diff --git a/README.md b/README.md
index df58131..25c678d 100644
--- a/README.md
+++ b/README.md
@@ -3,45 +3,56 @@ Just Another Conditional Quantile Estimator
 
 This Python module provides code to implement Jacques forecasting model.
 
-## Setup
-
 ### Prerequisites
 Before setting up this project:   
-    -  Python Version: Your machine will need to have an installed version of python that mets the `requires-pyhon` constraint in [pyproject.toml](https://github.com/reichlab/jacques/blob/BWRedits/pyproject.toml).  
-    -  That version of Python should be set as your current Python interpreter.  
+    -  Python Version: Your machine will need to have an installed version of python that meets the `requires-python` constraint in [pyproject.toml](https://github.com/reichlab/jacques/blob/BWRedits/pyproject.toml).  
+    -  That version of Python should be set as your current Python interpreter. 
 
-### Setup
-Follow the directions below to set this project up on your local machine.
+## Installing the package
 
-1. Clone this repository to your local computer and navigate to the corresponding directory.
 
-2. Create a Python virtual environment.
 
-```
-python m venv .venv
-```
+## Setup for local development
 
-3. Activate the virual environment.
+The instructions below outline how to set up a development environment based
+on uv tooling.
 
-```
-source .venv/bin/activate
-```
-**Note:** the command above is for Unix-based systems. If you're using Windows, the command is:
+Prerequisites:
 
-```
-.venv\Scripts\activate
-```
+- [uv](https://docs.astral.sh/uv/getting-started/installation/)
 
-4. Install the project's dependencies, which include the `jacques` dependencies:
+1. Clone this repository
+2. Change to the repo's root directory:
 
-```
-# if only need the dependencies necessary to run jacques
-pip install -r requirements/requirements.txt && pip install -e .
-```
+    ```bash
+    cd jacques
+    ```
 
-and the dependencies required for running the [demo.ipynb](https://github.com/reichlab/jacques/blob/BWRedits/demo.ipynb):
+3. Create a Python virtual environment and install dependencies. The command
+below creates a virtual environment in the `.venv` directory, installs Python
+if needed, installs project dependencies (including dev dependencies), and
+installs the package in
+[editable mode](https://setuptools.pypa.io/en/stable/userguide/development_mode.html):
 
+    ```bash
+    uv sync
+    ```
+
+
+### Updating dependencies
+
+Use [`uv add`](https://docs.astral.sh/uv/reference/cli/#uv-add) to include a
+new dependency in the project. This command will install the new dependency
+into the virtual environment, add it to `uv.lock`, and update the
+`dependencies` section of [`pyproject.toml`](pyproject.toml).
+
+```bash
+uv add <package-name>
 ```
-# if you'd like to be able to run the demo notebook
-pip install -r requirements/requirements-doc.txt && pip install -e .
-```
+
+To add a dependency to a specific group (adding a dev dependency, for example),
+use the `--group` flag:
+
+```bash
+uv add <package-name> --group dev
+```
\ No newline at end of file
diff --git a/demo.ipynb b/demo.ipynb
index c112c43..2ba5fc7 100644
--- a/demo.ipynb
+++ b/demo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -140,902 +140,902 @@
       "batch idx = 0\n",
       "loss idx = 1\n",
       "param estimates vec = \n",
-      "[2.9