi3dRrcANYtisaglU6VSKcxmMz6fjz179nD//n0tZrScZmVvr/YD6GZ39Fq0Y02Komh7q2OxGMViEbvdzt69e7HZbHWvU03W6e/v1+ZWOwGl02kePnyIwWDQqjVp21vsDkRwCcPSAn+4m+VTsYNdn97B/JKfN3eZwGRCiBKWxw+QXn51xfsTgQWkvn6kQpGS21tubWizIfurLS3Z60d6/BgpnwPKFrO0yo2tnmvWTQLbrGO65T2pCCEwGAz09PRUVbETQpDJZDQPjdpYxWQyVVUB26hoNdP93e3FOlrFN0KEW13WsVU/NEVRiEajWsehmzdv4vF42L59Ow6HQ5tXUZSG1tdqEe6UNdtuAS8UCgQCAWZmZiiVSrhcLvr6+tizZw+BQIBCoVC3AK83v1oUIpFI0NfXh9PpJJ/PE4/HX2xvyeexJqJkJQMLFhcDiwHiAq7enOPNl3dS+PwKpFIUPF6kdEprzlAlwLIBKALPs6dnnyDcXiSjiV9/vcSV6QhvjnnYJwTQ+gebdm4/amcCWDuFer3fsd1ux263YzabcTgc7NixQ2tqEY/HmZubo1gsahXDcrkc2WwWi8Wy5ntopgjr2dFtpNlfylbHhJuFEIJEIqEVyRBC4Ha76evrIxaL8corr6y5vkazpxsRtFbGhDd73dvhBiwWi0QiEYLBIMlkkmKxiN/vXxEOaDWSVG4+0NfXV7W9JXnnNtOLi3xvWOL38zkMkol7ip3Qb3/N54qPz9wHOJZ5xra5p4j+wXIhjkwGyWp9LsBw6W6IS7dS/FQkeXuwD+XpDIZdu7kyHSGYKvDp1wvsOeCAGi3h2iVq7aIbtwPVw0bXpHrbfD6f9rraozkQCPDkyRPNnW2327UkMDWxsJ447kZEWE/MagOt+EI26o5tZcw3nU4TCoWIxWJMTk7S09ODz+fTYoWVa1yPVorweq/XY2V3imZvJRJCUCgUePz4MfF4HEmS8Hq9mmfi8ePH9Pb2dk2cyTy2B8f0A/7dlw/zv09nMSWi+Mlgdrr4NOTBPDvNH01mjj59iq0kcOw9QE9PDwalhIjHkHpdXHkco5hI8Om9Jd4eHEQeGkaJhHhzzMOnXy/w5h4vmFfGlLcyzYpjdkNMeLX5Gn0wUHs022w29u/fr52TTqdJJpNViYWqQBeLRZxO56rFNprdorBe9JhwG2l1g4aNkM/ntf262WwWm82Gz+fD6XTy2muvrbuGzbymvt6ou3qzdDIxaz3qHTefzxMMBgkGg+TzeTKZDNu2bWNsbGxFWcFus+REqUSpxwWxCAZ/P1ImSUIy86ehvfzMk+SLBzmGB2wMjx8h//Aeqa9vslgskXN5MBfyOO12vuPM80cjvGVPILkPau/xbXeYt9/egezs5f79+9XzKiXNkq57rV3kjq6XZln33WwJ14skvejRXJmhfefOHaxWK9FolNnZWUqlktajubJ2dr0tCpv5UKO7o9dBCNHUH1wztiBt1i2quiyTySRffPGFlq24e/fuqvjgs2fPNjV+PbTDXd1t58LmLGFFUcjn89y7d494PI7JZMLv97Nv3z6sVis3btzA6/VuuK5vJ7axSQYDcqGAvGMXJ5OL/DdLA+yPzfDZ1Rv84oCBN068zMydrzEszmG32XB6/Qza7JCMIw69QnL6AePmGCOjecx+N3f/9nfYRnfS09uL02LFmMuhFELa+wNQouHyXmRnz4abN3SLwLYz87nb3dGNIssyXq+36l5XLBa1YiPBYJB0Ok02m+XBgweaMNtsthXCXI9Yb2Ttuju6jTRqCW/EHa1mxaouZrWAu9Vq5dixYx250XRShKEzhTE2cp3T6TTBYJBQKISiKBQKhVWLmqh04v1slsLQNkQsyruHB/hvvwgzb+rhu4tfwts/QwrMozh7EYUCcm8vkgSy0Qi79lK69gXuAy9hHRklOzvLXr+XYsZLKpkkmU6xmE6TN5kxlooUE3GiDgeGZAKzswfJ7d1Q84Z6aWdiVjPW8k2ICbdiPqPRiMvlwuVyAeXf39OnTxkcHNRKdKrubIvFogmzWnCoWWvX3dFtpBmW8Fo/TCHKhRfS6TRffvkliqLgdrvx+XyMjY1pT26hUKhjT/rNSEzbipbwWqhP4rdv3yaVSmG32/H7/Rw5cgSj0cjk5KR2g1htTevRjQJt2Lmb0uOH9FDkcPoZV/sO88GThyhjezHdvgE/fAtpYR6MJrDayiUv7Q6KN79EeuU7GGIRpF07MXm89Dy8T49RwnjodaB8Le9ev0bh4X2mnT3kZYOWPduDwOnOY/XULtjRbdetXZnPWzEmrB5TD/UmZlW6qCv/rmZnh8NhYrEYt2/fxmg0VlUBs1QkBW6koIfujm4jzY4JZzIZLa5bKBTo6enBaDRqN/HN0Mofa6uzo2vN3a7GBcvnVdcshNC270SjUYQQlEolduzYgcOxdlOCzczZrcg7xjiausJdxxBjxhyGl8chHKTk7EW5eR3h7MGwcw8UC4hoGNnlQTEY+Zu/+D2X0r2c6Inz9nYrKArS8zieJEkYjUasgP95eEX2+PiLW4v87qsgL/cbORZ8QE4I4vkC09PT2v5Us9m84nq16/rV811vp1DVI1LNop511xt7bVYy1XrbpiwWCxaLBb/fTyqV4uDBg5rhs7xHs91ux2azUSqV6ppXd0evgSRtrIFDvWM2Yunl83lyuRy3bt0ik8loyVQHDx7UPsTJycmmttlqJs1wRzdCJyzhYrHI0tISs7Oz5PN5rYTnrl27yOVyTE9Pb1qAN7umTlg9KsqjB4SH96LMLTGdNfLf/OV9Pvz3v4+0uASP7lHatZc/fXaDi/MSBZOVv3PAx9v2LJ/m3IRzJT6djnDC7UDq7UXy9/Nv//iAz0KCN8c8jFmsEFhAOlIu+vHZ4xjxAtyOSvx7x0aQBoa4fv06fqNM6tkMjxbN5EqKlszT29tLsVhsiji2i3rX0m1x43pFsVbOQztEeK3jDAYDbrcbt9utvaYoCul0mkgkQjqd5vr16wBaj2b1v8r3VSwWN5zb0Qm6U1U2yEYbMJRKJcLhMOFwWKsaoygKu3btwm63t2PJTaVRS7eVWcq1qPfhQFEUbc9uIpEgn8/j9/vZv39/lbuqGWva7Ho7gfpe5R1j/Pu+x/w/501IZhNXEwaUJw8xLM6Dpw+Dx8fXf3xGINdD2Gbl6lyWd783wBvbIvzlkwQ96Rj/dErw/V0eTpQWuPIsTbhk5NPpCHt9Mdh/EBEOIvUNlLcvPe+gJA0MlS3mZIKe/fvoHd3JwNIiksuDMBpJpVLE43GWlpbI5XJEIhEcDodW/Wt5XeN20CzR+ybHhDshwutZ6GoIxGAwkMlk2LdvH0K8qC4XDoeZmZlBURR+/etfk0qlyOfzLC0tadXoupUtI8LrfZD1WMLZbJaHDx8SjUa1ZKrh4WGcTieSJDE5OdlRAW7lNqFuzo5eCyEExWKRx48fE4lEUBQFj8fD0NAQ+/bt48mTJ/T09KwQ4Mrzm72mVpzXLCSDgdePHeDgwy95kDHwiiFG4XEcQyqB4YdvIc3PcnDIweNnBVyGLG+OjSAPbePHSBwwx/j/zJoIp4p8+jDMie+6eXObnatBheM+iZLLg2y1IhnLXZTeOdDP256CJsBKJIRisSBZyxmyau9iyeXRxNZisZDL5RgaGtIyZ9W6xqqrMZ1Ok8lk2iLM3ZQd3QlLuJ0ivJFylBtZlyS9qC5XWcRmZGSEzz//nM8++4wzZ84QCAQwmUy8/fbb/Mf/8X9c1zpWY2pqip///OdMTk7Wfc6ZM2f4xS9+UWXVL2dLiLBq6a73lFT5g1BjCmplqmKxSKFQYOfOnezatauppdG6ga0qwlB9IysUClrLv2w2SyaTwW63MzIysiIUsN68rbqBd1poayF7fIx6LGREHikc4X+4ZmZk70v8/dASksfP8Vd28b2ZRwilxKVwhP/sz1O8PuLgSDLBm3t3cHUmyfEBI4b+QX7aN8C7gQUkfz/3Hz4E0Jo3iMX5KgGWbHaEufphSO4bQAksgNdfTgRT/75Kmz7V1Xj//n0WFxd5+vQpUK7QpB5rt9ubKlTNOAaaJ+bfZBGuZ+sR1Pf7qicxy+fz8e677/LP//k/53/+n/9noBxyDAQCNcdfi4sXLzI2NsbU1FTd50xNTfHxxx/zi1/8Yt3jtoQIq5buWhdfkiRyuRyzs7OEw2Hy+TxOpxOfz8fIyAiKonD37t1NNbXfCLW+kK1yTXVShKExF20mk+HBgwfEYjEMBgNer5exsTHsdjuTk5PrupJa5RruNpdzvYhEjMN7tvH0t1P8wbmb7yQfkL37EPYc47cFD9f/1RQvHdzOT3KPuRLwEFYkPrsb4MDLu3i33867wybkvnIhBkmSkAaGyuNWXg+hgBDlzkpWW7k/cS6rHaPWnD6+08U7flHu9rR8jGWorka73c7OnTuxWCwIUa7QpLboS6fTQLmx/fz8vNbIYvnvqZkC003WcjNp9oNBtz1krIbZbGZkZGTT8548eXLD50xPT6/ZXraSLSHCq8V8VatJbX5gNBoZHR1dNUZYKBRafmOtZa2rQtcpEa5FuyzhbDar7dlNp9MYDAb27t1btd2r3nmbtaZ6x4XuFWglsAAWK98fsfNXfh/5ooNUvoedliClXJbfz4cImzyIz2/zzv/pLd68eo8roTzf2dsHUlprX7gWkiQh8jlEIl7uNRx5XsTD7kCkwZBKADyvOZ3nyp0F3v2HR6uu50aurZrU5XA4qlo/fvXVV8iyXFU60Waz4XQ66e3tbbiBRuVauknMN0K3WefN7I7UTcl763Hx4kVOnjzJmTNnah67ZUS4UChoRTLUZCqv18uuXbtIJpNks1mGhoZWPb9WzLgZNGptNnpz71R2dD0/5qWlJa0JgtVqxe/3c/DgQVKpFOFwuOUeio2yVbKjq5AkRCSI7HTxgzcOMfrHL9n/nTGGMhakaJQ3DBLX7s8yPbSPX/9xmndfHeVnHi/pdJrZ2TRKJAzFgmYJL0fkc4hCHtn3PPZWKKCUEsg8F2KjCRErW8BX7izw5sHhluyCUBvbq6UThXjRk3lxcVFrcH/37t2q1o+VD3jtvJFv1eStjezHrWe+Zo3VzHW1img0um4MeDld28BBCKGJrhoj9Pl8WmJO5ThqM/S1aGWDBpVmuHw3+2OsdU473dFqPD4YDBKJRIjH4ySTSUZGRrQkOBXVxbgZWv1Qs9ac7abu2KTHixIKQg9YF55y3+Dh2tcRYrZBTtsX+HH8j9wY/A5jiWf8aXGUd4sF7VxDMo60fQSMPShLiyuFuJCHZAJ5uOzOUwILSF4/hJZQkmUhFlYbmMy848+usIDrfR+b+f5LUnVPZkVRuHnzJqOjoyQSCQKBgHZ/UHsyG43GpvwW22ktN5NujFN3YqxW8cknn3Dq1Km6j+8aS1gIQSqVIhQKEQ6HURRF2/uZy+XYuXPnmtnL7WjQUMudXI/QrWeN13JnN0KrE7NKpRLz8/Paw5LT6cTv9zM6OspXX33Fzp07V31fzfhc1lpTu89t1edW97FGE4Zt2ynevs4fQgpXU1as+SKHItM8smV47Wc/4XtPU3wme5kwLkHPYQBENIIwW5Bsz39bbu8KIZaEgOdrUQILXAoIPv18mjfHPEyYQghL2QUs2R1I9rX3Zrfj5qlaXZXCrP69cjtLOBzm2rVrmM1mzWKu3GfaTBGuZ83tpFtFuJ7r0O2W8OXLl3n//fc3dE5HRTibzWpx3Xw+j8PhwOfzrahMFQgEagpYrS1KjVKPCNcjsrXGbwXNFmG1frbafUhRFHbs2MGePXtWxORamancrfHZ1WjL07vRiGHbdl6JzvPXc0m82RjDmRAvbfcgQkHesMObfQ6Me19HeXQf4fGB1YqwWF+s83kGdKUQKyYzGI0UPvs9hmPf5dPPn5R7Dd9Z5J13diEScVBKrX9/DVC5ncVms2E0Gtm9e/eq+0xNJpNmLatdgVajW2PCtehETLiZru2tYAmrTE9P89FHH/HBBx8wPj6+6vEdEeFwOMyjR4+wWq34fL5Vk6kq6aSA1TtHpzOU16MZc6tNEMLhMKVSCZfLRV9fH36/n3A4vGbm4XoPL620hFuR7d0JF/hGkGx2hMXKW8d286+//D3FUoqC1cbeI3tRbnyJfOAQhj37UZYWweFEmXsKY/u087XM5l0u3vG9eKCUVHf0rt0ojx5wfJefi5PPmC/IXHoQ4SeuPKbQ0rpra1eW8EbHqCybqJLP55mfnycSiXD79m0URcFoNNLb26vVNK7HpV3ver7pIlxvTLhea7nTIrw85js1NYXb7WZsbIyJiYmqY0+fPs3p06fXzZLuiAi73W7Gx8frLilWy8psV+KVoihrrrnRB4VWP0hsdO5isUg4HNbqMU9PT+P3+zl8+HBVZxK1VvNmaNRt3GnR60Yknx/l7m3mSxYOFhZwZmLw2e8x/Z1/iIhFKE7+EeHsQS4pmN74AfmF+XLMl4rM5q/Lmc0AIpdFzqSR9+1HzudQDEZOLDzjSq+FeCrPl7dnmDixD8XurOqqJJQSYnEByesrb2Oie/ZZ17qRqy5qWZbZvn07UBbmZDJJLBbj2bNnlEolSqVSVbOB1crattOtXS/d6o6uh065oy9fvsylS5cA+Oijj3j99de1bUvqvz/88EPt+Gg0yvnz5wE4e/Ysp0+f7i5LeKMXsZmtCDdLq0W2k5YwlL/cahOESCSiVRUbHR0lnU7z0ksvrTl2I3O36j23+8GgWwSGTAbJYmW3EiYjmRkSKXj1hygzj5AkiWI6g7Qwh/zqd1CePQX/APLCAiKf4/guF1e+fpHZLHJZRDJJqdcNgNTTW07C2rGb70dnuZbOcGRsENnnp+SYg2IRkUqCzYZYCiANDiPCobqXLoSAVBLhcm/6ejbrhr/8+6P2Cfd6X2zlWlhYIB6PE4/HmZub02oVqzHmnopGGI2uuZlx1a0swp2yhCcmJpiYmODs2bMrXrtw4cKKv7ndbj788MMqYV6LrknMWo9usYQbSbzqRhHO5XKEQiHm5+eZn5/H5/Ph9/vZuXNn1YPSel/6erO+N3vuZuZthktzM691A5LDiZBk/o/uGFcxcrlnnH/zeYE383NkjBYO9dt46+grEF6iVCqBfwDh9iIScd7xKdWZzbkc0vPCGZfuhvjj0yRvjnl4u19iwpHixA9HMQyPaNdEcrlRImFEYAF5524kSSpb5qEgIpcF89ohJwA5lQQhEIEF6B9s2c22WbFcg8GAw+Fg27Zt2t+KxSKJRIJEIlHVN7dUKmnCvLyzTzeKYrOt12aOtRV6BG+EjnVR2sjNrBss4W9CTFhRFKLRKMFgkHg8jslkwu/309/fj9vtroqLbXTs9V7vBN0ulq1CxKKIbJq9O4f4H++ZmC44+F7kAVMWF15ZEHiSwmQP8sOXd0KpCE9nwGh6sf+3AqnXhYjHkDNprj6TiGRKXJmOcMLXC243hudbln79dZC/uJHl7xgWmTCHkYdHqz532edHWgpoGdarrjseA1lG6ulFkqVNC3EzM5Y3I9RGoxGPx6PtfQ+FQqRSKXp7e7W9zIVC4UVP5udu71atZ62x6mmv2O6YcL1j1bOu9RLqug3dEq4Yo5XZz81ILtvMD0Mt87e0tEQmk8HtdjMwMMDevXu1sWZmZlqSyAS1LdbNCnirLOFac3Y7wmhEhJaQd+3lq3tRzGTJIvOSo8hiskhfMc7DRQs/HhhCiYYhGkEW67znXhcoCscHzfwxUOSNASNyTy94fFoG9ZVHEWJ5wZ9uPeXt7w8hP28hqSZ6vTnm4RWzGSmfX33NzwW4ZLWVH9JlA/T1t9QibpbLuh7BX601X6lU0gqLqNXjUqmU1vpRtZjV8dttmXYio7neLUr1jJXP59dN9u0mtoQIt8MSriXCzYgJN+qurodisagVOEmn09jtdlwuF16vlyNHjmxq7vVo9MfVjRbrZte01nnqZxIIBLDZbLhcrhW9T5uFZHcg79yNshTAWCxgEoIZm5//0vg1/4vJx1eWIV5xOSk9eVher8eHVKOwveJwcqKvh5+N5JDc3hV7io/vchMPBnl9/wiSx4MSCSN7vM8TvQp8em+JVw4ZkVzuFWOLfL66Epf6PmQD+MtCrNawrof1fsMiky5nkLcxTrvWMQaDAZfLhcvloqenh0gkwvbt27UOU0tLS1oze6fTic1mQ1GUtmWQd6v7u16rOp/Pbxm39ZYQ4XZZwrWaRHTSHb3WQ4IQgng8TiqVYnJyElmW8fl87Ny5U2tqn8vliEajm557PRq1Ztdjsw813ZA5nUqlWFpaIhwOI0kSXq+XgYEBCoWCtie1VCphsVjo7e2lt7d3RXnFzSBJEoZtowBYRIiMbGJHPgWKglXJYR/wMx1L8k+/SPD93T5+aIiDAKEoSGvMLYRA6nUhL49lRsNgNHKiNMPOsQKHXn4RG1Ui4XLf4XtLvLndgXCalw9bXq/ZjJBlRDaDEILf3A3x2aMox3d5eMdbQOpfvYzmRlFCQTDI5WSzdSx/7b210a2tHmMwGLTvghpnVhSFZDJJJBIhmUxy/fp1JElqqCdzN8aENyLC9RxXKBRWxN67lY6IcDfGhDud/VyvJS3LslbkJBgMUigU6O3txWQy8eqrr656E28keaoeGnlfnRbL5WzWBS5EueLb119/TTKZxOFw0NfXx/bt2zEYDAghtIYVau9TIV5Ucaosr2iz2ejp6SGfz2/q+qhCLPU843uzX+HJxblqsjB0eD+xpwvcNPnxZ5N8fr/ID4dHkBfmUBbnkQeG1hTi5ShLi0huL8rsE6Th7Yj0A0QiXo7pPreU33ZHeOfH25B6XczPz685luzxlVsi5nNcXYgQj6W58nWGd//dl8oW8QZY7SathIJITieSxYpIpyGwuOFx16LVQi3LMr29vVgsFrLZLPv370dRlDV7MudyOdLpNDabbW2PQAcs4WbFoOt1kxcKBd0SbiayLJczOdegmTHhzc7RqLt5vYcARVHI5/Pcv3+fVCqF2WzG7/dz4MABLe6hWsGbmbtVMVSVzcZua4UIOi3umUyGpaUlQqEQuVwOk8nE/v37V22xtxqStLIpuRCCTCZDPB4nkUiQTCYxGo1VscJ6LB9JkugrZRAlGMrFuZ/t5/9sSvD6O0f57f0Ij5/EeUVEEdMZFJevnFWtxmDXeJBTUQVYRELI23dQSia11yqFWLLZEYk4IptFZDLImRTCainHmZche3zIuTv8qNfCZDjBsdGhTQnl8s+9UoABJLsdYTIhx6I1x6lHqJohLvUcU2lNrteTORwOMzs7SyaTAVbvydyN7uh6Y731rkuPCTeZRgSsXmqVvmxH4pU6v2pVqRWqhBAUCgX6+/vxer0b/nE0aoU3Onar5t3Ma/VQy9q9e/cu8Xgcm82G3+/nyJEjxONxYrEYTqezoblVi8Zut5PJZPD5fDidTtLpNPF4nCdPnpDL5ZCkcuOCbDZLPp9f4XorBQP8yJPnybMSd3u24ev3cTVhYeGTy+z1O/jxSA/kBIrZgiEeKX8Wdse6QgzVAix5fUhGU1l0K665KsSlmUflY8NLSIkYYngEDAZELLp6fFgo/HiPl5+8eQjS6TWPWw8hBMZwECXSBwgkR1mAK5PEvjNgh0RCW+da4zRLqNoxjpptbbVa2bdvnzbuWj2ZFxcX6e3t1YR5tTXV84BRD816oIH6LWE9JlyDjd4k66kN3azErFa+vt57EEIQDAZ5+vQpmUwGh8OB3+9nZGQEo9HIjRs3VnQh2gitysxu9LpsJg5e75rXG7fe13K5nNaKMZVKYbfb2b1794pOXtA661u9wTqdToaHh4Fydq3aperBgwcUCoUXhSJQsGfT/KN9vXz4bABDOoU/EuJhKY+sGAnff4jI9SAfPAL5PCVfP8U7tzAeOFwW4sV5pKFtK9YhhIBiEYxGKJX4zf0oV6YjvLHTxQ6zGZHPITl7EMkkyuOHSL29iFAQXC4wm8v7jvv6EKnkCoFVwkEUiw158HkLRGcPIpnYkBALIRCL85S8fiSLFSWbgWgYBoa0JLEr0xFe97qQ87k1BVgdqxmWYL3iUs84G0WNHa/Wk1mSpKp9zGoIRLWYm/kQUo9wNjsmXCwWdRFuJs1wNzc6R6Pu5uWvCyG0JgixWIxMJkNfXx9jY2OrdotqRHTqsdIbaYCx2Xhzs9xdG6XWQ4G6nzMWi2mu/0OHDhEMBgHo7V158273e1EF1+FwcODAAaCiUEQ8Tmzyc3LOXmZKZraVkqTzeY73pbhrlpH37of4Q5RbU/D695EKeS4bBrlx8XNeOrCdd3dVW/Pq9ZIkCfoHy9ayw8nN393ibsbGtWdx/sFAlkMHywIsEjEkl/t5JjJIuRxKj4u/eRDm2lSC7+/18852qyawSiSEZHcinm/JqbRa39lmRiQTSM6eFddg+RrF4nzZip+bQ7LbkQEFYHGe47s8XH0U4fiOXqRoGMVbe098M0SoncfUgyRJyLKsibI6diaTIZFIsLCwQDqdJpPJYLVaKRQKq/Zk3sia6nVHN3O/tO6ObjJbpSxlLXd2LpdjdnaWYDBIsVjE5XLh9/sZGxvjyZMnuFyuNds1tjLjt94HiLUs0lpjNzrvWjTreuTzeUKhEEtLS0QiEbxeL9u3b2fPnj1V62/lZ9AMKgtFlAb/EcU//1/pKYBDyZGTDUzff8Y2l5E9WTMZARa3D3HnBvLgKJMxWDC54NYjfnr0zeqBi0Xt/0oGQ1nYAgu8OubnD18EsZrNfJUwIpLJcjclgxHZ40MoCn87+ZDLESMvGRNMpa0k0hmuTEd4e8CLZC+LveTyIJYW4fm11bY2PQzztrcXucYWJVWApYEhSKe1z0wTYgne8eZ4d+8uRCREWO5DyuVqjtks8axFu2O0y6kMgQwMlLPRZ2ZmMJlMGAyGVXsybyQru91WNbBqiKZb2RIi3CxLeL0PutG482qvl0olrQlCKBTCbDazfft2Dh06tOIL0uq4batKarZy3dAaK1qI8raupaUlotEoBoMBv9/Pvn37ePr0Kf39/bhcK5OHthJSOoX8o3fY+fn/D1c+zZKpl7/p2c0JEcCbXCJ9/C1St6+TS6cpBpbYu+O75PNFDo64UNJJDGojhlwWQyoBS4uIoW2IYADZ6wd/Pz+SH5I75OTTpRJ7LeUSlygKkrMHJRJC9vj4bcQIgUU+t/fwhiPBV24fb/QbkexOJOvzZClZhr4BjHfuIIQob216GOa4X64pwKtxZTbLP797r2xJ73je39jrR0TDyP2DiKVyxyclEgZFQfZtvFIc1P8Q2E5ruRnrUbFarXg8nlV7MgeDQS1x8Ouvv161J7NKJ7Yo6e7oJtOOYhzNECpFUbQvaCQSAcDj8TAyMkJvby+yLDM0tPpNpdUiXIvNupQbObeRpK565q1ELZixtLRELBZjfn4ev9/Prl276q6TXWvOrrKS7Xak6fvsf3mMqzcW+F7sAbMmN0c8Gdj3EvbrX8DBo/xhNsNU0sx3jAb+b54IKVOBm3OLMLeIw2TCZZTJO3sRXj+lOzeRto1CMIDUP4hhbB8/HUhyIpfl3vzic/ENI5lM/PZJiqnL0zg9XhL+Pn4Yu8eb33mDf2gyVAmwiiTLFN0eRGCBd/b187a3WLcAS5IEA0OIxXmEo4cvF/PkJIlP7wU5MSCXWzEWI1VZ2XIihuT1gMGAElpaUSyknUlX7RbhelhtPkmqzubP5XJMT08zNja2ak9mNZu/3gpduiXcZjbyhWmGJayOsdYHuNm4qerKfPr0Kfl8XmuCMDo6WvVEmEwmW5r41QiNupRbee5mLWEhBMlkUiuYoRYxURu579+/f91zNzNnVxEOgcvDa84H/M5g4U+9u/mz8G2GD+2Eh3fBPwT3b3NfOkDQ7OTebIATr7jxHTyMYXiEUiZNJrhE3GAiFXrC3T9cBbMZ9+Ln2A6/TM/8M8xD28qtCzMZpFwWANnjRYmE+d1MimReZjAU5J+97GDB8jJSOIi0a/cKAdaQZKS+AcTS4oYtYFWIefSQVwfM3I/JHB+2IBXySH0DSLJcLtgBEI2Axfqi8ldP7woh7rZ4b7MeCuplIxnNa/VkVoU5Ho9z48YNrUWkajFXtn6sN4FtIzFhXYSbSDcU41DPVxSlKqHKZDLh8/kYGhpCkqR1m9tvtS5L9bzeybjuWu5/1dqdmZmhr69vxQNRIzHubmNNz46/HykwjzEYJGLqwyVL/K99x9h5/SoHvRaIR8Dh4HVjEkMgzJF9I0iuHkQqUbZKohGc23fgSCaIF7LsOnQYKRggM3CQv/hims9C8F3zXV7Z5cEhSeQkmWKxiNFoRPZ4OT4c4+pThVf6JAx798P8PKJ3cG0BVtctyxsqU7n8Wgh/Pz8Kfc1/cHRPlQBDuZFEafoBQpLAantxntmyqhDXopkx4XbtN66XRuczm834fD58Ph+JRIKjR49qwhyNRpmdndX6MatNLJoVPwe9YlZNNvpFaZYlvNnEKnUTvLrfzuVy0dfXV5W4EwgEyGaza44vy+Wb1GbX18rktFaKMDTmjq5llabTaS3mLoTA6/WyY8cO0uk0hw4dWnddzaTbxFsymRCxKEV/H30PYzy29vHD1G3+tedlDpofl0V4dCfHFp6xZ9iFf9CEvH0Hss1ejv9aLCjBAJLZwh+SVn7z6xscOrKXHwcXuZV1UjAqfG1x8p61QMzXz9LTp9z5+muk0BImo5Ejozt5c5sHZ/8A0vOKYW25RpJEyddXJcCV2dZv+3tgbhbJ4azOwt7vq0pAa2cMthuTt1oxX6UwqxQKBa1inFqgZnlP5srY7kZEWI8JN5F2FOOovOEXi0UtoSqVSmGz2TAajQwPD7Njx46a56/1eqN7nTebwVyLRuduJK67URRFIRKJ8PTpU+LxOMlkkr6+Po4cOVLl3mrkBrJl4r7rIJIJZF8fxaUQRSnJT8K3uOray3ueJBSsYDDA42noG8AgSUjZDCIeRXJ7weVGPH6IYjRicPbwdSBP0dhD+utnvPXOPnqkOW5E8+y02zAODeMyW+gRCnv7fMiHD5PP50k9niZstjJz+zalUolisaiVV21Gjew13/fzz1XqdZW3QFU0krgyHeGdnSOQzUGpxJXpRDkLezrM255ClQXezphws8bpdhFeDZPJhNfrBcDhcLB9+/YVPZmLxSIGgwGn00k+n6/Lys3n81hreF26hS0hwq22hIUQ5PN55ufnefLkCbIs4/F4GB0d1UoQBgIBcutsa2jHFqdOiXAjsdlmuLKz2SzBYJBgMEipVMLj8eD3++nt7WVsbGzd+Tez5maf1wkkZw+USmAwsC0XYdHkoLeUYSlVBKcFFAGKAskEuZeOYRgeRFmcR/H3IylFKJUw7NqDiIbZ229jIabw2i4fkiyTECaGnQqJvFLe3xuPYUjEQIyAJGGxWjHvO4A7sIC0axcAjx49Ali1RrZauakWG8ojeR7vVRtJXJmOcHzEgchmUXaMIUdCHN/h48qTGMd9EtLA0IY/326LCbdbhOtNptrofMt7MkPZMEomkywuLmrFadQiNpWtH1Xy+fyW2eHQMXf0Rj68ZmVHV4pcLpfTmiCohfI9Hg+HDx/eVBOERl+vZalD41WvGhm7kXk3ui4hBNFolFgsxrVr17RszMOHD2supnA4TKFQ2NC49dDITaXbrGTJ5UYYTbj7veSWYiQlI8riNH9uHqSE4EB8hq9TRi5el/lOOMv/Y7cLikWUQADJ40OSJH6zoHB9qci7R7czsceFElrizd0ertxb4s3dXkQ2jYhHKQwOIw8MVpe97B9ELM4jDw5jNBpxOp2a1SPEixrZaklFtfrX8hrZIhEvV+mqiOOuReXNXBXiE944J2wGsEjIHi9ifh58fbwjCd7x96wqwN0mnt0owvUmU9VDLUE3Go243W5sNpsWZqrsyby4uEihUODx48dcuXIFo9HI+Ph4+8IgDbAlLOF6BKoWkiQRiUSYnZ0lkUho8Yl9+/ZhtVp59uwZBoNh3SYIra6o1YglDJsXgUas2Vrz1ivg+XyeYDDI0tIShUIBl8uFzWbj4MGD2Gwrb76tTFTbDN36QxdGE//RD8d4/y8CHE3NkDaYSS0GiPUOEPMfIB4IMT5/g2zMifzdEyihILLXi9zTgxIKcmU6QlQxcuVZmndHzEguN+9Ysrz7s72IbBrJbC23OZybR5IN/Dokc+XT67x5cJh3dznA8sIlWHmNJOlFgQi1etO1a9cYHBysqpFtyKbLbftsNhxe/8avs8EAhTySywMIRDajvST3rd0msVk373aKZzMFp9mlJus5ZqOCXtmTWWXPnj24XC7+5b/8l/zqV7/iwoULWCwWjhw5wi9+8YtV7yX1MjU1xc9//nMmJydrHnf58mUAPv/8c375y1/idrvXPH5LiPBmb7iVTRASiXLW57Zt21at+9sMS7aVlnKjr69Ho8lVmxlbCKHFdL/44guMRiN9fX1VnaFu3bq17viter/fhJiwirBYkAe38+PIX2Mv5UgZrRiHhxhKxhh22fjCsp2Rr7/A5xIUrn2BvHMX0twzpKFylv8bfXGiMXjTLyN5vEjGsidCZLMgBJLdjmQ0Ij1+gsikufooRqhk4NObs7wztAPZ4617rZIkVdXIFok4pVKJlCSX99/f/ZpEOsPNmze1pJ3e3t4VW13U74zI5xCJOIaRUe11JRJCZDM1y2DWdW27TKi3Yky42WM5HA5+9KMfcfv2bV5//XXeeustcrkcN27caKiM5cWLFxkbG2NqaqrmsZcvX+bDDz8E4OOPP+YnP/nJusK9JfYJ12sJqwUZgsEg6XRaa4Jw5MgRHj9+TF9f35pxgmbFZFv1OrTOHd0ugS8UClrBjGw2S29vL2azmfHx8Q2HAFpleday3LuJei0MclnmzB6OJJ8yb/FyypVk0unhjzEjP0h+zdAPDuFXCshGA9LMDLz2XUoP7mHYs4+fvjzMWPwBe44e1gQYQLJaEZkUIpNGyAauxC38qz+/Ra/DiUKBN3e5qgR4o9+f0uOHSF4/RrcHF+ByuSgNDXHvT39gz45RksWS5soulUqYTCZ6enq037EqwMu3HMkeH8wvlN3bNa5bO93ItehGUWxW0hnUX4SjXir3CVssFl577bWGxjt58mRdx01NTfHRRx9pInzy5EnOnDmjFTVZjS1tCavWlLpn98aNG/h8Pnbt2rUi0aMeS7ZWz+JWJl612tJej1aJsBDlMnfz8/M8evQIWZbx+/3s2bNHcwut1wdZHaMVtMLa7UYr2RgKoORT3HCPYVXyHI0/RpR2Mh3O8tLSHT7z7+WDeAzD8CDy7v1IRiPi7i3Yf5jSg3vII6MoZjPiySPE3gNIFeIle3xly1IycG2pSN5gZzQU5L/62SFkr2/FWuoVCCXwXCTTKURPL7+5E9S6NY06ezEk43iGRqoSd9Q9qNpWl2AQmyhhz+bp7e3lb59m+OxRlDfHPByVJCS6Szy34j7hZgpnM+PL0LmylePj4/zyl7/U/h2NRgG0XIjV2BIiXImaKRsKhSgUCvT29uL3+4lGo7zyyitrfnEabYfYaRFtpbUKzdtmVCqVNGs3nU6jKAp9fX0cPHhwwz+yRhPCNjNuLbpRaNdCCS0hKQqyx8tY6i6DuSgpk4278zHGM/NMWx28FbqFbHbB699Fyuf465iFv35s5838HG9vtyKePEIC5F27KT28i2H3fpAkpOfFT2SPDxbmecULD5NFXhk/sKoA13vdlMACktePVCyipBKwOM+n00lCz7cY7faEkVbZ/63meEiSpG11ycZjpAKLBLJZ/u2XIaI5QTwcYsdhM729rqaIVjfFe7vVEm7WfBuhkxWzKq3mX/3qV0xMTHRnTLjeC672TU0mk3z++edaebSDBw9WXWS1Zuny4uGV87UyMaoZiVndGhOu9XqpVOLJkyeEQiGg/NS3c+dOHA4Hjx8/xul0bvopt92i12qXczvfj+T2ImezlPIF3gpe5zPXPm47t2Ob/x2HLWkmbGlK/V6y+TxKcJHfug/xL648wGyU+Nxs5IQtgLxtlILbi2S2IO/aQ/HGFPLAMLLDqfX5lTxe3vQ95T98+2hD7nxjtCywktEIRmO5A1IqwXEfXMXIG25BsdejPQCsN48kSdhcbqwWK950kr8z3sundxZ4fZsdYX2xMwLKccQVGdld6GpuVqJUPbRbhJu53Qm6o2JWNBrl4sWLNRO5uq5ilhCiKqFKiPLWIavVymuvvbbmue0Q2a38+npsNB6tKIpWHjKZTJJOp7HZbBw9erQqSUYde7O00hLezLlbKV4MIBkM5L0+ir+/jHPnTv6g7Oa9wJ/42j6MOTNL3m5hTCiUnD1I0Qi/f/AAo8lETzrMW4YMcs8whh1jcP16+f3lchh27EJZmAeXG2X+GfLQNoQQiB5XYx6GVBLFbNbc3Wo1qx+M2Hlnp4O33VGUnbt5MD29/jjLPle1TObb7hDvvDOGZHfw9OlTHA4HXq8XRVG07VFqRrYkSVo/WrPZvObNvFkxUT0xq/nu6G6omHXmzBkuXbq0rhUMXeKOVhN2gsEgmUwGp9OJ3+9nZGREu6lHIpGaN8FWi+xWFvH1qCfDOZvNajWzFUXRukM5nU6mpqa0dmfNXlcn3L9byeVcC+uTaQxHXuEH0Rhv/9V1HKUszlKGa6PjmMN32aUUwWJDKSn8WeoxX/WM8OpLu/lu7B6Suxxz/cN8kV9O/QGQ+OlL/bwz7EWJRcu/iZlH0F+71nOtayo5nEiiLMaSw6lVufr9TIKf9Pdi2HsAUec2xeXfZ8lqRRratuqxasEHNSMbyp6dO3fukMvltMIQlaUUl2dkr0e7E7y2sgh/kyzhjz/+mDNnzjA2NqbFhdcS446JcGUTBKPRqHW42ew+rlaLbKdFtN2WsNqoYmlpiYWFBeLxONu2beOll17a8BNmK0StlTHwbxRmM5LbixIKYS0VkCVAkjmYnGVgwAd9NohE+NOinUcmF9+zF/jBNgvy2GsogUUku5MHc3Hm4tBTzPDlfcE7fQPIQ9uQzBaU0BLK08dQI9EJaj/slRxOKBYRqWS5p/CDIMcHLBhUAa1DhCs/V/X/L5+31g3fYDBgNpsZGhrSEjzVUoqVGdnpdBqz2YzX6121j249c3XimHqpJzFrrfDfRudstju6lTHhaDRaJahTU1O43W4t+/nixYuMj49rAvzJJ59w6tSpNcfrmAhns1lNeJvhhqgn8aoRS7YbLN1a72+zqHPn83mWlpYIBoMUCgXcbjcDAwNaVnMtt8pm1tXMm0a9NOJWbkVW9Wbnq4fM2D549hQRKWcL/7XnJWxKjrcT91CcMgztxXjvFtO2XoxShGuWYf4snULauRvjwDClm1+x1yXhiyzwzDnAgEjz//pTkFde6uHdg31l0fT4kB8/asr7lVxuRCzK24MG3u7vQ/a9aI+3EfevUBTE4jxIEiyriFVvnLaS1Uop3rx5E5vNVtVH12KxaNayw+HoShHuRuu1Fe7oZorw5cuXuXTpEgAfffQRr7/+upaApf77ww8/ZHp6mvfee6/qXLfb3Z0i3N/fjyzLTfsQuyE7uZsTu1ZDiPIWr/n5eQKBAMViUctkrvwCB4PBlrmUN5uV3UpX9Vrjtjsm3IwxDckEkq8POZclaMqSMNmISg5uiRF6Iotw9W9QHD0cKiX43c438WTD/PdfRTmQfMhPv7sHaWgbP7r7W3b85DBSOsl/O2NhKQeTX8/ytreI5O9HzueRjCZEIo7U04uIx8BmR1rW/abu9+1yQzaDVEeZyuXjCyGQhCiXzxwYKm9zWpiDweGq69mMWK7ao1r1DAlR3pIXj8e1GtmJRAIAj8dDT08Pdrt9U5/rt0GEm/lA3uyY8MTEBBMTE5w9e3bFaxcuXND+/9jY2IbvS10RE24G9YhYrX3ArbSkO+3uVikUClp5yHy+vIfS4/GQz+c5evRoQ2OvRbfFhDsVa+4EUrEA6RLmP/sJU9evcDT+mJDRwRPfGD9NRyEyiywZOPraXl4ZyfM/3TcSTBe5+/AZJ1xZJLuDwuA2jPsOgMXKd5f+miuGXl7zSPz6aY7PvnjI64MWjoYXENuGQQiQZUQ0jHD2IFksSHLZZVl5k1UiISiVkP1r5BKsIsBr3aiFKrqKApIBObwEL70MS4tIVhvC4awS4laJlSRJWK1WrFarliNx69Yttm3bRjqd1mpkS1K5ZGdvby+9vb11uWK3qgjXO1+zroFKJ7cobZQt0cChHhp1J3faHd2q8YUQWgbz5OQkBoOhqmY2lL+wi4uLm557PVqZNdxuV/Vm5lS9DYuLi0QiEYxGIy6XS9sSU09MrRGK3j5ku4XSnz7DCBQkAzuyS4SWchwac1AseikOb0PYrCiPH7J3//fh1kMOOgRKMY8hqVB0e8t9hoEf/N0fc3zyj0hDA/yzz+OEcoKvl55y4Ls7wWBECQcx7NwNFiul6XtIjh7k4eqkKCUSKjdXMBhRlhbXreG8nNXiuyKwgOTvB1lG+vSvEbv2wdIifxmSufIowA8HTby114tYmEN6ns29mbmWU68oqIKr1siuzMh+/PgxoVCITCajibJaSW6jc3WjCNcb663HHa2LcJfTaUuzm15X+yGrBTN6enowGo28+uqra5aHXI9mxJubfW43irt6nlqwJBAIkMlkcLlc9PX1leshi/IWvGg0ytOnT1EUBavVSm9vr9a0otnvzbhnP/lUkv+rfJWb+QzefAJ/JsYXt7K4Bvvp6+2Fx9N8gYcvo3d5adTHm/IisnU78thejFf+ln97e4mrjyK8Oebh7UNHKd38kuO7dnL7+n32v7wXORtEMpuRto1Smp9FMpow7j9MKbCAMvcM8dwLpQqwZum6vXUL8fLPpVKA1b3DYmAbhq+vw1vvcOWLhwRTBX63AG+NpKsSu5rxsLVZ0VuekX3nzh127NhRVfVreUZ2qVTakiJcb6y32WsvlUotf8BtFt8YEW6GO3gri3ihUGB+fp7p5/sol5fvXK88ZD1C2W2ZyJ1wka93A1BvoF9++SVCCLxeL2NjY9r1F0KQTqcxGAx4vd6qln7ZbJZ4PM7s7CyZTAZJKjcyyGaz9PQ0odFAIo5xaBs/3NXL9GKUoNHJ0dRTQrKTubk4xsVnuF8+yFTUSyqd4+ZMiBPfHQKHA7G0SLHXxcXPZ5hNK8xF0pxwejAcPMLb87P89P/wZ6SzWRZvPEVy9iAKBX73MMpfBRS+f1Dm7W02SvPPMCaSSBYTUv9AlatZMpk0Id7w+1omwACikEds34mYeYTTYuD6XIKdvcayWzqfQxQLbc00rveY1ToCVWZkB4NBIpEINptNs5aXZ2S3W4Sb2WmpHot5I2UyW/mQ3my+MSLcaMy2URGvZ33NFGG1kphaMKNUKtHf38++ffs2/ATYiqSvTp9ba9zNvFaJ6uYPBALaHnZJkja8hUuSJGw2GzabjYGBsjWoKArJZJJHjx7x9OlTZmdnMZlMVTffujNJSyWUaJjSo/uY+ga4YYf/YP73xCUr7nwCkyhyz+rl85kSfksUITv4jjGN1LMbOZOB/kGMsTsIkx9JySKKRWS3B6mnFwp5KBX57YMoV+4J3jY+5MfbrPxV2EAoW2Ty61lOmB0Yj7yCcu0azM7AYHkvrlqM480xD+/s99a9/agqucpqg2wGHE5tvCMeMz+0JcBqJpHKM9xjIpEpgtkMilLVhGIjc61FM45Za67KjOxSqURfXx8Wi4VEIqFlZJdKJaxWKz09PRQKhZqdgro1Jtxsd/RW4hsjwu1IjGqEZohwPp9nZmaGUCiEoih4vV62b9+Ow+HgyZMnm44xtlqEG3l42WxmdStQFIVUKsXt27dJpVL09PTQ19fHrl27SKfTPHv2rCkZmbIsa+5pj8eDy+Uin89rFtHjx48RQmA2m8lms2QyGa3c4nKELFOcfYIy9wzpyKv8KPI3/M51kJ+Eb7DNYuS+0YsBBXMuTcDSw3cKc/zW8hrRpzne2WVCTqcouLyctNq4umDkzR09ZQEGZF8fSmiJq9MRFoWVz2YS/Mhb4s+8JT4vCpxKln96x87382H2yRL0DaAsziMPDGnFOD6dDpezrPsHYXFpQ9dJ3c4kUkltvD+l8vzoiA3J5uD4tixXnyq8OeZGZLNaV6fKm7lIxBHJOJLHr1XXqpd2xmnVY9Qa2T6fT/u7mpEdDAbJ5/MEAgHNYl6ekd2JmHCz3NG6JdwFrPdBNcMSbkQsGmU1oVMUhWg0ytLSklbCc2xsjCNHjqxaHrIeIV3r+rXyAWSzYzfDYl3r3HrFPZ/Pa9nk6XQak8nEgQMHcDgcbf2hm81m/H4/fr9fW2MkEuHx48fMzMyQzWaRZVnbo1pZ1Uk2GCmazPCbf819xwBHE0/40rUbS2mB/nyY6d7d2AsF/m74Ov/iwN/DEpznc9nAT1/djuTxYrp7h3d/8DI/TSU1AVaRfX280RfkrzKCY7v7wFLiJ31F3nJL/LMnXkKpIp/eDbB3twFpeASpkENZnOf4aA9XZuIc98lI/YN1W0vLj1OF+Pg2O1dmErxiziM8Q0j+Pt4lwE8PD1QJcNV4iXj5PQyNoITLdaRVIW6mWDVjnLXWI0kvMrLVOGhfXx+ZTEYrLFKZkW2xWCiVSk15f92cHd3uB/RG2DIirIroWh9Cq7OXW406fy6X0wpmlEol3G43Q0NDDAwMEAwGtQzLtc6vNf5aP+RWWsLrsdl5W+mqzmazPHr0iHA4rN3UDh48SDKZJBaL4XQ6mz7vZtZpt9txOBzs378fKIcolld1SiaTBEWRnsA80vAoB+7fZN7sxVzK8USxkjJ58WbiWM0yw6NejhliXHd5+bulGUTvd5FlAyVnL0TCSBWFM1SEUmLipSGO2O6zc9T6vL1hGBEJ8mdewV89nSNi6eHTkJOTY0Auhzw8wjvWIO/095bjzuHQqmPXfS1cbt7dBW8PwFIc5NASDAwg+fsR8dgKARZCIKWSYLe/sOq9/iohblYGdT0021pWvxt2u31FRvbS0hKJRILr169rCWLqg9tGs4l1d3Rz6LoGDuuds547otWJV81gVXehEMRiMQKBAKFQiLt37+L3+zl06FDVjyIejzcslI1kGndbXLdRKtdU6XFQXXk7duxgdHR0Q+79Tr9Pg8GA2+2uqmz25ZdfYspniNp6KM3McNe5jZF0gP2peZbsbjAaiQsjh+Q4LKV4y9fHT9/6M3C9grh7m9Ke/RjSKa1ylRIKglJC7htAKCXEUgByORSHs9zWEJBcLmTgrfQMvx/ahiMW49bTGCd3LiJt34FkMCD1DSCSCSiVwOksj1uDdW/CZjOSsCMwICQJsTiPNDC0qgVMKgk9zpVWvddf3r/8fIp23fDb4dZWBRfK3/fdu3drD2mrZWTn83mKxeK6NbKbbb3q7ugup5a7uNOJVxshn89rPXfz+Twulwu/308ymdx0wYxuTa6C1gl4I+MWi0UWFhYIBALk83nN42C32zGZTPT19W1q7G7DYDDgG3+dfD5HiSKOuRh9hQQP7IN8P/+Ex0oOv1mhv1ik0OvHlEiiLC1i3r2fksmMcn2KYk85Y1cJBZGcTpANlBbnkQCpfxCRSmEI3EYUCpDNlPsYG4wY9h3keHqWz4w+Xko9AbvjxVai5wKstkMEMMRjm3qPIpNG5LLIbi8iPYdkNCL5+stlK5eVrHx+UaBUBJYliB3wQz4Pve62Wl2diC0D62Zkz8/Pc+fOHUqlEkajcdWM7Hqs12bWhNbd0R2mHQ0aWoUQgkQiQSqV0gpm+P3+qoIZQggeP3687voaff+bpVPnNnvcTCZDIBDQSgqq2eTWioSceDy+7pyNeBM6hUBg8PspzljYngmSNxjJSzIDe3bjefQAJSuQduwiJxmI57L89fUFvrz5N7zRb+Tvfv8Qpj9epWA3I/e6kS1WhFKCQgFhNiM/f9+Kx4fybAZhMmHw+MBoRETCvDMo804/TGe3Q6mIyKTL1u8yAZYsVhSbDSUUrKoXXfU+nt+ERTKByOWQfX6EEIhYFHmwvAf7868eMxmTOf4KvLN/sLyNaaC6y5NitiBZ7SiRUEWCWEQrw1mrX3Er6IQIr4aakW2323nppZeAF9vvlmdkp1IpUqkUZrN5TTGuR6jrpdlNHrqFLSPCtURGlmUKhcK6r7fz6ahYLGrWrloNx2QyrVswo5WWbiOWfqcs4fXOrdf9rtbxjUajWjlBv9/Pzp07cTgcq477TUNKp1GKRUgnCVmcGITAVMpDKgUeP0ouiz0cxPLqd3ENDSO+DOCWglw3DXDs7m24d5vpYgHTrt24kkmciRjG4W3l+O/SItgcSOkUUp8f5dkMJWMAee9BKBb5yyWZG9NLDPXK/MNDNjAYyzWmvWWhrbREh01myOdqvJdUeavRcxe27PMjLFaUdAoScf42ZaWQzZRrW/dLWjvGFePYbEgI3hgwcnURjvuk6qIfXRZ/bKe1vJy1MrJv375NKBTi2bNnCCFWzchudjZ2M5s8dAsdFeGNfECNdklqdWxSCEGxWNQSetTi7pUFG9YrmFGLTlvCnRDwzZyrVqtKJBJ88cUXuFwu+vv72bNnj3YNIpHIumNsdr3d6gKTXG6YfwbZLHGDnYTJxlAuymfxIoNyiUEBDGyDfBYlGmVo7y7yj55x2LjEDrmPB4dfZuzQYTKJBH99Y4arQYlDviRv+O8xmXFyO1zgZXuOnX4fn8aMXHsW5/tfX+LYB/8On3/2JYalAI+DwPj2clWt/kGUwAIKcOvaNEGplyvTEf5RbwTp4MG130gqCUJB6n3hPlVCQSSvj9L9O8jbtjM+HOerRROv+wQgkCwrtx1pCUxWGz89PMi728JI/UMrLOBvoghvJK66FpUZ2bt27cJkMiGE0DKynz17RiaTAcoubzXZ0WKxbGp3RuUx3fSZNIstZQk3us+32THfUqmklYdMpVJks1mcTueGE3rqoRnu9k7FdTf7er2Z1WpG+dLSEoqi4PP5sNvtvP766zXP3eycWwmRzyONjMKzGXpKAWJmJ72lLKVCgXkEQyTBakHy+DH4+vih2YDi8CAtPAPPAQpmJ7LJhDW0yOcFD3GniSf5DO/32fgfv8qRSOS4u7DAXXOCXy+5KRQMXJL9HP3jp/zImOf3bi9HCUEyiehxlbcBGYyQSXHo8E649YiD/n6Kpp6qzktV7yGZKJe+dL5IppIs5Szm0t3bGPYdRMSi/NmAgXcGrHi3b4dcDpFMIDmrq45V3swlm71cx7rL6aQlvBaVgr5WRvb8/Ly2lS6XyzWUkd2MB4huZMuIcKsbMEB9T77pdJpgMEgoFEKIcnnC0dFRHA4HU1NTLUvoaST7ud7z1zu3EZotXEIIrU3cF198gclkor+/v6paVSgUWvP8b+LT9LqYTIh4DEPfELedFo4kZygiEbA42JUIoPTaYPYpwmxF8fkRs48xmC0Y3nqXv5zJ8OvbQY7dusZjk5uRfBQsbl7zGLGOjjERfMxnD5Ic2O5h75GD/OBZnsn784z4LPz55E2uKP28fLiPvXIGcfsGiseLlEoiOZ3IPj/vmOZ5278TSiVuhdJrvgWRSYPNDtncympbhTySLCN5vPz+i8d8FTfwViHBOzsdiGyWb8Kn3e7krWasSZZlrFYrbrebkZERgDUzsjOZDJFIRKtzv9m1b0U62kVpIzfnWpZeo+5qWP3LpyiKVh4yGo0yPT1NX1/fqgUzWkk9MfFWuau7YZ+woiiEw2EtqcrhcGA2mxkfH2/60/FmrOSuvjmkU8guN8rCPIeST4kaHLikLHuSc4yaMsjpAtjtkEzyx8/vcN06zGujbr5nNPJvr8+xGE3zV7LA3Q95g4PjcoSruRHkmQwn9vl4ozBLcGCES3Erf5iNMNDjoPDwPr+2HUCy2ngyG+boUIFHbj/2/+0ixcNH6dm2vdz4vn8QSSiAhPHhgzXfgtw3gJh+gFQSWjLVlekIb/sV5L0HUAILyP2D3FrMYkin+dNthbcHd6xepOMbejNvt7W8mbHWysj+6quvNFd2qVTCZDJp1rKakb2Rjkxb6fP9xljCjYpUJdlslmAwWFUwY9u2bSSTSS1jsN1s9iGi8vxObFFq5NxisUgqldIKT3g8Hs3rUCqVuHXr1qYFeCu6lTeL5HAiZANKIoJFUiiKHGnJyLzNw7iSROQEpNNQnCVgc+BLPuWaxczr0/ew5ZPkjRaKThcH5DSv9ju4tWQlG1vi05LC2z8cRLz6XYzXJvk8KgiVDCzMzDFqtmMEcsEgnwkraauP//wHRzD0OsmnU/z5TJorjxc45BF8b9CI02Yjny+Qy+XWrn/s70e+f5fjO7Zz9UmcN/rK1q9kNCGMJkqPHvDygJnJmJ3jPjZchrKb6dQ2plo0Yyyj0YjZbGbHjh3a35ZnZCuKQqlUwmaz4XQ6cTgca/72t1IHJdhCIlxPzHOzMVEhBNFolFQqxeeff47FYqGvr4/Dhw9X1QJu9dNVI5ZWo+7qWrTr3FQqRSAQIBwOk8vlcLvdKwqXNLqmVln97WYj65R9PgQGvu8q8a/iNnZng3zu2sPPPDLKs2eQy4DLzVFlkcfCxV5TglLayN8dMvLLBQMmQ5GXRzz8ZEBG9A2SubfAd80RZPchiMcpjuzkeDHK1aQF584+ehdn+LE7xn+fcZIVZr4KFhFLi8heL7bhEa79doa8bGG2aOKlgztJP3tKsNfN9PQ0+Xweg8Gg7U1Va6ILIRC+Pt6xwzt+J5K/70VDhkwaye3luC/Cib29eHaOoUTCACtivsuFQVlaLGdGd7H11K0iXIvNbitaLSP76dOn5HI5zRu2VkZ2Pp+v2ciim9gyItyMmG8llTWBC4UCLpcLi8XCa6+91rEfYysTrzppCa+HEOUayIFAgHg8jt1up7+/n9HRUebn57Wn5I2O2wpqXYdOiHfdFksmg9HrY5snyM5giIjJybHoPRayeb6y7MZvyRFJm3jJnOMHfQoE7iEf+3d4c4+HP//z2ySsLm4vJPnpD9/g7WCAEw4H8tA+lMACWO1gMPCTH7/KDyf/CJJAeuUoYu4pn0s2fhcEn1Tgv7wa4odHt/POgJs3+55xNS5zfEcvUjRMz67d2BIpDj7Pji4Wi8Tj8aq+y6rlk+3vLzeseP5vrQa0x0spnuCvl+DLW/fKvY8Hs+X3XyHElZ+TsrSI5PYiFuZgcLirhXgrinC924rqydcxGAx4PJ6q+umVGdnpdJr/4r/4LxgeHiYcDvP06VNGRka6+jOFDopws2PCtV4XQlAoFHj48CGRSASTyYTf7+fAgQPaU9Pk5GTXfmCt3kdca+7Nstq8xWKRYDBIIBAgFosRCoUYGhpi3759VXO18rPoNjFtFep7kbfvhGAQPr9Kbz7FoBLFohT5nXUXfbkMX1r7+W7yIbecO9lpk8vFNh4/gO/+GS/5DEQTi+x941h5j66iIO85gAgGyhWmolEwWcpFM/oHkPsHoVBAsTn4v3OHf3x4gP/kSoYIJj59GOJtX4mf/fhl3o2EoVhA7htYsW6j0Yinp6eq73IgEGBpaWlF32WXzUqPBObnZSj/+DRJvEA5ZuztgYotTSqSJJUF+Lk7m8HhLSHE67GVRbjesZbfH5ZnZP/qV7/id7/7Hf/df/ff8V//1/81s7OzWCwW/sk/+Se88cYbm557amqKn//850xOTq573PT0NBcvXmRsbIzp6WlOnTpVVUZ2Nb7RlnChUNAKZuRyOXK5HB6Ph127dm25VPdG3dHNFtLVXl9vD6BarSoUCiFJEj6fj3379lEoFNizZ8+G523VQ8Vmr9NWuHHLZhOYTcxY/UiSjFQq8oPoHa669/FG6gFPB/ZwyFmCZBz58CsUF57B737DeP8w+/d5uJRO8Z/9b/O8eXCYd+QAktsLuWy5bOXiHEJSMOzcDYCIx5DyWYTLi5ib5ZUhD/dzRo73GcsiDavXdX6OEgmVK2tZrEg9vUiShMVioaenR4sdqn2XY7EYi6Eg4v59UhYbB902vo7JvOFj1QpYQghEMFDlzpYkqUqItyL1Cmw9jRKaWeWqWQms9bi2zWYzhw8f5sCBA5w7dw4oV8orFoubnlcV1ampqZrHvvfee5pQT09P8/Of/5wLFy6se86WEeF6LGE1/V1t/acWzNizZw82m43JyUntyXqzrPdFryVGjdAMd3SrCm6s9r6FKFerevr0KaFQiGw2S39/P0ePHq36UW4F8apkq1nJ6vUt3r1F/tokjO5kKmtjxtrHP5y7Sszm4u8sfYl7716iO0b5V8ECP7Il+W4+C24fJRQs88+QX/8OT//FXxHqHePWjQe8+w9fg1wWFAXcXuSnT5B69yOUEn/9p3tcncvxZ+4C3+spgMnMD/JL/PuvDWLZth2hKOW6ziYTsr9/xZqVSKi8f9dqK/f7TcSRenpXfMfUvsu9vb2wfTsil+XxrZu8tdvFjwMLxIwmrt+8id1u146zWq3ImTSSdfhFPLnyWnl9iPDa29u6mWZZuc2u91yPoNeb9VzPWMtjwjabreY563Hy5Mm6jpuenq7699jYGJcvX655Xkfd0RthLUtYrZC0uLhIMBjUWs9tpmBGvdbmZkW4UWu0la/Xoh7xUT+LQCBAJpPRmtKbTCat3d5Gxm2VJVxr3q0mtLUQpRKFr29COoXlez/gzmyY1+IPudM7wpzo5+9R4uuUhb95ksRiNvA3ip3PH8occktM+CRyfV5IJhl94zs4fvc3+H/wA02ApV4X5Asovj5KSwE+++w2F+8lcZWy3PTZeeNwDwwOIWZmQJLKAhxYQBocLrusg4EqIa4UYKAsvs+FuBaSxQqOHty5ND2vHkN6nsyVTqeJx+Na3+VELs+zmRl6Uml6Bwa5fD/ClekIx7c7eGebGdnXB8/mWvZ5tIpudUe3O8mrUChUJdS2i8uXL68w8rxeL1NTU4yPj6953paxhGVZplQqAWh9MdWCDF6vl+3btyOE0BI7NkM9yQH1WJudcHU3ainXGnstcrkcmUyGr776CiHEilKdiUSCZDK5qXlbxWbd0VvNatcQAuNLr1D4i39FPpHgSGoGBYmUwYIjn+FrywACC7vTCxhSgrzTzSPHDvoWn2J89WVKJQkhSbw9AOLn/wDlj1cRpWP85VyBK397j/FBC69aLYjAHFeiVnpFnsHYAgf27kfefwgpk0ExW1BSCUp3bmI4eKR8Lc1m6HVrQmxIxJFse5GstqqCHO8e7EPEoohUsvZnYDaDz6e5oCVJwuFw4HA4GBoqN3K4du0aPSPbSc7NMr+wwP9+p0QiD5lYjB+MHcSxRR/CtqoIb6Q4SD331k6JcDQaXfXv4XB43fO2hAgrikIikSAajTI3N4fdbqevr6/KtVksFltuwTQjQ7uTlnIzsqOFECSTSQKBAJFIBLPZjCRJHDx4cFW3T6Pvaa0Hi27cStRt61GRjEaMO8cQP/0HFP7F/4SkHMSm5NmWDZEzmHCLDLudgtl0Dq/DRN6RY8fClzx56fv8Vw8M2MLz/JsHCsd98KOJ7yC9fhzlyUOuTJsJpgr88XGa171LGN/8IT+c/QseynEWX3qV36UkxI1Zct4B/vJ2kb8bDvCj1/eW2wiqsdgKIS45HIhYFMlqqyrI8c4+L0osAvYeeF6TeC3quelLkoTH48Hr9aJEw/wd4vz+UZRXt/ewsLBAKpUiHo/z8OFDzY29Fba8dKMIN7PncL3HddsWpbXEWaVrRTibzbK0tEQwGERRFMxmMy6Xi7179676QbTjptysDOX1EpgaWVurLGEhymUib9++TSqVwul00t/fryW4Xbt2bc0nz1a6jTdLK7YadbuVLBlNyMUCvP4Gvt/N0FvM8v9n77+D5Miz/E7w4+6htY5IrYCEFlUAqrpEV1V3odTMUCyve244u0vy9mZ6brm2szTaXc/NURh55O5Yc8Xdnp1x2eSe7d3c3nC4VSSX7J6W1aq6UBooaJ2J1Bla6wh3vz8CP6/IyEgBIBNA9dQzKysAEf5zj5+7/97vfd/3fV+kniOgqKSsXobVGs/ErBCKQDLJsRef4E8vLTATnOJ2UeGAmuanrj28sLwIJhPK3gM8k5/lPU3meT2POrG301/Y4WTZ5aCaynDLHuPK+QJ+e5mm4uCT5SxffdGJZDKvlZ4ctSOZLSArSKEIWmKV5yb9HYh43Iu2NA9uDySTSE43WjaN5PZuqDO9lXXfX9kX4PUDNt44Mb7mOxcuXCASiVAsFjesXX7c7HF0wtuJXrcLM98LHH0vmtQ7ZT6fb13Um81mPz/saE3TKBQKpFIpisWiIZgh9IDT6TTl8sZw1Fayldux7eR0d4IctRtw9U5Hwt111PV6HUmSmJqawul0rjvXbjg1Me7Dtq3g6Mc12t3KtOVFJK8PudVAUTWGGjlKJgsBPYepLVOoAi4TrKzA2CSy3UXKY8GydIeQ00VreIqv+ppgciPpOlouw2uTbl7fq1Bs+SgmEuh2Kz9v+jHVlnA3algrGnFbGFSF4XaOwyemO2zqloP3b6dJ1zTevZnitcHBu72FFzowcijCqyR5bXoKbWkeye1FDgTRNTB9/B7a4aPIqgq+wDpHvF0HsqbUxbG+gYMkSbjdbtxuN0NDQ8Bntcu5XI7FxUUKhQI3btzA4/Hg9Xqx2+2PdDO2XdLVdtjRDxuO3m4t8eMMR58+fdpgZHfbyZMnNz3ukTrhRqNBNpslnU7TarXw+XxEo9G+0e6DylZux4Qj34jQ9aCR8G72NN7O79/q8261KkVRCIVCHDhwgEajwcrKCi6X657P/aBkt92yh+1MH+nirKpozQbqyiJ4fJwsniFnshNr5fFEPZgSKSxjE6CWAR0sVtrxZawVK7o3gF9q8veP2ToRcGwQNZWAahUpEoVKGd1qA7sdtZDn+YDG9zJO0q4h7JkEY60cVouD/8MhM3tOjCObTKh3bvOSr8371SpPDMfuOuDP7olwxN0OGIC5mU7ELcvomgr57DpHvJv31WQyEQgEDPLNhQsXGB0dpVAorK9d9no7rO2HaI9rJPwo4OjdioTz+fyayPbcuXP4fD4mJyeZnJxc893Z2VlOnjz5+EbCmqZx8+ZNY6HfatIepMRmu/agTnQ3IeGt7H42CLrekesU+d25uTlDrap7I9JsNrc8//2ymHcz8tzopd0posi9HCcgfZvN9tB1bSVFQdI0tPk7aNUKWbOLaDNPVnEQK8RRQ24cq9dheBxcblieB7uDJzxRJEXBbzHxw8UG7xfafHXxOi89OYmuaR0I2eWG5aVOPW+jytMelR9GBzCVGzSjgxyqrnJizIIa6ghm6MUCcjjKi+4aL+5tI21UPiLLSBar4YDbN66BBHKzgTy4H215AcwSeh9H/LA2PJIkYbfbsdvta9r3idrleDxOoVDg6tWrBoztcrl2lbi5U054J+uEdyI6v5fv7bQTfvvtt/nxj38MwB/90R9x6tQpo2xJ/P2b3/wmAG+++SZ/8Ad/wKlTp/j444+3rBGGR+iEFUXh8OHD216QdqoV4VbH76Y05OPghLvVqprNJj6fj4GBAcrlMocOHbrvsTezByGrPYgD3w27l/un6x1JzkQiQblcxmq10m63UVXV4Dh4vV5D83Y3Tdd1tFYLinn8rQq3HAPY1QZYbSiZFERjkEuBpAMKmMw8FZN5ytHiTrXOnxQU7Iu3+aTs58XJLFIw3IlYFQWcLkyXzyMdfQLzyBjuxetcquk8G1H422+cQrtzm5nECvrgAJjNSC630eNXr1bQCznw+NZCxJIEDid6pcwvzs1y+8Y83qOHeDZs4vs/u8B7FSvPTVh5NdCCXAbprgDIdu7Pbs71mtpl4OLFi+zZs4disUg6nWZubg5d19fULj9MVOZxrBPe6Ui43W7vqBM+ffo0p0+f5lvf+ta6z3qd7OTkpPG97dYXPzY54a1sO5HwTsDRDxLpPgzI/H7GrtVqZLNZarUay8vLhlqVravLzINEpDsBR9/Psffr3O/3nNv5rLtWul6v4/f7GR4exuVyUa1WkWUZRVFoNBoUCgVWVlaoVqtGDlLAmDvZJlMrFdGKRbDZILHKZfcE+8srVJCgVEBHh0qZc4FJFnM64akxvjzmQVdV5HoFbC5esjW45HZxMGIDWeGHd2trn5v081RAQvP4OzKWoQi36ibq7Ro3SwqUSkjhCFJxBkpFpNHxtfPmcKJX6Tji3jn1+tCyaa7eXGTOFMQ+k0AtWPmXsxo2paOA9IrcQjl4tO+92MgedirCYrEQCoXWaB531y4Xi0UuXbpktO7b6fvfbZ9nOHq7m4Nms/lIcsL3a58bJ7yb+VRhOwFH7yZ7ejPrHlvXO2pVyWSSfD6PzWbDYrEQDocNksm92oM44UfBjn6QReRez9lqtUgkEmQyGc6fP08wGGRqampdyVb3NVmtViKRCJFIR6hClOEJx6yqKq1Wy6gMsNls9/+bnC7UXAoWZmFoFOVOk9uuKE/mZ0FqILU0blhCXMmoJP1DlHItng+WkJwu5IFxyOR4adDGV/ZMoQNYzLx7fYlME969neGpAzJ6bBA5Eka9fhkdiVCjSEiR0CU3SmSQVqkGzQZaPsfPb6b4aKHA8UPjvLY/1BHjCIT6/75Gg4njhyndXKatqfzPtxposgldlXhGS2I6+OKarz9OxLnN3onu2uVGo8HBgwfX3X+BluxktPw4lijdS5/g7cLRjj5ku8fVHqli1r3c6J3KCT8M9vODHH+/L4CmaVSrVS5fvkytVsPj8RCJRNizZw+SJLG8vHzfucjtviD389mDRMKP0hqNhqHSJkmdulOfz8exY8fuazxZltc0O9d1ndu3O03uFxcXqdVqBtTp9XrvqURGkqSORGO9CShYWzoD7TQls5WL7QBBc51sW8Jul9AbdY56JfQWmPcdQi8XQQfJ5UKOdsQutFSCLw+YuXxzmaMRH1g6eVvZaoPJvfz2tXe5FJA56leQwwNIDgc6II1PoV25wC/nIdeU+fTKHK/47zZw2OheW628fijI63u8fPPPbmJVdNq6zjeG2/zaC09s+Hu3mo+HYffiOBVFwefzrSHxCLQkHo9TLBa5cOECLpfrgWqXH9dIeLvs6O1cV6vVeqzqhLeyP1eRsBhjMye8m+znnSZuNRoNUqkUqVQKVVVpt9tr1KoeZOx7OXa3crO7dc336/hrtRr5fJ6zZ89iNpvXaGE3Gg1KpdJ9XetG1yEiIbEwt9ttQ7RmcXGRVqtFu90mmUwarTj7WquJVshDOMIvbuc42EhiVRu0dYVVqw93s4nkcWOrFnjCpHHUZAdPFL3VQi8VeS+n8P/7pMTzkyZeG3eiN+q8HGjz8lMRJIeLQrmApMvACBQLvPjqU7xw6Tzy4eNQLvL9+Srfu1TjL1Vn+MrUKKeyK7xXtXPKWkTPZTrs6rsM6V6TA6FOXbDDxampIPpMlq9GFd54aj+Sey37WMtl0ZuNLef2YUbLDwKNd6Ml1WqVw4cPG0Ii/WqXt/O7dpJBvR3byZwwbG89eVQlSvdrnxsnvBOR8KN2og/6ua7rlEolg80sHMHhw4eRJIkrV65sCMNsx6HdTw51q88fJVntfq0b1i+VSiQSCfL5PGazGYvFwrFjxx6JNKnJZMLv9+P3+4GOfOudO3dot9vGomwymYxoWZSU6TpImo5eKCAXc1wzRzmkrmDSWxyoriDHYmQlB4q9haYBWqeTUuvnP8D8+l9m4eJVMlqTj2+s8oovCIoZvdlEjgwgmc3osglScdq3b6CMT2IymdG/9OVOk4ZwhDPvXoFCkXcXTLy8P8obLx3ltfk7SO4xJIcTLZ9D19QNF1jhiF8cMPNi0EfYbusQwrpMy2WRbDaUagVaTdiAdb2TsO52vrOTUbcsy5vWLpdKJS5cuIDNZtuwdnmnnPB2mcoPmwgGj06s437tkTrhe3khdiISFk70fuuAHzQnfD9OXtM0stksqVTKqEfsVqsSpqrqjmwg7scJw4M1RHiQY+933M2OqVarXLt2jXK5vAbWr1arLC4u9l18HgV0LkkSZrOZwcFBBgc77fdardYaJm6hUGD+wjm8ahtrIccnjkGO1uNcs4TY20gzLNXI5kt8PPEUNYeX30+8A2YHtFvg8aGnU4wOuAkWkxyNBsBkgmYDkFBvXcd07EmopZEUE0igl0pI/gA/vJ7m3dkyzwbLPLsnxEerczw1PgGKws8+uM5Hy2WOH3Lz2rQVSZbRzBaU8sZNGuRACCl1Hb1eQxoYMCJeyeE0HLBkd9D2+JDKZXSbDcmyFhnQ2y3Um9cx5zfX8t2O7WRE+SDPTnftcqlU4siRI9Tr9Q1rl1V1483Oblz3TsLR27XdrBPeDfvcRMI7oYi1He3nRxnpiuvrVqtqt9v4/X5GRkaoVCobNqh4GFH4Zsfez2eP8tju39PNaC6VSkbXJ5fL9VjnpfuZ2WwmGAwSDHbytOfPnyfk91L/xY8omM0MtcvMmUO8ljlP22ShKsvcMAVwlHIE2mUGx6OQzyMdPoFisyGpTb7cWmLi6ePQqKPeuIIciiCHIrzd8HH533zCxNQgX6KIFIlAs4FeKXNmNkem0uI9VeYfTOcYezLEvqCGVilzaSZBSnfw7kyWVwNt8Pggm+40eshnkX39243qstyBrU0mqFXRAT2fQ/IHkOyfIUBSMNSRujSZkQdHkEymjgO+dQNlzzR6PI6aWEW5m+PeLXvY+VfYuHa5VCpRLBYpFApcuXIFq9W6Ye3ywy4r2uk5+AKO3iXbCdjyYRCv7nf8SqVCuVzmwoULRknD/v371+T5tnI6Dwr77laZ0W5B2Q/ye1RVZXV1lWQySbvdNro/1Wo1SqXShsSnxw0638okScLp9yPvPQDv/Zy4KcRXM1fJm1xE1RKSBsONLKWylclCHgIjyC++ghRfgkAY06lnYGEJPZtCjg7y4UqNT9+5w9G9Dd6tOLnRdHP2wwWaR8N8XVM7karHy3OTfi6dv8FRnxfT/iPoH32A5A9Co87R8QDleBNLvcg/+qDJl2NFXjgxhTQ7g16tQh8nrBfy6CZTpz1huYzk8aHlMuiVEoqvA8//4FqK731a5i9kZ3j5+BiU7uac3R60OzMoe/chmcy0/SFotx/IEe+U49hpB9TPukl/xWKRffv2oWnahrXL22EX72Qe9ws4+nNi24mEt7qRD6vEaDuf6/pnalXFYhGHw4HZbGb//v04nc5Nf8dG9iCw7245+N2Gsje7pl5rNBokk0mWlpZQFIXh4eF1am21Wu2+NyOPo4OWJAnJZkcJR1AVEy9kr9M2mfjEO8nvWBZpLC0zquYYzWRoujwUkwnUH/572sMT2KPDuFZXQNOQXR60Qo6PE3VykoP3l8p8JdriTqWCxWrm5mwCDgSR/CH0eo1XLDleeXkK2WpHS8ahraL4A+i6zgvAl31J/vE1jVomx89MMb6cTaO6vMihMFomhRwMo92FjWVfoCPqIcsotSo/Kts588Ecz486edlrR1ucR9l3kDOzOSgWeHfVxMtTRaRIDEmSaJ35OcqJp43GEd+92uTXrRZe9Tbv2xE/LnD0vZo432a1y6JpTjwe37B2eacZ1F/A0Y/AJOneS5QeZDHfzhhbOfoHbWWo6zrpdJrFxcU1alXT09NIksS1a9fu+8F+kGh1q893Yu43O+/9fLYdEwtLIpEgm80aRLbBwUFcLhfhcPiBxv+8mJZMIJWLYDEhKRK/9B+goZg6pUeBIO5sCqvFCqY2eD2AAi4HP463OPfBBaYd0M4VOJ/SuVi3MtrMY/Y6+F9vl5EUM6rNyv6gjHqXmIWmI4UiyHcZz9qtax0oGfjh9TRnZss8OxbjBfcdfiJ5eCZsQnc4wWrt5HHdHtQ7t5CjQyB1SFe43MhXLqFP7uXMx502h79cqHD6iAXd7kDLZ3kpLPH9jJWnYlbDAeulIvLIGHoqgT40ylufrjKX1aifT/Da6ejd/Pa92+PIRN6Obcb7ELXL7XYbp9OJ1+vdsHZZtDDdzWu6X9tpxazdts9NJLwT9ijYzbVazWjJWK1WCQQC69Sqtjv+g9huM5x347z3Y4LRXCwWOX/+PA6Hg2g0ytjYmLHbXlhY2PR67sce69xxpYQejkEuy08DhxlrZEjLLrBJxJsSac8YZk3lyGgUKlXYux9Zlrk6myJaK3DFNcpvHTvOW//6HJKukbc5aa3EWdB8VC1Wjlp0XnBWUfYdR1u8gzw63mmy0G6hZ9JIE3uxzPwQrVwy+gS/N1/k70+6ecFu58cFK//wZ8tM2pocPAjayhKS17+mu5G6vIQajmDJZ3FZzVxcKTFubYPVhVStgmLipQEzY8UKo6eeuuvsO4per0ZcaMsL6OEoOp37pLXb6I0aysj4fU/rw4pydzrq3O51b1a7nEqlyOVyW9Yub+fd3mk4+gvFrHu0hwnH7EQJ0nY+LxQKa9SqRD3pysoKdru9rwMWxz+OTvhBx96tcbvH79Zodrvd2Gw2jhw5smHt7P1e7+MIOW9l8vgU0i9/CpN7sc63SClunijNEa+WSegubphD5PxhcqN7eKF4A+Zm0EYmeIIay24Xx51NpEoZdzhI+06G8aCJo8MDXLutomk6g4UllgZ9pJMp/Mi4VuO8V3VweTbFkSeneZUUDX8AbXmRZyfCvHcnxzNhGdOefej5LGcu5chUW5RKLdS5GSSPFzkQMq5fsjtQhoaRL5xDHximVCwyaAOlVgFlsKNFDUjhKK3VBNJdYli60rpL/vIgD4+jZ9J87XCA750t8Mak7YEc8OcVjoYH2zyI2mWn04nZbGZyctKoXZ6ZmaHVat1X7fJONjX5Iid8D7YbzMDNxtzKiW4HblZVdd2/C3bt0tISzWaTcDi8Rq2q9/q2uv7dsO0yGTc69lGQujYbV9M0Go0Gly9fpl6v4/P5DI1mSZK4dOnSpue8H3uso91NTLLaAJ0fl5w42wUsukrK7MHSaFMwW9FMZnSLheqNa3AwAiuL8OlHvPQbfwVlZIzFDz8AtU0GC0GXFblZ5QVbjXNeG7VSkbI7xsD+SSKRCOUb18hXq3xwfpU7Zh/Ln85x4oUBzMk48olTvJZJ8VrYi3S385EUjvJstMS7qzrTd/WgpbvErB9cSxnR7Gv7guh2J3KjzrMTfq7eWOTgZATZ6USyO9BLRWi10Jyd2uhnB22cWdZ5NiQb0LSWk3i1lWd8pMqB55/fcL52EmreqXEeRd54O9/pV7vcarUolUrkcjmDbPqgtcv3Yl844UdoW/UD3kliVrdalaZpBINBYrEYsiwzPDz8wONv9vn9PLCPCo5+EOsdt91uk0qlDEazpmmbKoTtxDnvxR7XKFkrl0AxkUxksElQstiZtwUZbWVRZYWkxc1YMYEUDsH1S/zSNcW8Ak9fmeXQ2CQ/dU8z92Eej1nCaZM47pQxv/gyT//sLNdmCgxFLUjAf/f9a3y4WuOVkM7TxyZpL1Q4NeGlVSyQ9Ye59u47SMEQHrcHr62Oy2RCkiR+7ak9vLyyxJ1kHXlssiPyEYl9Fs3OZnnV30ILhtGtVl5V47z20jiy12eUJkluD3qpiFQpI3u8vL7HxGuDZsMBA8j+ALrFQrOwubKZlknv2Nx/HqPlB70ms9l8T7XLO+00v4CjH6HthJPbKFLWdZ16vU46nSYej69RqxI3PJlM0mhsTPT4vDrhBx37fo+VJIl2u83i4iLpdGdh7C7dOnv27KalFLtV+vR5M8lkpn39Cna9xU37IGa1xdP1ZT6OHOZI9hZfKt3h4tBR9GIWBgN8qA+Aw8q1wgLTF89xsTxG2yThrOX5Px20Yjr2IgAvfOUEz47dJJ9IoN6+zrllOwP5Fd5pR/njgypoDt5bqWM7EGPQo3NwbJSmplFSNZLJJLOzs8Zi7HA40CzWzrMdHUBPrPLshI8zd3I8G5SQogOwsgK6juwPIoXCBtvZiJYnXEjq3Wja7Vknawkg3Y2UNzItsQoeL+Y+XZ267WHC0Z83J9xrkrR57XIymQQ66+dO9F3+IhJ+hPagOd1euFnTNHK5HMlkknK5jKIo2O129u/fv6Fq0m5tArZz/Ga2m9HsTl+XYDQnk0larRY+n48jR47cU3u33Vq07hd2f5SmLtxBcrm4YVfYX41jUhtULTZOpa+QcwaZKiyhJK9jGRmGVokXBs18WLURO/g0UvwCT+oZVswWnLKJf7ji5XlbhtcPdJjlpslp9FwOvdXk+aDO28o4/4G+hBw9xHuzaTJtnTOzOf63wQooClavDysYpTGaplEul8lkMhQKBaNO3uty8YK9yqsn3J8xnXUdSVGQgkH0dBIpOvBZtHwrzatRibbLs60USD/TEqtIwTC6JKF6vGjJOPLdPsW99qvuhHdS77mfddcu67qO2+3G6XTuSN/lL0qUtmmSdG8lStuxBy0hkiSJVqvFysoKqVSKVqtFIBBgZGQEp9NJNpulVCpt+IDuBPt6M9ttJ/wgEeCDHis0mnO5HHa7nWg0SjAYZHV1lYGBnVc2uh9n+jg72o1MrpZBNSNP7WfPBz/gji3EycJtnO0Sc2Y/zxTncIZDDGXucK4R5t/ahhg2mfjbJ33o+Qz66Dgv3Jlj/Jlj/HfvzJMrNTgzm+P1A3freDUdJAW8Af7mVIj/tFhAmfo11PlZnht08N4KPDtghZaE5FovgCI6Q1ksFprNJvv27aMeX6WUzbDa1qgvr6DcuIEjGqMhK53SGEWBUAQtscpzk37evZXm2QErciCEvrSy5Zz0u/fCAUsmE6gqKCakQHBDR/yrnBPebvvBnartFefbrHZ5fn5+232Xv1DMeoS2HdnJfp9XKhWSySSrq6sAjI6OrlOrEsc/aKTbj9i11fVtd/zNbLfh6Hs1Xe+IlWSzWXK5HD6fj2g0ytTUlDFeuVy+7/NuBXP/eTGlXkezmtATSyTMHp4t3CSnOChKEvvqSdyhEGSTXB87SiaRZj7oo7GQ5BQpTPsPYfb4qWkyoHP4yF6kS7c5MnbXAZutHXlIXUOPDCFHo0hDIwCY9u7nlVvXefVJN7Lbw6XZQt/r01QVknH0u7ldLZfB6vViiw0QmJ9BGhpE9/kpLS2ymEyyWCqxurqKzWbrRMu2Iq88F0IJflbzfa/3Vy/kOvC1ycQPrqX45UyGCWuLw4fN4HShF/JIG3R52sp24ln7vMPR2znfRsiiqF2OxWI0m00OHjxIsVikWCyuq13WNI1YLIaqqjvKtt5t+5Vywls5EhEJCwfQrVYl2My1Wm1TYtVuink86mh1t3PCmqaRyWRIJBLUajV8Ph8ul4uxsTE8nvX5u62uabdyt/c77uOYS27bHWiL82htjSPVZX7h3c+J8hzRVpVZaxBzJkUsEiOTq1CweRnPL7LHG0WyRpB1CWVsEj2ehFKR1w8c4I2DYdTbN0COQamAPDCEvrzEO5eXuXyuxHOTfgOqluwOMJuRXO6+c6OmkujxZeTpA+iZDEomhTQ6gmR3oOWyyOEB9GYdudHANzZOuVLG4fXhczpoWO0UCgUWm23qSysoqwlDclGt1zFt0EWpn0lef4eMpSh3Na/blIpt9EYdvV5HDobWHfO4wdE7+eztpBPeKTRARN6KoqzpJAaf1S7/6Z/+Kd/73vdIJpP84R/+IU8//TRPP/30riBpO2m/ck54IycpWn6VSiUWFxfxer3EYjFDrQogk8k8Uif6oONvZo/Kwbfbber1OhcuXKDdbhMIBNYwmm/duvXQr2mzzx7mYvcwTDdbUOtVKOSZsYeZqieoKiasko1Ys8Ci2c9gvcaQxUZdgYPmJjMNjf8yN8TLjSQvTpZQGjWk4SPo+Ry6BPLgCHqlhBQd6MzXrZss3ElRsqmcAV4/EEZdWkCyWJAsVvTyZ2xkLZ+FWg1NVqBeRZ6ahnIJvd3syGhIEj/5+BbvLdc4ta+zeL57bYbnpgIcctqQZm/BoSNYqxVisZhB9BHvd/LqZW5+UqVttWH3+42841ZN3uVgCC2T5tkRJ+/Oa0xZJPRyua8DhsfTCe9kZPowz7cd+Huz84na5d///d/n93//9/mt3/otfuu3fosPPviAv/N3/g4jIyP8w3/4D+/7+mZnZ3nrrbeYnJxkdnaWb3zjG2sETHq/+/bbbxMIBJidneVrX/sak5OTm47/K+WEe51Yt1qVJEmYTCai0Sjj4+N9j98p9vKj+nwze5hwdLPZJJlMkkqlgM4L1A/eF3a/DvF+87f3u3h8HvPFcqWM4vGhtlo0lBoJk508Fp4uztI2uxnVC3zPupePXBOckvMMUOE7bScj85d4d98JvnzuI9qeIJKmgaKg16r8cLnFmbkCz03KvBrS0GMDTO4xY5pLcCQWpvXpx8heL/LwKAB6sYDS6HRIkiw21Garo6w1MATNBpLdCaUiSJ1yqktXF6hrJs7M2tDRydTafDiX45g/CS43yAqS3Y6WSRtO0mQy4Ws3MUdiHHzySbRshpquU2g01/Ra9nq9tNtt1HIJpSdHLQdDvEaarw5bmb+V29ABw/YjvO1858+7E95pIpgkSRw7doxjx47xe7/3ew98fV//+tc5e/Ys0HGyv/u7v8ubb77Z97tvvfUW3/zmN42//97v/R7f/va3Nx3/V8oJAxSLReLx+Dq1KpPJRDwe3zQne7855Xv5/HF2wvdrkiRRr9eZm5sjk8lgMpmIRCIGo/ns2bMbOuAHiTx3C17/vES721rkLRaUwDj/4pcLmIBVxc5wM49uVmi2NYalOv93zz58tSKNVhGcKq9UbvKLwRN8pTmPdPxVLO+fQZ/o1OXi9nDlZ++TkdxcujjDa3/lBGQyPB+WCB0/iXb5HPL4XuRAEL1YQPJ4OwpYxQK0WuiqhmS1Ijlc/Gi+yr++k8NdL/PK4QgH2m20hTn2Hd6DfmOeY94GdZONj9pmTtsKqDYH0sQeJLMZ6nUkl8twxFoyDi43UrsFgBwIYs9lcfh9a3otFwoF9HSS6806uq5jjw4Y0bLZbO443mwG1ePbcm4fpyj3YTvh7RKztgtZP0wi2L3Y7Ozsmr9PTk7y9ttvb/j9f/Wv/tUaJ7wde6Ts6J2w7l6w2WwWn8/HyMjIOrUqcc5H6SQftRPe6vN7GVvXdcrlMolEgng8jsViYXx8nJGRkXsiRTwqAtXnxdFuZVsuzjYbyDI3JRc2q5lYNY0iw0fuKb5euIAeiPHV7FVyZidevQU2BxPDYabMRUyn30C/fQ3VYun07/V4kSSJQ0f34PrgE/YcmEBLJyCVRvcHkdQ2yt6DIMv8YL7KmdsZnhvJ89qkm7bHB+02eFzoy/Mo45O8/2c3UFdWWLK4+PRWgiP2JMr+l3hNlnj9iedoXzoPPhevBjUk6yBLmsRPZ4p8mmjw7JCD1ydAslg6UpeRGNSqfLpS59//2wuc3D/YYXAnVsFiRVKUjoiErrISjnLoySdRS0WqxQKFep1EImEI/zscDtrt9qbOYbtO72Hlex/HSHi779hObzJ2ct0Q0HK3BQIBzp07x5NPPrnu+4FAgBMnTvDmm28yOzvLK6+8suU5PleRsFiwm83mOrUqkWf0+Xxrkvbd9qDa0TtBvNrNTcBmthNjC0JbIpGgVCrhcrmIRqPYbDYURSESidzXte2GQ9zKud+P499qjh5Xxy7pOoF6iVC7SMHqoorEl5Qc01ODNK9f5tc9FSxWHwwOITk9/EyKcrsqseeTOV6f8iInU2C2dCBjj5fXJly09WH0egHw8NFiiQuf5jj51GFeHfWirSzy3pxCpqFzZrHCaxEJc6mAFDzOLz66yS9TGs9l5xmQ6nxgCTBkavCMkkXz+ZBCYWg10VNJTEefpH3hEwiEwGbnnatp/pfrNRy2TvnJq/4muqx0IuGVJUx79vFxxYZdrfHJ9RVem3SDydwpaQK0VALJF0BfiQOguD24ZBlXu2WQMRuNBul0mlqtxsWLF5EkCbfbjdfrXVMS8zChZtgZZ75d2+517xSEvJ0o91FFwvl8vu+/Z7PZvv/+5ptv8vLLLzM1NcU3vvGNLaFo+Jw4YRF1VatVPvnkEywWC+FweI1aFey+k33UxKtH4YQFo7lUKvHJJ5/g83XgPbfbbbxg9Xr9vq5JnPdBrnk37HFjXd/3+TQNUz6L5nYy1UjwZ8HjnKjMsc/WJmKTIZ1AG51EarUhHAazhf8Hezm/WES2mmApjX5yAuXmddBUJE8IvVZFSyXAZodsCk3XeKfqJJSf5d2bSbSSjfcSEm6pCU4Lzw5Y0TUVzWyBVouPl8q0ylUuVhVSsos9YTODjQZfmvCT1KQ13Y9ej0ooE3vR00nkkXE+XZrDZjZRb6k8E5TQigWU2GCHWT0yhpZJczRkZrHt4ksBFS2VRLmbl9YyKSSvD8ls5oN4m7eWbhpMbr2QQ6+UkZwurFYrgUCASqXC9PS0oezU3c7PZrNhNptRVfWBo+WddOY7ZTvlYO+FQb2Tm4yH8Z5t5JzffvttvvWtbzE7O2vkoz+3OeFetSqXy4XFYuHYsWMbqqE8DLj5cR5/M9tupAsdpmk6nTYgukAggMPh4NSpUw/9uu73s62c+0bzvNOkLU3T0DTNWLAFkiPLMpLUEazZtR1+tYLcbKLOJ5izBHghf5NPvJP8Z2M5WJiF6SPo8RV0iwVcDlBVZuNFVm0B2ppOQDLx0w+uM2qzdyLIuw5Yr9eR6jXkqX1oMzd4yu3i49gEf6k1z/eTU2RqKthN/MNDEph05Ogg7VwRPZPm+KiX2WsZ9gZ81AdivDeb4eSBPeCuoScTvHsz1YmgZ3O8NmxFkmWkiT3oiVWeGHFjWa3xwoiTV5xlTAcOo6eSyKEwNBr8cLnB5dUabzwZ5atD1jW9gqVAqKNJHR1gaSGDaq5xBnhtjw+92UT2boyeiZwxdJ65er3O6uoqhUKBixcvGoIjXq8Xt9ttpGMedpS7U8/RdjcG24letxsJP8xI/17M5/Oti3pF2rPXZmdn+fjjj/nWt74FwOnTpzlx4gR/8Ad/sClD+pE74e7JbbVaBszcq1YlSRIXL17cdKzdhosfNdy8m85OVVVSqZTRa7dboxk2hl/E2Jv97vu1B33pHhU8LJyupmmG44UOg1f8Xdf1NXMmnPGOOmWnC/PyApoiYZV0PvDtZX91kZ/Mq0Q9oxxeWUAPRTutAO1OpIAPf7kNqobbJNGWzdxI5hgdkdDrVWi30cpl9NUlTEef5CfLDc6t2jmZv8n/8cm9BIeOUZgr855k5rkBK5LfA5KEnklhLuaQ9u7hZVedr3hbSIEgICER5MydAo1xL8cUhWcjJt7PdrogAZ/pP0divDRzm7+034G9kcZ04GkkkxmCoU7jBquNM7N58qrCmdtZXhsdQupyrJIkQXQA9dplRgc9LKseXvA20fNZ5HB0zbRttuBLUkcH2e/3YzKZGB0dRVVVisUi+XyexcVFNE3D4XAgSdKWUqs7FQXudE/enTjfdhz1dr93L3D0Ts7D6dOn+0ayJ0+eXPdv586dWxOoTE5O8od/+IcbRs3CHrkTrlarpFIpstksiqKsW/y7bSfg3N2Eo3f7+nbaCddqNRKJBJlMhkajQTAYXAfxb9d2Y3Owm0S0nY6+RcQroHnxPSEw0LuACAfd/f9u5r54FsR/9+OY9UIeuVFHslmYs4c5UF5GQUVtNLmaN3PYpiDZbGjhGKzOo1VLuMwDHFFz3ND8uNUae549iVa4gbaygqa1kfIZfurcw0e/WCZfrGJD4xPVx/HbN2H/Id54OsZrM7eQBwIgK2jxZfRWE2o1MJmRA04kjwc9m0HXVM7MZsm0FT6YzXB02skbEwO8ls91OiR1N2Co11EdnbaFyp596MUCP0x0IuYvjzg5PQTPDtsplIo8O+pCstiQepp76Okk8p59PJf8JQeefRItk7yne9z7HfGM9QpI6HpHbnFpaYlCoUChUEBRFCOi7m5O8Diyo7djf17g6N4IdnZ2lpMnTxqR8Llz5/D5fExOTvLkk0/y7W9/m6997WvG9zOZTF8CV7c9Uiecy+VYXV0lGo0yOjq6Jat2O050t3O+jzO7eisT+a1ujeZIJMLIyAjLy8vY7fb7csCPAiZ6ENspyFk4ThH1aprGpUuX8Hg8+P1+fD7fpm01AeNz8VypqkqlUiGdTpNOpwkEArTbbeOY7ULYmqahLtxB9QXR60X2llexaU1qsg0UlRdLtyAyjlQuoszdQg8FIZvhDVOGf2TZy7PVJbTxw7y+x8OVCxbksQna3/s3KIePceaWztlUG1u+SMwGJ8acaCEXpOJoFivywBBaPg/NOtLgMFIui8QC6txtftzw895CiWfHPLwalnl20MZ78RYnY050h4TeaIDZDF3zpler6I06mtcPwSBUyuDx8e6122TaMr9crHB6wsUr3iZjUxoHD0bXOWBBypLMZlq+IHoxjzIwjN5qoaUS66LhB3mmJakjt+j3+/F4PAwMDNBqtdY1J3A6nQBbCon8qjth2F7k/SiIWdAhW/3BH/wBp06d4uOPP15TI/xHf/RHnDp1im9+85tMTk7yyiuv8E/+yT8xnPR26pQfaYlSIBDA7/dve3IfhhP9PBO3+lk3ozmXy7G0tEQ0GmVycnLNvO92vvl+jn0U17SdhaXVahk5XmGKonD8+HF0XTcami8tLaFpGk6nE5/Ph8/n25DPUL9bJpPL5Yz6diEq0w1rdz+f3c6/9x2SJAkadahX+JPbNS57Rwk0SkxU4rzaWACTCYo51JEJpGoJSQPTc1/hSGKV/8vSMv+jb5pAIs4PrjkZdDjQ81lMTz3Hz37+KYnmAGqxREmxUcfE844C+IaRXB50VUNrNNAWbiOFYvz4TpmLlxYZ1WB6v51PP7pN2uzjzHyRV5QGr436eeOwl7IOmds3kYaGkG22TtmRxYoUCqPXKsjBMOTy/GS2xLmlMl8NZHh2KsB7M1meHXF2csNWG7rEOgcMgCzD3Tn8MKHx1nKe5yYlXpvyQs89343o1Gw2EwwGCQaDxmflctmIlrPZLGaz2YiWRQpup69np2wnIfKdIqcJ2+nNyOTkpJHn7Y5ygXWiHadPn+b06dP3NP4jh6PvxbbjJFut1qbHP0r28r2QozY6fju5V03TyGazJBIJqtUqPp+PWCxGtVrlwIED931t93vdm9mDnvdhnFNEvM1mk2KxyOLiotHztB/ULBwufLbY5vN5bty4QavVwmaz4ff7sVqt5PN5Q1gmGo0yNja24aZUOON6vW4owQWDQSNS7s0rq80m1sQq75gOEG4WqMhWxppZsAJWGzjdKCuLaAePo+gtqJSR3D6eOB7lyLtzLNmCXL29zNB+J7RboKq8I0UZKCcp6xJz5iBWSeNnDR+nrTYkXwAkCfXdn6AcOg4mE1f+3QWSipO45uQ33V5OBONQrPKUpYY8eggqpU53pYU5NLujA49n0h1N6lwWPZ3qKHXd3fR8sFCk0ND5adbE3w/mef30aAdmHhxGstmRFhbQmw0ky9roUg6G0dJJdJeHS+k2bXOLd29neDWsI4fuvbTuQZ2eJHVKnwSMLRoUFAoF4vE4lUoFSer0WpYkaUuU8GFHwtuJTLebE97u+XbKoT9u9rlywo9DzvZBc84PGmlvZO12m0ajwYULF2i1WgSDQcbHxw3Iq5sgtJHtVv71fm07872Z3e/v6c7RikgTOvfn+PHjRiPyO3fuoCiK4XTdbnffiNTtduN2uxkZGTFyhQKSFD2q3W53p03fBr9JSIGm02lkWSYajXLs2DHjmerNK2vVKlq5RMvrJ5rPI6kaL+evUnO6wW/jcsPKXNpENBDlSGIZTj3DTy4s8a55kFfcRQ57JeqKmafDClKz0ZnLep2vTHr5WGvwmjXHTypFGg4f14pNXnY40SWJn//kY96rhHnuZooXJ3w47BYSySqxiBOtVOSrow5ebreQooPITie61Yp68xq6PwhtDS2T5u3lOr9czPLcpJ9XPDUkFPR0ElSVL416OLtS5UthE/JYFH11xXDAAKrHh14qgtuz3hGHIrSTcY4FJObaCs9GzX0d8E45tO04oe7viNLLcLjTAEP0Wl5eXqZcLpPNZjvdo+5Gy3a7/Z6ucycd9U7C0dt1rttx6O12+576jj8O9rm62geFkx/08+1c38N08t0azcLJTk9PY+/TQWarB/1xhKNhd0qUNhpL13Xa7TbVapVKpYLJZDKiSvGfUFTqbhxQKBRIpVKGxJ3H4zEcs6IoRqtMATVHo1H27NljLCqtVot8Pk88HqdcLiNJkkHgqdVqZLNZZFk2pEB7o6Luv2ua1omoqlVKlSqKLNPSJV7NXWHFHkBV7KBnWdXtNHSZRjwBvhjtj9/nE20ad2aB92p+/s9/5QVeSK6CxcLMpcvIe/eg2x28aK/y0lNfRr18gamrS7xrkjjolJGScdR8mp+2A2Tabd5LNHneGWdJt6LbzVyP1/jRYo3XJoIdZnQu07lPJhPS8Bhcu4yimJCGBvnlYpZ0pcW7M1lePeVBcnvQmw3k/Dyv7I/xF0dtSC4XerWyxgELk4PhjoxlINTpD9z9XATDPOe5xX+yb2zDCPhhO5iNviNKn+r1Oj6fj4GBAer1OoVCgaWlJWq1GrIs43a7MZvN29pkP45OeKfOB501casc++Nmj9QJ3+vi/aAlSLvZpUgcv9XnD+qEm82modEsVKoEo/n8+fMb5hy3c+2PG6T8oC/wVrnmbjKUiHgtFgsej4dbt26hqioOh8MgWfV7uU0m05pcn6ZpRqR848YNY1GIRqMcPHgQm822bgyz2WxEQM1mk3g8TjweZ3V1FUVRMJlMOJ1OFEVB07S+0GS3fGur1SJstzF88im0cx9x8NIVrrpHMLVaHCos8i+lYYIm8Jh0fE4n0uxNOHqK11dvc8Y2xMHjU6CpyIEQ6sIccr2KurqCac80P0hKnPlfL/PcZIhXngvwdCZJyuqFUh5d13hudID3FkqYzFb+wY0CI1qRqw0rZlni00SdN44OoZeK/DBt4r1znwlmMDiE6eMP4NgTPDfp581zq6xk6vwoG+C1oBXKJdRACLJppIFBJKsNVBW9XuOHd8qGwMeArqOrKuj6GnLXmrny+u8Lgu62h8lq7v6OzWYzNnLQue+CbJnP5ymXy9jt9jXdo+4lt7xd28mc8HZsu2O1Wq37Ipc+SvtcRcLbgXMfNBLezbzK/ThhkVNMJpOsrq5itVoZGxvbUKP5UTnSRxEJbzVuP+sWz2i1WsbzIPJukiQxMTFhnLtarZLP57l165bhUIVT7oUDK5WKsRja7Xb279+Pz+ejWq2Sy+W4deuWkRMW8qo2m41Wq7UGao5EIjz55JNrJBLL5bJRTdBut42FVtd1crkczWaTUCjEnj17sNlsqKvLqMvzaKEwy7YQdxwx9pWXiKseAuU0ZqeDp9opzlcDvOXbz56lIrI3QMPkpZVMoB+MIjebyG4PcrWCXiqgFwqcv7ZIRrVx5k6RV3025Im9sLQEE9MoisTpuVle+8ph/v4PZshKNiSLwu+M1/h+zsSJA8No8WWkgRHeW1ghXWl1RDn2+tBrNVr7j6Beu8Sr0wd59+oKGdXGuzMZXg2qyJEYeuEWUiTWccCA5OikWs5fuUZas3NmNsdvBsqo1y4hHzza9xl4mCSnh3EukQ7RdR273c7IyAi1Wo1CocDc3ByNRgNFUfB4PEZqaidsu9e9kwIi29Gk/8IJ77LtNvsYdjexv93r13WdQqFAIpGgWCzicrmIRCJYLBbMZvOGGs3b+X2bnft+ofjdjFh3YtzenKnJZFrT8EOUkvSeT5I6pSZOp5OhoSGgw2LO5/PMz89Tq9WM77bbbdxuN9FolImJiTVjuVwuXC4XIyMjQKc+O51Oc/nyZSqVCoqiEAgEGB8fx+v19r0OkVceHh4mnU6zsrLC/Py8QQwTEY9oPIDDgbo4z8dNNyu2IM/lrlORLSh2G2pNpVmtsez0ULJ6aMpWkm2Vi9ZJlFKe7y1b+PQ7l/mKrciJUwfQLBa0dIIzySbpnAmbWuS1EQVdGYVmA90fAFlGCYbQ6zXa1y7x3P4pztxM8txYgNePjzDxzs/YH2gjBfagFws8O+LkvcUKz4550HNZCIYhl0MeG0O7cYXnDgxyZjbDs2ETciS24T2WHE5OTIVhJsWJmB1TMov89DOQjKOLfsf38dw8bo56u8QsSZJwOBw4HA6jmb3otZzJZMjn81y4cAGn02lEy/eDnm03371TZCpN07aV6202m/eNBj4q+9w54d1sJbjbttn5NU0jn8+TSqUoFAoGo3l6etp4kLej0fw45nW3OnYze1ByVW85kXBoTz75JKqqks/nSSaTzM7OGrlY4ZT7LTI2mw2Xy0WlUqFarWK327Hb7bRaLSqVCgsLCxQKhb5j9JKrRkZGCIVCxr0X1yEiFzEGYLDdhajKvn371uT+m80m+XyelZUVKpUKsqoSKpb4n263OFRZZtHiY6BV5C8FmryTNtFoqdTaLcZiDtIWD/tMOfanP+ZfT53GtryCdWmJPwsNMfCD7yAfPI4+Psa5f/0B4UabiXaBU4FpfrTS4trcMoPDIV7Zb0WvVZElGckf5FWlxOsvjxiCG21fECkUQVIUpLt9e1+f8nbyuuEo+l1VIdlqRTp4lNeScV4L+dY44I0W9VeeGOPlETt6PsdlZaSzGY0OGDKV3cfslPOEB8v37vT1bDaOyWQypGc1TWN6eppKpUKhUOD27du0Wi2j17JAV7ay7cDDOwlHbzeqbrVaX+SE78Xu9Qbttizlblvv+YVGczKZpNls4nK58Hg8HDlyZFvH9/v8VwmOvtfnozviBVhaWiIcDhu9YgXJSpgsy2vYqKqqGjWbgr3sdrvx+XyYzWbS6TT5fB6n02nUWvdeY/cYd+7cMUo5Wq0WFouFWCzG4cOH1+3qI5GIgXCoqmrUGedyOVRVxeVyMTg4SCQS6RsRWCwWY4xKpcLK/Dwpl4+qpNFAZqKR4WpgilblE0ZRSLi8RBSJ4bCNI9YqxCZQNZX/InWWD9UGc0040Vjg5869XDiTYjjv44mjU5R+/nNCx46g1mp8shhnxeKlenORl0esqPUqssuFMjiMlklDV0Sig9HNCEAOhjqKXl0iGWIuJUlCig5s+77rlTKS2YK8Zx8f/PAsb/1ZJ9f82v71jninHOxORYI76fC3su5oWaAzAuER5XfJZJJiscjFixdxuVxG96heiPdxJWaJDcXnyT5XV/swIuHdzglrmsbS0pLBaA6FQkxPT2Oz2ahWq8zPz295/GafPypH+iD2IAzoXqhZfH9wcNDI58bjccORiXxuv7yRgIUDgQC6rlMsFllaWuL69etAx9GJSLlbTKF3DMFqlqSOdrCo9SyVSqysrBhIh9/vXwOdiaYliUSCer1OIBBgamoKq9VKqVQin89z9epV2u32OsJYtVolHo+Ty+U6mwQZJl98iZEP36JisvJu4CC/XrvJFVOQPaYkflOBa6Yw798o4Btxsl9JotucBNU2L435MEsSytgU/+ZHi6Q1C+XzN/G4m4w/8zzPDJqRhsd48fa/46OclfBgEOvKAu3JaWSHC6nV6nQsymWRPRg53HX3z+v77D6nEsj1OjC24f0W97zX9FYTydI5x6V0G9XcyTW/fiCMriigtsFk3nSMNeM9RNLV43I9FouFUChEKBSiUqlw+PBhyuWyUbcsei2LaHkntaO3M0fbjaq/gKN32XazzlbYbsDVtVrNgCHL5TKyLPfVaN4J9vTnEY6+10i42/GWy2VjHoVOs/izIFFBf3JTryMT30kkEhQKBSNSOHjwoHGdlUqFXC5nCG/Y7XZ8Ph8ul4tisUgqlTIi7H4Rrxgjn89z8+ZNms0mkiQZGwhR3+3oUX3q7eRTrVaNWuV6vW4wrPfv3985tt2i8ZPvgdbmnHcPTxRmKDqdFOtl3COj1OOrZEstUnYP6VSFAw4TpmYbzWSCg0d551aa9y7VCLht6Kk81kaVjx1DfDzf5EeLFULvzFE1D/H8UJMX9RUU235+nIIzH8/y7JiPV/YFwOujHV9Big2i6zqqqq5DIwC0xCr4g+j5HFoug+wP9r3vgi+ht1pI3S1MfQG0XKfByJGQiaW2mecm/R2pSq+/0+iha4zd3Gj3Xu/j4GDv5TvCRHmUSIdAp9eyaOmYz+e5cuWKESl391q+n/NtZV/A0Y+JybK8RuS+1x51zleYWGiFDKEoUTl+/Djnz59ncHCw73EPGuk+jo50O8duVwVMLOTizwMDA8zMzBjOMBAI4PP5+pYBdZObRkdHDUeWy+W4evUqlUrFkJgcHh5eJ+spxugmWTWbTZaXl5mfnzdgMJfLRSAQwOPx9CXTCLJXs9mkVCqh6x0NYRHN5vN5KpWKESn3Rtyi6YYQbti7dy9+v9/ICS8uLlKtVrHls3xwKUve4mZfZZms4uD58ixtX4BCOo5V15l06TjUEiM2K6Mjh/j5aptzJQtDF+N8VDRBpYzLauKgucINbDTqDWq6Qr1ZY0mxMxj1crGY5atHp5AadS7MrpLTrHy4WOI3jg5Qu3mVtGIhc+0adrt9bYOKfA7JYoF6DSUchWIRLFYku3NjR6zrSOkkus0Gbi9S132W/R1H/Iy/zdGnpj9zwPdJOnqcHOOjcML9zGq1GimPer3O/v37qVQqFItFlpeX0TRtjZiI2HTthH0RCe+i3cvivdWCvVWkvJsmGM3lcplPPvnEyBtOTEw8VG3s3YKUxdgPK4qAzxxvs9lcs4CLiDcSiRCLxdD1jpRjLpdjZmaGRqNhRMGiDKiXnCNqKwX7fGJiwpCRzGQyLC8vY7FYDNEN4QxFOVF3xHvy5EkjCmg0GmucoclkMsZot9skk0kqlQp+v5/R0dG+ZSPdJKtyuWz85kajgdPpJBaLMTo6uua5stlsxGKxznw06tTPfcR/W7ZRNcn4ahW+XLnD/gkPHpcJa8WLPDrBUaeLA9cvIZlU9EaDj/UwtewSPyoHODDhJ2W1sV9dQY942JMosF+P84vWEDOyF5PNhimf4ZbFxU9d07wW0vhy/ALv6EH2hqzc+N6/p+0PEg4EOLJnD7LVAiZzZxOVSaM7HKjLixAIoqkqP76Z5f35Iq8ctvHGpActm0YOhNbcMzmXgdEnkB2OTs4Z1jliudVEW11CCkb6OuDHxaHt9Lkedk9eXdfXPNvi34SYyMLCAvl8HkVRaDabhvjMdsqMNjrfdiPhL0qUdtEeBvHqXh5SodEsFlZRHH/y5Mn7etgfdzh6o5d4J8/bG/G2222uXr1qOFS73d43QhUsZYEyiFKiubk5arUaZrMZm81Gs9mkVqvh8XgM5aru32S3243Sju7oMpfLGXmxSCTCoUOH+r7sAvWIRqPouk4mk2FxcZH5+XkkScJmsxEMBo3f0s9E7q3RaBglTC6Xy+iCtbi4aOSVezs1qbUq+QvnyKYzDFUSzDsilC0eXh0MojZq1JfmqJksVFUdR7mEfe8hrMU8kqzwUnWGb1nHmKinKCfhH7w0isdyCL1QABk0lwfpwxuEMmWarSJn1CCKIvP21RWOH7URjtj5zeXruOsxAs+9gM0fQMtn0dIJJLcHyedHKheR/AF0iwXVH6DSaJC6cZ1f3KzSkK18dH2Fr1pySA4XcrWKYrN1nrt0krbHi3x3syMHQ30dseryIA8Mb/i8/SrD0Q9bX7p3rO73MBaLsbKyYqjMZbNZ5ufn0XUdh8OxpjzqQdnf3fZFJHyPdq8PxHYi4QeFo7fjiOLxuMFoDgQCjI6O4nK5ADh79ux9P+g74YTv1x5k7AdNA/S2BOxmcZ44cYJms0kul2N+fp56vW5EqH6/32ie3mtCVcjhcBgCGqqqGvKTYscuy7JBnOq2VqtlNEmQpI6Ah9/vN/LKly9fRpLWlzQJRCSRSFAul/H5fOzdu9d4PjZiYIvfkslkSKVSmEwmotEoR48eXbe4ClWufD7P0tKSUfvcbrdRUgn8qETnbhNtVtB0mazVjRKJwLXLONU2/zL2LO8sOBi1aVj0NiOhKCfnZolWSnztwAGuVfbya/4GbosZJAk5GEQZ7hCmXtZ1vvzJB/zxsoLF7abSaKKXcvxff2niCVeTr/gC4PGiWqxolU4UrwyNoJdLqHMzyLEh6rpOYmGBTCaDu14lPDTEbxz28clCnmdCCiavHzWbQtU1NLMZvV4HTUXT4QfXUny0VOb5yQCvHwijrS5DbHANs/pB7XGDiB921L1TJqJlQXYU/1atVikUCszOzhobzaWlJaMpSr+I914Us75wwrtouy07uZE1m01SqZRB3282mwajeSftUUbCsDUT+X5ZzP1MKFe1223y+Twul8twqL2diWw2GwMDA2si1FwuZ0C+ZrN5jVMulUrGvfJ4PEQikXURb6vVMpifQq/Z7XYb5CxJkoyIt5twYrPZCIU6MKlwqJlMhlu3bhl13D6fj5GRkTU13sK6GdjQga8XFxe5ceOGEWn7fD4jv90vupFl2SBp1et1g5zmdDqpNetI332L79XdLFmCTFQSDDVzyMPPo1aqsDyH+fZlhlwj3G4E8Ib8uLNZ9oTdaK/+Oq+pKl/K52kXCty5sIgzGMZ7/AROXecHl5b4+NNlps1+BqQlvly6xb4nD3Emp6Dl8rSLNQbH/FQtVt762QUur5TZM9pxli506okk2eVVJF+Aqw0355MOvjw1wpSlyeuBGm8MRzsQsmLCMjaJWizQtlgp3bpBymRFr5R5vySTq6u8czvDyyM2JJMZ+R6c1OPmPD+P39mu9XOcghPhdDoZHBxEVVWuXLmCzWZb02tZlEeJ8sIv4OjHxB5mTlj0d02n0yiKYrBdL168yOjo6I6co9e2cnS7uQnZbk74fs4rPheMZsEE1jQNn8+HqqosLy8bUW439NzvuiwWiwH5Qscpr6yscOXKFQN6FhKO/ZSwAOM7Xq/X2GCJnq5i8ajVahSLRbxe7zpnKJx1Op2mVCrh8/mIRCLoum4oam1UFiWi7FQqhSRJhMNhnnrqKUwm06YMbJ/PR7PZJJFIUCqV8Hq9DA8PfxZll4rUb1yGffv53lU7MbVA2uahZHfzxz/8lFGfhX0WBxGTSr6WIRj0grlBsFbmv9X28ZUr83z5119kUFbQdZ1GLkvx1g3uzMxQyGb5yaUSLV3mo4FBvvVXn6T9ztvIegKbzcH7TjPT4SFsfiv2SIz6+2cousa4VtBof7rIpVSLwyGFF306rkaVjxZlii345e0Mrx6zopfLSE4XKCZQFEqqRrxQonXtPexT04w4Hdi807x2dYW3c2aeGXKiVyro/gBaVytH8VztdgP47UKoO3Wuz6MTvhcYXZRHQcd5CzGRRCJhNFURqZyNygPhCzj6nk1Ajtu13Y6Ey+Uy1WqVTz75xFjojx8/ft9kgnu17eZlH+T43Tj3ZvdQwMuNRsP4uyR1ylTMZjOyLDM0NGSIBghi08LCghHlCifW2+i8WCwaDsnj8XDgwAHcbvc6JSwROfr9fuNz4QSBvjleAfnmcjkWFhaM3bnVaqVWq1Eul/F4PMRiMfbu3btmDnp7CedyOVZWVqhWq6iqitlsJhaLcfDgwb5lat0MbE3TyGQyLC0tcevWLWRZNkh/IvIH0Ntt6r/8KXK7xf9HH8csr6K2Zb4fOs5rjTuEcmlSup+D42H2jLo4nIpjjtVw5Rb5r0NP0arW+bnVy7Mzt1Cm9lJOp0murJBXLPhKecZGBrm5MMu/Lno42q5z49PztCYPElyc47i9xfP7Ylj2H0CzWMl8+glOt4Wx1C3Gj+zjYtqJ7jaxajKzf7+LSi7PsbDE+/MFxhp1ZldseJ12LMk435+v8E5Gxmsz4WlUOTZ9nF/bNwKahpbP8tLhIV4qFcHlRPIH0fVO5yvRvMJqta7pryyeNfhs/XiYzupxcp6Pwgnfj6CJ6AwlZFoBLl26hMViWdNr2e12GyVSAq1qtVo7qpH9MOxXKhK+V+vO3xWLRZxOJ2azmSeffHLXd9L97FFHwjsFR3c3SdB1HZ/Px4ULF4xm9sFgcMPdajexCT4jRy0vL1OpVIyIWlVVAoEAAwMD65ygyWRas7MWHYbu3LlDoVAAOo5ycHCQYDDY917LsozP58Pr9VIul1ldXSWbza6pQxZsULvd3hcCEzv6fD6PruuMjIzgdrsNx3zp0qV1zRzE7xD1ygKqn5ycNKL6fgxsPxpetY2mafzwToWCK4a/XmS0nuErnjqNXJPh0RBXYns5E6/z0vQeGokEb3OEQDFF0Rniudo8N+p7Ub//XWzhGJHpfUx6vejtFq23v8e8e4xj9RStgs5b1gFesMPE9D5Kc7PMZbLU3/kFH1VdzFSsvBwJ882n96JnkviiU5yZyfFcRMEUDOMNx/gPA0n+w/0B6iYzK/Nz3Ell0FSVuZtp6q4RstkKYbeNcryJNFPg3SvLPH9wiDcmHWjljkKWEGKp1+uEQiEOHDiA2Wxeg7QIJy1MaGtvFi3/eXawDzsS3m6uV1QiiJSUICmKuuUbN27w3e9+l0AgwPHjxx8qAe9B7XPlhHeKeJXJZEgkEgajuVuj+ezZs5s6YOGMHsUNfpTs6O0Qs9rttrEAirFkWWZ0dJTx8XGjjGh2dpZ6vb6mjGgjprDZbDYctohG7XY79XqdUqlEtVo1nJjb7V6X9+0X8SqKQqFQMJy7rutregDLsrxGtEM0Zuh29t2w8fXr12m1WgbrU8hOqqpKKBRi//79azYdXq93TUOIXC7HnTt3qFQqBkHN4/EwNDTUVxqzd6PSaDRYvn2LmXoLclnilv3IwLI9xF9MnuNIew5GAyhDXr59a5mSd5iZmWVkXUUyw3VrDL/aYiUcY18ug26xUW23SaVStFotvGob5cmn+Or7V/nAYaFQqLCymuGfLkj8R+4UkyMebql+zhVk4lUdWdL5xXyRvfUFLE4XT7izvPSEE1s4gmSx0mq1SDTbZJaXsZULBI4cZ/L4k1Apkwtm8X58jkQsRqNSZUJP8b330pRNDn52I8lz/jbJTJZiKoPf7+8rbNL9/ooNYavVMiRig8HgmmhZluU1EfN27GE7xsfperZrO6mq1TuWQLcEN+LgwYMcOXKEf/bP/hnf//73+e53v4vD4eDUqVP8rb/1t+5bwGN2dpa33nqLyclJZmdn+cY3vmEgXf3s7bffZnZ2lsnJSQBOnz695Tk+V054O5Fwv5uuqirpdNpYVPP5/BpGc69t9iD+eXbCvZ+LaFeQq1ZXV40yrV5yFawnWHU7oO58sMh9JpNJyuUyXq+XwcHBvixmce5EIsHMzAyAUZtoMpmIRCJ9YV/h/MXvELq5wqHa7XaDndwvyu2GjQcHB0mn06yurpLL5Yw6ZofDsaXAjIDq6/U6TqfTIGyJVnRLS0vG5qB7k6HrnRaGRiSotdj/G38Z08Is/rdm0XSZoXqa3w7kKFZlZJMD69I803tOMnLhHObRUbTUKuP1BtgiVKJjLGJh77EYkskEDiflcpn8nRnirTYtSSY65OGvFnKc0+v8j8lOR6aztmF+YyLIvztXpFltolicjJhavDARZP/YBA1ZodhsMpfJUlhcNogzsViM/S4rlhNfRc9l+OHVBO/eKfCSr8XvPD8J2Qzm57+CevsG/z6l8JPZHCOtBFevrGLy+Q3i2maLqyghjMfjnR7LXSpmwjkLVKX7vrbb7S3JPTuVE96pqHun6oS36xS3Y/cLR9/PWLIsMz09zf79+/n617/Oq6++SqVSMVKL92tf//rXOXv2LNBxyL/7u7/Lm2++2fe7b7/9Nm+++Sbf/va3mZ2d5ZVXXjHWpM3skTvhe3Ea99KKUDCaU6kUmqYRDAaZnp6m1WoxNTW15Tm2csJbXcNuOOlHyY4Wn3eXE4l/M5lMHDhwgEKhwMzMDO12G6fTaTi6jRa0bqes6zqpVIqlpSVmZmb65j77zano/tJsNqlWq+i6bjQ+r1QqJJNJGo3Ghp2RumFfp9PJoUOH8Hg8RpR77dq1vr9H5GoTiQStVotgMMiBAwfWOIVarUY+n1+zyRC5bdFaTkS1veIbsVine1D3JuP27dtroNZwOMzY2BhOpxO91URbWkAeGiFnzTFWXiHYLBIeDYJrgkazRaFWY++lH1EeGsFfvI3j1BO4amU+Mke4OTvH2MQBJJOpQ5ACnM0GDp+XQDDCmx/Ncna2yElbk1N+mb+s1TjfUtg/MUTVbuMpZ4KPFRe/7m/z0v4wUi6HbjZT1SAfX6Xh6OS4/X5/p0Tlzgw3Gk2kTz7GFRvg7NkFGprCB3U7X50eohGKknrvXRJ2JwfNKi897cMb3Y/i9RmN7AVDXhDgRPqgXq+zurpKpVIx9Ld7kRaRVhCbetFfWmzWx8bGDEnR3ryysMcJst6pc+1056PtnG+7Tn8719VdouR0OnnxxRe3NXY/m52dXfP3yclJ3n777Q2//3u/93uGw56cnOTHP/7xts7zyJ3wvdhWkXC9XqdWq3H27FmD0Xzw4MF72gltx9FpmrYhWWu75I/7tUcRCYuIoVarrfmOiPhEQb7X610jCZnNZtdAtSJ66YaXhYMRUo3T09MGQrFRKVIgEDC6GgmoWdzrXoffry7XbrfTbrep1+u4XK6+HZF6JS4rlQrZbJYLFy5QrVaRJMlQverW1+02IVwwMDBgSFyKul5FUbDb7Xg8HqMXcD9TFGXNfHm9XpxOp6HjK8qwfCYZt9nKv3jrfcbLBVQkEo4IVVuBnCahOdz4LFaCg0OYmk3aBw5TTiW4MzLOwO0bjEWtOEu3qGsx7EAtkya5ukpxdQWrfYHrSRcDtQI3w3v5j18YZObsHK5UG0VVia+uMmir8VesNdxmC6lbOfLhAeorcUKSztjhI9haTeRQp0uUvVYhdPxJJIuFdrFAMZtlKmRhZmaF0bbKuQ+XkGIDDA0McTQQwOQPoLfbnQidzxrZdxPgUqkUy8vLXLt2zdA9Hhwc3FDGtNu6o+VIJMKxY8eM97hfXlmgGzuxGX+Y33kUqloP83zQWTN2qkTp7bffNpApYYFAgHPnzvHkk0+u+ffZ2VmjP/m5c+eYnJw0IOmt7HPlhPuVIJXLZaO8xGKxIMvyAzGaHyU5ait7mCVK3RGvrusEAgFu374NdHKawWBww567kvRZLeDIyIjhlEXZTbVaNe5jMBhcU2bTbb2lSLVajYWFBaP8RwhyhEKhTbsaBQIBI0LNZrNGwwOTyUStViOTyRjlUv2EMbo7G4VCIaPloGBON5tNg3QmCFbAOonLaDTKqVOnjHO0Wi0Dxi+XyyiKYrC4ZVlek5OOxWLrap3F9ZUScfKryyQScT6aLVFXLEQbBcYHPDRdboacLkw2O5oiQzbDe1U7H9xqc/zQCV6zFtG/8jKNVIqcpnPjnZ9RlmQUs5nA6DgTzzyHNZXg9dIqPzeP8bqSQq/5+KBiI6c3uZhs8/Wv7KGiaSRv3yKZSaNFBvhorsyVnM6Xhpz8hcQqJrcHc7sN7TbIMpLFwvevJvnlTIZTpjzH7FWOj+h4x6awj09SnpslVSySXF7C7nThHR5ZBz83Gg2jjNBmszE+Po7P50OSJAOF6FZM65YgLZVKBtM2EAiwZ8+eDZ11N2wtpEdzuRzRaNSIlvvllR/HfO/DVNV62E4fdlasI3+3x3WvZbPZdf927tw5AoEAb731FqdPn+af//N/zuTkJF/72te2PM/nrkRJ0zSD0VwoFHA6nUQiEcbHx5FlmXPnzj1QSdFW0fZuQ8Kb2W7nhAWBpXeXL8uyodMs5l/0y4WOUxZNCzaClhqNBqVSiWazSTAYNGQZc7kcN2/eNDoa9bb3a98lCCWTSaAT8U5OTmI2mw0nJjSWhZat3+832gmKJhoixzsxMbHmmRMsS9G/V1VV3G43JpOJarVqQNn9CEAOh8MgWNVqNXK5HLdv36ZUKhl5xYGBgQ0lLkXnI9HPuFQqsbCwwOLiIsAaict++XBxb9w+H+rVizRVFV3TsKISbBY5Ui7wL294GYuZeNFf48OilQtVF/lSBcmt8ouPb3EmGOLopfPs3xdFVhQG/T48MuijUxQVE0t3Zmlm0kRsOr8TMuNxBdGyGZ6bjPCLWykOWKtcuHETZzBILBhkfGICJRjin/6rqywW6hTVJi8E2qzcuUMzMtCBjU0KFIr82bk02VqbmsvEq4MSllNP83bRxrtnkjw/McxpfRXd76MxOEKxXufWrVs0Gg1jg2i1WhkcHOT48ePrnrtuFAI6i3MikeD69etUKhXMZjPBYJCJiYkNm20IkyTJ2Cw1Gg1CoRBPPPEEiqJsmFcW79PjBFk/Cnb0VlDzdudou+vaw1DM6uecs9kss7OznD59Gp/Pxze+8Q38fv+2rvtzEQmLaCQej5PNZo0Ftbc0Bba+WVsRqx5Un3o75LH7td1wwt3kKuGIujWae18iWZbXkZr6OWW/32/U49ZqNXw+X9+GBcPDwwbUnc1muXnzplFTLBSkotFoX6i514n1LrSKohAMBpmcnMTr9W7oxEStoSh5EaIdwlqtlrFw93OmAvJOp9NomsbY2Bgej2dNKZKod+7Nb9frdeOcIkcuWieqqmrUKgvdXcHi9nq9VCoV4gsLtOZm8Djs3G57CbdXGKhmsEka5lwbl93MjDrEl+tJPszJtKtl2pgYLq5wyxSgGs9Qj0VwmmJ8dGWR11wqz57+EnoqweW5PB8vVDh29DCn9/opXDjHimQil1glvHKe34xECY+OMzQ11XFIuQySzY6ey8Ldd8wswejgIPrEJGUkFhYWuFksYqrXOOgwc1Wy8xV3HWlyD7Ki8L+cW2EpV6O5ssTp39iHIknYtTZ1dCOSC4VCmM1mo3tPIpHoS14DDHGTdDqN1WplamoKv9+/RvpTzK3IKwthFVGaViwW8fl8TExMrNuIiXdAPAeC8JXNZimXy0ZJlIiU7zdafpy+s1172EQw2Fkn7PP51kW9AnLutcnJyTUpEvH/ftB1rz22TlgwmruJNaOjozQaDfbt23ff426V031QR/e4R8L9FgzoOLSjR49SKBRYXFxcU0IUCAQ2hOq6nbJYfJaWllhZWUGSJKxWqyHTuFEZkiRJWCwWFEUx8qVCpapYLBKPxykUCsZ5ehmxve39JicnjevJ5/OkUinu3LmzTrRDkqR1Os8jIyNroHFdX9+H2Ol0GqVI2WyWdrtt9PLtXgBcLpdBsBL1zktLS0akrGkadrudoaGhdcQs6EDp3RseVVWNHsIi8g+gE1Pgv56zkL2zwFizQslk56i7jt/ro1BWOVJfxPfUCb6S+oSzupkxd5PjA3YuryR43zbKPq/OO2dvQbPN9y1hvjQ3g7LvEJd++hPSmo13bqfJLcxzfqHAnhGZv3ziEJ49U1QlmXyjydUzv0RqNbENjeK3mPCEI3x9j5N3lyVORczMJ5Jkmy3cbjdjY2PG3J8qFqiWihSXFplbjVNXTNQKZSLVEglbkJKmkUokqcgKIUVm78Qktq78u2jWISRIexnyjUYDi8VCLBZbp8HdO7fiPqdSKW7fvm0cKzaAGz274h0QG0mRWggEAhw9etRoUCDyy93HbDda/lV3wjuBFgjbyZzw6dOn+fa3v73u30+ePLnu37ab/+1nj9wJiwdUkta2iRM1lnv27FnzAjyog9sq0t1K+nI7x+8We/p+nbCAzHS9Iy4hzi1JHZ1msTsX+sPdnYh663oDgcCavGdvztTv97N3714j4t0Mvu5uWCDYvv0iXnHd2WyWW7durXnRGo2GgYz0OjJZlteJdohoX3RFEiVGe/bs2bD/b7d6TzqdZmVlhUwmg6IoRocjoW+72b0RDG6r1crQ0BCyLBvRnCjvEhuE7t9RrVaJx+PkcjlcLhd79+41yGClC+coterk7swTbhbJmFw8pSc5dWACpV5lYsBGoVxh9QffZd/xk5wyKdhCIfR0ir1v/AZ/OZOiUSzyY93Cj1MW9mgZrtZ9+M59QizqoXR7mQlTnuuqk5rNRyFXJ2iSkV1BbA4nvkwKafBLqJUylWSCnGZi+fynBFSNv+CqYcOJ0zPO+Ojoumde9nhxWiy4hkaIlYoA/MepK3xvwcm0u8L//N4trjecPDvu4i89MYy5VIA+JDgBLYs5bjQa2Gw2HA4H5XKZlZUVI5rtlg8VJvK8yWQSRVHYs2cPgUDAIAZ2s9vFPRIcBMHEzmazxrPUL3XQDVsLh1wqlajVarRaLQN16o2UxfP/eXTC29lg7CQbGz5Dz3bCeh3r7OwsJ0+eXBPl+nw+g4R18uRJ8vk8Pp/PqBXeKgqGx8AJ1+t10uk0mUwGSZKMus7dwvV3O+e7XWLXbjth8dJ3K1dFIhGuXLkCbE2ugo3remdmZgyoTTi6fjlT8Xu7I45ms8nCwgJXr141oKNwOEwoFNqU6GW32w2RBUGkEgpTjUaDZDJJq9Vas0EQJqKceDxusImPHj2Kw+Ew4N6LFy8iSeu7InWnQhqNBsFgkP379xvnEBFQLpczNgiCpOXxeIz6Y+iIhRw5cmRNQwhBOmu32xQKBVKpFLOzs8Z9bLVauFwuBgYG1uWz9XYLu8WMfWyccOMqWcWBGR3l6AlW5z6mhYzs8RDMJLGf/BLmbArLi6fRG3XwB9GW5pGHx7G6vfy6vMSvPRmhkEzww8vLXE63mfbK/G8iNSS5geyOcq5s48BwAAp58AXQMikkpxu9WkG3WqlqGpU7M0jhCBMOB3aHg1IqSXp5iZXlJaz+DjveJ+nY/AEkqw3J1hFeiWey5JcX2etV+K++HME1OMjf/0UcT2mVC3EbX5q7Q7WQR0um12xWKpUK8XicUqlEIBBgcnJyXdTajWYIFrTNZjOIeZqmEY1GjRpiYb3tMbvRjFwuR7PZxGq1EovFOHbs2Jpj+70H4n52O+0DBw4Ydcv98sriGXyYDOqHXSe8k3D0TmtHv/nmm/zBH/wBp06d4uOPP15TI/xHf/RHnDp1im9+85trvnvixAnOnj37+SlRymazWK1Wjh49uulDvF3b6iF7UIbxoyRubXWs2Gn3kqtExDswMMDQ0JARnQopx37Op9eEBGMul6PRaBCJRPD5fIYDun79uuF8utnI8Bm5qjviPXXqlFFvuxnRS4h2pNNpAx4cGRlZd43iOmZmZoxIyG63G63SBDTcyzDu7mgkcrCZTMbITUuSRCAQYHx8vK8mrSRJOBwOg6SlqiorKysGa9pkMuFyuQgGg/h8vg2fcZPJhNvtplqtUiqVMJvNRg/hYrHI/Pw8uVwOv99vQPWSydx5JjQVVZeZdcSw602ebizjO3iU8/NZ8gurzIZGSGZMXCqGCXznAnl3iFeiCl+NWSCdoOwNEFdB++XPcY1Nkiu0kC0OZqxB/qMnptDtTqKVCs9Wq+QbZS7mrARmf4xz+gDtVIrv3crzSV7i+ckAXzt6FKVRRxoagVQS97EnGJJl1HyORrtNIZXiTq1G6/p1mmYLbVn5DJJ/5jmolNFrVVBVTgfa/ESP8aq7ysTQKMqRowYkPzMzQ6lUMlrljY2NbTi/As0QOV8RGdtsNqPcKJvNGgz5fkz7bkKoqqqMjY0RDAYNWdLLly+j6522lCJnLyJuEWknEgmDrDc+Pr6OICieQbF51jSNWq1Go9Gg1WoZqNXjEC1vx3Yy8t7uOXcSjoZONPytb30LYB3TuVe0w+fz9YWvt7JH7oQHBwcNGGYnbLvErI1sKzh6t530Ztbv2O6IF2B1dRVd143cab8Xtjc67W7JJ5yygO6E9GO9XjecUXfE6/f7jUihmyFcr9eN6xK9cYW272bXomka6XSa2dlZisWi8fnY2JhRutPPRNTi9XqJx+NkMhlqtZrxbLXbbSqVClartW9+W9d1SqWSUbMcCoUIh8MGhH3r1i2DGNXtCMU1dwt3CPas2JFvpAzm9/sxmUwG90GUMW3WQ7i7oYTdbkdtqth++mPOevYSbZdZsvoJhyT0epnbJY2yOYCnJbNc0MHqZjWTYbg4ww/yQfy1Bg27A590h1goQOCZ5yGfZeqZE1TP3eSkt45k9mIKhvA5nXywUOfdVIknIhb22Wvkf/ET6iMTfJS1UNVkzq3U+dqJMbQ7t5CXF5GHx5Du3i/F58d85zayBG2TGfxBYpKOyeujUKuzuLjI6uqq8dw5sileOBDjJbsDrdGgXSmzWq6QymSwWCyMj48TCASM+yYkSFVVxel0Gix5i8Vi5GpzuRxut5uhoSH279+/Zn57NcoFL8FisVAsFqlUKgSDQfbu3bvm+REpGnGPyuWywdiv1WqdHs93N8C9KEjveyD+L7TO4/E4iqIwPj6Ooigb5pW3Ipt2P+MPG7J+2HD0F/2E79HEA/Swz/kgTnInIukHdcL9oGbAYN7m83lmZmaMjiJCJGOjHWJ3j1vhBJeWllhaWloDBW+m8Qyd3Jxg9faSq0STDBEp974oAlJOp9OYTCaGh4eNPF+hUDCcT7+ovTtn6nA4iMVi68Q3+uW3xZzk8/m+rQGFiRxQryMU5TLCeW5Ua9oL64t65zt37qCqqpHTDoVCGyqDiYYSDocDk8nUYZ1Xq9irZX4ycBJfMskde4Tp6grtfA3J6WbY3aLSLOGZPsIUKh+mi5j8CnKqxHNhK/uf+jI2j4cf/ehD/vXVJSbCKZ6c9HGYFWrTQ3x4awWpOc+XFRNVSeb7F5fI1lTKJRtvfCmCLRpGDkfIhBv86cdLrBbq/PGffcBTngZ2hwOP1cGHq23euZ3huCnL4ZiNkM3Cvn0HsN59jrRMilgshmS1dUrOcjnSVy9TMFuR4inMaNRMFkyKwoBZ5sjhQ5jMnz074nnwer2MjY2h6x1hlUwmw/nz5w2Rl3A4zIEDBzZtjxmJRIhEItTrdZaXl4nH44ajM5lMqKpqsOQ36vMs8sRCJtPv9xub04sXLxqqab0Rt67rBvmv2WwaEpu9nb26o+TuP9frdcPh99t4i3M8bnnjx5kd/bDskUfCO21b7Qp3ogRpN530RiZetna7TbPZNMYQqlXizxaLBbfbbYhk9Movut1uQ59Z7MpFJCeY6IFAwFiw4LN8pZBO7GaW2mw241gBNfeyhGEtuermzZtGPk2SOsIKYhHsFwX2K4lKJBJcuXLF0HkeGBjYVKRFOMJYLEa5XGZpaYn5+XkDqheQ8lb1ouJ3iN/qcrk6Mox3Nwoul4tAIIDX610T9Yg5jsfjBpt6YmICi8WyJtdYqVTW1TsLCFb0HxbzJGdSSKEIf/dffEzdbEUDjplKJFqgpVJU2nZu+0c5sTJHcMDDZKPKZVOUJ58+wa8fjqGjI+kqdzJ1bkk+zqbNDEetDB4Y4Ox3LrHUVFgsNXCqH+M1mXh5ch835tKcCGs4IjEkRUHLpHl92MO7tx1k8hVmLVH++n4buXKVxaUl/v35CpWWhIMKXz06wsfxNh++Pc+XpwK8cTACZgvqjaso+w5ittoI2m0og4PUShV+kVC5mpM5MWDhuF9ltVyjcukS3rHxdfMr5jidThOPx9F1ndHRUcLhMK1Wyyjz6hbu6CZYtdttEokEyWTSgIy7N3L96smFlKndbiebzZJOpw1iY3f5ZDdaJMhe3V3BxDsm6pb7cSvE+w0YUbHoIFStVgkGg8ZGoft57XbID9sJb2ed242c8E7C0Q/DfuWcsICTNytBehD288PMCXczKsWfbTYbFy9eNMqHgsHghuVDkrS2N203QWVpacmI5CRJMqQb+0W6JpOJYDBIMBgEOlHl4uIiFy9eNEgu0WiUcDi8oXKViKjD4TC63pEZFMxmi8ViLJTAOsEOYd01tVarlf379+P1eo3FcSNyFaxvDTg4OMj+/fuNaxXCIUJhqbuZhKg/FmVM/eqd4TNIu3uhNplMtFotdF0nHA6vgzNhbRQGnd18Npvl9u3bFItFY06E9rIkSei1KrrFgqQo5Fo6DZOD4XaRAwcmGYia+On1OD8tyTjUEu973PxmfIUL7VHKyHxQMPEbkQFIrqKXyhwcC/He+QwOl5W3cwoHrl5iNGimlGjy/KFRpgZMlFaX2Zu5xtSAB5ddIj97G/fgEKZgCC2T5vmwwi9UGwfIcTFhwe2wszfg4688M8yZmRxP+wPImRQ/npXJ1toU8gWOyBm87SbOw8cpLS6QKFco1usEzCbGBwb4k1mFltLidsXM33h2DLVcpmK2rHOEZrOZSqVipAJ6N4ECDhZIRDf0LAhWotnHoUOH+j57vV172u02S0tLzM7OGk0p3G43Ho9nUydgsVgIBAJG7bnVasXtdtNqtSiVSty4ccPYKPfbaIh3IJPJGLKq3chNNzrWK7nZarWMz3c7Wt6uiXTVVrbd8+20U38Y9ivnhHc7Un0YcLSQixQvkTivoijs378fWZbXMJUFEUlAyhvBMSKKKxaL6LpONBrF6XRSqVSM7lIi5yla+glrt9tG7lLTNEKhEKdOnTIcaHc+zWQyGbCzw+FY01JQqG/15scEyzibzXLjxg1Db9rlchkO2mq1EolE1pUibSQeIuo9BWFmeHi4b2tAwGC5xmIxdF0nm82yuLi4rpnEVvXObrfbINSUy2UsFgsOh4NqtUomkzFq3vulB0SddSKRMJSZDh48iKIo5PN5MpkMc3Nzn8lbWi04c1le2R/iu5cSOGWF7+RM7E2cZ6YxQFhu0dJlXpfTDEbCnK7Dv8+2WVjN8D/82Yd81d/CE47wVFAif9jP+3dyHKlnME8eZOCj28xHYrisZgJDg/jDYbRKGa3VoupwkstmWblwkbaioFttRCol/vfmGqF9B/Dv6USBeqvJ6/kcb0yMotdrSNZxfo0EZ+JNng9JyNkk101WKu+8g9lsJmJW2DM+iSccRi8VeG7IwZnlKs+NuNCrVUyhMF4whEpWVlYMYRVxTwUU3Y8l3/0O5HI5yuUysViMcDhMvV4nn88b1QPdrS3Fhr4fZHzixAnjPm4mlelwOMhmswZnIxqNcuzYsb65/+4cd7vdNiB0Aa0PDAz0rSsH1gnsNJtN4vE4qVQKi8WC1WpdFy13l0Y9jgxq8b3t2MNOb+6E/Uo64UdJrNrO8b2f90a8c3NzRn5XOKqt2gJ2K041m02jYUJ3mUyr1eqrkysUp3pznmKX2mw2jYYY/aDmfspVqVSK69evUyqVjJzz5OTkOkWj7nkRkHA4HDagwWKxaEDGJpPJmKfN5DHFhsLhcBhypvl83pCE3IgJLspdRLQsJA1FGZQYo1qtGrk9AUeKEqjNcsv90gNOpxOLxbJGIrMfItFb75zP50nP3OQ733+PTNvH6PA+PPYQ/4E5zeDgE5yaSfALbRB/epG38m4OKQpfP+Dg55oTkwpLOQ33uJfkrRvMujwMuWz874448dus2G5c4n3TONlyk49WVF7fW0fyePjlLy/wYcXKkcMTPBn0dVjcpSJOhw3ZJJP3+pnP5sh8/CHe8Y6CkNnnR6+Ukf0dFOX0dJDj0g1yiTjZ0QnuVB2cL7l5dtzPvgET6blZ7szNgcXCQbPMs4ccuDxW5EDQcCjpdNpIQXSz3bsdrNicivskuBKCnDU4OIjb7Tbm1+l0GkhPt5rW0tISzWbTSAdtBhn3SmU2m01WV1e5cuWKwUPYqhyvV8FNSLLa7XZDqlVs0sTv6n2fxCZSiMtEo1EjVdMvr9ztlEXZ4WaOdruR8HYh64cZeT+O9ivnhB81nLzdSLkf1CxJktESULArBXy1mWoVYJR5DA0N0W63WV1dNdi4Ykc+PDxsMHI3una32029XqdUKhnwt2gLmE6nDUfeKygBrIl4ocN8D4fDRq2tyF91s4PFLl+UIqVSKQMaFPq80HkRezsziZyciDKENGE/4Y5uBmt3SZR4FkS+vDcXKEyMK+p6hUO4fPnympz2vn37NoXkXS6Xwd5dXV0ln8+vuR+NRoNisbime1K3Ccg7c/Ma2uxt/tRykEgrTWT+Jv/F145RyjuIf/Auo/sO8p+aK/xnhQEaskQwfpP2uJnn2nl+1A6yz1FDK8P+l17G1t21amGeFbOVA5cX+VDzsSfopZxJYZmb4d2MxmK9xs2fXeLtaJCvjnj5jeePoSVWweZgNBzppDxyWQrLS9y4yxa32WyYkmkqlUoHCXG7mR4bxxyO8C+/c51Mpc37c3l+48h+AlobKRBEk+SOFGgiwfXbMzSuXMVsNhONRjly5MiGPZ67a3vb7faazlUmkwm73b6mxnsjEpzD4aBUKq1hW4vNT3c5ns/nW7dhajQarK6ukslkcLlcHDp0CLfb3bejlyhpEhyNWq3GysoK+Xwer9fbtzRObAhFdzHRztNms1EoFIya6X5Ewe68MnxWGlUoFIz2j5OTkwaE3ZtX3ukucTtVA/15tl9JJ7ybilcP4sTFcSKyFA+XiPSEapXdbjfkDhuNBtlsdkvYWZQ1iIg3GAxy7NgxbDabEYFls1lWVlZQVXUN7AwYTRIE1Nwv4hWLiBCUEE5bOAZJkjZUvep1YMIJFgoF2u02VquVgYGBvsQsMa/dnZkajQaLi4tGj12hzSxIURvt4mVZxm63G5C8yMm1222jHjefz29YM12r1Qwmtt1uZ3p62sgbi1xlb9tFEa30RtqxWGwNgUdsNERTi2azid1uN1o3ZrNZCoUCPq1FLJPgoz3Pkl6OE/e4eCl/jVgpy0CrhvSf/E1++Sf/lnNNB4Nm0Et5GgMDpD49y57DxzllKeH60gvozQZy1yJtsViI7tlLFNh36BC/uTRPvNFi5solmvUaw4qbjDtKvdpEL5V5P2nl13I5kGSUcITvX03y7kyW56cCvDa9D+fSIit1mUqlgs1mQ1EU2u022baGXirha7d4fipgHKMl40iBICgm8nfbCzabTYaHhw3IOJfLcfXqVaOHcC+0302A0zSNSCTSt3NVPB6nXC6v4RC4XC6j1EzXdWKx2LrmEIJb0Vt2ZjKZUBTF2JD1g4y7qxDEtZbLZTKZDDMzM8ax0WiUQ4cObbjp7t4QtlotVlZWWF1dNZ5vUX+/GZNbvIcCWRDvlUindP/XnVcWcyiu/0Fh6e3A0TsJfz+O9shLlHZjzMeJ/SweZEGWcLlc3Lx50yBWhUKhTSNc4Zz6wc6NRsOoKxQOsN/ut5ugJfrjCqjtypUrRv3r0NAQoVBowwdeLCIej4dUKmUI44uX3mw20263aTQamEymvvdXNE4X0bKAfAUMfuHChb7ymOJYcU5RFjQxMWFsaHqhXrFICx3p7uvdSPSjN1IWzl8smDabjVgsxtjY2Jpje9suCvLP3Nwc+XzeyO0NDQ3xxBNPbKgMJjYaw8PDVKtVlpaWmJmZQdf1TsSjtnCvzGM6+TQ/+MEcw+0q0WqWfYcn0bMpdMnEO2dv82Yjik1tMNrO8bUjDlztGo1DX6WxssS87MZx6ybewWG85RJmn3/NdRibhVwOf6XEnqeexhUb5PjKIn8hX+C7M20uxWvsVZOsXF3Bc+QJ3JrGuzNZEsU63zu/QKSsEpAlxg8cWhfJCQe2cGeGwVaLvzoRxt9eId2OkJuZNbSXe2F5q9VqEKN0XaeUTFBcmOP6PNS0z2DVcDjM9PT0Oo1xWJ86abfbrKyscOPGDcMJBoNBQqHQhps5EXELQZiVlRXDETudTiOarVarRlOJfuIaIr/carUYGRkhHA4bz43YdItSum7oWdd1MpkMq6urqKpKLBZbs9HohdO7mdwej4dSqWTkpkVVQa/ca/ffhSNutVrGBkeo14n52Ehycyv7Ao7+FYyEH5T9vBNiHaIrUTdLUTygsViMwcFBg40r6la3Q6yCzmIvcqOCCKQoCqVSiXQ6bZQYdRNKhImmGEL1JxwOs2/fPkwmk+EEl5eXjeigG3buJWaFw+F1LfpEJ6bu1oKC5SnE8YG+0bLT6ewrj1mr1YxrF2L8vfKCYt57meBiEbp27Rq6ruP1ehkaGiIQCGwKyYuoyGKxGHlp0au61WoZcPFGeb1uaN1sNjM9PY3H4zHKmM6fP2/MTXeZjDhWbBbE792zZ89nLO+rF6msyPzLiwluJ8tEmgXidj+/Pe2iXYHy3CzXrhZwaw5yVjfHJ3wMkEYeGMIUjqANDCC7PVRbbYrLiywXiqizd7Db7QahzOl0GrXWWnwFKRhCi6/wzlKNny20eS7m4795/RSND35Jdfowqds3udpsE2zopFsKz444OWSTsU5NI/VhkdtsNqIOO9HDR6jHV4inMyxqOlTryLK8JmrecAGuVjCh0/QHURfm8AVC+GIxA5G4cuUKNpvNyNsLiVNh3QIeXq+Xw4cPG5utjUqRfD6fkb8XqYSNuiv1ypBCh+wl+AOlUgm/38/U1NSajYbQbxctMgVhbHFx0WgFChhwcz+SoKgpFyiXqEZYXFw03kuPx0MoFNqwRWa3CRKcELER7/1GeWWB7m2HTLXTcPT9ajA8SvuVc8IPQ5ay+4ET1h3xFotFQ5lJwEO9i3U3GxfWE6uEyIYoqQ3SVgAAcmtJREFUSREOsN1uEwqF+u70RQmS6GSkaRoulwtZ7kCCwvH2g5q7X1oBOyeTSa5du7amhGPfvn19IwzolDKJKENVVeLxOMvLy8zPzxsM463kG2Gt6IfZbF4j+iEWv41EP3r7D0ciEQ4cOICiKEZ5Vj8402Qy9T22V061X6QsiDRCBx3oq3zVW4YkGLBCh1vTNKNHbl/VrEwK59Q09lCUK//f95F1jRWzF0WTuPPhe8jVKvrQCDaXhUbFxJc8GlqzybfyPk65PejXUrxPiOdGVF4LatiPHkdZXiZerVGr1bDZbJjNZmq1mpHW8DjdmGZuouw/zHvvX6BZqXImb+ern7xHbniC9I0bKB4Pe/12Tp4ap5zNUrp5letaGPnTT3F6vfgGh9ZsCpvpFKlcjmS5gllRiPn9jE3v7wsZC2KfgIxtNhvpxQUy8TiKP0AsFmPixa+g5zJIDieSzb7GgfVCxpLUEdMQeeNePe7eUiSBsKTTae7cubMm/79Zd6Xusj4BGcfjceMcomOYcIqbCem0Wi2q1Soul4tIJGI8g9evX0eSpDXtLbufmVarZTCjHQ4He/fuNYiRwrkLoqGoTReRe7vdXnPs0NDQGiKbuDbxTsBnndnEJlTXdWPT0K3u1b0WfgFHg6Q/wq2DyK2Im7MdO3fuHE888cSG37916xaRSMR4iXptaWkJk8lkOL9eE9KDw8PDfT8XghYjIyProGaxI8xkMuTz+TUsZb/fv+0icpFjXVxcNDRtBaMzHA5vKigBayNeIZMoHuTeCHezY4XTDgaDBilK6PWKfGd3FNfv2EgkYjhKIVSQzWbXkM5EXbDYaIgOWt3Hds+NUCASNZ7dYvyiHjcSiWw632LDIvLo9XonCguHw4yNjW2aIuieq1QqZdRcy7JspBk20+GGz4QlxFwJsYVSqWQszN2CHVRK6JqO7PHyz/70F7x9PY2vXea8Z4LnrUX+8VgFy959NHI5/qeP4iypCgONHHF7iIbVicNlR7c6yNQ1PHKLv3HYjl4pEQqFCHo8mCNRJIvVmJtKpUI+naK4uEDd7sDdbPCJHubCXI5TtTtMHZ0gOjJGOBxGLuSQXG60VBK9UsK075DxG0vLSx1FslSSst2JVCkjm80MTO0hFot1UB1NRU8mkKIDG6YwFhcXO3necgmrSSEwNsGnWZlzq3W+vCfIGwcjaFnhiG1r5llAqILMJ3LvvY6n99y9+WWhaiaibaGj3s2UF5Bxv2O7393umv3udIVAjiqVCqurq32P7X0GBfRcLBYNtTqxcRZrxlZOTGx8xCZX1zvSt0NDazdQG5lgZK+srBjXLHLfYm3sdjXCKc/MzDA8PLyhQAl0ApTFxUWmp6c3vQaA3/7t3+Y73/nOlt97nOyxcMKwvgRnI/v00085duzYht+fmZkxoq1+JvrcCuiz10QT+tHR0b6fJxIJqtUqg4ODax4skUfp3eUJ5yVa57nd7k3h4l6ClHAmpVKJbDZrvCDdTQ5EXrg3Wo5Go+vIWwISFbrMYhGq1+ubHttt4oUVjcvFZkRRFCKRCNFodMNoudvEyyXOKyQnI5HIlk6we5ETuTOxExfw4UYbn+563GazSTAYJBKJGJuEfD5vbHxEpNy96+/uqiTmSvxeEaXkcjkKhcIa4o/b7TYivH7H9ptjUc+qKAq2WpWmrvPf/3iBdqtN0uxhfzPJP3rSgfXLL0Mhj95s8LNbKZZmVxk/OIWplOFHeRtHzAXiFhdX8jInR9z85sEQbo8H2e1BstrQ0kkkj/czR9xuo2fTSOFoB9ZfmKe+soy1WqYd6MhrfqoGuZBs8eJ0mFcDLdA0fpS3GkSr1w+EKaTTpC9+StnmICCDMxSmYrXz/atJziebnBpy8htHBvC63ZiLeeRo573Udd1APoQiVCwWw6JrqOUyJcXEP/jhDOlyC68F/vazYfxaG+/oOIrVauQ9y+UywWCQgYGBdfPcbrfXzLG4V1ardY1edL9ju02wlcV7JYhLwWCQsbGxTaVehYk878LCgrHRdblchEKhDUuRuk1AxoVCAbfbvUbAxG63r4Hle61arRrHBgIBBgYGDM1s8ZsEciTWC7E29Du23zm6YWuxbtZqNW7dusX09LSBGvbLK4sNyZ49e7acxy+c8D3a/TjhCxcucOjQoQ3hzNnZ2TXiDb0mdpcCsuq1dLpTSjE2Nmb8m4h2BXx08+ZNwwnea4Qroq9CoWAs9JIkUS6X0XV9wwiw28RCn8lkSKfTxq43HA4zMjKy7SgumUwapVCiLCMYDK5h9G50/u6GBYKdWi6XjRIkMTe9C1CvIEUwGCQajWKz2QxZSxFhiMhAwM6CzBKPx6nX62uO7Z7j3o2PcMoC1q/VagQCAaLR6IYLpEAjxManWyo0FAoxODi4rcVVVVVWV1fXlGeJ694sUu6+BtGC0SbL2Bbn+K8+bXLRNsBULcHrwRZ/9alRZKcbOTaItrqEJMtIgRDVxQWyuQyVchn72CRhs4J5eIxiOkW+UKQCmByfbVhs1Qqyt+OIy+UyqSsXySpmvD4/l4sWzs4XeCkEXzk6Qr3V4u/+dIlkqYHbrPGfHzQTaNb4vyWDZKst7FKbvzGp4pUgMjSIyx8Au4P2jSsoY5P8vbcXSFeaeK0y//kJD+U7M9RcbixWG5qmGXWwAwMD6+ut6zX0apUfxlXencny3KSf5+wlUs0WyWzOYJULQuN23s9Go8Hy8rLRSENAxv1EO3qtm2XscDiIRCJGOVMvA7v3nndDxgIi761Nz+VyG0LGop6+99juZ0iIiOTzeQMZ83g8xibEZrMxODiI1+vd8J3vJj7mcjkqlQrtdhubzcbIyAiRSGRba7gINFZXVw1E0uv1rhEmgrWlUYJPMjU1teX4XzjhezRd1w1Fo+064YsXL/bNaQqbm5vD7XYbhfe9thXcnMlkKBaLjI2NGY63H4wiSn5EZCrg2d6XoJ915x6FAxXCGGKcjQgT/SLeYDBo5JTFLrpbsUqM0+s8RQQonJiA2bLZ7Jp6XtGacCPn2WuirCqXyxmkM9HNRtQZb+YAhdVqNQMuFjq7fr+f0dHRDdMN3abrHX3dxcVFQ/RD5JgFGrHRZk5smLp7EIsa6u4NlCiJ6l2gxbGFQgGPx0MsFsPlchnX1C9S7teQwu12d461WtBWlkBTeeO7KVylDDZJ51tjGaptFZOuYYvEsE1NU7LaKc3fwQ5EbFacY+NIuoY8MIxeLkGzgeRyI1ltBhs3l8t1yszSSRouDy6Ph5HhYTyNGnJ0gL/7ZzdJV1oMSHX+3vMxlKERoyTpuVE3z/uaLOQKvH9pkQ+bPk5GFP7CmANfOMwvVlXOLFeN6Lh94wo/rns5s1jhuSk/L3sapDRIptOdiN/WaebQ3Zu5N4oTjljzeEldv0qi3sR0l7nu9/upVqvG7xIoixhHrB3CIYhuRbFYjGAwaKxF3SStQqGwhkcgxDTi8TiSJBGLxTasLBAIlICMe0VwHgQy9vl8DA0N4ff7twUZd9cXi3nozrlvlKYSyMTy8jL1ep1oNGpIxubz+XUKYb3rV6lUYnl5mWq1SjgcJhaLrWtOIeaqW0cBMNY1kb/fjIX9hRO+DxMt77brhC9fvszevXs3hIfm5+cN1aV+JvJ//eBm4aRSqdSafp/9oObe4wQ8WyqVDIZtd860X0/dcDi8Di4WO03RU7Y7R5RKpQzHG4lENpyDbmcqnFe3RvR24F7oOMGlpSWDpCOii+3AzcLZCCcmfqemaX0XxN5jRQRYKpXw+XzGTlvMz2YdojZygIKUJ3JxuVwOVVXXpAgajYbhAEUtb7+NVe8CLX6XqqoGiUbs8jfblAlUI5VKGaiGmOehoSFjYW0vzkG1wn9zx8T3r6WI1TK8OB3md7+yl+b7v6TQaJJutahGBjGrbayyjDsaxefx4h4eQY+voAx2Np56tdKpEfYF+O6lOD+9tsq0u81zA2bCJtmAoCuVCiZFIaC2+EgL88l8gecnfLxxINRxgh4f6ZVlUnN30Lx+otEoQa8XuVSgCeRXlslrEv/9pRqllkTYbeUf//o+bM0GWrlETof0nRmaHh/R2ACRSGSdIxH5/+4oTqAa2dUVyOcI7J0mMji06YZK1F+LqFIoQ8ViMYaHh7cVLYsyt95acJH+2uqdKJfLRi9jl8tloEf9GNi91g/2NZvNRj64UCgYJZBizRC/qV6vs7KyYjDBBwcH1+RgBZNbbBLgM9lOh8NhdDfzeDwMDg721U2HzzYJAgEQrOZms2lUJvSSuzYygSAlEgmsVisjIyNYrda+AVG3U/7CCd+H3asTvnLlyoaNBgAWFxcNjeF+lkqlqFarBtzcDTWLovSFhQWj0D0QCBAMBrcFPQoTpTrpdNogXZjNZqM8aTuttgS7WNQgKoqCy+UiHA4bkelG1gv5iheyXC5vWQ4lIN9EIkGtVsPv7yyuDofDWBCz2Wxf0pnYLQshBJ/PRzQaXQMnCnhMRMrdztRsNpNOpw3nGY1GN5S57BYg6YaL2+22sdBs5QDFOJlMhqWlJSMyFV1vtkNIESVFovxKaGlDZyHbTDxENHtPJpMoimKQWbqduyRJeJ0O/NUy7sk9fP3/fR5rtUjJE+b/9ZdGyF04i6qYCKPh2XcQaXUJeWAY3B6K+Tw5Tae2OIfq8eO7247S6XSSW1kmtbLM//OWRE03E/PY+S+fDSJH1hIWRXvB4swtSjpIzk7pVrNYhHKRYCRCZN+BNc+R3mqipZIgS8huLz+Yr/KLW2mOR8wcJkWhUqFtthBQJIb2H8R/t2XlVlYul1lcXCSXy2EymdYR4TZLoXSXFYmoT8C9vT2ee9sd9jZNGBwcNFCN7ohbRO4i/2q329c8Hw6Hw5DL7IWMBdSbz+eNDa/H41mjm74dyFhEpmKzoaqq4cSi0ei25lk0pxA1zN1zs9EmofsaRJliq9UyNrBCHKc7r9y78emOthuNhlF3383HEAGF+HOv+/prf+2v8Wd/9mdb/sbHyT53TvjatWuMjY1tyKZbWloyxBj6mYCbh4eHjZsprDfiFUSdTCaz7VreXulGQawoFotks1kj1ynG6X4QBbM6kUj0jXh7c6bdTlBRlDWOdzPIt9sJCmdqs9moVqtrHO9GO15Ym3tNpVJUKhWg43jGxsa25QABo61gJpMBWKN8tZUTrNfrJBIJMpmMIeQgcnEbEauE9dbyRqNRI4UhnKCA/IS6mICdu9MJAl0IhUJrztGtwy2iFFH6Ip4RVVWNsqWNojhN0ygszJGr1fkfPsowt5hl1eLmZV+db+yz4XfYsQ4Ng9lq5FulZh0CIRR/EC2xihQMo4KhrGSU2bhdXMiZOJuD5yMmfu2Zjdmn5XKZ1dVVCoWCQaJp1KrIJvOmUKZWyFNvtYgXSxSXFnH5fMQmp7BareuIZ8J5dTspgUxkMhkcDgcDAwNrkIluOL23DaTVajXuscViMTZW/Z7L3vyroigoimI46MHBQQKBwJbrlHhHBYojSRKhUIiRkZFt1eQKlrGItrs3G70M7H7Hlkolg38gBEcE8iPah3bLbXaPI8hdxWJxDSHNYMvfjXJ7Nxs2m41Go2FE2z6fry9nYiNGuMvlMu7jVtF2twlH3Gw2+dGPfsSf/MmfUK1W+clPfrLlsY+TPXIn3N0YfTt2/fr1vuL4wvqxn7vLh/L5PLOzswb1XtTwbuf8wnlls1larZbRO9blchnOCDCg5n4QV3ckKMYRC7sokdkMau4ep1KpsLS0RDqdNhSihoaG+sJ6/Y4X7NNisbjGCXRLWm40Tm9rQLFj7YZ5uyPB7nFqtRqJRIJsNmsoUBkt+rpqnYUz7XaCqqquU77qzuP1/r5uuFgsgoIss1nphzDhTMX9bTQaRjOLsbGxbbHARbS9uLhIpVIxdKHFZmMzgpZYWBM3rvM332tir5cJSw3+3hMWqsUSpmoJy579+EbHcFlMSEhgsUK1giTLVG0OEncRGY/HYxCdjDzl6gqVZALVH1yXmxYayNlstq8DhM+aSRg5ZUkyiEPVapV0Oo2t1SDqceMbHkV29Ycju5nKpVLJaLtnsVgYGRnZVMmt2xqNBktLSwbvwWazEYlENi3L655rkXOt1WrGRr9Wq63ZJIja+14TjOxSqVP6FYvFkCSp7yahtyyqWzO6nxPbjKRltVoNYlh3pN7PRH1wPp83NhuyLBsiLcPDw9vitYjNhvi94p3oF+lvNNfinWg0GoY2QL9ezxvZnTt3+OM//mPOnj3LG2+8wV//6399Wxulx80+d0745s2bDAwMbJhbEHJsog5YlK10R7wCVhWLc2+5z3as2WyuednNZjOhUIhwOLzlON0Rb6vVMhYH0TRhs+sR+WfBEBZRq91u37KMqTfX6vV6Dbi4W794o3EURSGVSpHL5QxVpY0iXpEzFXrHovORECKJxWLbemFESdDSUqfeFDqtC4eHh/H1tFvc6HjR6L27ZrpbFWyj+9XLxhb3VxDPRP6sH/O1d679fj+xWMxY2DcrZRLCH93krH91W+VHNzJE6jl+LSbz117YC9UyDI5SvnGNPBJ5uxNZbePSNdpuL+VSEbvTxcDAwIYRoDDhlLsZ90IDeXh4eFs9XzVNI5lMGnXTiqJ8FsH5fHg20fQWDnBlZcUoG7PeLTUSzn0jhjFgRICiJCkWi2Gz2Yx8Z/cmoXec3qYJAwMD6yKxjcZxuVxGm8qN4Obecbo3G81mE1VVcTgcjIyMENwmNC82G/F43GApi1TVdlj3Yq7r9brhsKvV6pbzDJ0NuBCZCYfDRsDTnQ+WZdnYPHeP052fFmmfbn6KiIjFOIKhPj8/z9NPP40sy3znO9/hzTffJBwO8zu/8zs888wzn2tZy8+dE95MjEMQJ5rNJrFYzHC8m2mbikhHwNT9SFXCeqHm7oi3W0mp3ziC9NXdYKEfwanfOILFWygUDMcbiUQ2hWy6xxE5XOgoY213twsdiGphYYFMJoOu68bLHgwGt4TXuiFfkdMWzllETCJC6R2nW/hDCHCIqLV7fvqNIyC9zepxe+dZjCOclchr9zrPzeZZMF+hs9iKEpvtRAZis7C8vEyhUEBRFILBoFF28hf/+Sc4Cil8kso//c1DqHOzKJN7UQIBdE2jvbpCRjaTqFRQ6zVsrSZNl2dbi6rgEIgFPRKJrBFo6d5s9C7yvfW8vXMtxCS657kb2RDykYVCwZivfnPdW+MOHalTVVWpVCpGB6yt5rq7k1E6nTbSTIIIt93NhpCBFAxnUfbTnbboZ72bDaEdXyqVKBQKQP9exsIEuUtE26Kmt5eBreu6kY7xer38/9v78vi2yjPdR5tlSV4kWd6XOE5CCEkgZGEJARLblDBh6ZJAW7YuhEzboe3tlmk7vdOhvXBJp53pzG9uIXTaJikpxGErHQq2s5IEyGJaQkhC4n23LGuxFms99w/zfnw6PpJOUoht8j3/ECzr+Jyjo+/93vd93ufR6/UYGxtjlQ0lghZ/f+THycvLY9MKpIGel5eX8T4TaSwcDrP+9IwZM+BwOFStP0RW/fGPf4y33noLg4ODuPzyy/HAAw+gvr4e1hR6ENMJ0y4Iy8U4+GY9zfGePXuW9W8LCgpUMYEJRKoiZjG5o5DsXVFRERwOR0Y2ZSwWg8vlYl8YAOc8XkOlMVqUNRpNUl86HVmMZwgToYsWW7/fn9R7VSKi8L1WKheTKAjPBJePQ/EscI1Gg6KiIsWSL38c2vESgYn6RZlEQ+THob6XRqNBQUEBqqqq0irx8PD5fOjq6oLb7YZGMy7QT7KDmTYb4XCYlciNRiNycnIQjUYZU55XKVPabBA5i+8vA2BBp/H0CP58NoD80RFcMbsE31heBl1BIaKn3sForhUDoTFILicKikvgqKiEfiwEjc0OjSFLMQjm5+ez7w+Nf9ntdpY9KkGeuZN0aywWg91uT1sClR+HJ8KRKUhJSYkqIhw/VkQZIBHh0nEAgGSf3Wg0yu41Maepdy8PXgQ+26ZyMy/SwjOVqf1B50NGEakYyvz9kY9Fmc1m1n6yWCyK88CpjkNtlFAoBL1ez4xL1LZR3G43Ojs7WUmeRESoZJzuuynfMOTm5sLv97PvN01JpGKWBwIBPPfcc3j++edRU1OD9evXY+HChTh+/Dhee+01HDp0CN/5znewdOnSjNcylTHpQZjKMWqDcHt7O/uS8JKRQHLGS3Z5lAVS/1bNPF0kEkkaJzKZTIjFYohGo0zQIpUvr5KlYFFR0fhIxfvnIydVUUDnx3oCgYAiu5i/LjlZjAb/PR5PxnJxNBplx6EvKPUBySpNqdeqdBx+s6HValFQUMBs0TKBZ1NSMNZoNMxJKd1mQz7Lm5+fj8LCQnZtvImEkgAJlXxHRkaYfSRlwkqbDcq4KQOTM5uV+pbynikFZWKMUuaZSmbzlbd78fiBThTFA8grLcZXZkUR6GhH0JwDKSsLRdExlMyaDXPNHCAURLy/F7qqamgMyotjIBBAR0cH3G43gHEzBXqeM5UxycWKyGwk+EAZE3EA8vLyJupec20BahdRNiQnsMmDKX0vqHyaas5UPjJGQZCMOLxeL2w2G0pLS1M+U+SWxZOHeNZ9RUWF6ufa6/Wip6cHIyMjAMYrCbShzTQhQRuGvr4+ZmFKUxyZghcwsWRMtodyBjZPriIQyUopY85E0jIYDExAJDs7m40lydcgnlnu8XgQDofR0NDAHMp2796N/v5+3HnnnVi3bp3qzfR0xLQJwpTxdnR0MHlEfkYs3QA3PTik6ZxIJNiCSjtKKp2STR6VP/lASw+Oy+WC2+1mpCHa6TqdTkURDKXzodIOCVoQeYxYiWqyCmB8kL27uxsej4eVVEtLS1NuEnjw1wyAZRW02UinCMZrH8diMUYoo8WDNhv8Ik8Lj3yzQUxu/osm15nm7Q0TiYSqWV5gogAJbTZoA6N2sxGLxdiiSD1cu92Oqqoq1UQUCiQ8EY4qEkT4SRpdCfix6Y9vw9PTh+OWcqybl4srNE7kB32wOQqRsNrh62jDqM4AyWSGLR5F/mULkWu3T2ijUPCkPi8R4VJlyhSUASQFz1RkNiU2eF5eXtJoHGWPmdTgKJjSZ0a8hOrq6rQlUB6RSASdnZ1MApZnFyuNx8jPgb9mqhqQ93W6OXe5qAXJuOr1eha8aDSPFOGsVitbK/j+tNKGId1YFMmi8uNQqe6XJH3gicyvQbFYLIkMp6ZkTOdMrUCz2cy+V5nIVQS3243f/OY32L9/P2Pgl5aWYvny5VizZo0qycrpiikdhOWlZnpwiIJvMBiYzOK5lJyptDY0NASXy8UevHPpC1HPsre3F6FQiJWKqYSZ7uGTz9Pm5eUhJyeHqTEB47vmgoICxQVezi4uLi5mi6oSs5ifVeX72lQudjgcipsNueY1BUCn08nIM5mEO3gmOBksJBIJ1u9Uu9nwer2sXAyAiXBksn6kz4qyVqpsxONxlvUreRfz90JOziouLmbCIXJ1MfmcKeneer1elgnx10ztDxrVoczdaszCwTMubDvthzQWxLqcEdy4fCHytBroSkoR62xDvP0sdDPnQD9jJqI9XRjNs8H9vk0eMU1pxrykpES1+YfX62W9w3g8Pp4BFjpg0wA6mx0ac/rxEVqUaUOr0WgYwSZTzzQajbLPiqwc9Xo9y5hIXIWCl9zhivTEyWeXrllpPEYukEHlZrLsU9ow8MIflAnSmE04HGYz7pnGbGgjzldbKDBXVFSwZywTJEnC4OAguru7kzyN6ZlWoznd29vLRHGoP63G4ILkOp1OZ9I1K5GrqAUi5xMcPHgQW7duhd/vx91334077riD3XOv14tDhw7BarXi2muvzXgvpiumXBDmhb556TJ+jIif43W5XEkl3nSlYnoPTxYiktfo6ChcLhezdJNnb3RuJBmplPES89HlciEQCDANZRLX8Pl8GBwcTMlMJvDkEcqaaKzE5/OxwJuJXUzZyfDwMPMaJvP5iooKVaIhlNnSeA2VitWM1wCcQfz7JXKr1cqy3HRjTMDE8iftrjUaTUrBD/rs5RKdVJKTZ0CUDfAz3JT9u1wuRWazEvjMnWwjqSRaWVmpem56bGwM3adPwTU4gE1nzRgJa3C5wY+fXm2FwetGdu1qSCNOxM68B43dDsRiACQY5i9SHLGhe0UEv3SSqHJBi5KSEphMJvicQ+PqVxoddP5R5DkcsJaWJX328uDJZ9tAcoZLrHsKpuS1TCb1pOqWqq8rD6ZZWVmsXUSl6kybcqqO8cx5o9HI9KbV9EyJDU7CFGSVyAuIpGulUGUkHA6jqKgIVquV3SOSgUwlRJIuY041O03BlMb8BgcHWclYqZIkV8CiKolWq2XVjtLS0ozjY9SS8Xg8+PWvf43Dhw/DarViYGAAN998M77yla8kafVfbJgSQTgajbLSWKbAmwpKpWIixJhMJlZ2pcArz/54pJrjpcF7tbKPlLWTpSD1HcvLy1XJ5JGzEfnb6nQ6JBKJpD5wqsWCZxeTTGZBQQHLlCnrouPwAYYWiMHBQcYQ5sU7UjG4aYEnYpdSr1X+mcnHoWjHTdmcmlleWlBHRkYwNDSEYDAIrVYLu92OGTNmqBr8B8aDUHd3NxMOIfNzNUYdfBAiL1lgfGyDSt+8Drf8/ImdHAkGUWQ0oEVTjH/f0w57cAR2ay6+WxNEKBGHyTMCS3Ep8hYugjE8Bo1Oh6BrGEPhKDzSOPu9pKREccSGFmYaISFhDBLIpyDEB09pbAxSYBTagnEZ2Hg8Dl93F9yBALyRcX1n0kIuKytDSUlJxmwbSJaAJHW6goICFBYWqiJo8VmYwWBgBiB8hpuplUL+vqQZTTO0fJlXrlstF8RQ6k/Ly7y81aFOp2PiI5kyZnkw5QVEjEYj04zOtLmjagt9J6nEXllZqWrMj66JNAlos6HValOS2OSIx+PYtWsXtm/fDkmScM011yAej+Pw4cMIBoO44447sGHDhozn8XHEpAfhEydO4OTJk7j22mthsVjOKfCmQygUQldXF1MmMplMjDyTqUTDZ1HhcJjN8fr9fgBIO1ojJwuR/GJOTk5S9haLxdgCz38RwuEw2zAYDAY2LsIvSjQoT6QzygLz8/Ph8XhYpp6JXSzvu9IscSwWY9mfmnIxlXuJCKLX65lKkFrlGz4joZEvNYYW8nlcIrMRE5yCeyoJSZ7ZrCQcIi/LE8GPPjM670QikVb9SskhigJHMBhkDGGTyYSEcxD/dGgY7q4+9Opy8L8WWnDjdQsQfWM/ovE4AhU1GA4G4fV6YXrvBDCjBmUza+CwWqGzKruHye83BUAasaEKAN+blsJjkPyjeHVQSrInHB0dxeB7p+GPRJBfMj5T6/f72egQBRylKolcAYvmaulz5FnBvEEGVTeIGa3RKJsm0IaMJyBRD1ev1zPZWjX9ad63OhQKMWKU1WpFVVWV6lbK2NgYOjs72VpEPVOlMUgl+Hw+Rlykv0nf10zGC5FIBH19fUlBPzs7O2l8CFAei+LvNxlNpKpu0GdmsViYwUNJSQm6u7uxbds2vP7667jpppvwhS98YYKXO31XaSrgYsOkB+Genh5s374dBw8eRCQSwdVXX43a2losWrSIMWXVgg9g/DiRTqebQNCRS1DygZfvd8ozXl7UgDdZyM7OZg91bm4us6rL1Bfmgyn1K8lVRU1GQULnRAShjILK7Jk2MrzylclkSnI7kpd45aDsj0r7VC6m2T45qYrPAin7oyqB0iyv3NCCZzpTL8zj8TCnoVQEKV71ihYdnU7HesLpHHDknxllQbSg5ubmory8XPXnRXOxVJWhjU+SBOmoD79v2Ivno0W4em4JNtbPRuyvx7Dba0DjGTfm5cVw1aVFONXpw9GxHFxri2K+zgt/Tj6M7wccOU+CqhtUqpYHIaXetNVihk2vw6Nvh+EcDcOsjeG+mhhyrTaUmLKR63BAY5o4Z8oTvQAk6QeTaUKm+81XSch+EhiXgU0nWyvH2NgY86wGwFjdqYhVPGhz2N/fz8beSAErk940gJQ9Zp6lnEqyk8/0SQlPHvSVZqdJGpVsDgGgtLQ0LflQHkxpLUokEudkcEEboJdffhm/+c1v2H278847sWHDBlRWVmY8xsWISQ/CBMo6Dh06hKamJvzlL39Bfn4+Vq5cidraWuZ6JH/QKZNxuVwTAm86EDuZFiUqNav9gtMD19vby0YQSCVGSRdaDj6AabVaVh6nLyaZRyiN6MjJQrxOtDzgKPUCg8EgK02lGmXiS7x8/9b6vtMQMappDjhdKYrP3ElUHgCTuVO7oPr9fnR2drKSGsllqpkFl7O5KUOnRTCVQAuB9/Wlkq/ZbGaMYMq4KTPhN0BydjIRyniVMiL7+Lo6ENbosOPdEDR+H1BRjW9fbsKA243fv+lEf04RihMh/PT6IvzzO3G4IsCMiBv/+Okroc3JnVAKJVJSNBplvUM11QkaPevv7MTh04M4GrNhZUECt11eAbNOC11h8YQAzIOCPlVHyA+aGPyZRqLkpgmlpaXsuVdD0OIzZj4IESGK7hERqyiYkkpXJoIWML728JkyBfhIJAKfzweLxaLKOYhvFRA3Qa/Xo7S0FJWVlaqIosD4s9ze3s6+96SnrqZcTEGb+sR2u52Vw9XM9ALjQkpbt27F22+/jdtuuw333HMPvF4vXnvtNezfvx8WiwW//OUvVV3LxYQpE4R50LjO0NAQmpub0dzcjJ6eHsyZMwe1tbWorq7GCy+8gMrKSlx55ZVsIVbbi5JnvEVFRWzRoUzJ+r5FmbwMqqSZzAcwKl+OjIxMKDmT0AApbqULYPJ5YJPJBKPRiLGxsSS5ykwBjEgRQ0ND7JzMZjPKy8tRUlKiquRPi1pPTw/GxsbYF5w2CZlGdPgARoGcggUxr1MZNsh7rbznK8lHyo0xKMNJxWyWLyLyLJCqG2azmS2OmUah6D7xGyDKKAwGgypN78SICxqTCZIxGzvebMWxt9uwMD6IOZdWwFpaihMDEZw+O4Ar5lfjZkccrw1Gcbh1BIsWzsTqJdVJ94y0hHU6HcxmM8bGxlifk+6R0mIqF7QoKipCkc2KeFcHRi258AwOIOgbhc5sRn55xYT5a8r0SZRCHvSVMmUKyhaLhW2UdDodSktLU5IPeYIWbRIpyyRjjEzlZkIoFGJG82RuQQQtNdwP6X0dZKpG0bNHlZtMOshym8KioiIW4OUqY/IZbJ5klZWVhfLycrYeyWeeeT9k2rikcy3ir08+FnX69GmMjIzg+uuvxzvvvIPnn38eZWVlWL9+PZYtWzatZSQvNKZkEJZDkiR0dXXh8ccfx4svvggAqKqqwuLFi/GJT3wCV155JSMLKIGXjMxkSA98ELhcLhcjsQBgOs/FxcWKRCOl8ybdYxqtsdlsjBCh5v00yuT1epldIDCuDlRQUJB23INs1Hizg9zcXFZOJ8JQKiY4BTAl6cdMylmhUIjN8qYTDlEiZ/HlSxqjUmNKQdUNUoGSJAk2mw0zZsxQPV8ajUbR29vLxlz4kY9Mo3D0eff39yMSibDfJ/3jdAzlhNuFMDRo+Gsfjra5sKzEiDtWXAZLLALJ60HQnAPP4AA84Qjg8yAnJxdWjQRLgQNZM2ogabVwOp1M5pNKvqn8eakFQhuXrKwsDA8Pw+fzJfWn6dyg1UIKBqG12qAxmREZccHj9cAdicHv9yMWi7GeYEVFRVKmnw6kJEVCHEajEYWFhXA4HKq03Mlkgnr6RqMRoVCI9e/TzQXTukB688T05TNcalkosZ358TPy+OWfD2IXKzlFkU+vUvBUOk++hytJErKyshAOh9lnTbPI6UDVLXpOeMlOXv1LDXbv3o0tW7bgzJkziMfjWLBgAVauXIkbb7wRl156qerjCEyTINza2oqHH34Yn/nMZ/CJT3yCZYSvv/46Ghsb8dZbbyE3NxcrV67EqlWrUF1dzV4vLi5mgbeoqEi1L3AgEGAZr9FoZH+TJ0KlU80iqztiJpMpPR/c+REmOftycHCQZY68KT3/OyQ+wusnZ2dns2xbq9Uq2uzx4EVDaMyCWOryxTgdKGPt7e1lM9xFRUWorKxUlU0QOYOykXMhZwHj2QRveMDP8mYah6LPi3pockY2ZdxU4pXzCfhMP904k1y8n9jXiUQCvv4+ZBn02NqmRyIURKFRg29dUwxNWQUSXR3QFBRBV+CAFIsh3teNwNAAPOEohgzZSDgHEM3Jh6O4GNXV1apn5sPhMOuXSpKk2C9NeEagMWZDYzLjz+8OJRG0XB3tGBweRtSQhYKCAhgMBnjed+dJN14DpM6Y02XKFJTTlZsJfDuFnwumNtHQ0FCS21G6AMSX+HmClsViUb2hBsa/I7ThICEbIl5mcngCxjfV/f39jMlOG450pXn+fpDYTCKRYPeM33DwjHDr+1aHPHw+H3bu3Ik//vGPmDdvHh544AEsXLgQwLhP+4EDB3DmzBl873vfy3gvBD7AtAjC6UCla6fTiVdffRXbtm3D6dOnkZ+fj2uuuQY/+MEPmLpWJlCv1O12Mza1fARA3iulUSiaf3U6nUkKUunKYVRyprliErKg3p1ak4VwOIyuri4MDQ0xj04SKsgUQCnbpqBvNpthMBjY+fCiIUqLBN/vJG1ah8MxQTY0lUyn531HqFAopKhfTIFLSfNap9OlZDanuk6+f2swGBCNRiFJEpsjVlu+HBwcZGVu+ttlZWWq3k8ZGGV/VOEwjQVx1hnB0Ugerq/OR+2cAiTa26CbvxBaYzakWAySy4lQLIYB3yg8gSDs8QgK5i1AYmgAbmjhfZ/Bn8q0gd9wyBnGfG+azV9nZ8OOBKxz5uLHr7Ri0DcGkyaKL1wCOCChaP5CxWeMmPdut5uJmdCGY2RkRHEcSgl8UKbWDCnaqbWSpPOh7whZJKodP6PnlEwXKJB7vV5Wvk5H0KJsXT6WpGQDKf/cqNTd39/PgqcSIzyVEAmNZ9I8sdy1SA4SEHG73di1axf279+PuXPnorOzE5FIBJ/73Ofw6U9/+pzEkQTSY9oHYcIjjzyCAwcOoK6uDp/61KcQDofx6quvYt++fQiFQli6dClqa2uxZMmSpNJ1IBDA0NAQ3G53kvrUufRKKfvTarUwmUxwOBwoKChQ/ELyoGzb7XYz4gORIeSZm5ImsXwOmPSHSTs5XcmZN3igMSp5bzeVM5TVamWzpUBmchY/6kNBWa6cpXaWNxQKsXEPEsOgXb0aD2be89VsNjMrOzViFnx/2mAwsKDPjx+l6nETUWlgYADBYJDZ7TG2eHgMEfcIvO2t8IajGM3JgzkUQH5hEfJ1WuhKy+Ds7YG7vQ36kjKUzKjG6/0RHDw7gnp7DDcsrIRWp4UmZzzQebs64B0cgEejB7KyYDQamZMN9UszMV5Z+XJgACNn38M+vwWn/TqsqLbiztl5yCqvVNVSGRkZN6mnCgeACSXeVMfhAxix4OPxeEqZTXlw4gMYbUwpuNEoE31ucgUt3naP5GCVKhzyDQd54gLjsrKpsnU5eLYzMbAlSYLdbkd1dbVqAmM8HkdPTw/6+vqYmAnPTVGzcXG5XHj66afR2NjIvttOpxNWqxW1tbVYv369qnMRyIyPTRAOhUKKO3KSunzzzTfR2NiIY8eOQaPRICcnBx0dHfjCF76A22+/XbUZtFyJSa6aReMHcvUt2j3zvdJU2Tb9HSWW87m6DNG9cblcbEYykUggNzcXVVVVqob96XxotpRYtzk5OXA4HKpkQ/lyscViQW5uLiKRSJJMJ6/lLf/btOGgIELzuPKZaaWMm1eCIqatPNAqjUPRokWbJUmSMvanKSuhMihl2vF4PK0ueLyvB4jHsLc7iGNnh7BgdjFWLpqBrq4ueM+cRlyjhSXbiMLSUtiLS5DtKMQPXzqF4UAUBQYJP1lkhG7W3PH75XZBY8xGUAIGT56AOxaHMSeXCT2o2XDQJo/N5BYWwibFMZaTB+/Z9zCiMyCWpgzK90uVNJDlM+qUTVKJn7J1rVablqClpH1tMpkQiURYG0oNwYoX+6GNEvEo1LoO0XV3d3fD7XYznXK+NJ+OoEXz9gMDA4xFT9/3VOYWPHjXIvL5pSqLnBFOZefc3Fz2PCYSCezbtw+///3vEYlEcO+992LNmjVJmzW324133nkH119/var7IZAZH5sgnAlerxe//vWv0djYyEaRBgcH0dnZierqatTW1uLGG29U7O9QCY3EO86lvywvXVIZ7lyM0t1uNxuZ4C0NKbhn6mfRCBf1oAwGQ1JZlhZkeQCUs4vlhDY5O1keANMJYfDgZTpHR0cZgUWv1zNyTCpmMw8+43a5XGzDYTabUVVVpdowXZIkDA8Po6urC8Fg8Jw3HESIo95dXl4eotHoBF9evsQf7++BFAzi/x7zoMUN5HoHcfX8QtxebUFhRSUM0QhCwRC8sRg8w8PY3xvB3mE99BoN7lpYgDXVOdAWlyIyNIhBrxfOUT8j3eTFItDm26DhZoLlvWnqu8ZiMdYekJfopXgcknMAmuIyJmbCbzhIVY5cyyoqKs5JrrOvr29cNez9zRSVXtVyEoglrNVqkZWVxWwtM/kpU6uir68PoVCIbbRoQ02l+VTGDfLxHrkMJH8cXkaShFF4BS4yfEhFJOOlPxOJRNKoHU09qDEUoaD8/PPPo6GhgX3XVq9ejW9961tipvcC4qIKwgcPHkR9fX2Sm08ikcCZM2fQ1NSEvXv3wu/3Y8mSJbjhhhswPDyMl19+GRs2bGALklpil1z5ihRyeDcnjUbDdv9y4pXH48Hg4CCCwSAj+/B/mwKgy+WaQBYjTVu55nIqkwzquZJOtclkQjgcZjPImXST6ZyDwWAS85JKtlVVVao2HLwYhtvtZhsOYvBmCoDkZEULMQVt0q2lHncqzWu/34/+/v6kWWBa5DKNQ8l7rakIcXS/afxEq9XCJsWRXVgMr8+L197qxHNOC4ymbFyd5cd31l0FhILQ5OVDk2UcJ0plGfHDV9ow5AsiVw+srwzCZzRB4/NCZzKhbNZsvDWixaF2D1bMsuOWy4qQcA5CY7UpWhyOjo6io6ODPZMWiwWFhYWqDAB404RYLMbKsD6fj1VcUmmDA8nzwHl5eaxSQIQh6gPzeuU8iZHIfLFYTFFzOp1LVHZ2Ntsk5ubmpjUU4fvlnveNG7RaLdt4lJWVqWLwAx/MYPf09DBhFFKYyuRdTefi8/nQ09ODYDAIo9HIxDUyMcIJsVgMjY2N+MMf/gCj0Yhbb70VGo0GBw4cQGtrKy655BL8x3/8R8ZrEfjbcdEEYTWIxWLYs2cP/t//+394++23UV5ejoKCAtxwww2ora3FrFmzoNFoUn5JeHMI+mKlm1/mlaVIEpM2BhT81PRK6UtJLkPnow1LPWK32w2DwcAWGKPRyDLuVGVvvj9NJVsyS+dVqlLJfcqZzXL1q3QZt06nS2oPpPPmVepx5+TkIBYbH7OxWCyqCXFU4h8cHGTGFg6HA9XV1ao3apFIBP3tbRjq6cHrfhNOuCUsLspCTiyKv47qseoSB26yRqCbUQNN1gcVgIRnBK+0+bGnzYPL9D5cUTVuXJJn0MPvdsMdT+BXf/EjENehKNeE/3vHPGhdQyyDBT7I3pQMF+TqcnypmK6N761Tf1u+QVIybcjLy0N+fj77rtA8cKYqBc9QDgQCzOfX4XCgqqpKNVEoGo2ip6eHzUFnZ2ejsLBQlRkJnQf1qC0WC7KzsxEIBBRFP+TgNw28yxO/MaORSL5aQvcllWsRf3wlgpbf72e6AO3t7di6dSuOHTuGW265Bffddx8KCwsnnGsgEFDN0xD42yCCMIfHH38cQ0NDuOuuuzB37lxWCt69ezeamprQ1taGqqoqrFq1CitXroTdbsfw8DDeffddVsYmkpKaHTF9aQYHB+H1emGxWGA0GtmXWs0oFE/O4v+2z+eDy+ViwYY3auCFFcjUPtUsr9zMgrST8/PzmclDJBLJ2J+Wy32SRjgFVTVMWbpnwWAQvb29jA1Osn5qM5FYLMZ6bySmEY/HWe+OSHWpwGfMJJpCC2ymcSj5eE1xcTEKDHo8sqcTvVED7GYd/tfiPIy+fQxRnw/xuQtgLSpiAZBGe3zdXcjLNqJo/sKk7E0KBiBFInilL4I9p5y43AYsxiAi+Tbk5uVBr9djdHR0Qm89Hah/63Q64fP52OJeVVWl2nOWWhtdXV0IBAJM8IXKspkkVvlNA20MaRNL4z5yswX5Z0b9UofDgdLSUlbBSOenLB+J0mq1KCsrmzAHnarvSt85p9OpmqEsZ02TAInBYEBlZSUjl6m554FAAC+++CK2bt2K/v5+GAwGfP7zn8f69esnaDgLTA5EEFYJylDb2trw0ksv4emnn8bQ0BAKCgrwyU9+En//93+fsXRH4O39UikxpRqFstlsjKWrxqQB+MCQnjJu8mcmWb1zYYMPDAwkzQLT31cz50jBb2hoCBqNBmazGdFolJUbM2XcPKObrPYsFssEsQ+lkrPca5ZmgfkAJCcL8apgkiSxDQttGlJlzFSZoCqAJEkwGo3MtpP6fvx1Nh85g0Ndo1h2aTk+kR8GDAZoc3IRDgbgisQYJ4DaCxUVFSkXcgrEmrx8SEP9CFjy2EwuXS+RszLJRyqZJpB5PD83ncohijLHkZGRCdmbnHwoD4AajSZJ/CSVohP9Hd4gIzs7m/XiiQRJJvfnMhIVDoeTqhxqCVqJRAK9vb3o7e1FLBaDwWBgpKpUGwX59fT29sLzvq1kTk4OAoFARkY4j5MnT2LLli04ffo0PvnJT+Jzn/schoeHsW/fPuzfvx8OhwM///nPVV2PwEcHEYTPAZIk4Qtf+AK8Xi9uu+023HrrrTh79iwaGxtx+PBhZGdnY8WKFVi1ahXmzJnDDM2B8ZIrSUems/dL9XddLhfz9JWPQmXq18qNFmy2cacd6m1RdkvlXTn4zI83pqfyLmXcOp1uQsadjtnMI5UzFGURdN9KS0vTkn34krPX62W9MgCs76Z2IfX7/ejq6sLw8DArpVIWpEbQnhjCHo8H2dnZjJ18eDCGkx4tbrykEJ+88oMxn7jHDWmoH5p8GzSFxeP2hu1tSEQiKKyZhaKioqSNQio2OACEPW4421oxJGlgViizK5XmafNCZKH+/n5VjkN8BkgBUKvVslI2sZszPetULXE6nRgeHmYKdZWVlaoMNoBk/2syued5BalKxQSS/HQ6nTCbzSgpKWHHVBMAed1puc2h0n3ig3Im1yL5feJlLd966y2UlpZi6dKl+POf/4znn38eNTU1WL9+Pa688sqM901g8iCC8DlidHRUUZCdFrU9e/agqakJZ86cYSSS06dP4+///u9RV1enehSKl6wkT1+eIJVpFEquApVqlpdnuPL9ZOolqdVN5s+JAmAsFmMEqfLy8nMSVujp6WFZa1bWuCKTWilDfjwmNzcXJpMJwWAQgUBAsb/Jg5jwRDTiR5LktoZKJWeeGZ2dnY2ysrIJm4bv//EkhjwBlEVG8KnF48YddpsN1lgEiWwT+n2j8Pv9H8wSx2OQYjFo860TPjsiC5EuOPV0s7KyUFZWpjp4ye0oSfGMlKzUtAnIsCEYDDIzEdIqzySKIddApk0DZaXUwlDqlQLpTe7pdblcJz0HBoMhieBFvVqlDalS+Zo2pKOjo6pNG/hzIvUuklmtrq5W9X7+nLZv347nnnsOZ86cQV5eHm699VbU19fjmmuuEcIaUxwiCH/I8Hq9+MMf/oA//elP0Ov1qKioYD3fK664AnV1dbjqqqtgNBoVdZR5L+L8/HxW6s2EUCjE5jpprIZUhdR+Ccl7lHrMlHHz2W0qELOZfH1ph0895Uw9bt5TmMhdtGlQ4wzF+9Smy5iVjDHofIaHh5O8fTPpRFMZnDKbRCLBzNZJplQJfz4xgHf+egYLF9TgE6V6BLLN6OzogL+9FVGrHTm5uUzOMDs7G9JYaFy32V6geB5kURgMBpnAP/V9U/WmCRR85aYJxIeQm1rIGdN8uVnJsIEfG6O5aZ7BGwgEmIwjlelT9ah5vXK/3w+NRsOqCyaTCeXl5ao12YmVTdroVK5Wq5pGPe7e3l7WuojH40kev+k2jHyPm8aaSPaTyvypNKsJHo8HzzzzDF5++WVcccUVeOCBB3DppZciEong2LFj2LdvHywWCx566KGM1yMweRBB+ENGT08PXn/9daxZs4YFLUkat5I7duwYGhsb8cYbbyArKwsrVqxAbW0tIpEItm/fjjvuuINlP2olK5VmealsSQsfjUIVFBRMyGr4Pi15vfKM7lQZN43mZGI28+cp73Hn5eXBYDBgdHSUzQKrcb4hQs7w8DCb4TQajaioqEBpaamqzA8Y37hQuVmj0STpVKcqzfPX4/F40N/fj3A4zDLr0dHRJHUxuT2ilEhAGupHwlE8Xqno7UVWKICiefPH2wRDA4jaCj7I2kZ9MGsAa3VN0nwqr73Mj/bwoOyM701TgCDxlXA4zMbv0pXYI5EIvGdOw+P1wmc0MV5BdnY2KisrVRs2UGm3q6srqYVBpiiZiHW06SDJTwr4/PxtOktKftOh1+tRVlaW5OpFpKpUM8H8psNqtaKsrCwpQCqViume5+bmwu/3Z3Qt4v8WndPY2Bg2b96M+fPno6ysDI2NjRgdHcXdd9+NO+64Q9XGQWBqQgThSUAikcC7776Ln//859i1axcKCwtRU1OD1atXo66ujo0MpFrUMpWq5aCgRRrVBoOBqXfxLkVqZnn/VvERyvZpFpjew2tCpyt/8oplVC62Wq0sU6asKJWns9zbl2dly8UnaKNQUFDAshq+1J1OylBJ8/ptjw7tZ3pRUm7HkiLtB/cdEiSPG9rCcZZ1YqAP2pIyIDwGKRjAWLaZZe80nmMymVBVVYXCwkLVtnGBQAAdHR3M0ctkMrEWRjqBBzqnUaMJfX29iA30I2/2JUw/mZ4DuudKbQdqjwwMDDCvXLvdDgAT7rnSbHE4HEZfX58iwYu/53z2zlsJxuPxJDGOdJsOYjrThjEajbIyP2321G464vE4mwmmOXza9KhprRCGhobw61//Gm+88QbTeK+ursaNN96I1atXo7S0VNVxBKYeRBCeBPzhD3/Aq6++irvuugt1dXXQ6/Xo7OxEU1MTdu/eDZfLhSuuuAKrVq1iPR2NRoPW1laWCSi5K6UDb01Iiyb17HirvlS7cj5j1ul0bAaad6oBUs8C8+NQSj1mWkAp0PAOU0ajET6fj5VcM5WL+d4tlT8pgAITXZLS3TOfz8dEV6LRKEwmEyoqKlR7MQPjQaanpwf/vbsVgxoLzOYsfH3ZBxm30WhEwuOGJisLGrOFBT2N0QiNrWCCv29OTg7LtjKVnOlz43uttOmQl3eJtMfLKwaDQQydOA4XtMi32VBizoZJowFCQehq5rC/I2cn82V+p9OpKvjRPedni8fGxliZn8Zz1G46gsEgOjo6WKUjJydHtQgJfW59fX1MvIXGu6LRKNOZTtXnVnItcjgc7PurlCkrmW3s2rUL27dvBwDcf//9WL16NfuMBwYGsG/fPlRWVmL58uWq7onA1IMIwlMMkiQhFouhpaUFjY2N2L17N4aHhwEACxYswM9+9jPVAhyUMff39yMQCChaE6YahSooKEBOTg7LOtXMlco9hnU6HZsFJka42gyChPv7+/tZdltWVoaysjLVGTc531BQTyQS0Gg+sH1MV/JXchuy2WyMUcz3SZXmiklYYXh4mI00Hewdw9tvt2LRZVVYeYmDZbcxnxcWoxHWqhkscPGM9HSWkkrjUPn5+dDr9YwcR0pSme4b+d/y1pa5Y0EUX7YApeXl2HXkDA52B3HVvDLcXGaANDKcFIgJY2Nj6OrqgtPphEajmWCRmIlZzjOMif1PZX4gtUMU3Y9UrkNkjiIXIeFNJHgJylRjTUp9bgrKZrOZWaCqnQmmoNzR0YFNmzZh/vz5iEQiaG9vx80334wvfOELYqb3YwwRhKconn76aWzZsgWlpaW4/fbbIUkSdu/ejZMnT6KkpAS1tbVYtWoVioqKACSXrpXmaVPJ8clBDiwU/MhZqri4WJWLCx+8SLc5FouxQEz95FQLMR+8SPs4Pz8/yfc4Ho8zlqxc5EFeLparjvFjVcS4pYzbZDIxkhPfo053rlTmp+qCXq9nxDjeJpBHYngImjwrNFlZkAJ+JKJRjBmyWMZK9728vBylpaWqNh107X19fXC5XEllfjV+zPIed1FREWw2G0a7O+EeCyPs9WJrKzCis6A4z4RHb545fh1WO7S5eRMELXjTBSXHIn5mVqfTIRqNor+/H8PDw7BYLCgrK0s5hcDLftKmymw2w+v1wufzMdOGTGx8ftxrdHQUkUiEkdMqKytVCb8Ayq5FdM8zyUcSotEo/vznP+Ppp5+GwWCAxWKBy+WCTqfDNddcgy9/+cuKylYC0x8iCE9RvPfeexP8UiVp3Du5u7sbTU1N2LVrF4aHh7FgwQIsWLAAx48fh9frxUMPPaSofpUKSsxm6jGTLSIJWMhHoYDkUnckEklJ9OHtDPkRn9zcXAwPDzMRj1TBiyBnS0uSxDKdnJwcxbGgVIjFYujv72f9Qupxq1nE6d5R8AoEAuNs5verGWnZ4MNDgE6HBDQYiY6bJgBgxDh5mV/J9AH4oMdNLFv5tct70+QORZsOIhqls+uLdbRCm2/DnzpDeOatPsQiYdQWxnHDvCKY4zEExsYwptVNmItNd8/8fj9cLheGhoaYKxfpjKsJWsB48Ovv70dvby97BkiFK9OmA0i2SczPz4fD4WBl9VR61TzSuRbJNx3ECJc/C2fPnsWWLVvw9ttv47bbbsM999zDeuXAeFXh8OHDWLBgQdLPBT4+EEF4GsPpdOKZZ57B008/zViuWq0Wy5cvR11dHS677LIkwRA51DKbeVBGSgsVbQxoplStbjKfcYfDYRiNRjaWpKbPTSzXoaEhAGAm6fJ+cqpSIJ9x8yNN/AiTkjMUf+9IiCPVaI4SG5zK4H6/H/3tbQh7PLAX2FE8d15KhitvskHm7+R3S161aqVSyTygt7eXOSiRAleqz06KxxE/exq6GTX44Stn4RwNw2wA7p4ZQ/aoF3G7A9L7n0E6mVUCZeyUtZaUlDB9cL6krqTnzLOjiWHMl9qVHKL4PjdfrgaQ1udXzk7Ozs5Gfn4+mxigakWm7wz/LLz00kvYuXMnCgsLMTg4iMsuuwxf+9rXsGzZMtW97o8DWlpaAACLFy9GW1sbPB4PFi9ePMlnNTkQQXga40c/+hEWLVqEv/u7v4PJZGIZxr59+9Dc3IwTJ06gqKgIK1euxKpVq1BaWoru7m6cOHGCWRKmshdMBbk3b25uLsbGxjKOQgHJrG6+R52dnT3BzEIpkPIC+OnMGnjDBxpjodK12+1O6vOmy7j53p+cJUsZd7q51D+/O4QDrSNYMcuOmy91YHh4GN3d3RgdHR2Xn8zSo2jmLORYLEAoAK3dkfbek4qVz+djPcyxsTEY9HoUIA5rzRyY07gAUY88EokwkhRPiFNyhyLEo1GMHHsTLw4Ab43qcePsAnx6hgm60nLGLOezdyo5U0lWkqQk7ed01QpqGbjdbiZnyRtt5OfnK7KjlUB9burTxuNxWK1WzJgx45wqRT6fD52dnWwUip+hz6TCRTh+/Dh+97vfob29Hddeey2ys7Nx9OhR5tz2k5/85KIJxBs2bMDmzZsBAPX19WhoaGAOXBcbRBD+mIIy1N7eXrz00kvYtm0bent7UVJSgttvvx3r169Xzaw+F19gfhSKAqnZbGaBjFjdmQRIIpEIy7gDgQDi8TiAceUv+WxmOpDedU9PD0KhENO7djgcqghuvIgIZbNUvgdSs8EB4IcvnYLTH0GOLo5754z3Z6lXGnMOwRePY8QfGA/KiQTsJiNsM2exAEv3gSd4Ua+Ut70M93bDq9XD034WAaMJ2eYP2O40muPxeNKOVNGx5KM5WVlZiMViiMfjKLTbUWTQwVheCWmwL8mVSelY5NE7PDzMgl9lZeU5aZUPDw+zTRedC/lNZyo58ypcFPjNZrMiOUtJQS0SibA+tdJYFH+v+Nlik8mEvLw8AOMbp507d+LFF1/EpZdeivXr12PhwoUTrvPMmTOYO3duxnvyccHmzZtx5513AsBFG3wJIgh/jCFJEtavX4/h4WF88pOfxO23346Ojg40Njbi4MGDSCQSuPbaa1FbW4uFCxcmla6VRDzUSiAC4wtYb28vC14kpEDErEylU14UgZin5DGrNL8rh9wbmIIPzyam7J3OixZ0nlWeTjtZzganfqvNZsPY2BieO9qBtwYjWDHLjjuvnsVKpgmXE5qcXGiMH5TKo9EoRvr74envhV+fBUmSmGRneXm54r2XJAnS0AA0jiJo3r+ficF+BLLN6OrpgcvlAgBWLrfb7apIXrxlXlZWFoxGI0Kh0Pg4lMUCuxRH3uxLUh5rbGyMzYGToAWNmfFmDbyBhJxYqOR4RFDqc/OBdHR0lAliZFLhovaD2+1GKBRCVlYWE6GhUr+a557fwDzyyCM4fvw4u6YHH3wQ9913n5CP5LB582Y8+OCDk30aUwIiCH/MkUrrmsqHBw4cQGNjI44fP87KwydPnsSCBQvwla98JaU3rxLSGdvT33O5XEyUQT4qREYTQ0NDSVmjPNNRkrEkRSKycFRj9kDnTAu61+tFNBplGr6VlZWqWeXAuIxgR0cHU8zKy8tD0fs2hHwQSQz2JwVO+jx8IyMYeu8URo0m5Ofnw2AwwOfzKUp+ygMwKaf19/cjMdSPgtlzUVRaCp1Ol1QmjsfjimIYJILS398PSZIUg49cgQv4gDBmNpuZSxUvxpEuS5UbSGi1WoyNjTFrSrWqcdFoFE6nE729vawCU1JSck4cBXItGhkZYaXlWCzG7AjVlJxdLheefvppvPLKK1i6dClqa2vR0dGBffv2YWRkBJ///Odx3333qTqfjzs2bdqEmpoaAMCRI0ewYcMG9v8XG6ZcEG5paUFzczOA8Q/nySefZOWKtrY27Ny5EzU1NWhra8ODDz74N792sUOSJBw8eBBPPfUUTp48idLSUmg0GgwNDWHevHlYtWoVVqxYkVLFivq0mZjRchDZyOVyweVyIRqNMhnBioqKcxoPIXvFaDQKnU6HnJwcxuDOtHDyGr4GgwGFhYXsmjKpbwHJhg3yuVJ5uZJ3q9IMD0HjKEL4fdce19AQrFIMxfMvnxD45SSveDyO/CwDbDk5yHIUYueRdrzZNYqrq3Jx1zWzka1BWq1pfgMTi8WYTSeV+tUabVCpu7e3lxGXzoVcx/epw+EwcnJy2LXyCmp8eV7+fpfLhb6+vqSNA9/nJsa7EsuZxqr6+/vZsydvsygZP1gsFrZJTCQS2L9/P7Zt24ZIJIJ7770Xa9asmfCsUAuDytQXO6g9Aoyv+evWrUNra+vkntQkYcoF4U2bNuF73/se+/czzzyDY8eOAQCWLFnC/t3W1oaNGzeioaHhb3rtYockSfj5z3+ONWvWYN68eexn8Xgc77zzDpqamrB//37E43FcffXVqKurw4IFC3D48GG0tbVh/vz5acUkUv1NmscNhUJwOByw2+0sU84U/PisTynwh8Nh1k9WyiLJKam/vz+jCIl8rIrmW6lcL0kSc91JV7LkAykFeO3IMHSFRaiqrIQNEvQlZaruXzQaRWdnJ4Y72hFPxLGtPxchyYCiPDMeu6UGCIUUAzD/fio3m0wm5OTkIBwOs7npTPKhfK80NzcXZWVljDiVbhxKyfRBSX+ZzpGCH/Vu6ViJRAJ9fX3wer2w2+0ZNw7EcibymV6vRzweZ0ImNFqUCVRyPnnyJB5++GEMDQ3B4/Hguuuuww9+8AMsWLAg4zEExtHS0sLY0CRs0traelFmw1MqCLe0tKCuro5p27a1tWHWrFlsh7Ru3ToWTAHAZrPB7Xajra3tvF4TUAdiCT/77LN48skn0draitmzZ6O2thaf/exnUVlZCSC11jUhEAhgYGCAjfWkExFRmik2m83w+/0TZpkznTsFP6fTyXp9BQUFqK6uPueNQ1dXFwtWvF6ymjEVXgzD4XCM9y/bW+H3+5GwO9IGP9p49PX1IRqNstEcbSiIl0+7sLdjFAtyo7gyexQam50di+QZ5UpS1ONXkrnk5UMNBgNjONPnB6Qf7SGQGIbb7UYgEGDz0yT9qVY9DRh/Hjo7O+F0OgGAEfzkpf5U4Cseer0eJpMJoVAI0WhUldUiHaOpqQnbt29HVlYW7r//flRVVWHfvn3Yt28fPB4PHn/8cVRXV6u6posV8nWegjBxCC42qJPiuUBYvHgxnnzySfb/JFRgt9uxY8eOCcPqdrsdLS0tOHr06Hm9drHOpZ0PvvzlL8NsNuN//+//jZUrV8LpdKK5uRmPPvooent7cckll6C2thbXX399UimSLBbJX7e0tBQ1NTUZF1+z2Qyz2YyioiL09/cz3WkAyMrKgk6nQyKRgCRJGQUZhoeH4XK5kJOTg1mzZrFy86lTp5IUs5T0hPmsLS8vDzU1NWzjQP3kwcFBnD17lmV+vIwl73aUn5+PGTNmJDFsHY7xsSTKIvv6+pJGtEwmE1wuF7xeL2w2G2bNmpW8ccjNw9/NBf5uhhnQ6aG1F7AsksahqORcWFiIOXPmpCUIkcEAqTORnGJrayt0Oh0TtZArlSkhKyuLsZFpfI363J2dnRgaGlIchyJQCZfsAouKijBnzhzo9XpW6n/vvfdYtUMui0mld9616PLLL0/aePBjaCdPnmTCGl6vF7Nnz4bNZkNHRwe2bNmCo0eP4pZbbsHjjz+epF512WWX4Stf+QoSiUTa+yEwjpqaGjz22GPs/5ubm7F27dqLMgADUywIA8DatWvZv5955hnU19fDarWygCwHryp0rq8JqINGo8FTTz2VtHiVlZXhvvvuw7333ot4PI6TJ0+isbER27ZtQygUQkFBAbq7u3HZZZfh+9//Pq644opz6vMSwUur1aK4uBjLli1jiz5lazwRh+8Byz1ySYmJDxo2mw0AkgJWMBhkikvxeBwjIyOMZFRdXT0hQOt0OhQUFKCgoIAda2RkBG1tbfD5fEluRzNnzky7WdDr9Wx0KhaLobe3F11dXYjH49Dr9cjLy4PFYlEsmWtz8yBlZ0NjyGKfVyQSQTAYRI5Oi4J8G2I5eXC73Xj33XczZn58udpsNmPWrFks01cKfnKBDvn7lUzueTbx6dOn2ew1VQKcTiecTidycnIUCXLkH1xeXj4hkBK5LhaLwWazTdj48NBoNLBYLLBYLKisrGSz9nv37sU//uM/YmhoCDk5OfjiF7+IP/7xj2lnk9VODlzssFqtWLp0KTZt2gSr1YrW1taLuj045YIwwePxYOfOnUll5FS/92G/JjARqQIo+fAuXLgQIyMjOHHiBHp7e5mC1jvvvIPvfe97uPHGG1FXV4eqqir2Ph5Kfd7LLrtMMUjIszXqAb/zzjsIBALMnvHSSy/NOBZiMBgYmcjtdqOnpwddXV3Q6XTQ6XSs3J1IJDL6C/t8PjidTkSjUcyYMYOxtQcHB9HT05MkXiE/Fi9EQr3KZcuWsd+TZ2sTjqU3sHIzvf/y2bOgTSSgyc6GFAygbP58xcyPjkUew7FYDCUlJYobJ3nwo1I/+fvSOFpFRQUWLVqUMjBpNBpW7aioqGB93rNnzyISibCNlZI5RqpjkWgMuUFRC+G9995LcppKFyxPnTqFrVu34tSpU7j33nvx2c9+Fu3t7dizZw/uuecezJs3D4888kja8xHIjMWLF4tK5PuYskF448aNaGpqYiUKq9U6IXslYsf5vibw4aK7uxv/9E//xMgVJBgyNDSEXbt2YdOmTeju7mb95Ouvvx7vvfce9uzZg7q6OthsNtTU1Kju0wLJ88A2mw3z5s1j4zanT59WHIXiwRs+2Gw2zJkzh/19yopGRkbQ3d0NSZImHEv+fnm5ODc3lwUs0kvmj2U2m+Hz+dj5Tyg3vw8+YFGZdmRkBB0dHUw+1G63o6amZnweOuCHFI1Ca7WxYyRGhqG1O5IyP7L7O3nyJDQaDRvJMZlMGTM7jUbDzBdisRgcDgeT5CSRDF5+UqkSEAqFmJiIzWbDokWLkJ2dnTQO1dXVBUBZP5tGizweD+x2u2K5nT9WZ2cngPHNl9PpxPLlyxGNRvHcc8/hhRdeQHV1NdavX58UIOx2O5YsWYLvfOc7mEIUGoGPCaYUMYuwadMmrF27FjU1NSxjHRkZUSRYtbe3n/drIhBfWBDr+uDBg/jFL36Bw4cPY8aMGViyZAk+85nPYMmSJdDr9Rn7xXLdZyW7OQKvu0ym6vn5+YjFYvB6varnifljOZ1ONlZlNptRWVmJ4uJi1SQjcgyimVytVovs7OwkW8R0x+JJRjQTq9FomGiIIRqBNccC24yZ2Nfpx8FWN1bMsmN1TS6kYABSvo0FyaysrCT5TblOtRJbmsr9AwMDMBgMiqM98nvPH4tIXoODgylHgzJ9jtFoFPF4nJX7z4XklUgk0N7ejsceewxvv/02hoeHsXLlSnz961/HVVddJcrKAhcUUy4T3rlzJxYvXswC8I4dOxTnetva2rB06VKW7Z7PawIXFhqNBv/n//wfnD59Gg888AAaGhoQj8fx+uuvo6mpCY899hhycnKY1vXMmTPZ+8LhMDweDwYGBpjuc7pyJ4GIV1arFcPDw+jr62OCEoRIJIJYLJbRbJ7EKOLxOGpqamC1WpngRF9fH3OYUiIaKZWbly5dysq91Jvu6upivWnqc2dlZU0o1yuRjIiASDKW3d3deOnoCEajWoRCISwtrID71LsIWHJRVFyMhQsXTugx80Q14IP+e09PDwt+pCSl9P50x/J4PGhvb0dnZyd0Oh0jU2XadNCxyBNakiSmfU4kr76+voxzxXQOO3bswP/8z//g8ssvx44dO1BRUYGDBw/i+eefx8MPP4yf//znbFxPQOCjxpTKhGkkiYfVak0aWXriiSewbNkyHDlyBN///veTBDnO5zWBC4tUbGYqXbtcLjQ3N6O5uRkdHR2wWCwYGxvD6OgonnzySVRUVKgaSaFjZpKfDAaDzKeYRqEKCgpY75BME0ZHR5njT6o+M+95HI1GkZubC4vFwsaq7HY7SktLVckXEmnJ6XTC7/ez8vXMmTNVqXiRKtdLxwfQ9G4/5pjHsNw0CuTlI9deoMrtCEiWsMzNzYXD4WAGGbzhQyoxk3QkLd6xisQ+6Fj0GfFZfzrXIt4bmAQ6Ojs7MW/ePFRXV+PQoUPYtm0bfD4fPv/5z+OTn/yk6udouqKlpQXr16+fwKsR4kVTC1MqCE81pHuIm5ubYbfb0dbWxkrn9JpQ7vrb0NHRgd/+9rc4cOAA5s6dC4fDgePHjyMUCmHJkiWora3FkiVLYDAYFAM6P1bEi0lkAgXtoaEhRrCicnNRUZHqMiUFHr7cTPrGvEZ1KvDlXio3Z2VlMbZ/IpFAfn4+CgoKJvgLAx8oQQ2fPIGEzY7S8grYpDi0+TbAYEhyhorH4xNIS2okLOl+0WZBLmYCAAMDA4zkpcZqkT9WKBRiZg1lZWUoKytTza4Hxp+BrVu34tlnn0VnZyeKiorwwAMP4M4777wofHlpnVmyZMmEPrYQL5paEEE4BdI9xLyqFzBuy/XEE08AEMpdHwYOHjyIeDyOFStWsIVfkiSEw2G8+eabaGxsxNGjR5GTk4MbbrgBq1atQmFhIbZv346qqirMmDEDJSUlKCgoOKc+odPpTCp38+XmQCCQJM4hV2iicnNfXx/i8TgT06DAITd7IBEMKp/KfXLTGQ+Q1R+vu0zn5HK5EAqFUFhYiJKSEuhGhgEAGlsBNAqZH09acrlcCIfDbKZ4xowZ52Q6EAqF0NHRAZfLBY1Gk3S/Uhlt8JArcdlsNiYbmUgkFDWv5YjH49i9ezeeeuopSJKE+++/H6tXr0ZnZyd2796NPXv2YMGCBfjBD36g+rqmM0iohSDEi6YeRBDOAPlDDCQHU+CDICyUuy4cKFv7z//8TzzzzDOIx+OYP38+brvtNqxevVqVR7JatyTC2NgYy9RIC9psNiMQCMDv959TuZlKsU6nE16vl2W3M2fOPCd9YXKrGhwchCRJrNfKbxakeDzJLIKH3O6vuLiY9Z9ps8ATxuTgNy9arRZlZWWMJKW0WZCTvPjNSyKRSJt185rXwDizPRqNYsWKFRgYGMC2bdtw6NAh1NfX44tf/CJKSkpU38ePK+Tr1+bNm9HQ0ICmpib2s1mzZqGhoUGMDE0SphwxazqARhYaGhrQ1taGm266CQBYiVr+u0K568PH6dOn8fWvfx033HADXnnlFVRVVeHs2bNoamrCN77xDfj9fixevBh1dXVYunRpUuk6GAxiaGgILpcLubm5qKioUFWuzs7ORllZGQoLC1m52e12s3IzMB4UjUZjxnIzmV7o9XpceumlMJlMbOSIetNEKFMKSLxxQUlJSRKrlzYLZ86cSenAxGfdSiQvUvIiAZKOjg6EQiHWtzUajRgaGoLf7085001Bl8rTvMjK6OgoE9VwOBwZlbw0Gg3y8/ORn5/P7uFbb72FJ554At/+9rcRDAZx++2345e//CXmz5+vugJysUGIF009iCB8HmhoaEBdXR1mzZqFBx98kJWihXLXhcPcuXPR2NiYtNjOnTsXc+fOxde+9jVEIhEcPnwYTU1N+Nd//VcmA3nmzBmsWLECX/3qVyeoaKUDr70cj8dRUlKCJUuWJFkB8hKW8gySN7knCcb58+cnBS5etYmyvs7OTib5aDKZ4PF4GEls7ty5isYFtFkoKytLEtTo6elBMBhkJC8SFEkHg8GA4uJiFBcXIxaLobu7G21tbZAkCQaDgc06q7mP1G8mElZlZSUSiQRT8krHLudx9uxZbN26FX/9619x6623YvPmzdDr9Xjttdfwm9/8BqdOncKzzz57TvPmFzuEeNHkQQTh80BzczMee+wxtLW1YcOGDQDAArEShHLXh490iz71I6mnPDAwgI6ODhiNRsybNw8tLS348Y9/jNraWqxcuZJVKJSyJ14MJJUYBJ0PP45DbN2zZ8/C5/MxAtTMmTNZNpfu/Cnri8Vi6O/vZ+VarVYLs9nMxpYygRSy3G43dDodLrnkEuj1eoyMjODs2bMZXZN4u8FIJIKSkhJcffXVzCOaxEx6e3tZSV3eA/b7/ejr68Po6Khi1lxaWgoAE2QszWYz3n33XdTX18NgMODFF1/Es88+i7KyMjzwwAP42c9+lnS+a9aswZo1azLek4sZQrxo6kEE4XNEW1sbjhw5wgTI6+vrsWTJEmzcuFEod01BHD58GN/97ncxZ84cAGDeuW1tbWhqasK3vvUt+Hw+LFq0CLW1tbjqqqvg9Xrx3HPP4aqrrmJiILNnzz4nkpfb7WZ90ksuuQQmkwlutxudnZ2Ko1A85DPBJSUlSTPFNCLU2tqKcDgMs9nMMkgqi8sD37x585KyS6USMelw2+125OTkMDtI6lXLHas0Gg1yc3ORm5uLGTNmIJFIwOfzweVyoa2tDZFIBPF4nL0+Z86ctPdQLonpcrlw6NAh/OIXv8Dg4CCWLl2Kb37zm1i5cqUq60GBiaivr1dMGJYuXToJZyMACGJWRsiJDTt37gSQbDSxadMmZjQhlLumFyRJQjQaxeuvv47Nmzdjz549cDgcuPrqq7FhwwbMnTsXWq1WFclrdHQUfX19CAaDjJ2sFCwoWBHJiLJRi8UCj8fDDM+VfHaV/i7NOpP3biKRQE5ODqqrq1UpgfHn1d/fj56eHkQiEVZulgf4dOfCuxY5HA5kZWXB4/EkBXi73Z62/zs6OoqdO3fij3/8I+bOnYsHHngA8+bNw5EjR7Br1y4cOXIE3/zmN1FbW6vqui5maDSaCRaB8imNDRs2JBG1BC4sRCasArQoAuPC40888URSEHa5XIrkKqHcNfWh0WjQ0NCA3/3ud/jEJz6Bxx57DBaLBXv37sVvfvMbvPfeeygvL8eqVauwatUqRliiwCYfq1FD8tJqteyZiMfj6O/vR29vL2KxGPR6PSwWC8xms+q52HA4DJ/PBwCorq5GdnY23G432tvbM5abgXGWcV9fH3w+HxwOB6688kpW7qaZ4hMnTiAej7NyM5/B8/cgLy9vgmtRcXEx+z15Bt/d3Y1FixbB4XDgyJEj2Lp1K5xOJz772c/ixRdfTArWy5cvx/Lly1Xdk4sdzc3NLLA++uijWLZsGVuzGhoasHHjRiZeJMYkJxciE04BeohpJph/iJubm9HS0sICaH19fZJYh1Duml7w+/0p+6GSJKGjowNNTU3YtWsXPB4P5s+fD4vFgoMHD+Izn/kMbrvttowG9/LjyklaxcXFLGtWGoUqKChIcl8aGxtDX18fy3JSZc1UbpbPOufl5cHr9bLRpPLy8pT62wSaKSZ/42g0ikQiAYPBgIqKChQWFp7TPQiFQnjiiSfw0ksvoa+vD+Xl5fjKV76CO++885zmkwUEpjNEEJ4mSKXeBYxvCtra2thGoL6+HoBQ7/qw8de//hW//e1v8cYbb6CoqAjxeBw6nQ4rVqxAbW1txtJ1OBxGf38/XC4X8vLyUFpamjFr5t2X3G43wuEwEokETCYTKisrz0mQRJIkOJ1Opk9NDG6HwwGbzaYq8+Zdi6xWK0wmE/x+P/x+v+pycyKRwP79+/H73/8e4XAY99xzD2655RacOHECu3btwsGDB3HDDTfg29/+tqrrEhCYzhBBeBognXpXc3MzGhoamFjITTfdhNbWVgBCvevDxPDwMH7yk5/gi1/8IhYtWgTgg8xw7969aGpqwunTp1FSUoJVq1ahtrYWhYWFCIfDOHDgAIqKigCMM4HPJWsGxnukvb29CAaDbOTJ6/VidHQUer0edrs9re9uJBJBX18fC/5lZWWwWCxJ40ukSqUkh8kLcpCMpJIYCpWbSXnLbDZjdHQU1dXVKCwsRH9/P5566ins27cPN954I774xS+isrLyPD4NAYGPD0QQnkZQUu+aNWsWjh07lpThUnYr1LsuHKh03dXVhaamJjz33HM4ffo0zGYzrr/+evzzP/+zovFAKvDGBxaLBWVlZYozvbwJQigUSmJKezwe9Pf3A1AX/OUKV3RNJMNZVlammpVM5eadO3di27ZtzHbx/vvvx0MPPSRmeAUE3ocgZk1jtLW1sTGnlpYW1NTUsJK0UO+6sNBoNNBoNHjrrbfw7LPPoqamBj/+8Y8BAE1NTfjyl78MrVaL5cuXo66uDvPmzZtQupZLOKqxaySDh5KSEvb+zs5OnDx5Enq9Hg6HA0VFRYqjUHJotVrk5uYiGAzC6/XCaDTCYrEwdyi/359kr5gOXV1d2LJlC44ePYpPfepTuPvuu9Hb24vm5mbcddddKC8vx69+9Sv1N1hA4GMKEYSnMVpaWmC327Fz507U19dj8+bNqKmpwdq1a4V61yRh4cKFeOGFF5J6oldffTUbYaJe6MmTJ1FcXIxVq1ZhxowZ2LlzJ+bMmYPVq1dnlHCUQ+66VFNTg/z8/CTlrY6ODsaULigogNlsZhsA+WhRcXExrrjiigk9YrmYRk5ODrRaLUpKSpCTk4NwOIw//elPaGhoQEFBAb70pS/h0UcfZX+ntLSUzaPG4/G/9VZPOaTibbS0tAAYn6xoa2uDx+MRm10BBhGEpzFGRkbQ1tbGZpQffPBB2Gy2tEpKQr3ro8Xs2bMVf67RaJCXl4dbb70Va9asQTAYxK9//Wv853/+J2KxGMrLy5GdnY3W1lZmopCudC1nWBcXF2PhwoVJs7wajSZpPI6Y0t3d3YyYBYyTrUjGkh8tkkMupuH3+/HKK69g8+bN8Pl8GBsbw6c//Wn89re/RUFBQdr7dC62hNMBxNuggMvjiSeewObNmwGMkyYF90KAhwjC0xg1NTVJiyz9l8anhHrX1ITf78fnPvc5fOpTn8Jrr72GnJwcxONx/PWvf0VjYyNbsK+99lrU1dVh/vz5rHTtcrkQCATgdDqRm5uLyspKVeYTAFh5WqvVoq+vj0lDAh+Qv+SjUKkQDAbxwgsv4IUXXsDll1+O++67D+FwGE1NTbj33nths9mwbdu2cyKgTWfwugFyLFmyhPEtpuJ3LBQKwWAwZBRjEfhoIO76NAb1f5WQTp6upqZGSNdNInJzc/GnP/0p6Wd6vR5LlixhDHi/34/9+/fj6aefxjvvvIN4PI5oNAqdTocnn3wSixYtOmeTexotUtLA5kehuru7mcmDxWJBQUEB+1tvvfUWtmzZgp6eHtx5553YuXNnUva8cuVKAONB/WIJwGow1YJvIpHAgQMHsGHDBsRiMbzxxhsZqxcCHw1EEJ5m4NW7ampqsHTpUvYzYkYL9a7pDdJkvvzyy/Hmm28CGO8rFxcX49ixY/jqV7+Kyy67DLW1tbjuuuuS+rs8lEaLampqFH+X14EGxnu2Ho8Hu3btwq9+9StotVp4vV5cd911+Na3voV58+alvYZM7kwXEzweD5O7PXLkCDZs2JB2A/1RYmRkBK+++ir27duHoqIi/Nu//Rt++MMfIhwOT8r5CIgRpWmBdOpdHo8HGzduZHO/GzduFOpdHxO88cYbiEQiuP7665NIVPF4HMePH0djYyMOHDiARCKBa665BrW1tVi4cCHefvttHDt2DIsXL0ZhYSFKS0vP2fBAkiQcOnQIW7duhc/nw2233QaTyYS9e/eiq6sLc+bMYRKfAh9AaYyQ3zi3tLRg3bp1bJb/QiAajWLPnj14/vnn4XA4UFtbi1WrVrHX/+u//guzZ8/GzTffnJGLIPDhQwRhgfNGOhUvwsaNGycEf6Hi9eGBBDeamprw+OOP4/jx45gxYwZuuukm3H333aioqACgbNOoBKfTie3bt6O5uRnLly/Hl770JcycOXPC3zx79uw5OUtdLFAKwvzon8fjgc1mQ2tr60eeDQcCAWzbtg3/8A//gEQigZdffhmrV69mr5M1Zk9PD3bt2oX777//Iz0fAWWIICxwXkin4kVoaWlhpBQKpkLF68PHj370I7z11ltYt24dPvOZz8Dr9aK5uRm7du1CX18fLr30UtTW1mLFihWKGtmJRAK7d+/G73//e0iShPvvvx+rV68WRJ3zgDwIt7S0oK6ujhGzKAjLnY0+CrS2tsJkMuGFF17AN7/5TTQ2NmLlypVMbjUajbIKyenTpzF79mzmE03XIvDRQ3zLBM4L6digBF7Pmv6fR01NDZqbmzO+JpAe3/72t5MW9JycHNx///247777EI/HceLECTQ1NeF3v/sdYrEYrrrqKtTV1aGwsBBPPfUUDh06hPr6evzyl79EaWnp5F3IxwRy3gZ5jwPjraW1a9dekArPrFmzAABXXnklYrEYEokEgA+Ca39/P/Ly8mC1WjF37lz2PhF8LywEfVHgI8HOnTsnBOp0Kl7pXhNIj1QLukajgV6vxxVXXIHvfOc7ePHFF9HQ0IDrrrsODQ0NuPfee7Fq1So0NTXh+9//vgjAfwOam5uxceNGAOPWgUTEslqtWLp0KTZt2oTNmzdPinVgPB5HdnY2qzKRcMzBgwdhtVonVLLa29uxceNGbN++HadOnbqg53oxQmTC0wTxeFyVufxUAJ8JyH+uBKHidWGg0WhgsVhw0003Maet6fA8TQfU19ejvr4+KeslLF68eFIUsohkZbVakZWVxTa0Op0OBoMB//qv/4q6ujoUFRWx333ppZfQ39+Pa665Bvv27cNXv/pVHDt2jGXVAh8+RCY8TXDmzBn86Ec/wvr16/HnP/+ZmbhPRezYsYMt8mogVLwuPEjrWuDjC/p8FyxYAEmSmGlGd3c3vvvd7yIQCLDArNFoEAqFMDg4iAcffBCf+tSn8O///u/Iy8vDf/3Xf7FStsCHD5EJTxNceuml+OlPfwqbzYb//u//xtq1a1FcXIxrrrkG11xzDaqqqs55DOWjQHNzM+68807F14SKl4DAhQWRsG688Ubs3bsX3/jGN5BIJHDJJZdg5cqViEaj7Hd1Oh1++MMforKyEjfffDMAYMWKFfjLX/4ihFc+QoggPE2QSCTwyiuvwOv14pFHHsE//uM/IhwO4y9/+QteffVV7NmzBz09PfjsZz+LL33pS5MqlrBjxw7277a2Njz66KO46667hIqXgMAFhk6nw9jYGGbOnInGxkasX78eCxYsAACcPXuWsaE1Gg2ysrLw3e9+F36/n71/4cKFE7gaAh8yJIEpjXg8LkmSJJ06dUpasWKFtHjxYqmnp0fxdxsbGyWNRiMdOHDggp0fAMntdqd9vbW1lf3/4sWL2b9bW1ul+vp6Va8JCAicP771rW9JGo1GOn78uCRJkpRIJKRwOKz4u7FYjP27vr5e2rdv3wU5x4sVIhOe4pDeZy5u2bIFBw8exJNPPslcbIDxXg6VnIxGIx566CFcd911H/l5kYoXMM4G5VW8gPFeLhkRPPbYY9iwYQMWL16MhoYGbNy4kSl18UzRdK8JCBCITQ+My0A++eSTQvAlBWhtmDNnDoDx2eEFCxawzFcJpBPe3t6O66+/Htdddx3GxsbQ398/QbhF4EPAZO8CBDLD7/dLBoNBWr58+YSs0+fzSdFoVJIkSdqyZQvLghOJxIU+TQGBC4LHHnss6d98BUVeTVm7du0FPbepBloH9u7dK2k0GqmxsTHje6j69qc//Ul6+eWX2c927Ngh1pWPAKLbPsURCATwi1/8AjqdDg8//HCSN+zBgwfR19fHlI1yc3NxxRVXALh4R09IpUvp55s2bcKmTZuwbt26JNZ1W1sbNm3ahJ07d2LTpk2qXxO48GhpacGjjz7K/n/t2rVoaWlBW1ubEHxRAK0DsVgMer0eQ0NDAACv1wsAimp3RMJKJBJYvnw5jh49iuuuuw5vvPHGRbuufKSY7F2AgDKoL/M///M/UnV1tTRjxgzp61//uvTb3/5W2rdvn/Too49KDz74YNJ7fD7fZJzqlEFDQ4N07NgxSemxPt/sSWRWUw8NDQ3s3/R5u91u6YknnpjAI6ipqZGOHTt2oU9xyoCy2vb2dik/P19atGiR9JOf/ET6l3/5l7Tva2trk2655RapvLxcmjlzpvTMM89IkUjkQpzyRQfRE56ioN3oz372M/T09ODNN99EZWUlXn31VTzyyCPo7e3FI488AuCDvk9ubu5F7YKSSkqTsqfvfe977Pc2btw4IXMChJTmdAD/OT/zzDOor6+H1WoVgi8K0Gq16Ovrw/PPPw+fz4eSkhKsXLkSK1asSPu+YDCI2bNnMw6KwEcHEYSnMMj386GHHmKKO/fccw/q6urw3HPPsS8Sb+5+sQbgdFi8eDGefPJJ9v+0WNvtduzYsSOlXObRo0dTvjYZCkgCySCf3nQuXvR7FzP27t2L/Px89PX1oaSkhP083YZ9/vz5+I//+I8LdYoXNUQQnqI4e/YsfvrTn2LevHn42te+BmC8RxONRjE8PIw1a9bAZrMlvScQCODMmTO4/PLLxXC9DOeTPYnMampj48aNaGpqYjwJIfiijM9//vPs34lEApIkQafTiQ37FIFYqacY9u3bh8HBQWzZsgVHjhzBww8/jEsuuYR5fxqNRixcuBDV1dXsPW63GwcOHMDOnTvxD//wD2hvb5+8C5jioOwp0/iTkNKc2ti0aRM2btyImpoaeDweeDyelFKpQvDlAwKWVqtNqpwJTD5EEJ5iGBwcxI9+9CM88sgjiEQiCIVC8Hq9aTNbq9WKFStWoKOjAwUFBcjLy7uAZzy9cC7Zk8ispiZ27tyJxYsXswC8Y8cOWK3WJNtMYLynv3TpUvF5QbSppjI0kpTCkV1gUuH1enHgwAGcPXsWfr8fP/zhD1P+LmXJ99xzD+bNm4fvfOc7MBqNF/BspxbkxuqETZs2Ye3atWzxBsaD6rp165L6ijabDe3t7WlfEwv75KCtrW2Co4/VaoXb7WavP/HEE0zw5fvf/774rASmNERPeIohkUhAo9EgPz8fa9asUfUerVaLWCyGU6dO4e67776oAzBBbqeolD0pqSnx2VO61wQmBzU1NYobLP51shNMxZYXEJhKEEF4ioEvO5N9WLpSNGXBb7zxBrKzsy9q389UUpptbW1Yt25d0u9arVY8+OCDANLLZQopTQEBgY8Sohw9zUEzwv/yL/+Cjo4O/Nu//ZvI1AQEBASmCUQmPM1BTMc33ngDt956K3Jycib5jASU0NLSgvXr10+YaU1nOHC+rwkICEwfiCA8jUF6sIFAAIODg7j22muZjrTA1AEFy5aWlgmv8cSvtrY2rF+/npW8z/c1AQGB6QOxYk9jhEIh7N+/H5s3b8bx48cZQ/Rilq6cikhFEEoni3m+rwkICEwviDnhaYzc3FysWbMGTz/9NA4dOoSRkRGcPn1aBOBpgubm5pSymOf7moCAwPSCyIQ/BjCZTFi2bBmWLVs22acicA44X8lMIacpIPDxgciEBQSmGM5XMlPIaQoITD+IICwgMEk4X8lMIacpIPDxgQjCAgKThHSGA+f7moCAwPSC6AkLCFxA8HKa6QwHhJymgMDFARGEBQQ+YqSS0wTOXzJTyGkKCHw8IGQrBQQEBAQEJgmiJywgICAgIDBJEEFYQEBAQEBgkiCCsICAgICAwCRBBGEBAQEBAYFJggjCAgICAgICkwQRhAUEBAQEBCYJIggLCAgICAhMEkQQFhAQEBAQmCSIICwgICAgIDBJEEFYQEBAQEBgkiCCsICAgICAwCRBBGEBAQEBAYFJggjCAgICAgICkwQRhAUEBAQEBCYJIggLCAgICAhMEkQQFhAQEBAQmCSIICwgICAgIDBJEEFYQEBAQEBgkiCCsICAgICAwCRBBGEBAQEBAYFJggjCAgICAgICkwQRhAUEBAQEBCYJIggLCAgICAhMEkQQFhAQEBAQmCSIICwgICAgIDBJEEFYQEBAQEBgkiCCsICAgICAwCRBBGEBAQEBAYFJggjCAgICAgICkwQRhAUEBAQEBCYJIggLCAgICAhMEkQQFhAQEBAQmCSIICwgICAgIDBJEEFYQEBAQEBgkiCCsICAgICAwCRBBGEBAQEBAYFJggjCAgICAgICk4T/DxQ4K06YSJZ5AAAAAElFTkSuQmCC", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.yscale(\"log\")\n", - "plt.xlabel(r\"$k$\", fontsize=15)\n", - "plt.ylabel(r\"$\\|x^k - y^k\\|_\\infty$\", fontsize=15)\n", - "plt.plot(\n", - " results_FDS[0].all_error_criteria, color=\"#2980b9\", label=\"PGM\", linestyle=\"dashed\"\n", - ")\n", - "plt.plot(results_acc_FDS[0].all_error_criteria, color=\"#e74c3c\", label=\"Acc-PGM\")\n", - "plt.legend()\n", - "plt.savefig(fig_path + \"/FDS_error.pdf\", bbox_inches=\"tight\")" - ] - }, - { - "cell_type": "markdown", - "id": "4ca0b2ad", - "metadata": {}, - "source": [ - "## FDS CONSTRAINED\n", - "Minimize\n", - "$$F_1(x) = \\frac{1}{n^2} \\sum_{i = 1}^n i (x_i - i)^4, \\quad F_2(x) = \\exp \\left( \\sum_{i = 1}^n \\frac{x_i}{n} \\right) + \\|x\\|_2^2, \\quad F_3(x) = \\frac{1}{n(n + 1)} \\sum_{i = 1}^n i (n - i + 1) \\exp (- x_i)$$\n", - "subject to $x \\in \\mathbf{R}_+^n$." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "coupled-worcester", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T16:55:41.849567Z", - "start_time": "2022-05-25T16:55:41.806701Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T19:35:04.898637Z", - "iopub.status.busy": "2023-04-12T19:35:04.898205Z", - "iopub.status.idle": "2023-04-12T19:35:04.941993Z", - "shell.execute_reply": "2023-04-12T19:35:04.941246Z", - "shell.execute_reply.started": "2023-04-12T19:35:04.898610Z" - } - }, - "outputs": [], - "source": [ - "n_dims = 10\n", - "problem_FDS_CONSTRAINED = FDS_CONSTRAINED(n_dims=n_dims)\n", - "start_points_FDS_CONSTRAINED = generate_start_points(low=0, high=2, n_dims=n_dims)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "oriental-supplier", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T21:36:48.716952Z", - "start_time": "2022-05-25T16:55:41.852638Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T19:35:04.943548Z", - "iopub.status.busy": "2023-04-12T19:35:04.943060Z", - "iopub.status.idle": "2023-04-12T23:27:12.764225Z", - "shell.execute_reply": "2023-04-12T23:27:12.763554Z", - "shell.execute_reply.started": "2023-04-12T19:35:04.943526Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 1.4min\n", - "[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 3.4min\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 4.7min\n", - "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 6.8min\n", - "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 9.0min\n", - "[Parallel(n_jobs=-1)]: Done 45 tasks | elapsed: 11.7min\n", - "[Parallel(n_jobs=-1)]: Done 56 tasks | elapsed: 14.5min\n", - "[Parallel(n_jobs=-1)]: Done 69 tasks | elapsed: 16.7min\n", - "[Parallel(n_jobs=-1)]: Done 82 tasks | elapsed: 20.1min\n", - "[Parallel(n_jobs=-1)]: Done 97 tasks | elapsed: 23.9min\n", - 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| elapsed: 108.2min\n", - "[Parallel(n_jobs=-1)]: Done 496 tasks | elapsed: 115.3min\n", - "[Parallel(n_jobs=-1)]: Done 529 tasks | elapsed: 123.3min\n", - "[Parallel(n_jobs=-1)]: Done 562 tasks | elapsed: 130.6min\n", - "[Parallel(n_jobs=-1)]: Done 597 tasks | elapsed: 138.5min\n", - "[Parallel(n_jobs=-1)]: Done 632 tasks | elapsed: 146.4min\n", - "[Parallel(n_jobs=-1)]: Done 669 tasks | elapsed: 154.7min\n", - "[Parallel(n_jobs=-1)]: Done 706 tasks | elapsed: 165.0min\n", - "[Parallel(n_jobs=-1)]: Done 745 tasks | elapsed: 172.9min\n", - "[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 182.2min\n", - "[Parallel(n_jobs=-1)]: Done 825 tasks | elapsed: 192.3min\n", - "[Parallel(n_jobs=-1)]: Done 866 tasks | elapsed: 201.5min\n", - "[Parallel(n_jobs=-1)]: Done 909 tasks | elapsed: 211.2min\n", - "[Parallel(n_jobs=-1)]: Done 952 tasks | elapsed: 221.0min\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 232.1min finished\n" - ] - } - ], - "source": [ - "results_FDS_CONSTRAINED = run(problem_FDS_CONSTRAINED, start_points_FDS_CONSTRAINED)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "b43ad931", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T23:00:27.066240Z", - "start_time": "2022-05-25T21:36:48.721160Z" - }, - "execution": { - "iopub.execute_input": "2023-04-12T23:27:12.765531Z", - "iopub.status.busy": "2023-04-12T23:27:12.765176Z", - "iopub.status.idle": "2023-04-13T00:34:52.773097Z", - "shell.execute_reply": "2023-04-13T00:34:52.772454Z", - "shell.execute_reply.started": "2023-04-12T23:27:12.765507Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 28.8s\n", - "[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 1.0min\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 1.3min\n", - "[Parallel(n_jobs=-1)]: Done 25 tasks | 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"[Parallel(n_jobs=-1)]: Done 825 tasks | elapsed: 55.9min\n", - "[Parallel(n_jobs=-1)]: Done 866 tasks | elapsed: 58.8min\n", - "[Parallel(n_jobs=-1)]: Done 909 tasks | elapsed: 61.7min\n", - "[Parallel(n_jobs=-1)]: Done 952 tasks | elapsed: 64.6min\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 67.7min finished\n" - ] - } - ], - "source": [ - "results_acc_FDS_CONSTRAINED = run(\n", - " problem_FDS_CONSTRAINED, start_points_FDS_CONSTRAINED, nesterov=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "538ca218", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T23:00:27.145238Z", - "start_time": "2022-05-25T23:00:27.072471Z" - }, - "execution": { - "iopub.execute_input": "2023-04-13T00:34:52.774833Z", - "iopub.status.busy": "2023-04-13T00:34:52.774185Z", - "iopub.status.idle": "2023-04-13T00:34:52.821879Z", - "shell.execute_reply": "2023-04-13T00:34:52.821136Z", - "shell.execute_reply.started": "2023-04-13T00:34:52.774809Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Acc-PGM': {'execusion_time': {'max': 73.01789879798889,\n", - " 'mean': 32.38448367094993,\n", - " 'std': 9.095739273097353},\n", - " 'nit': {'max': 651, 'mean': 268.968, 'std': 86.530347139024},\n", - " 'nit_internal': {'max': 21000,\n", - " 'mean': 9416.054,\n", - " 'std': 2682.9857385166997}},\n", - " 'PGM': {'execusion_time': {'max': 345.1270561218262,\n", - " 'mean': 111.05938012957573,\n", - " 'std': 55.36683633818737},\n", - " 'nit': {'max': 3112, 'mean': 934.443, 'std': 506.89862571425465},\n", - " 'nit_internal': {'max': 101739,\n", - " 'mean': 32410.08,\n", - " 'std': 16334.077640185258}}}\n" - ] - } - ], - "source": [ - "stats_FDS_CONSTRAINED = {\n", - " \"PGM\": get_stats(results_FDS_CONSTRAINED),\n", - " \"Acc-PGM\": get_stats(results_acc_FDS_CONSTRAINED),\n", - "}\n", - "pprint.pprint(stats_FDS_CONSTRAINED)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "11fcf773", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T23:01:04.653386Z", - "start_time": "2022-05-25T23:00:27.148447Z" - }, - "execution": { - "iopub.execute_input": "2023-04-13T00:34:52.823471Z", - "iopub.status.busy": "2023-04-13T00:34:52.823147Z", - "iopub.status.idle": "2023-04-13T00:35:20.160205Z", - "shell.execute_reply": "2023-04-13T00:35:20.159169Z", - "shell.execute_reply.started": "2023-04-13T00:34:52.823451Z" - } - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ed75e59822bc474b91672f4f31f4c5a6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "show_Pareto_front(\n", - " problem_FDS_CONSTRAINED,\n", - " results_FDS_CONSTRAINED,\n", - " results_acc_FDS_CONSTRAINED,\n", - " fname=\"FDS_CONSTRAINED.pdf\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "1bd61a5d", - "metadata": { - "ExecuteTime": { - "end_time": "2022-05-25T23:01:05.594585Z", - "start_time": "2022-05-25T23:01:04.657749Z" - }, - "execution": { - "iopub.execute_input": "2023-04-13T00:35:20.162640Z", - "iopub.status.busy": "2023-04-13T00:35:20.162147Z", - "iopub.status.idle": "2023-04-13T00:35:22.571871Z", - "shell.execute_reply": "2023-04-13T00:35:22.570916Z", - "shell.execute_reply.started": "2023-04-13T00:35:20.162609Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.yscale(\"log\")\n", - "plt.xlabel(r\"$k$\", fontsize=15)\n", - "plt.ylabel(r\"$\\|x^k - y^k\\|_\\infty$\", fontsize=15)\n", - "plt.plot(\n", - " results_FDS_CONSTRAINED[0].all_error_criteria,\n", - " color=\"#2980b9\",\n", - " label=\"PGM\",\n", - " linestyle=\"dashed\",\n", - ")\n", - "plt.plot(\n", - " results_acc_FDS_CONSTRAINED[0].all_error_criteria, color=\"#e74c3c\", label=\"Acc-PGM\"\n", - ")\n", - "plt.legend()\n", - "plt.savefig(fig_path + \"/FDS_CONSTRAINED_error.pdf\", bbox_inches=\"tight\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae4f2fce", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/PGM_experiment_with_various_a_b.ipynb b/examples/PGM_experiment_with_various_a_b.ipynb index e1d5226..cac5059 100644 --- a/examples/PGM_experiment_with_various_a_b.ipynb +++ b/examples/PGM_experiment_with_various_a_b.ipynb @@ -31,12 +31,12 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import scienceplots # noqa: F401\n", "from joblib import Parallel, delayed\n", "from matplotlib import rc\n", "from tqdm.notebook import tqdm\n", "\n", - "from zfista import minimize_proximal_gradient\n", - "from zfista.problems import FDS, FDS_CONSTRAINED, JOS1, JOS1_L1, SD" + "from zfista.problems import FDS, JOS1, SD" ] }, { @@ -83,9 +83,9 @@ "nesterov_ratios = [tuple(map(float, t)) for t in nesterov_ratios_f]\n", "\n", "\n", - "def generate_start_points(low, high, n_dims, n_samples=1000):\n", + "def generate_start_points(low, high, n_features, n_samples=1000):\n", " return [\n", - " np.random.uniform(low=low, high=high, size=n_dims) for _ in range(n_samples)\n", + " np.random.uniform(low=low, high=high, size=n_features) for _ in range(n_samples)\n", " ]\n", "\n", "\n", @@ -99,11 +99,7 @@ " verbose=False,\n", "):\n", " results = Parallel(n_jobs=n_jobs, verbose=10)(\n", - " delayed(minimize_proximal_gradient)(\n", - " problem.f,\n", - " problem.g,\n", - " problem.jac_f,\n", - " problem.prox_wsum_g,\n", + " delayed(problem.minimize_proximal_gradient)(\n", " x0,\n", " tol=tol,\n", " nesterov=nesterov,\n", @@ -120,10 +116,10 @@ " problem, results, s=15, alpha=0.75, fname=None, elev=15, azim=130, linewidths=0.1\n", "):\n", " fig = plt.figure(figsize=(7.5, 12.5), dpi=100)\n", - " if problem.m_dims == 2:\n", + " if problem.n_objectives == 2:\n", " axs = [fig.add_subplot(5, 3, i + 1) for i in range(15)]\n", " fig.subplots_adjust(left=0, right=1, bottom=0, top=1)\n", - " if problem.m_dims == 3:\n", + " if problem.n_objectives == 3:\n", " axs = [\n", " fig.add_subplot(5, 3, i + 1, projection=\"3d\", clip_on=True)\n", " for i in range(15)\n", @@ -147,7 +143,7 @@ " ax.set_xlabel(r\"$F_1$\", fontsize=10)\n", " ax.set_ylabel(r\"$F_2$\", fontsize=10)\n", " ax.tick_params(labelsize=8)\n", - " if problem.m_dims == 3:\n", + " if problem.n_objectives == 3:\n", " ax.set_zlabel(r\"$F_3$\", fontsize=10)\n", " fig.tight_layout()\n", " if fname is not None:\n", @@ -156,19 +152,13 @@ "\n", "def get_stats(results):\n", " nits = [result.nit for result in results]\n", - " nit_internals = [result.nit_internal for result in results]\n", - " execution_times = [result.execution_time for result in results]\n", + " times = [result.time for result in results]\n", " stats = {\n", " \"nit\": {\"mean\": np.mean(nits), \"std\": np.std(nits), \"max\": np.max(nits)},\n", - " \"nit_internal\": {\n", - " \"mean\": np.mean(nit_internals),\n", - " \"std\": np.std(nit_internals),\n", - " \"max\": np.max(nit_internals),\n", - " },\n", - " \"total_time\": {\n", - " \"mean\": np.mean(execution_times),\n", - " \"std\": np.std(execution_times),\n", - " \"max\": np.max(execution_times),\n", + " \"time\": {\n", + " \"mean\": np.mean(times),\n", + " \"std\": np.std(times),\n", + " \"max\": np.max(times),\n", " },\n", " }\n", " return stats" @@ -194,9 +184,9 @@ "metadata": {}, "outputs": [], "source": [ - "n_dims = 50\n", - "problem_JOS1 = JOS1(n_dims=n_dims)\n", - "start_points_JOS1 = generate_start_points(low=-2, high=4, n_dims=n_dims)" + "n_features = 50\n", + "problem_JOS1 = JOS1(n_features=n_features)\n", + "start_points_JOS1 = generate_start_points(low=-2, high=4, n_features=n_features)" ] }, { @@ -240,9 +230,7 @@ "with open(data_path + \"/JOS1_ab.csv\", \"w\") as f:\n", " writer = csv.writer(f, escapechar=\" \", quoting=csv.QUOTE_NONE)\n", " for k, v in stats_JOS1.items():\n", - " writer.writerow(\n", - " [k, round(v[\"total_time\"][\"mean\"], 3), round(v[\"nit\"][\"mean\"], 3)]\n", - " )" + " writer.writerow([k, round(v[\"time\"][\"mean\"], 3), round(v[\"nit\"][\"mean\"], 3)])" ] }, { @@ -275,9 +263,13 @@ "metadata": {}, "outputs": [], "source": [ - "n_dims = 50\n", - "problem_JOS1_L1 = JOS1_L1(n_dims=n_dims, l1_ratios=(1 / n_dims, 1 / n_dims / 2))\n", - "start_points_JOS1_L1 = generate_start_points(low=-2, high=4, n_dims=n_dims)" + "n_features = 50\n", + "problem_JOS1_L1 = JOS1(\n", + " n_features=n_features,\n", + " l1_ratios=(1 / n_features, 1 / n_features / 2),\n", + " l1_shifts=(0, 1),\n", + ")\n", + "start_points_JOS1_L1 = generate_start_points(low=-2, high=4, n_features=n_features)" ] }, { @@ -322,9 +314,7 @@ "with open(data_path + \"/JOS1_L1_ab.csv\", \"w\") as f:\n", " writer = csv.writer(f, escapechar=\" \", quoting=csv.QUOTE_NONE)\n", " for k, v in stats_JOS1_L1.items():\n", - " writer.writerow(\n", - " [k, round(v[\"total_time\"][\"mean\"], 3), round(v[\"nit\"][\"mean\"], 3)]\n", - " )" + " writer.writerow([k, round(v[\"time\"][\"mean\"], 3), round(v[\"nit\"][\"mean\"], 3)])" ] }, { @@ -357,7 +347,9 @@ "source": [ "problem_SD = SD()\n", "start_points_SD = generate_start_points(\n", - " low=problem_SD.lb, high=problem_SD.ub, n_dims=problem_SD.n_dims\n", + " low=[1, np.sqrt(2), np.sqrt(2), 1],\n", + " high=[3, 3, 3, 3],\n", + " n_features=problem_SD.n_features,\n", ")" ] }, @@ -398,9 +390,7 @@ "with open(data_path + \"/SD_ab.csv\", \"w\") as f:\n", " writer = csv.writer(f, escapechar=\" \", quoting=csv.QUOTE_NONE)\n", " for k, v in stats_SD.items():\n", - " writer.writerow(\n", - " [k, round(v[\"total_time\"][\"mean\"], 3), round(v[\"nit\"][\"mean\"], 3)]\n", - " )" + " writer.writerow([k, round(v[\"time\"][\"mean\"], 3), round(v[\"nit\"][\"mean\"], 3)])" ] }, { @@ -432,9 +422,9 @@ "metadata": {}, "outputs": [], "source": [ - "n_dims = 10\n", - "problem_FDS = FDS(n_dims=n_dims)\n", - "start_points_FDS = generate_start_points(low=-2, high=2, n_dims=n_dims)" + "n_features = 10\n", + "problem_FDS = FDS(n_features=n_features)\n", + "start_points_FDS = generate_start_points(low=-2, high=2, n_features=n_features)" ] }, { @@ -474,9 +464,7 @@ "with open(data_path + \"/FDS_ab.csv\", \"w\") as f:\n", " writer = csv.writer(f, escapechar=\" \", quoting=csv.QUOTE_NONE)\n", " for k, v in stats_FDS.items():\n", - " writer.writerow(\n", - " [k, round(v[\"total_time\"][\"mean\"], 3), round(v[\"nit\"][\"mean\"], 3)]\n", - " )" + " writer.writerow([k, round(v[\"time\"][\"mean\"], 3), round(v[\"nit\"][\"mean\"], 3)])" ] }, { @@ -508,9 +496,11 @@ "metadata": {}, "outputs": [], "source": [ - "n_dims = 10\n", - "problem_FDS_CONSTRAINED = FDS_CONSTRAINED(n_dims=n_dims)\n", - "start_points_FDS_CONSTRAINED = generate_start_points(low=0, high=2, n_dims=n_dims)" + "n_features = 10\n", + "problem_FDS_CONSTRAINED = FDS(n_features=n_features, bounds=(0, np.inf))\n", + "start_points_FDS_CONSTRAINED = generate_start_points(\n", + " low=0, high=2, n_features=n_features\n", + ")" ] }, { @@ -547,9 +537,7 @@ "with open(data_path + \"/FDS_CONSTRAINED_ab.csv\", \"w\") as f:\n", " writer = csv.writer(f, escapechar=\" \", quoting=csv.QUOTE_NONE)\n", " for k, v in stats_FDS_CONSTRAINED.items():\n", - " writer.writerow(\n", - " [k, round(v[\"total_time\"][\"mean\"], 3), round(v[\"nit\"][\"mean\"], 3)]\n", - " )" + " writer.writerow([k, round(v[\"time\"][\"mean\"], 3), round(v[\"nit\"][\"mean\"], 3)])" ] }, { @@ -590,7 +578,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/examples/cameraman.ipynb b/examples/cameraman.ipynb index 0273a99..2ceee91 100644 --- a/examples/cameraman.ipynb +++ b/examples/cameraman.ipynb @@ -4,7 +4,15 @@ "cell_type": "code", "execution_count": 1, "id": "fa664339-29f7-4447-82b1-413828137298", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T15:41:14.305751Z", + "iopub.status.busy": "2024-11-07T15:41:14.305337Z", + "iopub.status.idle": "2024-11-07T15:41:14.350262Z", + "shell.execute_reply": "2024-11-07T15:41:14.348925Z", + "shell.execute_reply.started": "2024-11-07T15:41:14.305723Z" + } + }, "outputs": [], "source": [ "import sys\n", @@ -26,7 +34,14 @@ "end_time": "2022-05-10T09:04:16.178090Z", "start_time": "2022-05-10T09:04:16.171844Z" }, - "code_folding": [] + "code_folding": [], + "execution": { + "iopub.execute_input": "2024-11-07T15:41:14.352250Z", + "iopub.status.busy": "2024-11-07T15:41:14.351583Z", + "iopub.status.idle": "2024-11-07T15:41:16.109579Z", + "shell.execute_reply": "2024-11-07T15:41:16.109037Z", + "shell.execute_reply.started": "2024-11-07T15:41:14.352222Z" + } }, "outputs": [], "source": [ @@ -36,6 +51,7 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import scienceplots # noqa: F401\n", "from joblib import Parallel, delayed\n", "from matplotlib import rc\n", "from pywt import dwt2, idwt2\n", @@ -52,7 +68,15 @@ "cell_type": "code", "execution_count": 3, "id": "b57015e5-bfa1-44da-8172-4c5c22a0ca01", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T15:41:16.110561Z", + "iopub.status.busy": "2024-11-07T15:41:16.110265Z", + "iopub.status.idle": "2024-11-07T15:41:16.139300Z", + "shell.execute_reply": "2024-11-07T15:41:16.138508Z", + "shell.execute_reply.started": "2024-11-07T15:41:16.110540Z" + } + }, "outputs": [], "source": [ "fig_path = os.path.abspath(os.path.join(\"./figs\"))\n", @@ -79,12 +103,19 @@ "ExecuteTime": { "end_time": "2022-05-10T09:04:18.173606Z", "start_time": "2022-05-10T09:04:17.905718Z" + }, + "execution": { + "iopub.execute_input": "2024-11-07T15:41:16.141165Z", + "iopub.status.busy": "2024-11-07T15:41:16.140611Z", + "iopub.status.idle": "2024-11-07T15:41:16.384246Z", + "shell.execute_reply": "2024-11-07T15:41:16.383431Z", + "shell.execute_reply.started": "2024-11-07T15:41:16.141131Z" } }, "outputs": [ { "data": { - 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", 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", 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" ] @@ -118,12 +149,19 @@ "ExecuteTime": { "end_time": "2022-05-10T09:04:18.308346Z", "start_time": "2022-05-10T09:04:18.176217Z" + }, + "execution": { + "iopub.execute_input": "2024-11-07T15:41:16.386133Z", + "iopub.status.busy": "2024-11-07T15:41:16.385581Z", + "iopub.status.idle": "2024-11-07T15:41:16.530422Z", + "shell.execute_reply": "2024-11-07T15:41:16.529475Z", + "shell.execute_reply.started": "2024-11-07T15:41:16.386100Z" } }, "outputs": [ { "data": { - "image/png": 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2t9tNx4aNx+N0ZBhShlqtVmobtsJD265WKzOztJiLY8Y+fvxof/31l/3jH/+w29tbGw6H1u12T/JspYhO6YfU3RP5rV47dMmiFADnikwf/XwopWPsT0h4AAkpQXjp4isidHwEMkLfyLNjJs+1Wc0HNvcYGJyd0Gq1bLVaWbvdfrPbdrfbJRCOx2O7vb2tnLuHAy1RD0xZ1rrQpv1+366vr+329tY+fPiQAHV3d5fMvibJsdHY8xhFvphHh0Rp6wCqQRn2PetA0xQsdTGDJjGFs4bNc+StGbHfM5lMks/DB0oCUAAczsDT8xi8NSR18uvaxPewyQdQdbtdWywWyfTD/XCSe72ejcdju7u7S4Aaj8c2Go1sMBgk34fXl3AcM85DX6/XlXQjAPLq6ippJvblougj90/7yaYq2s/7rprSsX6MXovxR5vMrCIkOShR52+VtPsYYJ0kbK7UtPEIcU8mk5RXh5y6+/t7e3x8TEdtISUIwQdeg1CNVLrWw4DTPrCWarfb1uv10rHJCAiAmRGxQ/7eYDCw6+tr+/Dhg93e3tp4PE4gYGlr9hrBw/aMXq+X0pbwQuABAQ0zS9FFaGa+jvsSLX6q32f2ysBmlszSY6mpkNZlAH7ogtlrv70+1dVf14ZSYHl0llOPmmipzWaTThe6v7+3Hz9+pHQg5NkBSPAv9GQgZn4Nn5aCiq/h0CzKwGecAIvQdbvdToGJ5XJp+/1r2hF8LhyzPB6Pk6/D2z84UsjHN2OLB8CBe2Aespm3Xq/TybQcTuf2e0KGlyEgEHhtDAm/p6RDTURuC8bZ86FKyiwB+CHtPPupR7nG8PkNWCdCfh2DCaYdzqzT8vBZz2poYmbwviX2H7wNgGwCdrtd22w2Nh6P0yZElIVtFghGDIfDdEYfMzJH9TiyhwxyED+oAKcoLRaLyhM88FQPvRfjoz4iL0Ws1+sUgIFfBrP0nNTEetClDL1W+9ek/lIQNjb5jony1TUG9wFMyGLg04V+/PiR/CXkqqlG4rojreSZNyzxuW1ejhi0Bu7jwAOA0+12U5oTAA8JD7OQH1sDIICZAQrec8W7cnWhd7lc2nQ6NbO/17wYSKPRqNKfXq9nZtXjzbjP6Bc2MS4WCzOzFMHs9XoVIVJKx+5jaxIQwfXe4m4T/4mvB53M5CulphoLDdxutzabzSo5djiV9evXryktaLFYVMwVlKEayaujZBA1qwGaB8yD0D1MOXxnjQLmZ+AAnPiOreow9/A/tmTw1nkGE2sRBG24HZwlAfCpPwSfA1qTfROAlSOrGBcIikNCygy+U20SjayOUjP+kH4cEpw7WEMd2sDdbmeLxaJy7p2eysq7ZDW/rQRM3u9sKniSDL4RP/gMDI/TjmAeIUw/m80qTAczzcyShkAAgiN33W43mVe4D5kW8M8QBUQEEdv05/O5mVkCEz/Vg1OiuE4dH2UwDzhNTeaIIs1WCrRTtCGnaZpor5P7UFpBE0JjsJ3758+f6cy7L1++JN8JRxx7ZlwOTF771M9iUHnXY60Ja0T7/d8PC+h0OsnMgnnEZpeZJd8OEb6Li4tKgIDD7/BNAGBE6HgBGdkU7XY7PZfq+fnZer1eMitxvoSalbgP9esc4LPuOu52u2ZmlTar73Iq4kCLNxdKnuVxjOA/pF8nCZufkrBrFqYejjj+/Pmzff/+PZ0uxKSNr9tDpMDxghfRhF1cXNhgMKgEEDabjY1GI/vx40fKwnh5ebFOp2ObzSYxLzQfHHhoDmYcDquDgQeDQSXChjWn8Xhsg8HA2u22zWazZC5ibQoaEA9ngxDAO7SbFwlFe9hENHs1e/kZwBwtLF1fauK7ROahx+hNAX1q3ym6nundALXb7Ww+n9vT05P9+PEjaSd+NIx3bBbIC4l7VAImT0tBA15eXlq/37fxeJzWgobDoZmZ3d/fJzNwMpkk30PXlfhoL150xYv/58VKaC7OF0S5qAtlmVkCP8xDmJMoT9dqNHzO/7OZiFSp3HOpdI5ywNI5iEi1VqkV1DSIUVIeUxMgvwug9vu/MwH4EEne6AfNpIDJRZZy2ga/R1JOf4ckR6ABUno8HtvFxUVi5O12m4CEgMlms6lIdG8NiDcEwtfhAAj7UvifTRJe9EXYHf8hC53L5cVugEc3TeIa9A1gYjMSIfMm65IaSczNVUkUrQ4odW3jdw3Bl1KT698FUIjqPT4+VsCE8Dife8frQF6I2+ytL9B0PULvwffVamXz+Tyl8CDa1u/3zcySaYaTkBD94+gZzCa0XTdALpfLBAyz6vHNAPV8Pq9ke/CmSQATwmc+n6cACsw4XvBkQLH5p4DitpSSjrP3zp810JGLoh0TmWMQcZ3cVhZkdYD57TQUTCSsOeFsB47ogRhQpRvH6rRV6XUAC0CF0DdAgnA5NK6ZVR5Rs16vK/uS8JA1gGq9XqcgAyZeNQDC9MvlMoESYXL8h2wR5PldXFxYv99P5aNsTzOxhuJ1Nph9MCcxBzAnNZWJ33nudA69sfciiFGgqIQik08FCQgC59Cnl+TobIBCJ/kAfph7OL5rMplUJCTfx1Leo1NJNC4PgOJMbzArMsqRscFaFSlB3q5ZT0utVquKv8A7emF+mf0tiODH8S5lfij2y8tL0lDeTmJPWnOmBq+rIagyGAxS/7fbrV1dXaXMeNynPi36ztHBXBAjN185ky/HE5okq1qa24D25gIuh1BjQEVOqA4CGgozCiez/vz5Mx1xjJ2znvllZrWgYioBU4kGQ24hnp273+9TeHu326W9V9AgAAtL5ygTA9E5PkmWGZ6Zgc04EIMAwAb4EDLn3btMWg6f6Y4dz7z/6uXlpZIGhQCKPpQb/eDgCgd+SnwcnmMvnH1IxjuDiV/QTjwWKgiPAVcIqMhPyTmibJvCecYayuPjY8oef3h4qDzNwuuEpuZ7QYZDwqKRY4zfsYiqWgoLtre3t+n4ZkTI+NQhLZuPSIbpuFwu0/8ckcP1fEY6h7KRjMt5fLgXi8EMKh5Xnj8AindC44w/RCGn02nSWLzYjSx/Tr/So6eVF3icOcKoEUcVRtG8lWZweFqKdyZw2Tw/nIHSlFxAeSHWnHpmCcaPWIGJhDMdcNrQ8/NzhVlVOvHgm72VUB7j8nsTCeNdCyEwnU5T4AHMjPMccEwz8vfYHMJ7v9+vTD5PLhgX2yMgNDCZnU4nRds4usfHnUHDv7y8pICGRhoj6ct5hNgp/Pj4aNPp1C4vL+3q6sra7Xba+4X+o88AAC9ke0zKdfK8Rj7WIelOXlkeD6Ne3QiK33UpA5FVzfPMURZQvPjHmdAglU5mlsDFaTvYmoFz8LxDUlQylYJKfQR8jqRbHejU7EOAAtpkPB6n7HHsFua1HM2nY4DhxetRYH6sQSF0zVvm8b3VaiVteHl5aS8vL8mXwo5lMAYYQUGFz2gbstd5Z7TZ34D78OFDSnfCI0gRGOFFbDb18K718djzfLHfc0xgooTQbzZ3ma8hILAUATNflzJAngZzAcVJk3ygIh8cAmmKSs1en3i+3+9Tvh72NWGjIEw9z3fgyWbiAc85ut5vkfMLqYRrtP94LCefzorgBBZeB4NB2uyoUg8Mm3spiAAuZFBg6wdy/+A/QbO32+3KeRpgdIylJ12ZMaDVcKQA+tpqtSqH3Mzn82RGaqgddfFaGVssSgCZajOeL/WrMZ8sNHmccxaUCtzdbleJmCLtCzwPMPX7/TeHnur6oBeBdgEFMwcgQsUwb+DIYU8QnwGO96enp5T4isdqYs1JJRJ3lu3oyF/KSa1oorxrvAACjwE2Pk4mk7TIa2bJHAPTQ6PxybMclu73+2/OnACgEKhAsuxoNHrTb4wXsih4OwvSoTiQouZjmmxZSGYt9fz8nDQe2oUxmE6nKQuDgzAADR+txhqX51bnhX28OjBgvPBZ/SK1ZtTkwwttR6CMD8PhXExOCcOcssCLBLFZAKjpdJoAxUiGpmL7mSvGofzL5dJ+/Phh//znP+2///u/7X//939TehEiSTypOqheECIX7asLVqjJwSahF2GCWfDy8pL8v6urqyR9AajRaPTmTAsWOPBpNGeO6+a9URcXFzYcDlM2+2KxqOQDcoBkMBikzHP4QtjVjDLZ7DKzym5hZjwwGMDZ6/Uqx5q9vLwkMIGRoYX0iYrsu3kMHpmDOvfqv0PgsA+kghEv5SWNamLHAIMK64qwEMDruB5C4yBATSaTNxoKapJX6bH9m1G/3W5tOp3at2/f7J///Kf913/9l3369Ml+/PiRnOdI7XuAKPk9Ig+s6nth0LU9vEcI5tDV1VWyrUejUZqU+XxeMYcg7cBweva45qyxCYhTj3D2hJo/bPOzsOFoKh8Yw/di4yD3G2BGkAj3MfNh7QtjCWHqRcJYWESaxFuv43v4OADON8R4sYkGc5O3sGAuUR9cFwaICkEWbNx2XkDXLPzGgIK9ySv1fBgJVwy7Go/W/Pr1q3369Mn+53/+xz5//mwPDw8p/421T87+ZVIGYS2TM+v0nlz5Ork4vwHnRcA8g3bCeYF8Lh9nH3AQQfuJhWDct91uk0+EB6u1Wq3EyNxOAAj+FBgFkUnO2OeJh8bkOnnBmceKhSm0pVmVgRkwbMpyv9QVwO/eOhnawoeV6vEA8PvYSoLFwEsLzF/aT7UW2NdjgccmIuaWx7M4KIHHTzJKIRUwGBx+ha29WCzSOeK81wmPi9FAAEuSCFgekDzyylGQqPNbV5fXd2Sj7/f71F/0AxOAyWfHVaUan2++3+/TXiT4pWB8rFlpO5HzBzON05IYhKxNcC/aC4GhYXBsW0G5ABT7TdgUCUHLW1g4uMT+sc6dApHzHdnFUG0Cnx2Bl16vVwmYQVPxArSakBxUwyI68wlHR6ENdQG/WEOxhGMpxJUwqrEWosmveP4SHHZuCKt9ZZZSxs+R+l5eXdGgRPUgGnd1dWW73a6ygMr+gn4G43J98I00SAFgLBaLtA3dM3uxDoVr1Sxn82qz2bgZ7mqe8fjjdwYN7xJutVoVEwgA9QIfnrBkM8xLzVIwKbDgx3U6HRsMBhXLh/vk+Vq8XUbN58gCYsDleLPoXD4eII7Q8KBzAixOLMKaE9v0IA2Vc0PrAON12iOvPNVYdffjXoAJm/6w1samiAYDFAiQ+GaWmI99SvU3OLqlxP4Pa1CQZgTUnVzE7ebFef4fdaF+/I528v4p1lKqtTBW7EaoBtJ1IgYTL+vAauh0Oml7DJZv1EJggQKT1QMT90vXEPFbRC6gcgEDllBmljqJdB2c+opDVniiFTga9aoDVom28kw/jRCWBjR4MNF3lOeZJwAHH4zCxMzI5z3A3OP24eWtteB3fuF3PUeCiTMAmNG5Xt46jxebiCw8OIKG5YHIGsB4cOCBNRGb1sq8anqjLK1H71MlgDHwSP0iDZAw8FGXB6yiQ1pYenFHUCFCtk9PT+kAfyRb5tCspoBn2nD9OVWr9+jvrAlyfWXCAAI4CKN3u93KQigWB81eTblcuhQYxMySpjN7zYVT7cTSEt9xtoXWD3Aq4CAEdY0I2hafEcXknED8zyZaq9WqmGfz+dwGg8GbJQjUw4EItMlbbvA0ALSj+mGYTzVF2U9Cn/E/38svjTxi/NHHy8vLtE8NSqIYUKqhtAMcBYEjDK2Ed2QP6OB6g43fPE2mYMLvJVqGywcBWDmzkeuET/P8/Gzfv3+vJI/e39+np7AjascTy6DSwed9U3wNh4p5rxJeCM0jwoiDQPnUWj4CDGVxu/T0VQUUMgX0bArMN9rF2rnX66XlAxUe6oOjbrRNQaQmMzM3L8BiHL0sFM7oj3iFNRivY5lVrQm+XzWW0lHHiPE6DV6YXM/Ua1KHx+Qgtsn1P76eTUf1bTQbIzInkYXw+PhonU4n7ZCdzWYpnQqSy8uZU1MCn1V6mr066nzUsp7Px4m7/EQSjDsmOQdm9JfbCSBiCzy21cNM4+UTzC9v50BghLUAR+8wPuyfadQN7VINrylw0Li8hKN+Pr5H62U8x542xXizicvrWIiuKmWTYz0CUMBoOOAfpt58PnePAfO0lEZm6nwkD1TetdE93v1cjlc/A2q/36fjwxAyf3x8TKHtnDTkOnANMxYzDW9r1wez4TGnz8/P6cEKGHcwubeuwksUYEi+hrUTm3sQnNxGHAGA8YNG4Cif2atPg/sRFOBrQKzVvYCHPq4IGSPgNWZ+NpEhHDgowjygFoKCSV8MLt5pDnIB5V3IkhYdnE6n6ckYAFTuCYEKHM/MKwGVSrTo2txvZtUMCQU4SzwcMIMsEDNLmfS8WB31t4SgBRaLRWVNByDZ7V6z9/nxPsiPRGoOgKGHXmp7eNwBOICJ0500WMA+j5pv7FubWYURkSOoPh3qxoKw+loAJNKgOE3ISxHCmhkSmDebTdon5gFZw+sc2WPgeOthxRrq5eWlMuhQ1XBgt9ttSppkvwlZ2RED86R6oIquRVs8f8grK6dhvaggBwVQDptjMLX2+33Kn+NtDF57UbYXCPGCLJic+Xz+RmMiHxD7lvDsLAgz1hiIMPLJsRx9BFB5LAAmXM/9Zu0E6cyaQE1MBaqmTmHe1H/05oQzFTgFipceOFroLQjDLNxut2+EhUYF+TtbDLwAzpkWnuJwAXV/f586xqvRMAOw5gTNBN9Jw+QRRdqFv9dF8jRg4QGzBFSq9jHY7FuYWWWNiU2Cusgj6lCGM7PEbOgLMyfGnTMqeP0GEhsmNuYKgAFIOCQPEw6A0ugiB0oYPBo8YCHC4PSsDU0E8KJ06v9gTFCXBmbYLIMWgVDR8rU8BF8gxDS7Rc06/MfgVFeFyQXUly9fXi/4vxnlfPrNcrmsPF3QM/ciJldqGpxA2Z6/FDme3v3cRgWyamZcy33L9cuT1syEfD8zAa+VYA+UtlEZiaUkg5znje9nPyQaNzAfrA3WUGwVoF/cB/YPtW3sswJk0IocpOCxQ1mcPwgtzDvEWesgFYrTpJjAx2zOemts8M+4zxwN9Na0XEB9/fo1TSR2i/JDlrGtHacXsR2vmoMnKQJHLlqnk6f38jsmrYQ8v8zsdcmA61KA6P8ReYETMLwuXML/MXsVYug7FmI9H8AbU2gn7C4GA4NB1FQ1s4rgAFNhLNjE4XHi8dK2sCZRjcjhe148ZsbmvnB+I2sI1W7qM6LtfMoU2tNqtdygA2tG1o5oC8AbLZ67vyKRlRcd4VTu969JoQATh8pLKefER8BSZs+Zf3V1R8ELDlSAiTD5rFW0nVyvZxKp2cbSUQGFx4wiEXc4HFYCFOrrqT+GCcdDtlEunHqWrF4gBm3hx2+CMZnhc0Elz1Tj/znixzthmeHRTl5DY9KlCH6p5kPQBv1hQLHQ4H4xX3EdaFexyQeJyIXDOcXaE8K2k8kk6zup1OKBixg/51uhYxh0Zeo6h9crk9ukEkp9Bq5L7fVcn9QHYhsd5UNSI+OAdwav1+tkxuQIbeDEUd5qgfUlBCnMLAEVT/3A9n5sqOMIJIICHAZnqQ2mY+ZkwYy2cQBEd+9iblkD8cZFNYFVg6gpqMIRAgHvrH2Zd3SOGVBesMksANTd3V1iAEwGHD9dB0Hyq2dHo1H8W3RNHQBALC0U9CXleWD1TBWeAE8TeGafRuf0WjAmL1JqsicLMwSDeAFVtZunBbg98MfMLG2UQ3Y2R7xgIl5dXaWnzQ8GgwQGztXkrfJ6OpMGUbxx553PfEIT8xDfz1FmBZRexyCH8OG5BFj5PvaPAV52MbjtDOpiQP37v/97JZTIp4jiOUUI1zKY1HfKAQu/lQBJr2NQqZouIY0oaRuYQRlUvD7imTHqM6n/wNnUXva0OuZmrwfGYCs68iSRAqSCzNPmYBCclcD+MW/Qu7q6squrK7u5ubGbmxsbjUZpjLGIz5kZZlbZ/o6wNDMsgMDgwloZAxDzx5niLKAYeNxPDsur2aoCjwMx6jqwVlTLQ3kix7MuoD5+/Ghm9iYcCbXPO1g95y0yp/gd/x0CKg8QkbmpvlXupe2LtBFLNG4XpLkHPNUu/FKpjPvW63U6IGUymVSiqrAMzKpBE09ggcng3OOe0WiUNBb8LZw7eHNzk87mgyDFtnwEqmAq8hl20AoYHwYTzwGbh54Q4DlVUy8yybjfOl/8WX1vVQbsTjDActYAyAXU7e2t7ff7BCbYnfw4TD06y0N1HVBKfSrvPq98r179zNpGJwiD6A2g57BqNIrf+X7d5xONG+rZ7/cpZ8/M0lkTP378SAm5mBvNpOZn+fJYgQAqfg7VaDRK/tPV1VUCFQ6mQUIwCDtdkbGg4NjvX0/E9QQpxknHin0lvcczzT3t7AlGngv97kWOuUwWBhHfVcbX+xHHXkF6ckoHXnXaSZlUB86jJqBi8gCkE+D97gFK2wOBosSgYMnGaT4AnYKJMw20TCwizufzlCe43+/TY1QfHh7SyUYIXLC5xZniMNegScxeQ+o4F+Pq6srG43FaGsGD1vQEI4BoOBwmwHBwwvNDdSyjOfO+e2V5c6o5iVF9Uf25ZZZIC3mmNShMjl2v1xUnFO9IL6pbE8pVrOArAVKunMh3izRWrq7I9PP6yyFvmAdIEMU1mgun/ia3G+M+m80qyabwYR4fH9MTS5jJsd6E6ByieziPnf0DOOz8PF5+uDasET4qG+3iPVwArC6VsPlaSp4bkLsG16mGK6EmoMvd2whQSMLE9gB2RGFq1DVEpb9e10QTRWBS29fTPnx/DuCRP8d1aFSII0UcRgbjAVCaM+aND5uHyBfEORQcZeN9T2Dsq6sru7u7s9vbWxuPxykIwVoKBH8HgQRvERQAgnaCQOHsDI6sefNfusDuWQDeHOX45VDLxiunlBoBChoJhzxivYkdYc/f8BrVdHA8iiRW5LeVmHWedvO0nFcXR0DZdOPUITBhZOIxwzHDs3YAoGAGIsOBzZVOp2PD4dBub2/t3/7t3+z6+joFR3TzHgIIrJl4Ry7C8JxJzqT91nHC9yYMHvl6Sk2Y/dzU2IeCnf709JReLB1zfodXsSf1S8toQhpw4N8OlXAKPHxmsLCZw8EK/s5AhVTntrEpCC2Ax/0oE6NsgKvdbttwOEzBhG63m/Zx8YlTHMDQbe7s80VmHD6X+EPHaI33Ak9kXuo1Jb+BXEB9+/YtHVgJu11PL/KYlM2wksr5/yaDH01qzsyMJr1JnfyZQ9y8yo53zxczq0pkD1QMnlw/drvqEV8wORGYwJaP2WxWqQuLsbyw6mmjyOLQ36N5qxNiOcr5y4eS196SNp4MUHh6BnwnLxBxalDx92jCVFPwNTmfTRddPfKkqlemZ/6p6eeNTS6thesHYD0fkO+Bv4V9aS8vL2/OqEOWBWtuzijQMakz4VWw6Fh5nyM6hQYrBV+Jn5b7r7StLqB+/vyZ1kGQZqLM5g08mz5sknCjSiRb3f+RtMG7Mi1rD/zf1CTJMQvqQH/BtNwe1Z4RoPhd+8P9h3mGndMPDw82Go3MzGw4HCb/yAOxCgAFagmYvDE5lBGbWielAjrSak2BH5Xl3Rv6UMiSUEc4VxhPBAOqiQ8TaQn9jjr0Go/ZzfxoUhPSSeDM5qiPCi5uO671FoJVOEQg3O/3abPnw8ODdTodW6/XKYVoNBpVNBP8M84jRJ6ftquJwDnEbNfrS/gjatOh5mBdew8JlIRnm/MuxaYSRCNAmn/Fg8Pv/B/fD4q+e36dJ129RciobO8/Lp8XPDXypoCrK5f9KrQx8lVVe2y32+TvtlqvZ+Xt9/uUGMupUAh2cOoYb0fQsa8zr5qAosl9Hk+oJo3uz/Uhqj8HnlLtZBYACmtNXrjXkxTqNwFQXmpOjqJB5A7kJiJnHnG7StsTEYCkvhTaq9oLlDOhPLMw0uxaDnL+uE/dbteGw2EKQLAZbmaVJ/dp7lxOqCk11WK53yNLpEl9Hm9GgjhXdyTw69oQPiyAQeLZ1BGiPeblhrAvE0kdz/zg36PrtMPeIGmfSkwc7z/On0N/UWYOTJF/grZ4wIqu5/9Wq1UC1eXl3w+dhu/L2xgQHURKFGd2aFvqKDdeTe89B3Bz/OWBKeKtOpOTyQWUJgQqE4B5NJfPYxp21iMw5aRD3aDlzAC8VBCwr+IBV+vg/xisEaC4fgVxJJQ8868J7Xa7dKZft9tNKWLb7fbNWetsAqKunBDyqIkbUEd15nbpWETavE7g5rRnxB8R1R506TEBQBJpMW6Ax0hNOqXgKh1glfTan8i0rCNlPo7mef2Myqgr17u+zvyB9uHAA+7hU2AhDKJzEbSOpn2qu75uzEsB5FkZUV3e7yX1eW3M1RcCCu85EyX6r475IzCVgiqqy6NIaumERhNcx8QALLRvTlNHwkXLqzPvPE2I/7y0KOTumb1GO/mxPN485OZdf4/mP9e/nMlfWlZ0TQlYmghRr1zvu1nm+VBRI+sGz5uMnEkWNcxrT51ky6n2pgOrv6vdnau3jtmiMasTWhGQtGzNtkC+HyfKeqepeqZ0CbiivkbXlPglTYDU1GXw6si1oYk5XPTAtab2vFaak8x1zqGW1cQEKRnQOi0F7ROl53ivVuvtGXPROJRMUm4MI1CBEDrHtg5cgwRZPnHIG5um5lcdlQpRr+4SbRnd2/T6pmWC6o1oy6MXn6MGsdRks4jB08TpqwPlIX2JrvMO5NCAjbdR0BsbHaOSfkflsm/IfYJvhHw+3pqORFj0A3l8EaCbOOS5OYmElN6f81X4GtUakQBqwhORBtbxrqvXLAOo0oGsQzYzE2dORBN3CKnE98ykErOPX7wYrUd3MZBKNaEHrMhk0X7l6sC9yM3DVnbsvAWQeGv8fr8PNVOOdAx13upAkasvKqvUqtFr68w5via6j9vluQ+NfChuTE7ilkguBhNrKa2jxI71Ol3HGE2uZ82kZp53jhvK5wHnhVLeY6RmYI7ZcuaiXguzbjQa2fX1td3e3trNzY2Nx+O0nZ3Pz/PWCHNMdcy1Xv/qggaeL1f6X6Sxono9Aed99rSsV1ctoErAFKlM7x5N06lr4KHkgT1n3nj3K+N5629MWFTlx4JifYjzInm8ctu4c4ILUhNHgI3HY7u5uUk7dz98+GDX19cpWyIKYjT5XEc5S8P7T4WLvh8SGKlrn36PeNhrWwkV+VBaQYTq3L1gRF4LqZNOWkYTCaT/lZbnJdyqQFBtwQJCd8NibQiZ+3zyKoIX3haK6MVmKdKLoJXu7u4SkD58+GA3NzeVwAP3uQ683FeMRZSuxWCJhFgdKEosnSb3lJZbByj+XHJORjGgcpV6E+Q1nDVUlMF+CDiiNtZNhJoLePHARVpZJX6r9bpz9urqKp1hZ/Z3KtdkMrFer1c5qJKPYc4JEm0ntrHDxPvw4UMC04cPH9L5eldXV9br9d4IBnxWJudH1/BuZICJzUYFUKSdVOtEc4G+KTg9AdBUqNbdU/q7XuNdlwVUDq1NVW8EKpV6df6N15aSAEquHzyJamd7QPK2beC8u/F4bHd3d3Z1dWX9fj9t8Ht+frbxeJx2QOsJUixgtE4QfCUEHlgr4XVzc5MOroSWjMZGwYQsdN7egWx0jhTW7fL1fi8xmxR80XyXfo/6WhLIOJQamXw5gOXuYynjSWRvS4V23DMdorq4vR4oot+jPuN/D0iIAuI4r6urq8Tco9Eo7VGaTCYVUOGZWjADkbCqppbZ6+EqfJYe/CVop7u7u3R8MmuSqE8YJwRY+FQlbKvH1h1oRNZWdT6ZzrE3xsdaIfy5qTDNXR8JdT58x+u/WUHqETfAY8qSTiiouFGeBtBORdEZrcsDGzOo1u/1Se9lMOFdAaXMDjPs+vo6PQeWT2kdjUYJWNi+zsdbsykIsA4Gg1T+7e1t0k4A0vX1dQIw9w3jhz7zsWD7/T5pJfbv8IQQtiDwALXokM7c/DcBXnRN7r9SqyV3X51lxKRPgmQqivKVSvS6csx8pz9nYzcBk/7Ge4A4QndonxhE+I5ABJhdXziBCI+lGQ6HNhwO0+GUOKINoGIzEKe84txxBhPOH7++vk4nv3a73cr2DDOrRBtxgiz+wwm1eLEPpQEJ7XuOcmZVEyrVPrl7S/8v0aD63bMAipNjm5J3n5arAQo1R7ScugHm3xVMutVEw9cg1ZBgosjcQ54cjvC6vb1N4Woc0cXn4QFUANz19XXF/PMApWeOI+AAbYecvN1ul86f5xOqNJwPLcgPedbjw5CJDt8JRz17Z4/XMZ5H3jh7c6kWRF3ZJUGJSLso1S3rFJt82gAtpGTAcqRaSiNrvG9K25IDlWdW8iJs3ZaT3Mt74Fen00nm2/X1dWJ4LKhyhgU/lAxnhCNKBw2FJwwyoABCfm4Tjlzmx7QCIDAhOTSPepEp4T0lHePOz1ZCAEKPHYvmvyTwEAFJhWgUkGDTtVRb5r7niMelrlxQNvWIO6bMnQNWnf3rBSm8a6Jyc6DyAKXmnt6rWsjTRvofHrk5Go2S1gCYsDXCYzDVWAAWHhPED2LAOXsADw70R/QOWgSnzOK03+fn58qTU/RZttipywzDIEIdqAf9j0Lm0bzrd81qVx6qMxOZVzxQRe1iKgE9l6HCvo6yPlTk06gf0ZQ8fwq/awQlJ6n4nf9XUNX5SswwniZiUGkQAprp9vY2aQ+cKR71HWWwGcghazA7gMsha35AGcqDL4QHouGkX5TBCbO4B7+zRkKkEnmATfP9eK5UWJac4eGZ3qW+bVOfqfTaEveCyQVUbhA5SsTM1tTXUlCx/5Srv878U9u7LujAYNFnsyqwIe3x+EwASXPnvGRaj1jb4RFCfBi/agYvTQmAwvl8T09P9vDwYLPZLB34z2YbB1IAGmhbBDj4SRzRWLPphfHBe6RdmFdygPH+y2mJkuAFt4t5juvTa3NxgKi+RoDiAEKTqE9E3EEd6JzJx5+jDpYMMohNOn2WK1/TarUq600IW0M7DYfDyrZyNWu9FCPVgHxCEZP3G6J38J/m87n7HFzeysGmInwq9Gk0Glm/3w/PtWAm8xi8TrAyA0fz41kk+h3z4iUGcNtyflZUpmrJHKgaA8obAPUp+Bm0TbWUNq7EjIzs9EPqRl2en6Rahk09ZENgLej29tZGo1H2yYHa5pwTjraAMXLSGUEXfagbP9RaBR+vLSGczwEOz4etA1Ndn3GfHtRTx8BqtvO1JWNTYmrm2l7n1ynV+lA5UOnEe/eVkIKqidN4KlJQKUOB+ZDVzaYenpgemUlN0qv03sg84S0iHGyBz8Xg1rljnwqmX7fbrYDJA1UJqVZWEOQAkNta4lkfKiSaBA9US3pKJEeRSdhoP5T6E/oqUZd1dGqgNKHIzMR60HA4TCk/ePEWiVx5uYmKfvMkslnVlGEhAN+OF273+9c9Wdvt9k2Qg5/WgSwLbwy8NqmfqULACzp5fUQZJWDyxgK/5fIWvfZ7oFIzn9tQ4pcXb4GPTD68vEoOBdWpqU7a6EBqP+C0ayAiByYuS01Zz/fkyVOweD6Baqp2u50eFoC2z2azlKfHD8rjJxhiwRZA43A6CPPL372cTM83KmFujHed36x1MZgiINRRLl5wSHnFybEqFSC5ObXFA9CvApWnTUEMmLpET5hHyG5AmJzTfeoYQZmC6/OYAcyFceWzxz1CJG80GqW5QaQQjxfFdTDx8IJ2AqDMrHKen6eBUJaOdekYMNX5y551hPrVQorqrmtDKeWsBlCj/VBckAeqSBW+J6gic9RT32ZvI0Gw/3EPn9OA/DmsN/EZDUxeVC+aeG27+pGaNcLEzAaNg1y95XJpi8UiLfDu9/tk3uEp8Awq+H/tdtt9iiG3j7/rf56Fcqjp74EaY+JpeM8PKqm/ia9Yd22RD+URJhqZt/zyspGbqM2mpCYSm6U5QKndzM50q9WqpP1oEALh5SZRJp3o0pw4zxfjcvb7feV8veVymXYNv7y8JKAg+wHaiMHEkb8SX6qO6jRP7pocaPmaJu0ozZBv0h6PDtJQKLiJ6acNOgWwPC0EEOm7gkZBhf/wP6Q4/CbO7EZEzywO25ZoI6XSiWPfg7d5oE5OaMXr4uIiZbr3+/2krfjkWBC0FCfYcrtK5w7t0THmfujYe2Oh5FkAvEHTG7umVpJ3LZcflXVQ6hEop6XqOnCMGVgKIk9D5dZ20Hak4fBmPl685QMj+T5uG0izCTyNyf3SPmr52lbepsHvCKQMBgPbbDbW6/WSYACokIGhZi6bfh6jq2bXNtat/dRF//Q/1sTM1Dy23jh646dgrGtL5MNFAq+RhvJMEk9L8eNTTg0qBYz3OWfueZpDhQQyB/jwE15v8hZ9GVxch7YJ16vk9sZXy1f/j1OU+F5OcsWmw36/nwIqg8EgaSdOIGZ/DWBDdBAUzVedRPf6o8nKPCY6pl59sIjU9OY1OpD6hLiP7400pNePyNyvPegy+s6/e5kF+M9b5fbKrQMe18V1eloJ13oSlM0kvg5M1O/3k8/08eNH++uvv96EyHUsImBEZqcCvY48DWFWfcgbMyPWpFqtVtIy2LOFPvCaDYOK29hut1PGBfrjtd/T1JoNrtd6TMzX4XOJ9tLNkHVaCBRlUkRaCZTTwEW5fHVSlCN8HOJFWhIzcZ0U8OpV88576SQrmPAeSR428xhMHz58cFOLUJ5Xn6cxPfLa6H1XzYTfAAJ+miLqBvARSse+rV6vZ2ZVZsNTDLUO9IEP+PQElrbvkNQk1U64vk54eeOG/7kdEXCjnMQmIGJqFOWr01LaIEwGmxM88DnpxN9zYGINUNIXHSx873a7STN9+PDBPn78mMCE47h0UCNTgOth7aGSr84Jz40VgBCNG2tcRCsRTGEzcb1ep/I5wx19abfblXUpXhfTcagzB6N3LSe637tWxzYqj7WdV48naJWvVGN6VtlBC7seeYBi+1Yja9oJrx5mfs+0q9MAWq7HwJDm4/HYbm9v7a+//rKPHz+mtCKN6ml/vfojk0j7542D9zliPPgROcDyhkaOBPJYImPdEwIIozOocm32nHxtn27Z4TZ7GskDQg6cnuUQWSdK+J3HqgmFZ0o0LQiNUQkNQHlOaFQGf/a0lE68134ui7Ujt6nVek3ZYc0E7YSIGIMpF7HTPjCIuU2RCep9V+KonAc0BsNut0tA4v9VymIhl59oz4yFdSloRAYJt7WJqReZarnxaEJe+R64tU78hncFcR0uGq9DeQ3wGsPSD/dwVkFO/ap08TpYCngGAOpFWy4uLlL0S8+48/L0cg6stt37D7/ppHpaO/L1QFE2hvd/JKXx/+XlZeWJ8By5bbVa7kKvx1yRSeyBxeOjOmES/ddkDnI+l7Y1pxn1d9DBqUdRQ3PaoUQr6ffcf0ra2UiD4DpkZ3sZ5AhCcD+0DO5rZOKV9DM3Plp2JGH1uwc4XKfRLWgp/Z+1FI4o4/BzTmJ7c8iClSOKOc3WBBxaL9+fA5L3H49fZFV59Redy1enDSITBuSZQPq7956rq2m79TOberzzFlvZo6RXkJeRXRIJ8vpYZzaifHzmDAZYAegvA8xzqpnYDAKo4EtpmZyJnrMuPPJ8Gl3H8cpUIRX5SyXjqRS5CsyjJRs8lbLHiOUq9X6PvucQHtmrTckDMzMMCMzD602cp4dDVrQtdbltnAOY64enNb3Vfu961j77fTVDBdeiHPhPHlC9+niRF8sdfAYfQAXQRe2NmC8HbmZiBrFey/V5wI7uA2kd0X/8myqLOgUTrkPlpECplsg1mKWBMk3U4Mgn8T57ZTCY9IAVmHlIeuVVeLP6Q0JKhIHXR9zLpoXew+9geDad1HxRc0+ZwjMHUQfvm4K2wrWIFurD5kA5jcNmJJuS8Nu8e3ImstaN/3Pak/ktxzcR8Eq0c+3JsZFNWtdY7bw2FA1UUOUcW6+N/N0bYL4WGQQ4/kvNPM5vM6uuM0SpJlxP9F3bqX32zLWIeAnCG4/9fp80jPpP/O6ND+pnIDEAODuFQZVbQK0jFlyqbXlMuPxI8Hr8qeCMTEelqG91957Eh4rA5DVQv3vgjQbTK4s7ntNQMFmwHYMPWUF+G0wkzQhgQGraktahUTXvWs+n8KSkZ37gmDHWnFye+j4RsbYASFG3aimO2GKxl7MndC60v7k1uzqKGLdEq0f31fFUiVUSUfE6VB1gIhAx46jJ4UljXa3n8iOthDJV0uogAVCc+ApTTzMhPHuczSwmbwuE19acSeIJGB0jLMBizYj77Wljb07AhJ7Exv9mloAEbcQL6dBSyLLw+stt8Ey4yJrgNpVoHK8PJZS7R+eobs6Yas8297SPBxSQ+gbeoPDiJNelg5hT+8poEWPhN0jWfr+fnn5xdXXlHk7J2RieBq2L5tVpYzVzuE/e/fhNd9J6QkQTWT1iJldthn7zUwwBKO4/tFSUC6dZGdwP5RcOo0fj4AEzMuPqTDnPwtH/m2pSULHJp5WorauN03siEKq2wn+5TGB9cVoTazcdLD7xB9u/+fxus7cPVVPi9nlmXG6yQLrlQJkhmlDOCIcJ5o0L2qnjpiD22tlq/b2kwGPL0T+MFXL8PEdds1l43LxxUWCoJYNyvCwVfNcsnDpe8IDnAdTj+RxgG6ce5f7ztFU0wWa+rcr3K6giMPFg6kKyZ9Lgpfla+j3qK2uvHIAiG5xTfPQ+T4Kr5Ob8Pb5OARe1KRJWrDmRv8dA4v+hpXS/FOpQyyICC5POpze2npbKCaRonKOx1/Z4bYz+MyvcvnEowOrK5s+etMbvylSslVQ71ZlNDD41Fb2+eJKsFDiR6cJBBfzmtdczeWFOKXE4XX06FVLMuFofQMPrUvv9/k2bASiAyhsj3r7j8Yg3jgo8tWbURMwJDtVsnib1hC5fmwOVR42fAp8z/zzytBTfz5PEdrRXbk7zRP4W7gPD4chifj4SMrJZmqlJFTF9jnisSkGIMeH+8HoTr5HpmDET8Vir0AGjoy4lBDwAFoydRj5h+nGYvaR/Oj7MH+gvJ+IqRfOgfJHjI88Py5Wt93ogNCtc2M0VXEJ6nQJLJ1Xt4boyS9rBoAKw4HjzomPOBEBd3LY6xtGJKyEFE+oCoHhCoU28/70x4JcXsWQfi8EMJmdzN9JSWndd3xmg0boUj4tnukV99UxEvkbfo7ZHAFIq2gKvTKaobsIsWkcJqLjenO0M8sK1DCjWTnoQCU8mZxSodlbJj3pY8ucmG3Vp/7gcvBAuhzmnJpu2lceXy9d6PY3GbUK/IYTUTwOokOfHffPawP/nTMFonqN+RdpG6+T7lbfqBKlHXtuLn76hDYw61ZR4YPUVXa91q2kWvcwsMQcDSkn9OWUy/o/bgfqjNkeTxp+9Z0tBELBvwyF09gN171KdRMa7N+bss7CZqGOKlKT1el0LAP7fMzcB2ijoU2exqODjfkbg1u9eH7SMyArImnwR4rnhUYVNKNJUXkg2qpN/19A3M/Ru9/qUdE6fybWb+x+louiEct+8z3VlcCQO7WUzT4MpeBBAlD0QMaKCituqJiR8JV7oNXtdNAeQIzOXgwlKuXHyxigiZfg6ECl5cxG1q1hDaYE5VZpT6U1IAaHl1d0baTmAFIygUa4S29jrP+7T37w1GPxXBy4ze5NWhN/4IBWO6PGLQ9tRvdHYaf90XKGleF0KGtPMKickMdC1rxxgicYh16aIBz1Bq5Rrl1e3/l7C040fZ1NqXx5iBnqgihhef1cA8YsBgWAEZwLkbPncd68dnsRX5sDvHvObVdffEEBRc4+BBKnPib0RQ3rjpf1RwpodA1uzJ1A/TDa+FxStL7EFkdOueg0v/PK79oPnoSmYPDMX5TTSUF6hkZl3SlJ/QzWBDpSafRGYcC8Dip9ny1QqDLSNOeZkJtCnaXgTydpJ13lYuwJgSOz1xk7LVyDpZw0I4XcGFWt+3I/lB93eEQkWHhttoycw9T+tQ4VETqt4c5YTrN6YFQMq6og3MeciTFi0oq5SyQOUtg+MyGBi/yQyNXL9VEB7/6FuEKR7bkEYbdGwvme2cvAg10ZlPF5Xwv9ct6cJIJTUnEZZnJLkCbwIYNE8c/tA2l7OX/TmPmdCehQBxwO1UhZQOgDauHNTZBt7HWQwRcwN6asaiiNo0SSyX6MS0ruezTaPWfC/mi3aZjVJ2czT/9VM9gDS1IxVbWdmlecqc+oRTMNut2vL5bJyfwkPedfW8Zqn7VgAeX3w6vf43GsLf/fmtUhDNQXQqTRXZPZFqt1TzVoegwmhc/goKuFLNJNej3u8dTW9pg50YA6YW7iOX9B28F8iRvUEgCd0PIGh74iUol4Oc/O6VDRGnmmnWrCOobkMCCa0w0s1iqwFbYfylWcWeuMJyvpQkbTO0bFg8gbbazybG1xvSf2afsR+VK4cvkbzwNT8wf0aQND/vUwF/Z1D1+o/QbPqTlqtU8dOSQHkfef7dWsHh8OhpVarVdgnrTdnLut3FZ4AMv6L7vWERFQffvNyLnnslWpNPu/7KSmn+j1AlTiGXnnMwAAUR/tykwpTkSkCci71B2V6GQogrgfA4YP91YdScw/38eI0awBtP39XX8MDFepiU1mDGK3W6wKzp+U4U0MBVWruqYlbR9E1kR+LOlTLHaWh0JAmwPIY0/s/B6TcbzmJG0k7Lod9D83nM3sb5s3VHflZ0f3KNJ4Zq+Fy/s8z9wA41WBeON4jTmWKzGZPQ2HPFJ84y9dx9gSDn6/jgIo3jszE3I/ItPbm37Ns9D/mDTYZPeI+eFRr8kV2pDYuV8ahv2kdnvTMMXhEnCDLklbL0G3mXr9zg5/rmzIoiMPl3CYACL/rSa+YJ9YaWj5rh6h+BnUUMAGgePyYyXWxl8/z80DFwiTSVJFG0HZF4+uBUa/jPntBDe96peLtGx4zRcAqUcElxJLZI5ZuTcxRZjzPbOEJ8BYjzaoJuBHYuB+oV4WBRuZQHgOGAeX5T2zOqobS9qEuEIPSW5DFvZqF3mq13iz2eqclqd/BbWcByX5YdB+PT1MzTy0jngcVMl7Iv5SnG5965JlUdWZeCUX3q7njqWlvw11EOpAKKu9MBp5MlqReilTO5PQm0+s7t8l7MBrAttls0pMIvTo8u1/LKols8hwwYwEECspW63XPFQsEbQvPA4IqUejf6wcLKgZydC1f5wkZLYf/y/mXTEUa6r1ABVITJCfhcU1uu4RH0FKqqdTc8QZNzQb8xtKO782ZfB5zKLC1rfw/nw6r2lTnhCWvmoVR5reaTtx+jDuyzHVcMCd4GIHHxF7wREEVWQkKEAWTHqLpmXA6z2hvZBpG4wE62OQ7JYCYPAmhoFLbvsSPibSAF62q61ckUKKJ95zYqA4wgppsHDxh7YKQeVSXpw32+1cTTs0evsdrm0prtFEz0HlueOdvZKIr2Ll9qh3wv7bDaxu30ROEMFvZ+vDWsvizVz7ooKdvaAei700p53/oZw0vs5bKSVa91yNMZGTSsa3P0t4rNxp8Xd/wpCEnm3r1YLcsa2fvjECmEmHBnyMNwNdyGJ2fLM/3eWdPqDbD9WqqYW7ZPPTaq2YdIpEoP2fOeTzEZXoWy8EmH1MdaCLpcCyhs+ovcOhVB8Iro6QtnvbBO6QjJ4B62k3tbS1b24qJ442DnB3BAQmUh2PR2OyLzF+0jTWHlzzs9VnHRvuDaKi3zQQA160lDBRutwpFBhPazZpWx9BrX07YogxOWPYEKX+OtKDZAU/fOOS6U5iHGGx+7AqbQDzwpXWpieJpQzU3PVB5UruuLM/fYJOI2+b5Tzi0k4MSWq9HHgN6TKakGkO/M6gwR/gffWHTT+vR9njjzp9ZW0bmHAMyEqZsAnuWgkZ/ud3FGkrVtff/KU29OgKQ2CzYbDa2Wq0qhy2yHa7tybVPzYFIauO7SqpovYZ/U8mnIMD/HCrnKBoHT8AA8J90oqO2a33Rfd4YRmPCbef9WtwH1qi6vUM1VTR23rir+a39UY0XaScvaqvmuwqRxhqqjiLUngNYrdbfqSwwb8z+zsdbLBa2WCxsuVy+Gdic6adl4z2SYmq24TevHHxW59ysmgGQC1ezicSBE86eQCaCSnHPp8gBy8z3KRlU/JnfddxarVYlM6LVar2ZF35wG9+fsxS4nXWmNBNbE/yb91nBi7brmh7f59V59NM3TqWtImlo9vrg5NFoZJ1OJ0m5+XyeJP1yuawwVZN2RNJXpWCk+TwGKKlb7wPDACxog0YkWTt5kj0CkNadM+1A3sKz/q/MCA3E5jjaioe2rVarN6aWJ9z4dz4uzdNWXt/QnpxJmBsjftfxKQbUsdqnKajqOtVqtazb7dpwOEwPkt7tdjabzSr3rlYr1wRQUu0RSUGdGO4bh20j7eRlWfOYehMMJoO5h4Xc9Xpty+XSVquVdbvdN2awt3DK7c6Rgitn8nH/uY9mb4MoEBI83vy8XtVyWhd/1kwKBbjXX/W3vL6i7IhfcuadR0W5fHV0Sh/KKweMg4el9ft9a7Vabx7d6ZkcXpmeGRCZdZ4/GTEa31cyHsog7MjjN/iKy+XS5vN5OumWTSVlGq89HilI2MTzNLEKVmZ2Jt61q5E9s78fFo6+6X9ePSD1cXKM7vXfE5AsSLXOknFTapxtHjHKKUAVmVNsMvR6PRuNRtZut63b7aa6+Z1BFa0/eH4DNEOd36FM10SCKXHb0SaO7q3Xa1ssFjafz202m9l2u7Ver+dKeH15jKnz5DF8RDlprfVyBBT1sokHLVVXn5kfwOGxj0DilRVpXvzHgjjS2rl2F5t8TTVV6T2lIGy1WmkhE4+hYUCxzQ7pV+dPcT85tMvM7U1Wzqb2hELkcHPbdbsGg2k2m9lkMkkmrm6O5GgfBI+CStvIZitv/edrVMproAGki60om7PM1fSDQIQv5VkDahVE48u/1wm6Er70TEK9PyrnJCZfCTX1w0CYHE6evLy8tF6vZ71eL5XNoWUGWAQoZSAFlCddtf0w0VSjcXn4ztJZ28D7slg7rVarFMmcTqc2nU5TcGY+n9t8Prdut+s+tb7O9lcp7X3XcdC1Mu0LC6J2u11ZN2PgYj50W7+nERCA0gwQNnc5NM/latsjgOm1+M6CB7/VmZonyTZvQtH9uTJ5nYBBdXl5aaPRqBIFY+aE9IvaoYDzzrXj670249GZXhhYfYtcfpgeFAMNxaBaLBZm9jcAl8ulzWazlHiKFCRoBfRNM8E9RlFzjT9Hi7Dcfs8MgnCCL4U5MnsbQPGsCU+DRhqTeSqXfuZZXp6w9IJI3tx71PiBa/g/V+gpiQGk0RiEYUejUQIQH76CCJknVXmNgSfbMyuYvInRifWiRvw7+xcsDNAn/O6dHaiAm81mtl6v06Lpbrez5XKZmBTawcys0+mEi9hR1NLslR/QRlyv/gbfx1kt3Ab4twxEDmBwuzw+UyGlZruafx41cUcicEZUnHqUM5ui605hMrJW4pVvlI/1mtFoVAEUg0rNPrTby0Lg/vB9Kvl4gOH7aBDE87sYSBwW5+wC/h9jwM8H7vf71ul0rNVqpX5ibLbbrS2Xy4o2ABjAjJyu5PkgHql/w1LcM3nxDq2JA1ugkbRsaGd+TheDBwBFucp7ke/E1/HvOc3rLcrrNZHl02g/VJ3az91bWgeXzx2M0kharVZitvF4nMC0Wq1stVolKa+gYkbWPDkFH9vhynxqnvDY6MRqubxYy6YOE/dvMBjY9fW1XV9fpwdtc+Ks2WsGCfqMMgE6fMcCqwckFjgqALhdOj5MrFEvLi5SNgvmhX2t6Ag0Nsn4ZFzmCQ/YEaCiayK+4zK8yJ7X76NSj0q1zzGaSk0+djjxDtNvMBhU/A6YPgwU3MMA4ncvGZK1Ept3rL08xtKJ9ZiGgc2HnXDGeafTsfF4bN1u125ublLGCMwl+IqcjsVHePGBKev12rrdbiXrok4QKDNpHzyNgXmC1mQ/EOtpZpbmrdvtWqvVqmgpjA/q5PFXIPF4RgKR/+e2e+Z5bh4j7WR2ZJTvFCadR2oyMJjY2WcnHgu/w+HQlstlJc+PE2gxKerDMJgik5VBxIOqkhLXRuPFwEYGhEYo4TvB3EMA5ubmxobDobVaLVutVgl48Kn4UBSuUxnQY5qc2aaL4dGcqSWBeYKWZFDt93vr9/tmZpVUKwCLy1ZARZE3zxLgvqkLUOd3NeXxg4ISx9zTpFzusJdZDd8Av3c6Hev3+zYajdL6zXw+t8ViUWE0fQogvxQo3D9994AFYgb2/CrWKPP53NbrtV1eXtpisUhgQWAB44B1ODAfNE+73a7k+HW73XQNTEb8xvun2GzmtjFFUTMNkvB8Maj2+33SsvgOnwpaCqBSnyky3/h31qAMHp07jSyaWYV31GdTXzny25SKw+bHaKNTmXteUELNLwbVbDaz2WyWfApdvUcZnqnnObmetPOuN7OK2aJjAaZCOtFkMklngSP5F/WxL4Q6+bD+drud1qHAFGBejFWn00naG3uoEGbXQAi30dNS/F37xW3gcgFoBEN4zY+jtxAQXsTRG2NPO6kGUkGgJmxklntmIY8NPitlHwlaR+cw97hsZoooXGr29iy1brebImJYBFVQ8Qo+yvNAxfXlbHNPauUWE6GdOAvCzBLjcfgcUh59RYAB7QETYsEb5hkYFFtfACRoDA4GqAbGZ1005fc6fwLXAuS9Xs/6/X7y+Xa7Xeovt5PBwHOhv2nYXtuo/fLaylkeCjYV3GxmRv0+6kwJpghcdaCr03yQXOw7saaIpGe327XBYJBe8/k8+Rxq2ulkKHi8Or12Rv3QwWezBzl60+nU9vv9G8YHYNiv0ACL2et2ePwGJoaJhzUoXSTXcrw+6++5PrK24/G8uLhIQg4L0bvdLgENqWQcMNGx1TH2ABa1a7/fV8aNy4g0kmcN1VEWUFpASaGn1FpqQnAwgicMEpklM6QhtnywL4WB5Unj+jDICCtjwPXMPm+SzaqT6KWq8KIzg2qz2SSTFYwGbdXtdpNW0eUDtEUDOAATwMn95Hbit8g0ivqnoXSzqqmr9yH8j/A5rmdAoY8Is/M4e4T/FMCeKahBJxAvH5i9zWhh/vI0eaWPUSNPQacoh7WTql28ox4FFQMK/hRH1Jg8xlfTkM1DPVwz8s0ik88LgjDI0E+YbcxwKmjMqgEBAAjvehIRjx/u9fqhFgGDjn0VnoecRmu1Win/krUua2VeoNYFYB1Dz7wGsFgQemMd9d+rx+w1b5PLbuxD1dnHev0hANJ7uCyYPF4oFu2LzDREuzi7oN/v22KxeKP6vSgRf4+yKFSj8b3eegfe0SeYYoPBIJk/Wi7ABI2LAITa9d48QLh4jM9trjNXPeEThao9M4nLhqBDuzjyyPdycq22x5t/aCm2YngePL/HA0VJGF2FB1ORyVcKrKak9rD+x1LW01J1vhdnGAyHQ5tMJtbpdNJ2eU9qsVng+QMeuBl43uTxvWgX/BtkP6BdcNhZs0AwDAYD6/V6FX+AwRWZmjr5OfMJ72BQ7o8KC4/Z4S/hOl04xvwhU0PnlgUim1mR8FTzmz97UV3cnwNNjurcniINVVJhpGnqfovKYvOFfYHIf/DKhh/CwYnpdJrWbTjthzWRBkDYlNTQvVlVYjOjeVKb+9jtdu3q6sra7bYNBoO0JsVZDhcXFxWHXaN/npZgs6fVaqU1LgCLw9I6N3wfg9XLWFBt6s0h7tE6FCweDyC5VrUNPjNAvTZ4Jyxpn9VsjPxjNoNziqZWQ0Xa6RgzT+/zytKFTF2z8ADlTaoXReKMbc7n48xolZpYKI2ijepjsWbDZ5SHSYY/BE21WCys2+2mhV6ADn2A1uJDR3R+PA2LtqP9bArq+Htgwn/q2DNDeyBVsEfgV23H5hvq1+t4YRn94nrRLq2X+8l+Kn73TGhvbCKqDZvnQBVdX/d79BnfdZLU9PMAFRE7vRo6Nns1jxRQfL+GmnmSeaIVYCiftRzGE2YdzFJsbUcdWJdSoeJZDcz8yjzcFtY00VxEJhr3U5mLNTczvIblFVi4nseKSc08vlYBqODXzwom/K/85PGSjomOG9NJ1qEO1VJRGTpZHsi089FgAJC8RZwJYPJy4Ph+HlReVFWzIPLNlInMLLULbeLscfh56Df7FIiCeePHgGXtGAFQ265l8X1crgcm1VLqbyigFKw87rAgdFzVN+R7tP9qTkZWjKfNQB6/aVsr5e2bqJ8/9If+UJbqnwPzh/7QHyqmP4D6Q3/ohPQHUH/oD52Q/gDqD/2hE9L/Ac/ZImKy9w5TAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -167,6 +205,13 @@ "ExecuteTime": { "end_time": "2022-05-10T09:04:18.343737Z", "start_time": "2022-05-10T09:04:18.310794Z" + }, + "execution": { + "iopub.execute_input": "2024-11-07T15:41:16.536026Z", + "iopub.status.busy": "2024-11-07T15:41:16.535690Z", + "iopub.status.idle": "2024-11-07T15:41:16.571648Z", + "shell.execute_reply": "2024-11-07T15:41:16.570741Z", + "shell.execute_reply.started": "2024-11-07T15:41:16.535997Z" } }, "outputs": [], @@ -245,6 +290,13 @@ "ExecuteTime": { "end_time": "2022-05-10T09:04:18.375627Z", "start_time": "2022-05-10T09:04:18.346802Z" + }, + "execution": { + "iopub.execute_input": "2024-11-07T15:41:16.573383Z", + "iopub.status.busy": "2024-11-07T15:41:16.572852Z", + "iopub.status.idle": "2024-11-07T15:41:16.606980Z", + "shell.execute_reply": "2024-11-07T15:41:16.606170Z", + "shell.execute_reply.started": "2024-11-07T15:41:16.573348Z" } }, "outputs": [], @@ -278,6 +330,13 @@ "ExecuteTime": { "end_time": "2022-05-10T09:07:51.217585Z", "start_time": "2022-05-10T09:04:18.377676Z" + }, + "execution": { + "iopub.execute_input": "2024-11-07T15:41:16.608688Z", + "iopub.status.busy": "2024-11-07T15:41:16.608267Z", + "iopub.status.idle": "2024-11-07T15:47:05.774045Z", + "shell.execute_reply": "2024-11-07T15:47:05.771112Z", + "shell.execute_reply.started": "2024-11-07T15:41:16.608657Z" } }, "outputs": [ @@ -286,13 +345,13 @@ "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 out of 15 | elapsed: 1.2min remaining: 7.6min\n", - "[Parallel(n_jobs=-1)]: Done 4 out of 15 | elapsed: 1.5min remaining: 4.2min\n", - "[Parallel(n_jobs=-1)]: Done 6 out of 15 | elapsed: 2.0min remaining: 3.0min\n", - "[Parallel(n_jobs=-1)]: Done 8 out of 15 | elapsed: 2.4min remaining: 2.1min\n", - "[Parallel(n_jobs=-1)]: Done 10 out of 15 | elapsed: 2.5min remaining: 1.2min\n", - "[Parallel(n_jobs=-1)]: Done 12 out of 15 | elapsed: 2.6min remaining: 38.4s\n", - "[Parallel(n_jobs=-1)]: Done 15 out of 15 | elapsed: 2.7min finished\n" + "[Parallel(n_jobs=-1)]: Done 2 out of 15 | elapsed: 2.2min remaining: 14.2min\n", + "[Parallel(n_jobs=-1)]: Done 4 out of 15 | elapsed: 2.8min remaining: 7.6min\n", + "[Parallel(n_jobs=-1)]: Done 6 out of 15 | elapsed: 3.9min remaining: 5.9min\n", + "[Parallel(n_jobs=-1)]: Done 8 out of 15 | elapsed: 4.8min remaining: 4.2min\n", + "[Parallel(n_jobs=-1)]: Done 10 out of 15 | elapsed: 5.1min remaining: 2.6min\n", + "[Parallel(n_jobs=-1)]: Done 12 out of 15 | elapsed: 5.3min remaining: 1.3min\n", + "[Parallel(n_jobs=-1)]: Done 15 out of 15 | elapsed: 5.8min finished\n" ] } ], @@ -316,18 +375,25 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "id": "d68572f5", "metadata": { "ExecuteTime": { "end_time": "2022-05-10T09:08:02.226452Z", "start_time": "2022-05-10T09:07:51.235014Z" + }, + "execution": { + "iopub.execute_input": "2024-11-07T16:18:15.134026Z", + "iopub.status.busy": "2024-11-07T16:18:15.133649Z", + "iopub.status.idle": "2024-11-07T16:18:53.684682Z", + "shell.execute_reply": "2024-11-07T16:18:53.683480Z", + "shell.execute_reply.started": "2024-11-07T16:18:15.134004Z" } }, "outputs": [ { "data": { - "image/png": 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kyUXybibDMKDrOizL2tor00REtP58FVt3ft4EvEOJiAjnyaDklebk4whJ9Xo9t4116hARXZSu66lb7RffoAacJ0Y8z1s7Ka7qQwiByWQC0zTR6XQQBAFGo9HSFVrLspbWlkh+J35LWrxNVZZHNRbVPqj2XdWmql6v14NpmjKJFe/H4jFvtVpotVpr3QFLRETXb535KlZmft5kTCgREeH8MYbFySBJCIF6vb50EpVUVCe2uN4GEdFFlLlF/tWrV4U/VlVJcVUfvu+n1qDTdR0HBwepBMnh4SEsy4Ku6wjDEGEYwvd93L9/f6m9eA2ivDIV1VhU+6Dad1WbqnpxEit+jbRhGNA0DZ7n4fbt26n2gOUFvomIaPOtO1/FyszPm4wJJSIinJ9MTCaT1DbHceSjCMPhUK6hAQD9fh+WZS21o6oDnE868WKuRESXQdd11Ot1mcBJJjJi8fpBi5LJcVVSXNWHYRhL8fPNmzeyf9d15eNdYRhiNBrJNpO3+Hueh2azWVi2KJm4Uo1FtQ+qfS9qM69erVaTfQ2HQ7k/lUollYiK75DKe2sdERFtrnXnq1je/LwtuIYSERH+dnvq4rZ4glhc28JxnNSCtGXqAOcL0vJtPkR02eJkdq1Ww3Q6zXybZNYdOv1+H3t7e/I1xXE7e3t7mEwmqaR4Xh/xehGHh4fyB3Qc54QQaLVaqT7j9Ys0TUO9Xpf1giCQ/anKgPMf7HH/8T40m03lWIqOU96+l21zsd5gMIDneZjNZmi1WjKJZBgG2u02bNsGAIzH49S6UkREtD3Wna+StnEx7thH0cJKhL7vy0mWV0qI6EPieR7CMCy9MOqqi6iGYYh+v781i+49ffoU+/v7nA+IiD5gnAuIiAjIzhXxkTciov/PNE3MZrNSC6N6npe7dkaebUomERERERERqTChRESU0Ol0Sn0vfu30KphMIiIiIiKi9wUTSkREC7b5TQtERERERERXIXdR7pOTE5yenl7lWIiIaIO8ePECAOcDIqIPGecCIiICgNevXy9tW1qU++XLl7h37x7Ozs6ubGBERLSZbty4gbdv3173MIiI6BpxLiAiIgC4efMmnj9/jrt37wLIuENpZ2cHZ2dnODo6wu7u7pUPkIjoMjx79gyfffYZ7ty5g59++gnfffcdPvnkE/z888/453/+Z9y6dauwjdPTU3zzzTd4+vSp8nvffvstvv76awDATz/9hB9++AG/+tWv8PPPP+P+/fu4c+cOAODJkyf48ssvL75zV+Tk5ASPHj3ifEBE9AHjXEBERMD5udH+/j52dnbkttxH3nZ3d/lqUCLaSr7v49NPP8UXX3wBAHj48CGm0ykAQAgBy7LgOI6yDdd1sbu7ix9//LEwFn788cfyO57n4ZtvvpFl3W4Xw+EQAKDr+la96S1+tIHzAdF28DwPQgjoug4A8k2UruvKv8usEef7fipuxoQQcF0Xuq5DCIFOpyPbKyrzPA+VSgVCCDSbTTlG1diEEBgOh6hWqwiCAAcHB6k2VWV5Yyk6FnnHcJ39U9XxPA8AEIYhJpMJ2u32xsZZzgVERGq+7wMADMOAEAJhGKbiZd68uur8vJGiBdPpNAIQTafTxSIioq3QbDbl30EQRIZhpMo1TSvdVkaYTHEcJxUvF/vqdDqpz71eLwqCoHT/1+no6IjzAdGWGI/HMt4EQRDpui7LACz9GwwGme3EMS0r9iXjWxAEqVirKlvsKxkXVWPTdT2az+dRFJ3/Pk3WU5WpxqLqT3UM19k/VR1N02RsHQ6Hqb42DecCIiK1Tqcj5xTTNOX8FEXqeXWV+XkTZOWK+JY3InqvhGEorywDkFeNkyqViryScFHj8Th1BaJSqaBWq8kr1o1GI/X9drsN13UvpW8ioli325V3P+q6jvF4DOA8JjqOgyiK5L/BYIBer5fZTrPZzLwLRQiR+qzrurzLRlUGAMfHx5l9qcYW14+v2BqGAdu2AUBZphpL0bHIO4br7F9RHcdxUsd5a69MExERarUa5vM55vM5xuNxKqbnzaurzs+bigklInqvjEYj7O3tyc9hGGZ+bzabXbivMAxRrVZT2+JH6arVKhzHQbPZTJUbhiFPUoiILoMQArPZDJqmwff9pcR6Mg65rrsUl8pQJeeLEveqRHve2PJid7x/eWVFY8nrT3UM19m/ojrxIw7A+bzR7XYz94mIiLaDpmkrXxy4jPn5ujGhRETvlSAIUidSefJOSFZh2zY6nU5qm+d5GAwGGA6HsG078yThMpJZREQx3/dRqVTkej22bcs7IZM/bsMwxGw2KxUjF6mS80WJ+7xEu2ps8ToUyX2M21SVqcai6k91DNfZvzIXM3zfh2VZaDQaS3MJERFtjzAM4bouXNeFZVlLd6lmuaz5+brlLspNRLSNwjBMBWhN05YSOIsnFusKgiDVjhACk8lEPjJhmiZqtRosy9rKCYKItsNsNoMQAqZpQtM0dDod3L59G1EUpb5nWdalvxRAlZyPy+JEuxBCJtnjlxXkjU3XdQwGA9i2jQcPHsgf55VKRVlWdpyL/ZU9hmX3b/Hu1axxGIYBXddhWdbWXpkmIiKkXrqg6zoajQaCIChd/13Mz1eFdygR0XtF07TUD/bkYwVJ9Xr9Qv34vr+0PpLv+6nH7XRdx8HBwaXcDUVElEfX9dSt9vH/Ta4VF4YhPM9bO5muSs6ryuJEu2ma6HQ6CIIAo9EodfU2b2y9Xg+macpET7yvqrIyFxGy+lMdw3X27//+3/9b6mKGpmlotVpotVqcK4iItlRyTovf7FnmLiXg4vPzdWNCiYjeK9VqdSmoJwkhUK/XUycLRQE/60f+8fFx5vpIk8kkte3NmzdLC/GprqITEa2qzB2Qr169utCPVVVyXlVWJtGeNzYhBHRdl4+4GYYhv5dXVuYiQlZ/qmO4zv795je/ya3jeR5u37691HfZkw8iItocvu/j/v37S9vL/t6/6Px83ZhQIqL3immaS0kdx3HkIwXD4VCudwEA/X4flmUtteN5ntze7/dLvZktvsX18PAQtm1nrqGUdWcTEdFF6LqOer0ukzTJZEssXiNokSqpnkz6qJLzqrIyifa8sdVqNTmG4XCYehwgr6zoIkJef6pjuM7+ffHFF7l1KpVKKkkV3wWV9RYgIiLabPFj2DHP89BsNjOTRFkXqfPmwG3BNZSI6L0S32a6uC0O9It3FTmOk5ksMk0TpmlmPs/sui7a7XZm/3G9PMfHx3ybDxFdujhxXqvVMJ1OM98mmXUXTr/fx97ennxNsed5sm5cFsfNuI+9vT1MJpNUcj6vLJloj39cZ8XArLENBgN4nofZbIZWq5WKraoy1ThV/amO4Tr7l1fHMAy0223Ytg0AGI/HmE6nS+MhIqLNp2ka6vW6nAeCIEjNO6p5NbbNa61+FC2sNuj7vpxIeaWEiLaR53kIw7D0AqerLoba7XaXFpQtIwxD9Pv9rVl07+nTp9jf3+d8QET0AeNcQEREQHauiI+8EdF7xzRN5eujkzzPU95RtCgMw9y39xTZpmQSERERERGRChNKRPRe6nQ6pb4XvyK6LE3T5KMhq2IyiYiIiIiI3he5ayidnJzg9PT0KsdCREQb5MWLFwA4HxARfcg4FxAREQC8fv16advSGkovX77EvXv3cHZ2dmUDIyKizXTjxg28ffv2uodBRETXiHMBEREBwM2bN/H8+XPcvXsXQMYdSjs7Ozg7O8PR0RF2d3evfIBERJfll19+wa1bt96bfq7ayckJHj16xPmAiOgDxrmAiIgA4PT0FPv7+9jZ2ZHbch95293d5ZsciGhr2bYN0zSh6zqEEHBdV/7d6XRKrZvk+z4ePnxY+Dpny7Iy10eyLAsHBweyr8PDw7XXX7oO8aMNnA+ItoPneRBCyNcPJ184oCpLcl1Xlqni5GJ8830fAGAYBoQQCMNQxg1Vm6p6qtgthMBwOES1WkUQBKmxlI35i/tQ1J/neahUKhBCoNlsymPpeR6A85c2TCYTtNttGIYB3/dl2WQywePHj0sdr03DuYCISK0opuedU5SdczdatGA6nUYAoul0ulhERLQVptNp5DiO/GwYhvw7CIKo2WwWtuE4joyHRXq9XuYYAETz+Vxum8/nmd/dVEdHR5wPiLbEeDyOOp1OFEXncU7X9VJliwAs/RsMBqnvZMW3Tqcjv2+aZqpM1aaqnip267ouvzudTuX+FdVT7YOq3uIxSPanaZqMk8PhUB7fZJ3BYJBqX7Xfm4ZzARGRmiqmq84pysy5myQrV8S3vBHRe6ff76PZbAI4v6qcpOu6vGKs0mw2S12JdV0X7XZ7aXvyToBY8ko3EdFl6na78k5JXdcxHo9LlSWFYQjHcRBFkfw3GAyW7qzMim+1Wg3z+Rzz+Rzj8VjGu6I28+qpYnf8f+PvGoYB27YL66n2oaje8fFx5jEDAMdxUvOFpmnwfR/9fl9uazab8H1f9pO330REtH1UMT3vnKLsnLvpmFAiovdKGIapk4T4EYWkSqUib029qPF4vDRJuK4rE1qL2u02XNe9lL6JiIDzZMhsNpOJjGQcVJVlScaurFimim+apmUmRorazKqnit1hGGb2Hz9iVhTzs8ZQVK9SqaBWq8lH3xqNhvxe8vFBx3HQ7XZhGAYeP34st8djTvaRd7yIiGj7rBPTi+bHbcCEEhG9V0ajEfb29uTnvBOP2Wx24b7CMES1Wl3apppMDMPIvTuAiGgdvu+jUqnI9X9s25aJa1XZomTsCsMQs9kslXxSxbcwDOG6LlzXhWVZ8k6cMm1m1VPF7niNiuT+x2VFMT9vH4rqOY4DAKhWq3AcZ+lHv+/7sCwLjUYDnU4HQPpE4fj4GKZppu7cytpvIiLaPuvE9KL5cVvkLspNRLSNgiBAvV4v/F7eycMqbNuWJw6x0Wi0tG3RZSSziIhis9kMQgiZsOh0Orh9+zaiKFKWqWS9bEAV35ILWOu6jkajgSAICtssUy8pvsNqMBjAtm08ePBA/nBfvMNosV7RPqjqeZ6HwWAAIQS63S4AYDgcyu8ZhgFd12FZ1tJV5vhEI7kY66r7TUREm+uiMT3vBT/bgHcoEdF7ZfHqs6ZpSwmc+PGPiwqCINWO53l48ODBhdslIlqFruupW+2TbxJTleUJwxCe560U35JXY+O3pCW3ZbWpqlcUu3u9HkzTlMmy5HHIq6faB1U9IQQmkwlM00Sn00EQBBiNRktXoDVNQ6vVQqvVSl20sCxraU2NouNFRETb4yIxPW9+3BZMKBHRe0XTtNQP+bxXY5e5i0nF9/3UGhqx0WgE27Zh2zaEEOj3+5e2XhMRURbVLfLr3D7/6tWrzB+2efHN933cv39/6fvJO4ay2lTVK4rd8aLa8eNvhmFA07TCenn7oKrn+37qUWpd13FwcCBPAm7fvp0qi8cHAIeHh7AsC7quIwxDhGFY6ngREdF2uGhMz5tztwUfeSOi90q1WpUnF8DyyZQQAvV6PXWVXtM05UlX1pobx8fHS7emLp6QdLtddLvdpbZ50kBEl0nXddTrdRmrkskWAMqyrBgYr7uUpIpvYRim4qHneWg2m6m4mdVm/OhaVr2sO5mSsbtWq+H169fQNA3D4TD1Fru8emVj9GI9wzAwHA5Tj7G9efMGhmEsJaPi42kYBlzXlY/ChWEoH7dT7TcREW2XVWJ61jlF1vy4TZhQIqL3immaSz/8HceBZVnY29vDZDKRi6sCkK91Tm4DzieDePHsfr+Pvb290m9eCMNQvsJ6MBjIN/4A+Xc2ERFdRBznarUaptNpavF/VVkc3xZfU5yXZM+Lb/V6HYeHh9A0DUEQLMXUrDY1TVPWU8XuwWAAz/Mwm83QarWW3rSWV0+1D3n14vUw4nECkOsoGYaBdrst2xuPx5hOpxBCoNVqLe1vvM5GmeNFRESbryimlzmn2MbFuGMfRQurMvq+L39wLL4Km4hoG7RarZV+nK/6ms74bUnrxEjLsnKviG+ap0+fYn9/n/MBEdEHjHMBEREB2bkirqFERO+dbreb+1rsyzAej9f6UR2v7bQNySQiIiIiIiIVJpSI6L1jmiZms1lqce48nuflLsaaJQxDVKvVtcbV7/e39pWgRERERERESUwoEdF7qdPplPqeaZorLYSqadrSWiNlMZlERERERETvi9xFuU9OTnB6enqVYyEiog3y4sULAJwPiIg+ZJwLiIgIAF6/fr20bWlR7pcvX+LevXs4Ozu7soEREdFmunHjBt6+fXvdwyAiomvEuYCIiADg5s2beP78Oe7evQsg4w6lnZ0dnJ2d4ejoCLu7u1c+QCJ6Pz179gyfffYZ7ty5AwA4PT3FN998g6dPnyrrffvtt/j666/l5++//x4///wzPvnkEwDA559/Xtj3un2Vqfftt9/iq6++wq1btwrLnjx5gi+//LJwvJvi5OQEjx494nxARPQB41xARETA+bnR/v4+dnZ25LbcR952d3f5alAiuhS+7+PTTz/FF198AQBwXRe7u7v48ccfC+PMxx9/LL/jeR7++Mc/YjgcQgiBRqOBIAiU9dftq0w93/fx5MkT/P73v19ahymrTNf1rVqYO360gfMB0XbwPA9CCPkmyfiFA57nATh/qcBkMkG73VbGtfj7k8kEjx8/TsW3vD5UZUIIuK4LXdchhECn05FtCiHgeR4qlQqEEGg2m7K+aiyqNpMsy8LBwUGp/lT14vE8fPgQ0+m09LFX1VH1tUk4FxARlbfKvFN2Ltto0YLpdBoBiKbT6WIREdFams1m5vaMEJTiOE4qFum6Hs3nc/k5CILSY1i1rzL1HMdZGlNRWa/XW2nc1+no6IjzAdGWGI/HUafTiaLoPDbqui7LNE2T/zseDoepskWDwSD1t2EYpfpQlSXbCIIgNSck+4uiSLZRNBZVm7H4N20yDqv6U9WL54isOSFv31V1VH1tGs4FRETlrDrvlJnLNklWrohveSOidyoMw8yrv2WMx2N5NVQIgdlsBk3T4Pv+hdot6qsM13XRbDZXLmu323Bdd60xEhHl6Xa78u5HXdcxHo9lmeM4qfiWd/XT9330+335udlswvd9CCEK+8gri+vGdF2Xdx0BwPHx8cpjKWozlrxjqKi/onrNZjN3jsjbd1UdVV9ERLSdVpl3ys5lm44JJSJ6p0ajEfb29lauF4YhqtWq/Oz7PiqVirwt1LbtS0vMLPZV5vt5J2SqMgAwDCN1EkZEdFFFCffkY2mO46Db7Wa2YxgGHj9+LD+HYQgA8jb9vD5UZfFt/kmVSgW+78u/a7WafCSg0WgUjqWoTSA/sZ/XX1G9PBe52LFqX0REtLlWnXfKzGXbgAklInqngiBY6+qrbdvodDry82w2gxACpmlC0zR0Oh20Wq1LGeNiX0VGo1HqBK1sWWw2m600PiIilTIJd9/3YVkWGo2GMt4lfwwfHx/LmKvqQ1UWJ4IWxXHQcRwAQLVaheM4qf7zxlLUpiqxr+qv6IJAlnUvdqzTFxERbaZ15p2iuWxb5C7KTUR0Gdb90RwEQaqeruvQNE1ui/+v7/sXXiR0sS8Vz/Pw4MGDlcuIiN6VrIT77du3EUWR/I5hGNB1HZZllbozJgxDuK4rF5NW9VGm/6z2gfO4ORgMIISQd04Nh0PlWIraHI1GuUkzVX+qennW2fd1+yIios207ryTJS/RtKmYUCKid0p1NTmP7/tLjyG8qzUmsvoqMhqN5N9CCPT7fbTbbWUZ34xDRO9K2YS7pmlotVpoNBqYz+fKRLplWRiPx6m3VOb1oSrTNG3pamv8iJgQApPJRK4/ZJomarUaLMtKxfzFsajaVCX2Vf0JIda6ILDOxQ5efCAien+sO++o5rJtwoQSEb1T1WoVQojcH9ZZdzAdHx/LwBvTdR31el1+P170Lm43PnFRJZ7K9qWqt/g4W7fbRbfbzew3r2zxeWkiootQxT3P89BqtTCfz1PfVcXlw8NDmdSJLwio+lCVmaaZeSW2Xq/D87zUGnu6ruPg4CB1ESJrLKo2X716lZvYF0Io+1vngsC6Fzt48YGI6P2xzryjmsu2CRNKRPROxcEy+XiF53lyYep+v4+9vb1SC5M6jgPLslCr1TCdTlOLW8dvA4qfU75oX0X1wjCEbdsAgMFggG63K08EVGXr3BFFRKSiSrj7vp9KhMfJ97xkvOu68vG4MAzlbfyapimT+qqyJCEE6vW6HMPi/PDmzRtZTzWWvDZVSX9N05T95dVblLzIUHSxI6vOKhcmiIhos13WvJOcy7ZKtGA6nUYAoul0ulhERLSWZrNZ+ruO46wdfxzHWfn7Vx3rer1eFATBlfa5rqOjI84HRFtiPp9HnU4nGg6HUafTScUZx3Gi4XAYDYfDqNlspsqazWY0GAyiKIqiIAgiAKl/mqaV6kNVFgRB1Ov1Isdxol6vF83nc1k2Ho+jwWAgxxfXKxqLqs14PIPBIAIQdTodGcfy+itTr9frRQBkv0X7rqqj6mvTcC4gIiq2zrxTNJdtmqxc0UdRlF410Pd9efWft90S0WXwPA9hGJa6M6jb7SoXqlNZ9RXMF+lrHWEYot/vFz5itymePn2K/f19zgdERB8wzgVERARk54puXPOYiOgDYJomZrNZ4eLcYRiiWq2u1YfneUu3nL6rvta1TckkIiIiIiIiFSaUiOhKlHk9sqZp6PV6a7Ufv7K5rIv0tS4mk4iIiIiI6H3BhBIRXZmtW2SOiIiIiIiIMuW+5e3k5ASnp6dXORYiItogL168AMD5gIjoQ8a5gIiIAOD169dL25YW5X758iXu3buHs7OzKxsYERFtphs3buDt27fXPQwiIrpGnAuIiAgAbt68iefPn+Pu3bsAMu5Q2tnZwdnZGY6OjrC7u3vlAySi7fbs2TN89tlnuHPnDgDg9PQU33zzDZ4+faqs9+233+Lrr7+Wn7///nv8/PPP+OSTTwAAn3/+ubL+999/DwD45Zdf8Oc//xmNRkPGMM/zZP1bt26VHsfp6Sl++OEHAMCf/vQnPHr0SNb/6aef8MMPP+BXv/oVfv75Z9y/f1/us6rsyZMn+PLLL5Vj2BQnJyd49OgR5wMiog8Y5wIiIgLOz4329/exs7Mjt+U+8ra7u8tXgxLRSnzfx6effoovvvgCAOC6LnZ3d/Hjjz8WxpOPP/5YfsfzPPzxj3/EcDiEEAKNRgNBECjr379/H9999x0Mw4Bt2/jd734n69RqtaXvDwaDzEW5F8fxhz/8AQBweHiI3/72t5hOp7Lsm2++kfW63S6Gw2Fhma7rW/O2t/jRBs4HRNvB8zwIIaDrOgDIN1/6vg/P8wAAk8kEjx8/zl3TTgiB4XCIarWKIAhwcHCQ+q7v+3j48KGMhcntAGAYBoQQCMNQxg0hBFzXha7rEEKg0+nINlX1VP2pxhnvaxiGmEwmaLfbsk3XdeVxyToGqv7y9iGvP9VxLxrHJuFcQERUnmVZqTlJCAHP81CpVCCEQLPZlPO0am7ZGtGC6XQaAYim0+liERGRUrPZzNyeEWpSHMdJxRxd16P5fC4/B0FQ2Pd4PJZ/D4fDyDCMKIqiaD6fR47jpL47GAwKxzGdTiNN01JjACDHErcf63Q68m9VWRRFUa/XK7VP1+3o6IjzAdGWGI/HMtYEQRDpui7LkjFvMBgsxaikZPydTqep+BXHyKyY3ul0IgARgMg0zVQMT/YXBEFqrlDVU/WnGqemaTJuDYfD1LGI+0r+i4+Pqj/VPuT1pzruqnFsGs4FRETlxHNIci5bjO155wyLc8smysoV8S1vRHQpwjCU2fZVjcfj1JXs2WwGTdPg+37pduMrvQDgOA663a783Gw25d+u66Y+543DMAw8fvxYloVhCACoVCry/9ZqNXnVodFoyO+qygCg3W7Ddd3CfSIiKqvb7co7H3Vdx3g8BnB+x02/35ffazab8H0fQoilNuK7aeKro/Edn8m6eXeo1Go1zOdzzOdzjMfj1JXZJF3XZT+qeqr+isbpOE6qXvy9MAzhOA6iKJL/kner5vVXtA9Z/amOe9E4iIhoOyXvEo4dHx/nfjdpcW7ZFkwoEdGlGI1G2NvbW7leGIaoVqvys+/7qFQq8vZP27ZLJ19834dlWWg0Guh0OgDSjxKEYYjZbJaZoFocB5BORB0fH8M0Tdme4zgAgGq1CsdxUt9VlQHnJz/xyR4R0UWpEvFFyfGkuGxR/FhaEU3Tlm7Vj2/zT6pUKqk2s+qpFI3zMi4wJBXtQ1Z/Rcd9nXEQEdHmyovleReay8yP2yB3DSUiolUEQYB6vb5yPdu2ZfIHAGazGYQQMnnT6XRw+/ZtROkXUmYyDAO6rsOyrMygbllW7tpFi+NICsMQruum1tTwPA+DwQBCCHmyklxDKa8suZ9ERJchmYg3TRO2bUPXdRkDVcnxpHgdo2S7QLl4FcdJ4Hy9oG63C13Xc5M/cZt59VTKjNP3fRwfH691gSFr31T7kNdfmeO+yjiIiGgzhWGYe2HEcRzcv38f1WoVnU5HnhOUmVu2ARNKRHQpVIFUJQiCVD1d11NXq5MLt5ZZDFTTNLRaLTQaDczn89SjDp7n5Y5xcRxJlmUtPcIxmUxkcso0TdRqNViWBQC5ZTxhIKJ3oWwiPis5nqTrOgaDAWzbxoMHD2TSJutupkXJhUR1XS98mUL8Q3rVemXHeZELDGUlTwZU/amO+2WMg4iIrtdoNMq9MF3mQnNSXqJpU/GRNyK6FJqmrRwAfd9fWl9onaSL53m4ffv2UhvJK9ivXr3KTRhljSN2eHgok0FhGCIMQ/i+n3q8T9d1HBwcFJYREb0LqkR80mJyPEuv14NpmjJBFbdfJBlv47fVCCGgadrS1db48TxVvSJlxhlfYGi1WqkYXHSBYVHRPhT1l3fcVx0HERFtHs/z8ODBg8yy+CK0aZrodDoIggCj0ajU/LgtmFAioktRrVaVJwFZCZXj4+Olq8a6rqNer8vvx4vbxXcnZS0mW6lUUmtY+L4PTdOWXj2dd5U9axzA+bPQ8VXnMAwxGo1ku5PJJPXdN2/ewDAMZdnimImILkOZhE9WcjxLMuYKIWAYRuGPW9/3cf/+/aXti7E5qV6vK+sVyRvnRS8wZFHtQ1F/quO+6jiIiGgzjUYj2LYN27YhhEC/34fv+8oLzaq5ZZvwkTciuhSmaWI4HKYSM57nycWn+/0+9vb2Si086jgOLMtCrVbDdDpNLWAdvzUnXvgaOH/UoN1uy7f8jMfjzEcLVrn7SQiBVquV2hY/ShI/lnF4eChPBuJbWFVlMdUdUUREq0om4jVNW0rEZyXH41vz4wR8HB9rtRpev34NTdMwHA5zH8dKPuYcP4IW8zwPzWYzc7FtIQTq9brsM6+eqj/VOC96gSFv//L2QdWf6riXHQcREW22xcRQt9uV6wHGc1Ty/CfrQjOQnlu2CRNKRHQp4kcVkkzThGmamSckruui3W5nthUH3yyO42S+9S0ZqLOeYc57HXPeOHRdVy4EHu/bqmXA+R1Ri0kmIqKLyEvEq5LjwN+S/XGMHAwG8DwPs9kMrVYrFcvyLhJomoZ6vS4T6UEQpJL+8dj29vYwmUxkWVE91UWJvHFe5AKDqr+8fcjrr+i4q8ZBRETbJwxDORcMBgP5xk/Vhea8uWWbfBQtnDH5vi9/jJRZAJeIKOZ5HsIwLHUXUrfbVS5Ip3KZr1i+yDjWEYYh+v3+VizC+vTpU+zv73M+ICL6gHEuICIiIDtXxDWUiOjSmKaJ2WxWuAB1GIaoVqtr9eF5nvLun1VcZBzr2pZkEhERERERkQoTSkR0qfJemZmkaVruI2hF4tdiX4aLjGNdTCYREREREdH7IHcNpZOTE5yenl7lWIiIaIO8ePECAOcDIqIPGecCIiICgNevXy9tW1pD6eXLl7h37x7Ozs6ubGBERLSZbty4gbdv3173MIiI6BpxLiAiIgC4efMmnj9/jrt37wLIuENpZ2cHZ2dnODo6wu7u7pUPkIho0S+//IJbt2698zrrusq+rtLJyQkePXrE+YCI6APGuYCIiADg9PQU+/v72NnZkdtyH3nb3d3lmxyI6NrZtg3TNOWrlX3fx8OHDzNfA51kWVZqvSLP8yCEkO2UWdh73b6S2w8ODuSaT4eHh1e+ZtNFxI82cD4g2hx5sczzPADnLxuYTCZot9u5/7st+m5e7HNdV/a3uJadqkzVnxACw+EQ1WoVQRCkYqaqbN1j4fs+AMAwDAghEIahLFv3uMQWY37R+PPqbRrOBURE5WXNBZ7noVKpQAiBZrMp5y0hBFzXha7rEEKg0+ls7FyQK1ownU4jANF0Ol0sIiK6UtPpNHIcR352HEfGqCK9Xk/+PR6Po06nE0VRFAVBEOm6Xlh/3b6SYwcQzedzuW0+n2d+d1MdHR1xPiDaIKpYpmma/N/qcDhUxjnVd1WxD8DSv8FgUFim6k/XdRknp9Op3L+isnWPRafTkeMzTTMVo9c9LvH4FmO+avyqepuGcwERUTlZMT2eC2PJucAwDPl3EARRs9l852O8iKxcEd/yRkQbq9/vo9lsys/NZrPU1VHXddFut+Xnbrcr7yDSdR3j8biwjXX7iiWvmseSVyqIiFalimWO46RiluoKp+q7ebEvDEM4joMoiuS/wWCAXq+nLFP1F98RFH82DAO2bReWXeRY1Go1zOdzzOdzjMfjVNk6xyW2GPOLxp9Xj4iItldWTD8+Ps79bpKu63Lu2CZMKBHRRgrDcO0f2ePxOPU4xWw2g6Zp8H3/Qu0W9RVzXTeVCEtqt9twXffS+ieiD0NRLEs+xus4Drrdbm5bq3w3KRnXFuOcqiyvvzAMM/uJ9y+v7KLHQtO0zITbusclK+arxq+qR0RE2ykvplcqFdRqNfnoW6PRAAD5GNzid5PzxDZgQomINtJoNMLe3t7K9cIwRLValZ9930elUpHPJ9u2fWkJncW+4m2qOwMMwyh1hxQRUVKZWOb7PizLQqPRQKfTKWyv7HeB9N06YRhiNpvJJI6qTNVfvI5R8jsAMJvNlGUXORZhGMJ1XbiuC8uylq4Qr3pc8mK+avyqekREtH1UMd1xHABAtVqF4zgy6ZR34SGeJ7YFE0pEtJGCIFjrTiLbtlMnAbPZDEIImKYJTdPQ6XTQarUuZYyLfQHnibCiBb+3baIgoutXJpYZhoGDgwMEQVCYOF/lu4ssy8KDBw9WKsvqT9d1DAYD2LaNMAxlAqZSqSjLLnIsOp0Oms0mms0m2u22vFK87nHJi/mq8avqERHR9lHFdM/zMBgMMBwOYdt24d2veYmmTcWEEhFtpHWv3gZBkKqn63rq8Yb4/17G7aSLfXmel3uSRUR0EWVjmaZpaLVaaLVahT9KV/luLAxDeJ6XGZ9VZXn99Xo9mKYpE0TxvqrKLnIskncNxW/VWbxLqexxKYr5eePnXEFE9P5QxXQhBCaTCUzTRKfTQRAEGI1GEEJA07Sli8zx49zbhAklItpImqatnKH3fX/pavO7Wuw0qy/g/AqFbduwbRtCCPT7/a17FpqINo8qlnmeh9u3by99N+sFAKt8N8urV69yf+xmlRX1Fy9gGj8iZhhG6gUGWWXrHgvf93H//v2lOpVKZe3joor5qn3jXEFE9P7Ii+m+76eW8NB1HQcHBwjDMPeOpnq9flXDvhR/d90DICLKUq1W5Q/wLFl3MB0fH8u3/sR0XUe9XpffT/7AB84TQ0UnKGX7WpwYut0uut3uUtuLC/ARERVRxTLf91PxJ45rWXGuUqkov5uUFfvi9YuyZJUV9Ver1fD69WtomobhcJiKq3ll6x6LMAxT7Xueh2azCU3T1jouRTE/b/xl5woiItp8qpgex//kYt1v3rzJnFuEEKjX61t3hxITSkS0kUzTXArAnufJBa37/T729vZKvSHHcRxYloVarYbpdJpaFLvf78vvJK3bF3B+shG/HnowGKDb7aZO7LLubCIiKpIXywzDQLvdlnFnPB5jOp3KenEM6/V6hd8tE/tUiY/FsqL+BoMBPM/DbDZDq9VK/TBXla1zLDRNQ71ex+HhITRNQxAEMvZf5LjkxXzV+FX1iIho++TF9EajIecdAKk1lOK5bG9vD5PJZOl8ZBt8FEVRlNzg+76cnDmpEdF1arVapQNr/LafdeLWqq9uvkhflmVtzZXop0+fYn9/n/MBEdEHjHMBEREB2bkirqFERBur2+2WfvvQeDy+sh+66/YVrwm1DckkIiIiIiIiFSaUiGhjmaaJ2WxWuDh3GIaoVqtr9eF53kqvbr5IX/1+f2ndJSIiIiIiom3EhBIRbbROp1P4HU3T0Ov11mrfNM2VFr+7SF9MJhERERER0fsid1Huk5MTnJ6eXuVYiIhog7x48QIA5wMiog8Z5wIiIgKA169fL21bWpT75cuXuHfvHs7Ozq5sYEREtJlu3LiBt2/fXvcwiIjoGnEuICIiALh58yaeP3+Ou3fvAsi4Q2lnZwdnZ2c4OjrC7u7ulQ+QiD48z549w2effYY7d+4AAE5PT/HNN9/g6dOnynrffvstvv7668ztX331FW7dulXY97p9ff/99/j555/xySefAAA+//zzwnE8efIEX375ZeGYNsXJyQkePXrE+YCI6APGuYCIiIDz86b9/X3s7OzIbbmPvO3u7vLVoET0zvm+j08//RRffPEFAMB1Xezu7uLHH38sjEEff/zx0nd838eTJ0/w+9//vnBtpHX78jwPf/zjHzEcDiGEQKPRQBAEhePQdX2rFuaOH23gfEC0GXzfBwAYhgEhBMIwlP/bVJVl8TwPQgj51snkywl838fDhw8xnU5TdYQQcF0Xuq5DCIFOp5MZZy3LwsHBgSxTjc3zPADnLzyYTCZot9uyTAgBz/NQqVQghECz2cx8S+Zif6oy1T64riuPw2JbZfZ9sS/Vvq363+s6cS4gIiova07Km1dV887WiBZMp9MIQDSdTheLiIguXbPZzNyeEZ5SHMfJjFOO40S6rkfz+bz0GFbta7H9IAhKj6PX62V+fxMdHR1xPiDaIJ1OJwIQAYhM00zFF1XZovF4HHU6nSiKzuOXruuyLI53WXHRMAz5dxAEmfE7rlt2bJqmyRgzHA5TYxkMBkv7X6Y/VZlqH+IxJv/FYyja96y+VPu2yn+v68a5gIionKy5QDWvquadTZSVK+Jb3ojo2oRhmHm1uYzxeLx0pdR1XTSbzcsYWm5fQgjMZjNomgbf9zP3QTWOdrsN13UvfYxE9P6r1WqYz+eYz+cYj8epq5mqskXdblfeKanrOsbjsSxrNpuZd6EIIVKfdV2Xd+Asfm8xJqrG5jhOqr9k2fHxce4+qPrLK1PtQxiGcBwHURTJf4PBAL1er9S+Z41DtW+r/PciIqLtkDUX5M2rqnlnmzChRETXZjQaYW9vb+V6YRiiWq0ubXsXP8gX+/J9H5VKRT76YNt2KkFUNA7DMFInb0REq9A0LTfGqMpiZZLiWeJHz5IqlYp8dAtQJ9PzxpZ81M5xHHS73VT7tVpNPvrWaDRSdVX9ZZUV7UPy+8n6RfXyxqHaN6Dcfy8iItoO61zYzpt3tknuGkpERO9aEASo1+sr17NtG51OJ7VtNBotbbsMi33NZjMIIWCaJjRNQ6fTwe3btxH9/xdmlhnHbDa79HES0fsvDEOZwJ5MJuh2uzIZpCpLSibFTdOEbdvQdb3wR2wYhpnb43imSqYXjc33fRwfH6PRaKTip+M4uH//PqrVKjqdDobDYapNVX9ZZap9SF49DsMQs9ksdWzz6hVdRMjbt7L/vYiIaPOtc2E7+f3FeWebMKFERNdm3buKgiBI1fM8Dw8ePLi8gSn60nU9dVU5uejsbDZ7Z+MgIkouBK3reuqFAKqypKKk+KriZIsqmV40NsMwoOs6LMtaujNoMBhACCHv7omTSqr+Vr3AsJgwsiyr1MsTwjAs7Ctv38r+9yIios130QvbZeedTcRH3ojo2mialnvlN4/v+0uPPQDngdy2bdi2DSEE+v1+6lGMdWT1VXTl4F2Mg4gISK8BFL9tLN6mKktSJcVVNE1bursyfnSuKKlfZmyapqHVaqHVaiEMQwghMJlMYJomOp0OgiDAaDSSj7/l9acqU+1DLAxDeJ6X2pZXTwhR6iLC4r4B5f97ERHRZrvohe2seWeb8A4lIro21WoVQojc1xBn3cF0fHy8lMFPrlMBnC84m3x8wPd9aJqmTAaV7UvXddTrdfn9ePG9rH1YHEdscS0OIqIivu/j/v37mM/nqe3xWj55ZYvWvZ3eNM3UI2exer2OV69eYTQayW1xMr3dbgNA7tg8z0Or1ZJl8dji5EpyjT1d13FwcJC6Iyqvv7wy1T7EXr16tTQX5NX7j//xP+b2NZvNcvdNdUyIiGj75M0Feec4SVnzzjZhQomIrk38Iz25dofneXLR6n6/j729vdIL1IVhCNu2AQCDwQDdbheGYaDf7wM4X48jad2+HMeBZVmo1WqYTqdLi2znjQPIv8OKiEhF1/VUgtvzPDSbTZkszysD0kn1VZLiyUT7YiJKCIF6vQ5N05RJ/TAMc8dWqVRSdeNxGoYBTdOW5oc3b96slLwvKkvuQ3IMi4mdvH3/p3/6p9y+fN/P3TfVMSEiou1SdGE7KesCdta8s02YUCKiaxPf5p9kmiZM08x8jth1XXkFOoumaej1ekuv23QcJ/Umtov2FZ/orDoO4Pyup8U3/RARFdE0DfV6HYeHh9A0DUEQyCS5qgz4W8I8jkmqpLgq0R7X29vbw2QyWUrS5yXT88ZmGAba7basMx6PMZ1OAfxtXaG4HoCl2KlK3ueVFe1D3PciVb28vvL2rei/FxERbZ+8uaDMBextXIw79lG0sAqj7/vyB0aZW7SIiC7C8zyEYVjqzqBut6tM5Kis+irOi/SVJwxD9Pv9rVl07+nTp9jf3+d8QET0AeNcQEREQHauiItyE9G1Mk1TvnpZJQxDVKvVtfrwPG/pdtR31ZfKNiWTiIiIiIiIVJhQIqJrV+Y1m/FjZOuIX49d1kX6UmEyiYiIiIiI3hdMKBHRRuBipERERERERNsjd1Huk5MTnJ6eXuVYiIhog7x48QIA5wMiog8Z5wIiIgKA169fL21bWpT75cuXuHfvHs7Ozq5sYEREtJlu3LiBt2/fXvcwiIjoGnEuICIiALh58yaeP3+Ou3fvAsi4Q2lnZwdnZ2c4OjrC7u7ulQ+QiLbbs2fP8Nlnn+HOnTsAgNPTU3zzzTd4+vSpst63336Lr7/+OnP7V199hVu3bhX2ndeX53n4/PPPAaCwncVx5LV5enqKH374AQDwpz/9CY8ePZJt//TTT/jhhx/wq1/9Cj///DPu378vj8eTJ0/w5ZdfFu7LJjg5OcGjR484HxARfcA4FxAREXB+/rO/v4+dnR25LfeRt93dXb4alIhW4vs+Pv30U3zxxRcAANd1sbu7ix9//LEwnnz88cdL3/F9H0+ePMHvf//7wjWWVH3VarWl7w8Gg8yFt5PjULXpeR7+8Ic/AAAODw/x29/+FtPpVJZ988038rvdbhfD4RAAoOv61rztLX60gfMB0ebzfR8AYBgGhBAIw1D+71ZVtsh1XflWzMW4K4TAcDhEtVpFEAQ4ODiQ3xFCwHVd6LoOIQQ6nU6psnh8Dx8+lDG0bH+qMs/zUKlUIIRAs9mErusX2gfVOD3PA3D+htDJZIJ2uy2Pr+d5EELI/uNjqzrOm4ZzARFReZZlpeYWIH/+2Ka5IFe0YDqdRgCi6XS6WEREpNRsNjO3Z4SaFMdxMmOO4ziRruvRfD4vPYbFvubzeeQ4TmrbYDBYaRyLbU6n00jTNPk5CIIIQBQEQRRFUWQYRur7nU4n9bnX68nvbrKjoyPOB0RbotPpRAAiAJFpmqm4qSpbFH8v+S+Omcl4PJ1OU7EtGfeCIEjNB6qyOO5mzROq/lRlizG+bL11x6lpmoyTw+Ew0nU9iqIoGo/Hsv0gCOT2KFIf503DuYCIqJx4nkjOs6r5Y5vmgijKzhXxLW9EdCnCMJRXYFc1Ho+Xrnq6rotms3kZQ0u1o2o3axxZDMPA48eP5ecwDAEAlUpF/t9arSavkjcajVT9drsN13VX3Q0ioly1Wg3z+Rzz+Rzj8Th1pVNVlhSGIRzHQRRF8l98N2d8F05c1zAM2LYN4PzOniRd1+X3VWXAeXzOiruq/lRlAHB8fJy5f+vug2qcAOA4Tqosbr/b7cq7UXVdx3g8BqA+zkREtL2Sd6TG8uaP92UuYEKJiC7FaDTC3t7eyvXCMES1Wl3adlm3fSbbCcMQs9ksM/GVNQ6VZFLq+PgYpmnKvhzHAQBUq1U4jrOUwDIMQ55YEBFdFk3TcmOnqiwpLwEfJ84X+b4vHy9LqlQqhWUqqv5UZXH7WUn9dfehSPy4AnAe/7vdLoQQmM1m0DRNjjk595S90EFERNthnVj+PswFTCgR0aUIgmCtO5Rs20an00ltG41GqR/ol8WyLDx48KD0OMoIwxCu68okEnB+FXwwGGA4HMK2bXS73aV6s9ls5b6IiPLEsch1XViWlbrjRlWWpErAx+svxeJEy2w2y03UFJWpqPpTlQH5Sf1196EM3/dhWRYajQY6nQ5830elUpFrMtm2Le9MLXuhg4iItsM6F8Pfl7mACSUiuhTr3lUUBEGqnud5uUmfiwjDEJ7n5Y5xcRxlWZaVeoRECIHJZALTNNHpdBAEAUajUe4JHBHRZeh0Omg2m2g2m2i326lHbVVleRYT8LquYzAYwLZthGEoY9riXT1JeUmaorKi/orGkpfUv+x9SDIMAwcHBwiCAK7rYjabQQgh717tdDpotVpL9VQXOoiIaDtc9GL4Ns8FTCgR0aXQNK30D++Y7/uZJzaj0Qi2bcO2bQgh0O/3Sz12oPLq1avchFHeOIocHh7Csizouo4wDBGGIXzfTz36p+s6Dg4OVj42RESrSCat47eUxdtUZVnyEvC9Xg+macpESdyepmlLd/LEj3upyork9acqK0rqr7MPZWmahlarhVarhX/7b/9t6jHD+P8m57KiCx1ERLT5LnoxfNvnAiaUiOhSVKvVwhOURcfHx0vPCscnAfE/4Hxh0+Trr4vu9snqK378IEvWOIradF0XhmHIZNJoNIKmaTAMA5PJJPXdN2/eLC3Gp7oiTkS0Ct/3cf/+/aXt8RpAeWV58hLw8WKj8aNjhmFA07Tcq7L1el1ZViSvP1VZUVJ/nX1Q8TwPt2/fTvUHADduFP/EVl3oICKi7XGRi+HbPhcwoUREl8I0zaVEiud5sCwLANDv91d6s1kYhjg8PAQADAYDGZT7/b5sc9W+Vn0uOa9NIQRarRYajQY++ugj3L59W35P13U0Gg0cHh7KiWVxDaV174giIsoSP8oV8zwPzWYTmqYpy4DsJH1eAr5Wq8nEzHA4TL3BLEkIgXq9LvvPK1u0mLjP609VVpTUX2cfVOOsVCqpZJTv+9A0DV988QXq9XpmIiv5XV5cICLabkUXw5NWvei9Df7uugdARO+H+FGDJNM0YZpm6iQg5rou2u12bnuapqHX6y29OtNxnMxkkaovALmv4FSNI69NXdcRRVHu2ON6eY6PjzMX6iYiWoemaajX6zg8PISmaQiCQC5MrSoDzpPle3t7SzEyKwE/GAzgeR5msxlardbS280sy8Le3h4mk0mqD1WZ53nyrZfxWOI7RlX95ZUlk/pxMigZb9fdh7xxGoaBdrsN27YBAOPxGNPpNNVerVbDdDrNfLvnNi7ASkREy8IwlHPBYDCQSSXVPBfb5rngo2jhrMj3fTnxZWXViIjyeJ6HMAxLvfKy2+1iOByu1c9lvlbzIuNYRxiG6Pf7uYmvTfL06VPs7+9zPiAi+oBxLiAiIiA7V8RH3ojo0pimqXz9ciwMQ1Sr1bX68DzvQm9RuKxxrGtbkklEREREREQqTCgR0aWKnx1WiR9nW0f8CubLcJFxrIvJJCIiIiIieh/krqF0cnKC09PTqxwLERFtkBcvXgDgfEBE9CHjXEBERADw+vXrpW1Layi9fPkS9+7dw9nZ2ZUNjIiINtONGzfw9u3b6x4GERFdI84FREQEADdv3sTz589x9+5dABl3KO3s7ODs7AxHR0fY3d298gESES365ZdfcOvWrXdeZ11X2ddVOjk5waNHjzgfEBF9wDgXEBERAJyenmJ/fx87OztyW+4jb7u7u3yTAxFdO9u2YZqmfJ2m7/t4+PChfC1zHsuyMtcrsiwLBwcHpdZhuuy+Dg8Pr3zNpouIH23gfEC0GXzfh+d5AIDJZILHjx/L+CKEwHA4RLVaRRAEyjgnhIDrutB1HUIIdDqdVDuqsrw+isaW16bruvJFC4vjVbWZtBhrhRDwPA+VSgVCCDSbzdQrmT3PgxBCbov7V9XLG6dq/Koxli3bBJwLiIjKy4rpqnOKvDlpa0QLptNpBCCaTqeLRUREV2o6nUaO48jPjuPIGFWk1+tltgcgms/nhfXfRV/z+Tzzu5vq6OiI8wHRBhkMBqm/DcOQn3Vdl/FmOp1GnU4nt51kvSAIomazWapM1YdqbKo2ASz9i9tStRnLirXJelEUpcY5Ho/l5yAIIl3XS9XLG6dq/KoxlinbFJwLiIjKyYrpqnMK1Zy0ibJyRXzLGxFtrH6/j2azKT83m81SV0dd10W73V7ansz+F3kXfSWvnhMRrcL3ffT7ffm52WzC9315Vw3wtxhjGAZs285sZzH+6Lou66vKVH2oxqZqMwxDOI6DKIrkv8FggF6vp2xzcX8WY+3x8XHmvgNAt9uVd5Tquo7xeFxYL2+cnU4nd/xFYyxTRkRE2yUrpqvOKVRz0rZgQomINlIYhmv/yB6Px0uB23XdVHLqsqzaV7vdhuu6lz4OInq/GYaBx48fy89hGAIAKpWK/HuR7/tL2+JHupIqlYp8vCyvTNWHamyqNgGkYmUydqrazPr+Yvu1Wk0m2xqNBoDzH/qz2Qyapsl9Ss4zefVU48zbXjTGojIiItouq8b0ojlpWzChREQbaTQaYW9vb+V6YRiiWq0ubXsXa1Os05dhGFt59YGIrl/yh+rx8TFM04SmaTAMI3XnTpysmc1mS23kJYZms5myrKiPvLGp2kzGyjAMMZvNUj+m89qMv58Xax3HAQBUq1U4jiPb8X0flUpFrudk23YqwZ9XL2+cReNXjfFdzUtERHT11onpRXPStmBCiYg2UhAEa2XpbdtGp9NJbRuNRu9kgbt1+8o6ySMiKisMQ7iuKxMguq5jMBjAtm2EYSgTP4t3BhW1qSor28fi2Mr2Z1kWHjx4kPvdxTZVsdbzPAwGAwyHQ9i2jW63C+A89gohZGKq0+mg1WoV1iszzqztqjG+q3mJiIiu3joxvWhO2hZMKBHRRlr36m0QBKl6nuflnqRc1FX2RUQUsywL4/E4FX96vR5M05Q/TgFkJuU1TVtKasd3C6nKyvaxOLaiNoHzeO95Xm7MX2xTFWuFEJhMJjBNE51OB0EQYDQayXUt4v2MxwZArs2UV69onFnbVWPkXEFE9P5YN6ar5qRt8nfXPQAioiyqRyXy+L6fWvMiNhqN5N9CCPT7fbTb7Qu9/vgq+yIiih0eHsKyLOi6LmOkpmmphUDjdY2yEjSmaWI4HC5tr9fr0HU9twxAYR9ZY1P1F3v16lVuMimrTSA/1gohUo9L67qOg4ODwrUpfN/PrVc0zrzteWNUlXGuICLaPuvE9G1cLykLE0pEtJGq1SqEELmBOOsOpuPjY/mmhNji7afdbhfdbjd1UqRpmjKoX1ZfsVUeQyEiirmuC8MwZHJlNBrJx25rtRpev34NTdMwHA5T8SkZ5xbjkRAC9Xo9dZU0q6yoj7yxFbUZjy8rLua1qYq18diS6y+9efNGziX1el3G9DhBFifGVPVU48zaXnY+KCojIqLNtkq8T55T6LqeOydtEyaUiGgjxVe1kz/uPc+TC1r3+33s7e2VfptCGIbyFdeDwQDdbheGYcjXUi+u9/Eu+gLy72wiIlIRQiytrRCvuQCcxxrP8zCbzdBqtVI/cOMYFr/O3nEcWJaFvb09TCaTVPxTleX1UTQ2VZuxrESXqk0gP9Y2Gg0cHh7KH+3JtZDisdRqNUynUxnndV1X1ssbZ9F21XygKiMiou2SF9NV5xR5c9I2+SiKoii5wfd9uUOc1IjoOrVarcKFXWPxGxLWiVurvubzIn1ZlrU1V6KfPn2K/f19zgdERB8wzgVERARk54q4KDcRbaxut1v69Znj8fjKfuiu21e8Fsc2JJOIiIiIiIhUmFAioo1lmiZms1nh4txhGKJara7Vh+d5K73m8yJ99fv9pXWXiIiIiIiIthETSkS00ZLrZeTRNE2uDbIq0zRz3y502X0xmURERERERO+L3EW5T05OcHp6epVjISKiDfLixQsAnA+IiD5knAuIiAgAXr9+vbRtaVHuly9f4t69ezg7O7uygRER0Wa6ceMG3r59e93DICKia8S5gIiIAODmzZt4/vw57t69CyDjDqWdnR2cnZ3h6OgIu7u7Vz5AIvrwPHv2DJ999hnu3LkDADg9PcU333yDp0+fKut9++23+Prrr2WdH374AQDwpz/9CY8ePcKtW7cK+16nr6J633//PX7++Wd88sknAIDPP/8cAPDkyRN8+eWXhWPaFCcnJ3j06BHnAyKiDxjnAiIiAs7Pf/b397GzsyO35T7ytru7y1eDEtE75/s+Pv30U3zxxRcAANd1sbu7ix9//LEwBn388cfyO57n4Q9/+AMA4PDwEL/97W8xnU6V9dftS1XP8zz88Y9/xHA4hBACjUYDQRAAOH+72zYtzB0/2sD5gGi7+b4Pz/MAAJPJBI8fP06tHef7Ph4+fFgYM4UQcF0Xuq5DCIFOpyPbidsPwxCTyQTtdlvGDSEEhsMhqtUqgiDAwcFBqn/P8yCEkG/AjF+UUDTumGVZqTZV/anaLBpnXn9FbXqeh0qlAiEEms2m3M+yx/26cS4gIlJTzYFAfrwvO+9stGjBdDqNAETT6XSxiIjo0jWbzcztGeEpxXEcGaem02mkaZosC4IgAhAFQVBqDKv0VVRP1/VoPp+nxpLU6/VKj+u6HR0dcT4geg8MBoPU34ZhyM9xfCuKg1EUpeoFQZCK35qmyVgxHA4jXddlWTIuTqfTqNPpyLLxeCw/B0GQqqcadyweezLuqvpTtamqp+pP1WayLIoi2eYqx/26cS4gIlJTzYGqeF9m3tkkWbkivuWNiK5NGIbySu2qxuOxzPwbhoHHjx+n2gWASqVy4TEu9qUihMBsNoOmafB9P3P/2u02XNe9lHERERXxfR/9fl9+bjab8H0fQgj5uWx8S9J1XV6RBQDHcVLtLN65FH82DAO2bcvvdbtdedemrusYj8elxp0cVzLOqvpTtVk0zrz+isZ5fHy81Eb8Pd7tQ0T0fsibA4H8eF923tl0TCgR0bUZjUbY29tbuV4YhqhWq6ltzWZT/n18fAzTNC/lltGsvvL4vo9KpSIfCbFteyl5ZBiGPGEiInrXLivhHj+2lVSpVOD7PoC/PaYGnP+w7na7qf4WxUmXvCR8mXG7rpuK/UX9qdpU1VP1VzTOSqWCWq0mk1aNRiOzHyIi2l55c6BKmXlnGzChRETXJgiCte5Qsm0bnU4nsywMQ7iuC8dxLjq8wr4WzWYzCCFkMqvT6aDVamV+j4joqlxGwj3vh28ynvm+D8uy0Gg0ZNw0DCN1V1H8Q3k2mxUm4VXjDsMwcx9U/anaLKqX11/ROOO5qFqtwnGcpYQUERG9H7LmQJWieWdbMKFERNdG9QNdJQiC3HqWZWE8Hl/agnaqvhbpug5N0+T3kwu2EhFdt8tOuMdtxgzDwMHBAYIgkIkhXdcxGAxg2zbCMJQ/niuVSukkfNa4R6NR6opwTNWfqs2ienn9FY3T8zwMBgMMh0PYtl3qqjUREW2frDlQpex8tely3/JGRPSuaZqWe9U7j+/7uY8MHB4ewrIs6Lou271IYknVV5Z114MiIroKF0m4a5q2dNU0flxt8XutVguNRgPz+RyapqHX60EIIZNHwHm8jOtnJeGT600sjtvzPDx48CB3rHn9JWUdi7x6Rf3ltSmEwGQykWtEmaaJWq0m5ykiInq/ZM2BKmXmq03HO5SI6NpUq9WlBVaTspJNx8fHmY8MuK4LwzBkMmk0GqVOTlT9rNpXXj1d11Gv1+W2ePHWxYX4tu3KAxFtv8WE+6rJ/Ly7c+r1OjzPw+3bt+W2+MdwHHeTsVAIAcMwoGlaqR/NeeMejUawbRu2bUMIgX6/L+8GzeuvqE1VPVV/eW36vp9aJ1DXdRwcHKx87ImIaHMVzYEqRfPVNmBCiYiujWmamEwmqW2e58GyLABAv98vdcuoEEJeDfjoo49w+/Zt2UbcTvLzRfoqquc4DizLgm3bGAwGSwtwr3rXExHRRakS7kmLiY5kMn4x+SOEQL1eh6ZpqFQqqYST7/tyXSIAqNVqsu3hcJh6q5sqCZ83btM00el05D/g/G1xRf0VHYu8ekX95bVpGMbSHPfmzZuliwxMMBERba+iOTBpMd6r5qtt8VEURVFyg+/7qNVqmE6nfJ0pEb1zrVar9Hoe8cKt68SmrLfzvKu+VCzLQrfb3YrbWZ8+fYr9/X3OB0RbTAix9KZKTdMwn88BnCfIx+MxDg8P0ev1sLe3J2Nlq9XC3t4eer2ebGs4HGJvbw+TyQQHBwcyGeO6rnwkbjweYzAYyDhn27ZcM0nX9dQP7zAMYVmW/O0Z3+VTNO64rm3bsCwLnU5HJnny+itqUzXOvP40TSs8vvHJBXCenIofo8s77puGcwERkZpqDlTF+6J5Z9Nk5YqYUCKia+V5HsIwLPVDutvtYjgcrtXPqgmli/SVJwxD9Pv9rbn6wJMIIiLiXEBEREB2roiPvBHRtTJNE7PZrPCW/zAMl64Cl+V53koZ/4v0pbJNySQiIiIiIiIVJpSI6NrFa1KoxG8KWkf8SuqyLtKXCpNJRERERET0vmBCiYg2wra90YCIiIiIiOhD9nd5BScnJzg9Pb3KsRAR0QZ58eIFAM4HREQfMs4FREQEAK9fv17atrQo98uXL3Hv3j2cnZ1d2cCIiGgz3bhxA2/fvr3uYRAR0TXiXEBERABw8+ZNPH/+HHfv3gWQcYfSzs4Ozs7OcHR0hN3d3SsfIBF9eJ49e4bPPvsMd+7cAQCcnp7im2++wdOnT5X1vv32W3z99deyzg8//AAA+NOf/oRHjx7h1q1byvrxldbd3V389NNP+OWXX3LjXrKv77//HgDwyy+/4M9//jMajYaspxrHkydP8OWXX6oPxgY5OTnBo0ePOB8QEX3AOBcQERFwfp6zv7+PnZ0duS33kbfd3V2+GpSI3jnf9/Hpp5/iiy++AAC4rovd3V38+OOPhTHo448/lt/xPA9/+MMfAACHh4f47W9/i+l0qqw/HA5h2zaA84W7HcfJXcsp2df9+/fx3XffwTAM2LaN3/3udwiCoHAcuq5v1Zvekgk3zgdEm833fXieBwCYTCZ4/PhxKp75vo+HDx8WxsXLaMeyLBwcHGTG08WyuK8wDDGZTNBut2W88X0fAGAYBoQQCMMwFYvyxiKEgOu60HUdQgh0Oh3Zn6pM1d+6ZXn7XXScNwnnAiIiNdVcBqjnq+FwiGq1iiAIcufOjRYtmE6nEYBoOp0uFhERXbpms5m5PSM8pTiOI+PUdDqNNE2TZUEQRACiIAiUbQyHw2g+n0fz+bx0X1EURePxONWGYRilx9Hr9QrHtSmOjo44HxBticFgkPo7jktR9LcYVhRXL6OduDwrrmaVaZomY8xwOIx0XZdlnU4nAhABiEzTTNVTjSU55iAIUvOMqkzV37plefutOs6bhnMBEZGaai5TzVe6rsu5YTqdRp1O50rGu66sXBHf8kZE1yYMQ+i6vlbd8XgsM/+GYeDx48epdgGgUqkUtqNpWuGVgGRfwPndTDHHcdDtdkuPo91uw3XdwnEREZXl+z76/b783Gw24fs+hBDyc5k7Sy6jHSFEblzPKnMcJ9VmMh7XajXM53PM53OMx+NUWd5Y4rHGdF2XV45VZUX9rVuWtd9Fx5mIiLaLai7Lm6/i+Sf+bvzkw7ZhQomIrs1oNMLe3t7K9cIwRLVaTW1rNpvy7+PjY5imWZgoCsMQruvCdV1YlpX5Yz6rL+D8hMCyLDQaDXQ6ndLjMAwD4/G4aBeJiEq7SFL9MttxXTcVA8uU5SXoY2WS/kme5y2Nt1KpyEfM8srK9LdOWdZ+X9Z/LyIi2gxFc1mWOPYvSs5J2yB3DSUionctCALU6/WV69m2nUriJMVJoqJ1QgCk1s7QdR2NRkOuhVTUl2EY0HUdlmVlnjCoxjGbzQrHRkS0inWS6pfZThiGud9TlQHnP56Pj4+XEvRxHAXO1xnqdruFd7Xm/UCfzWbKsqL+1ilT7fdl/fciIqLNkDeX5YnX3UvWB7bvPIEJJSK6NkUnGXmCIMitZ1lW5iMHWYQQ8hbUeIHWxUcTVH1pmoZWq4VGo4H5fJ763irjICK6LKsk1S+zndFolPsDWlUG5CfoyyT9y8pLJiXLVP2tU1a033Hfl/Hfi4iIrlfRxeZFuq5jMBjAtm08ePBAJpe27W5VPvJGRNdG0zTlj/wsvu+j0Whklh0eHsKyLHllWNW27/u4f//+0vZkEM/qy/M83L59W36Ok0/JKwyrjIOI6DJdVjJ7lXY8z8ODBw9WLkuKE/StVkvGzGRcTSb9i9pZvLo7m83kI2l5ZUX9rVr23//7fy+137z4QET0/siay1R6vR5M04QQQj42t+76steFCSUiujbValV5cpAViI+PjzMz/q7ryisDYRhiNBqlXs+ctRjrYDCQnz3PQ7PZTP2oz+qrUqmknpP2fR+apsk7nVTjSLZBRHTZLiuZvU47o9EItm3Dtm0IIdDv9+Xt+3llqgR9maR/lmR8TqrX68oyVX/rlP3qV79SHhOAFx+IiN4HZS4254mfjIgffzMMY+suMPCRNyK6NqZpYjgcppI2nufJRav7/T729vYKbxkVQqDVaqW2aZomHzWI36bjOE6qvF6v4/DwEJqmIQiCVHkewzDQbrflWxjG47F8VKFoHID6DisionVlJbOzHrdafNQ4TorHP4DXaWcxUdPtdnPXO0qW+b6fm6APw7Aw6Z81lsU+hRCo1+uZi2Yny1QXGdYp+6d/+iflMSl7nImIaLMVXWxOWpyDa7UaXr9+DU3TMBwOU/PJtmBCiYiuTfxoQJJpmjBNMzOguq6Ldrud2U4URbn9OI4jF0xNMgwj9xXYeX0B6cVUkycAReMAzu96KvPmByKisoqS2apEffy51+tdqB3g/IdynGwfDAbodrsyxuaV5SXoi5L+qrE4jgPLsrC3t4fJZJKql1em6m/dsrz9jh+JyDvORES0PVRzGaCerwaDATzPw2w2Q6vVyr2TdpN9FC2c/fi+j1qthul0mnuiRUR0WTzPQxiGhXchAedXeIfD4Vr9lFkc77L6yhOGIfr9/tZcfXj69Cn29/c5HxARfcA4FxAREZCdK+IaSkR0rUzTVL7OORaGIarV6lp9eJ63Usb/In2pbFMyiYiIiIiISIUJJSK6dmVu89c0Db1eb632TdNcaYG7i/SlwmQSERERERG9L3LXUDo5OcHp6elVjoWIiDbIixcvAHA+ICL6kHEuICIiAHj9+vXStqU1lF6+fIl79+7h7OzsygZGRESb6caNG3j79u11D4OIiK4R5wIiIgKAmzdv4vnz57h79y6AjDuUdnZ2cHZ2hqOjI+zu7l75AImIFv3yyy+4devWO6+zrqvs6yqdnJzg0aNHnA+IiD5gnAuIiAgATk9Psb+/j52dHbkt95G33d1dvsmBiK6dbdswTRO6rgM4f7vAw4cPU6/jzGJZllyzyPd9eJ4HAJhMJnj8+HGpNZXW6Wtx+8HBgezr8PDwnazN9K7EjzZwPiDaDHkxSQiB4XCIarWKIAhScSerDeD8NcdCCIRhKP/37bqufIHBYn1VPdXYispii/GyaJ88z4MQQs4N8biLxpnXXzxHhGGIyWSCdrst6wkh4HkeKpUKhBBoNpvQdT13+0X62kScC4iI1Iriumr+LjOPbLRowXQ6jQBE0+l0sYiI6EpNp9PIcRz52XEcGaOK9Ho9+fdgMEj9bRhGYf11+4rFdefzudw2n88zv7upjo6OOB8QbQhVTNJ1Xcaa6XQadTqd3HY6nU4EIAIQmaaZilHx9uS/OH6q6qnGViaWZsVL1T6Nx2P5OQiCSNf1Uvun6k/TNBnrhsNhqs3kHBL3odp+kb42EecCIiI1VVxXzYNl5pFNkpUr4lveiGhj9ft9NJtN+bnZbJa6Ouq6LtrtNoDzKwL9fj/Vhu/7EEIo21inr6TklfNY8so7EdEq8mJSfFU0ji+GYcC27dx2arUa5vM55vM5xuOxrBeGIRzHQRRF8t9gMJB3VebVU42tqCy2GC+L9qnb7cq7QnVdx3g8Ltw/VX8A4DhOapzJesfHx5njztt+kb6IiGj7qOK6ah4sM49sOiaUiGgjhWG49i2f4/FYBm7DMPD48eNUuwBQqVQuPMbFvmKu66YSYUntdhuu615K30REcUxbFD/6lUXTtMwkRjJuZcWxvHoXkdWPap+EEJjNZtA0Db7vZ84VqnHmxef4kTng/MSg2+3Kz5VKBbVaTT6a0Gg0lNsv0hcREW2fdeN60TyyDZhQIqKNNBqNsLe3t3K9MAxRrVZT25I/6I+Pj2Ga5qWcFGX1FYahsm3DMFJX04mILiJeKygWJ5Jms1nm98MwhOu6cF0XlmXJusm4FYYhZrNZKlGTV+8i8uKlap9830elUoHrutB1HbZtp5L0qnEWxWff92FZFhqNBjqdjtzuOA4AoFqtwnEcOafkbb9IX0REtJ3WieuqeWRb5C7KTUR0nYIgQL1eX7mebdu5QTw+0ShaZPsifY1Go8JJJO9Ej4hoVbquYzAYwLZtPHjwQCZQ8u7C7HQ6MtGh6zoajQaCIEh9J+tFA2XqrSovXqr2SQgBIYS8MNDpdHD79m1EUVQ4zqL4bBgGdF2HZVmpu4s8z8NgMIAQQl51Hg6Hudsv0hcREW2ndeK6ah7ZFrxDiYg2UtHV3TxBEOTWsywrd02NdSz25XkeHjx4cCltExGV1ev1YJqmTLQAyH1kOHnHTvymssW7eDzPW4qTRfVWVRQv8/ZJ1/XUI23x/43vYsobZ9n4rGkaWq0WWq0WwjCEEAKTyQSmaaLT6SAIAoxGI/zP//k/M7dfpC8iItpuq8T1vPll29Za5R1KRLSRNE1b+Qe27/u5zx4fHh7Csizoui7bvUhiKa+v0Wgk/xZCoN/vb/wroYlouyUXfvZ9H4ZhZMY33/dx//59zOfz1Pbk3UyvXr1aqlum3jpU8TJvn1Rr66nGKYTI7W82m6HVasl6cR9xMir5+LWu6zg4OMAPP/yQuT2eX9bpi/MEEdH28Txvrbju+75yHtkWTCgR0UaqVqvKQJx1B9Px8fHSYxrA+cKo8W2oYRimHkXwfb/wJKVsX8kF+YDzNxF1u92lti9rQXAi+jAtxqRarYbXr19D0zQMh8NUbErGuPhRspjneWg2m6m24jWKksrUyxtbXllRvMzbJ13XUa/XZVtx4skwDIRhmDtOVX++76fK42MWJ7GGw2Hq0YU3b96g2+1mbs+as8r2RURE26dSqZSO68l50DCM0vPIJmNCiYg2kmmaS0HW8zy5oHW/38fe3l7h88lCCLRardS2eN2NuB3gb4viXaSvWBiG8hXXg8EA3W5XTg6qu6iIiPKoYtJgMIDnefLul+QP2/i7vV4PmqahXq/j8PAQmqYhCIKl2AcsPy5XVE81tqJYmhcvVfvkOA4sy0KtVsN0OpXtl9m/vP7a7bbcPh6P5Vp78TpMcZsAZHIoa/tF+iIiou1TFNfz5sEy88g2+CiKVzH8/3zflxP0tmXHiOj90mq1Mk92ssRv/Fknbq26IOpF+rIsK/OupU309OlT7O/vcz4gIvqAcS4gIiIgO1fERbmJaGN1u93U66BVxuPxlf3QXbev+JnobUgmERERERERqTChREQbyzRNzGazwsXpwjBEtVpdqw/P85bW1nhXffX7/cw1noiIiIiIiLYNE0pEtNHitY5UNE1Dr9dbq33TNFd629tF+mIyiYiIiIiI3he5i3KfnJzg9PT0KsdCREQb5MWLFwA4HxARfcg4FxAREQC8fv16advSotwvX77EvXv3cHZ2dmUDIyKizXTjxg28ffv2uodBRETXiHMBEREBwM2bN/H8+XPcvXsXQMYdSjs7Ozg7O8PR0RF2d3evfIBE9OF59uwZPvvsM9y5cwfff/89AOCXX37Bn//8ZzQajdxY9O233+Lrr7/O3P7VV1/h1q1byn5/+uknPHv2DHfu3MFPP/2krLPY1/fff4+ff/4Zn3zyCQDg888/LxzHkydP8OWXXyrHtElOTk7w6NEjzgdERB8wzgVERAQAp6en2N/fx87OjtyW+8jb7u4uXw1KRO+c7/v49NNP8cUXXwAA7t+/j++++w6GYcC2bfzud79DEASZdT/++OOlOOX7Pp48eYLf//73hWsjtVotTKdTaJoG3/cxHA4xHA4L+/I8D3/84x8xHA4hhECj0VgaY9Y4dF3fqoW540cbOB8QbT7f9+F5HgBgMpng8ePHMvbE28MwxGQyQbvdzv3fdNF3fd/Hw4cPMZ1OU/WEEBgOh6hWqwiCAAcHB6kY7HkehBDyLZfJlyHklanavEh/6+yD6vjm1VMdS1V7m4ZzARGRmireq+YW13Xl/LSpc0ChaMF0Oo0ARNPpdLGIiOjSNZvN1OfxeCz/Hg6HkWEYmfUcx8mMU47jRLquR/P5XNnveDyOdF1PbcsIiZl9LbYfBEHpcfR6vczvb6KjoyPOB0RbYjAYpP5Oxk5N0+T/jofD4VLsS1J9N46FWbEyGe+m02nU6XRk2Xg8lp+DIEi1qSpTtbluf+vug+r45tVTHUtVe5uGcwERkZoq3qvmFgBL/5Lzw6bJyhUxoURE12Y+n0e9Xi+33DTNaDgcZpYlg3HMcZwoipYTPlnihE9SXuxL9hUEQaRpWhRF5/Eyqx/VOKbT6UZPFEk8iSDaDtPpVMalKDqPUwBk8rpsor7sdxeTMUUJelUSPq9M1eZF+ltnH1THt2icseSxLPrvtWk4FxARqeXFe9UcMZ/P5TlDbNPPEbJyRTeu5j4oIqJlo9EIe3t7S9t934dlWWg0Guh0OkvlYRiiWq0ubVvlVlHDMCCESPUJALPZTNmX7/uoVCpwXRe6rsO2bbiuW3ochmFgPB6XHicRURHDMPD48WP5OQxDAEClUgGQftzLcRx0u93ctlb57mJ/i3zfhxACs9lMPlochqF8DE1Vpmpz3f7W3QfV8VXVyzuWRf+9iIhou+TFe9UcAQDNZlNuc1039XlbMKFERNcmCILMH/qGYeDg4ABBEKSSNTHbtpcSTaPRKBXMi+i6jsFgANu2EYahTC4t/qBf7Gs2m0EIAdM0oWkaOp0OWq3WSuNYTFoREV1U8kfo8fGxjFGxokR90irfBdQJelUSXlWmanPd/tbdByD/+BbVyzuWRf+9iIhou2TFe9UckYz5YRhiNpuVugCyaZhQIqJro7qbR9M0tFottFqtpex+EARLi68+ePBg5f57vR5M05QJIgBLgXyxL13XoWma3La4YOs64yAiuixhGMJ1XTiOk9pelKhf97uAOkGvSsKrylRtrtvfuvuQtHh8i+oVHcu8/15ERLRdsuJ92bnFsqytPYfIfcsbEdG7pmlaKlnkeR5arRbm8zkApB6LSL4Zp9FoLLU1Go3k30II9Pt95ZuM4u/FfcSPNSxe0V/sq+jKwTrjICK6LJZlYTweZybr40R9o9HAfD5X3hGzyneB8wS9EGIpQR9fhc1KwqsS9IZh5La5bn9FcVjVXyzr+BbVUx1L1X8vIiLaLlnxvmiOCMMQnudt7TzAhBIRXZtqtZpKFlUqlaVXO8ePFMSOj48xGAxS7Sw+YtbtdtHtdlPJIk3Tlk4MarUaXr9+DU3TMBwOl9rN6kvXddTrdXl3VZyUyjpRWRxHjOtkENG7cHh4CMuyoOu6TNa/evWqMFEfK5PUz5OXoFcl4YsS9Kqk/zr9FSm6yJB1fJPzQLJemeOe1x4REW2PormzaG559erVVsd+PvJGRNfGNE1MJhP52TAMtNtt2LYN27ZxfHyM6XRaur0wDHF4eAgAGAwG8jnlfr8Py7KWvj8YDOB5HmzbRqvVKr0Gk+M4sCwLtm1jMBgsLbKdNw4g/w4rIqKLcF0XhmHI5MRoNIKmaYWJ+ngha6BcUj+2+ChyrVaT25IJ+mQSHkAqCa8qU7W5bn/r7oPq+ObVKzqWqvaIiGh7FMV71dwSf3+bLzZ/FEVRlNzg+z5qtRqm0ykf0SCid67VapVeOyJeZHWd2LTqmxMu0peKZVmZdy1toqdPn2J/f5/zAdGGE0IsvflS0zR5tdR1XblI9Hg8xmAwkDGo1Wphb28PvV6v8Lue52E8HuPw8BC9Xg97e3syrtq2Ldcw0nU99eM6DENYliV/X8Z35RSVqdpct7919qHo+ObVyzuWRe1tGs4FRERqqrlTNV8B53erBkGA4XB45eNeVVauiAklIrpWnuchDMNSyZ5ut7t2sF01oXSRvvKEYYh+v790ZWJT8SSCiIg4FxAREZCdK+Ijb0R0rUzTxGw2W3r0YFEYhktXdMvyPK/042wX7Utlm5JJREREREREKkwoEdG163Q6hd+J35Kwjvj10WVdpC8VJpOIiIiIiOh9wYQSEW0ELkZKRERERES0Pf4ur+Dk5ASnp6dXORYiItogL168AMD5gIjoQ8a5gIiIAOD169dL25YW5X758iXu3buHs7OzKxsYERFtphs3buDt27fXPQwiIrpGnAuIiAgAbt68iefPn+Pu3bsAMu5Q2tnZwdnZGY6OjrC7u3vlAySi7fbs2TN89tlnuHPnDr7//nsAwC+//II///nPaDQauXHl22+/xddff525/auvvsKtW7eU/Z6enuKHH34AAPzpT3/Co0ePZJ34iuru7i5++ukn/PLLL6XHcXp6im+++QZPnz4t3d9PP/2EH374Ab/61a/w888/4/79+7hz5w4A4MmTJ/jyyy+V+7IpTk5O8OjRI84HREQfMM4FREQEnJ//7O/vY2dnR27LfeRtd3eXrwYlopX4vo9PP/0UX3zxBQDg/v37+O6772AYBmzbxu9+9zsEQZBZ9+OPP16KOb7v48mTJ/j9739fuMaS53n4wx/+AAA4PDzEb3/7W0ynUwDAcDiEbdsAzhfodhwnt73kOFzXxe7uLn788celsan68zwP33zzjfxut9vFcDgEAOi6vjVve0sm4jgfEF0/3/fheR4AYDKZ4PHjxzKWqcqy2gEAwzAghEAYhqm4F78Vc7G+qgw4j31CCOi6DgDyu0X1YpZl4eDgQH5HCAHP81CpVCCEQLPZlG2r+lt3/9Y9vuuMQ1Vv03AuICJSi+eHMAwxmUzQbrdlvBRCYDgcolqtIgiC1DwHnM8VDx8+lOcRWydaMJ1OIwDRdDpdLCIiUmo2m6nP4/FY/j0cDiPDMDLrOY6TGXMcx4l0XY/m87my3+l0GmmaJj8HQRABiIIgkH3P5/PCdvLGsRgqi/pb3M9Op5P63Ov15Hc32dHREecDog0yGAxSfydjjapsUafTiQBEACLTNFOxMd6e/Be3rSobj8cy1gVBEOm6XqrNWPz7MzmWxe8kY6mqv3X3b53ju+44VPU2DecCIiI1TdNkjBwOh6mYnjyXmU6nqbksPvfISMtspKxcERNKRHQp5vN51Ov1cstN04yGw2Fm2WLCJYrOA2wURaUSSsnvR9HyiUlev2XGEUXLCaWi/kzTjAzDiIIgiMbjceq78fcXT5Q2EU8iiDaHKpFdlORelJdkn8/nS/EqjlWqsihajtVx30X1YlkXEFTJ+bz+1t2/dY/vOuMoGv+m4VxARKSWdxF9PB4vXTDIOq/Y5oTSjcu/54mIPkSj0Qh7e3tL233fh2VZaDQa6HQ6S+VhGKJarS5tK3rEbVGz2ZR/Hx8fwzRN2UYYhnBdF67rwrIsCCFKjWPd/hzHAQBUq1U4jpP6LnD++MN4PC7dFxGRYRh4/Pix/ByGIQCgUqkoy/JompYZZ5PxynXd1Oe8MiEEZrMZNE2D7/sIwzD1aJqqzbxt8fhrtZp89K3RaJTqb539W+f4/p//83/WGkeZ8RMR0fZIPrLsOA663S6Av80Xi+JHot8HTCgR0aUIgiDzB7FhGDg4OEAQBHBdd6nctu2lRNNoNFp7LYk4eRQndQCg0+mg2Wyi2Wyi3W7Lk5Kicazbn+d5GAwGcu2meFJJms1mK/dFRB82VSJbVbYoL8me/H4YhpjNZjKuq8p830elUoHrutB1HbZty3ivqhdvyxtnXnJe1d+6+1d0DLPK/tf/+l9rjaNo/EREtH2yLqLHa+glvwO8Z+cBZW5jIiIq0ul0lLfsj8fjpfUx4nqL30t+p+wjb6pxJOPZfD7PfBQk73G3KFLfhrrYXxAEqUf/giCINE1b6k+1vsmm4GMORJtpPp/nxkZVWfI7sel0mrl+T6fTyW1jsWw4HKbiexxny7SZfCR5cdyO40Tj8Vi2H8fpov4uun9lj++64yh7vDYF5wIionLm83nU6XRSj1cPBgP5CLTjOJnxdJPngCQ+8kZE74ymaanbOj3Pw+3bt+Xn+CrwYpY+626h0WgE27Zh2zaEEOj3+6VuDT08PIRlWdB1HWEYIgxD+L6P+/fvL303+ShI3jjW7S/56J+u6zg4OMi95ZWIaFWWZWE8Hmfe2aMqiyXjsK7rEEKktoVhCM/zMtvIKtN1PfVoV/LNaKp6nufhwYMHuWOcTCYwTROdTgdBEGA0Gsm3oqn6u8j+AeWP77rjKHO8iIho+2iahlarhVarJX/793o9mKYJIYR8AuN9esyZCSUiuhTVajX147lSqaQeW/N9H5qmpV45fHx8vLRuRnzyEP8DgG63K+v5vp+5BpLrujAMQyZ3RqOR/ME/GAzk9zzPQ7PZTJ0oZI1j0WJCKK8/wzAwmUxS333z5s3Sq5ZVa5sQEeXJSmSXKYuVSbK/evUqN9mSVVbmh3Fem3kXEFTJeVV/F92/VY7vP/zDP6w1jvfpRIKI6ENXdBE9vpAQP/5mGMbKa8Vusr+77gEQ0fvBNE0Mh8PUAqftdhu2bQMAxuMxptNp6fbCMJR1B4OBTCr1+30ASK1ZJIRAq9VK1dc0DZ1OB5qmoV6v4/DwEJqmIQiCVF0Vz/Pk4tn9fh97e3toNpvK/nRdR6PRkP0BWFpDad07oojow5aVyI4T76qyOKGv63qpJHu8xk+WrDJd11Gv1+V6SMkfz6p6i2vldbtddLtdeQdPck4B0sn5vP7CMFx7/9Y5vuuMI56XVMeLiIi2Q9FF9FqthtevX8t5LTk3JK3zUqJNwIQSEV2K+Hb+pOSJwOKC167rot1u57anaRp6vR56vV5qu+M4S4uX6rqOKIpy2zIMI/eHumocpmnCNM2lwF/UX1wvz/HxceZC3UREeVSJbFUZ8LeEeK/XK51kV91Fk1XmOA4sy0KtVsN0Os18k2Vem3kXEFTJ+bz+1t2/dY/vuuMoc7yIiGjzFV1EHwwG8DwPs9kMrVYrdY6Qd/F6m3wULZwV+b4vJzdeKSGiVXiehzAMSwXCbreL4XC4Vj95r5hex0XGsY4wDNHv93OvTmySp0+fYn9/n/MBEdEHjHMBEREB2bkirqFERJfGNE3MZrPCBajDMES1Wl2rD8/zlHf/rOIi41jXtiSTiIiIiIiIVJhQIqJLtfhoW5b4cbZ1mKZ5ac8XX2Qc62IyiYiIiIiI3ge5ayidnJzg9PT0KsdCREQb5MWLFwA4HxARfcg4FxAREQC8fv16advSGkovX77EvXv3cHZ2dmUDIyKizXTjxg28ffv2uodBRETXiHMBEREBwM2bN/H8+XPcvXsXQMYdSjs7Ozg7O8PR0RF2d3evfIBERIt++eUX3Lp1653XWddV9nWVTk5O8OjRI84HREQfMM4FREQEAKenp9jf38fOzo7clvvI2+7uLt/kQETXzrZtmKYJXdfheR6A88W0J5MJ2u12bpyyLCtzvSLLsnBwcFBqHSbf9/Hw4cPUqz8v0tfh4eGVr9l0EfGjDZwPiDaD7/syDk4mEzx+/FjGF1WZSlZM9DwPQgjoug4A8kUIQggMh0NUq1UEQZCqp4rP69Yr2l/g/HXNQgiEYSjrqdpctz/VcVEdS9U8UtTepuBcQESkVjQHep6HSqUCIQSazaaM+67ryth/WWvEXrlowXQ6jQBE0+l0sYiI6EpNp9PIcRz5WdM0GZuGw2Gk63pu3V6vl9kegGg+nxf27TiO/H6Rsn3N5/PM726qo6MjzgdEG2QwGKT+NgyjVFmerDg1Ho+jTqcTRVEUBUGQirO6rsvvTqdT+b0oUsfndeup9qnT6UQAIgCRaZqpfVC1uW5/quMS79fisVTNI0XtbRLOBUREamXnliiKUnNgPI8l/y1+f5Nk5Yr4ljci2lj9fh/NZlN+dhwndXU0L5Pvui7a7fbS9uSV4CLNZrPUldhV+orHK4QoNQYiopjv++j3+/Jzs9mE7/sQQijLVLLiVLfblXdc6rqO8XgM4G9XX+M4ZhgGbNuW9fLi87r1ivapVqthPp9jPp9jPB6n5gPVXLFuf3nHJZZ1LFXzSFF7RES0PVTzzvHxcWadMAzhOA6iKJL/BoPBVj3NAABMKBHRRgrDcOnHefJxAMdx0O12M+uOx+OlH/Gu66aSU5dl1b7a7TZc1730cRDR+80wDDx+/Fh+DsMQAFCpVJRlebLilBACs9kMmqbB9/1UHI7bXBQ/epYXn9etV2afNE3LvLCgmivW6U91XIDV55ei9oiIaLuo5p1KpYJarSYffWs0GrIsOXe8q3OVd40JJSLaSKPRCHt7e0vbfd+HZVloNBrodDpL5WEYolqtLm17F88lr9OXYRi8Ek1Ea0n+0Dw+PoZpmjLeqMoW5cUp3/dRqVTgui50XYdt2zIBHq9VlPwuAMxms9S2xfi8br2ifQrDEK7rwnVdWJa1dDeWaq5YtT/VcVlnflG1R0RE2ylvbnEcBwBQrVbhOI6ca5JzRxiGmM1m23lxocxzcUREV63X6+XGofl8HnU6ndT6SrHBYLC0RtJwOJR/J9fyKCMjTF64rzJrm2wCrptBtJnm83lufFGVxfLi1HA4TK0DNJ/PUzFwMBhEw+Ewms/nkeM4mfEhKz6vW0+1T8m/p9Np5hpERW2W7U91XMrE/MV5pOg4bxrOBURE5WTNLY7jROPxWMb+5BpKsU6ns9L5yXXhGkpEtDVUV301TUOr1UKr1Vp6nCIIgqW38jx48OCdjPEq+yIiilmWtbRuUJkyQB2ndF1PPUa2+Ea1Xq8H0zQhhJC392etFbcYn9etp9qn5B1Juq5DCLF0l5KqzVX6yzsu/+2//be1Yn7RcSYiou20OLcIITCZTGCaJjqdDoIgwGg0Ss1XYRjC87ytfcsbE0pEtJE0TUv9yPc8D7dv35af45ORxUcpks8lx0ajEWzbhm3bEEKg3+9f+If7VfZFRBQ7PDyEZVnQdR1hGKbipKosKS9OFd1qHy88HT/GZhgGNE0rjM/r1svbJ9/3cf/+/aXxVSoVZZvr9qc6LuvE/K18pIGIiDKp5hbf91NLeOi6joODg9T8/OrVq61NJgHA3133AIiIslSrVXniAZyfKCQXvPN9H5qmpRbEPj4+lm/NiSXrAOdv1ul2uzLYx+2ofuBn3S21Tl8x1UK5RER5XNeFYRgy2TEajeQ6DaqyZJwrilP1el3GvGQiCDh/s9rr16+haRqGw6GMgUXxed16efuk63oq/nqeh2azCU3TlG36vr9Wf5qmZR6X//Jf/ovyWCYl5xFd15XHmYiItodq3onnveQafW/evEnF+3hdvW3FhBIRbSTTNFMB2DAMtNtt+brp8XiM6XRaur0wDGXdwWCAbrcLwzDka6LjBfNinufJxbP7/T729vZKv3khry8g/84mIiIVIQRarVZqm6Zp6HQ6yjLgbzEs+SrivDjlOA4sy0KtVsN0Ok29RGAwGMDzPMxmM7RaLfkDuig+r1NPtU9xgufw8BCapiEIAhnDVW2u2x8A5XHJO5aqeUTVHhERbQ/V3KLrOhqNhpyvAGS+pXqb71z9KIqiKLnB9305ufFKCRFdp1artZToyRO/LWeduLXqazov0pdlWblXsDfN06dPsb+/z/mAiOgDxrmAiIiA7FwR11Aioo3V7XZLv0p5PB5f2Q/ddfuKn5fehmQSERERERGRChNKRLSxTNPEbDbLXVg2FoYhqtXqWn14nre0psi76qvf7y+tu0RERERERLSNmFAioo0Wr1+homlaam2QVZimudKbFS7SF5NJRERERET0vshdlPv09PQqx0FERBvm9evXADgfEBF9yDgXEBERkD0PLC3K/Ze//AW7u7v4l3/5lysbGBERbaabN2/i7OzsuodBRETXiHMBEREBwN///d/j9PQU//iP/wggI6EEnCeV/vrXv1754IiIaLP867/+K3Z2dq57GEREdI04FxAREQD8wz/8g0wmATkJJSIiIiIiIiIiojxclJuIiIiIiIiIiFbChBIREREREREREa3k/wFH/pi5BQjWbgAAAABJRU5ErkJggg==", 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", 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" ] @@ -337,23 +403,25 @@ } ], "source": [ - "column_labels = [\"$(a, b)$\", \"Total time [s]\", \"Iteration counts\"]\n", + "column_labels = [\"$(a, b)$\", \"Total time [s]\", \"Iteration counts\", \"$F(x^{200})$\"]\n", "cell_text = []\n", "with open(data_path + \"/cameraman_ab.csv\", \"w\") as file:\n", " for i, result in enumerate(results):\n", " cell_text.append(\n", " [\n", " \"$(\" + \",\".join(map(str, nesterov_ratios_f[i])) + \")$\",\n", - " result.execution_time,\n", + " round(result.time, 3),\n", " result.nit,\n", + " round(result.allfuns[200][0], 3),\n", " ]\n", " )\n", " writer = csv.writer(file, escapechar=\" \", quoting=csv.QUOTE_NONE)\n", " writer.writerow(\n", " [\n", " \",\".join(map(str, nesterov_ratios_f[i])),\n", - " round(result.execution_time, 3),\n", + " round(result.time, 3),\n", " result.nit,\n", + " round(result.allfuns[200][0], 3),\n", " ]\n", " )\n", "fig = plt.figure(figsize=(15, 0.2))\n", @@ -366,18 +434,25 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "9b1b0e87", "metadata": { "ExecuteTime": { "end_time": "2022-05-10T09:08:04.259927Z", "start_time": "2022-05-10T09:08:02.230130Z" + }, + "execution": { + "iopub.execute_input": "2024-11-07T16:10:54.449001Z", + "iopub.status.busy": "2024-11-07T16:10:54.448655Z", + "iopub.status.idle": "2024-11-07T16:10:56.129817Z", + "shell.execute_reply": "2024-11-07T16:10:56.128985Z", + "shell.execute_reply.started": "2024-11-07T16:10:54.448973Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -406,19 +481,26 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "c095f4b0", "metadata": { "ExecuteTime": { "end_time": "2022-05-10T09:09:48.967644Z", "start_time": "2022-05-10T09:08:04.263015Z" + }, + "execution": { + "iopub.execute_input": "2024-11-07T16:10:56.135316Z", + "iopub.status.busy": "2024-11-07T16:10:56.132503Z", + "iopub.status.idle": "2024-11-07T16:12:35.978300Z", + "shell.execute_reply": "2024-11-07T16:12:35.976689Z", + "shell.execute_reply.started": "2024-11-07T16:10:56.135275Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ee64e76f20447ca86a39d1320a1172d", + "model_id": "b5abfc6d3fd040c8852d4dffd562d9de", "version_major": 2, "version_minor": 0 }, @@ -442,19 +524,26 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "71301275", "metadata": { "ExecuteTime": { "end_time": "2022-05-10T09:09:50.664948Z", "start_time": "2022-05-10T09:09:48.975336Z" }, - "code_folding": [] + "code_folding": [], + "execution": { + "iopub.execute_input": "2024-11-07T16:12:35.986442Z", + "iopub.status.busy": "2024-11-07T16:12:35.985687Z", + "iopub.status.idle": "2024-11-07T16:12:37.446126Z", + "shell.execute_reply": "2024-11-07T16:12:37.445158Z", + "shell.execute_reply.started": "2024-11-07T16:12:35.986404Z" + } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -502,7 +591,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index 8ac67ae..037fc22 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,19 @@ docs = [ "sphinxcontrib-katex", "sphinx_rtd_theme", ] +benchmarks = [ + "joblib", + "matplotlib", + "pandas", + "SciencePlots", + "tqdm" +] +examples = [ + "zfista[benchmarks]", + "jupyter", + "PyWavelets", + "scikit-image", +] [tool.mypy] strict = true @@ -37,6 +50,7 @@ module = [ "jaxopt.*", "mpl_toolkits.mplot3d.*", "joblib.*", + "scienceplots.*", ] ignore_missing_imports = true From bbd26531cbb2aca89b970678c03d7ff45f702c27 Mon Sep 17 00:00:00 2001 From: zalgo3 Date: Fri, 8 Nov 2024 02:07:34 +0900 Subject: [PATCH 2/2] fix readme --- README.md | 27 ++++++++++++++++++--------- pyproject.toml | 4 ++-- 2 files changed, 20 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 34f2881..cbded40 100644 --- a/README.md +++ b/README.md @@ -22,35 +22,44 @@ Note that FISTA also requires $f$ to be convex. - Documentation: https://zalgo3.github.io/zfista/ -## Requirements +### Requirements - Python 3.9 or later -## Install -```sh +### Install +```Shell pip install zfista ``` -## Quickstart +### Quickstart ```python from zfista import minimize_proximal_gradient help(minimize_proximal_gradient) ``` -## Examples +## For developers + +### Installation +```Shell +pip install -e . +``` + +### Examples You can run some examples on jupyter notebooks. ```Shell +pip install -e ".[examples]" jupyter notebook ``` -## Testing +### Testing You can run all tests by ```Shell -python -m unittest discover +pip install tox +tox ``` -## Benchmark +### Benchmark You can run the benchmark by ```Shell -pip install -U joblib matplotlib pandas SciencePlots tqdm +pip install -e ".[bench]" python runtests.py ``` diff --git a/pyproject.toml b/pyproject.toml index 037fc22..3eb31d2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,7 @@ docs = [ "sphinxcontrib-katex", "sphinx_rtd_theme", ] -benchmarks = [ +bench = [ "joblib", "matplotlib", "pandas", @@ -33,7 +33,7 @@ benchmarks = [ "tqdm" ] examples = [ - "zfista[benchmarks]", + "zfista[bench]", "jupyter", "PyWavelets", "scikit-image",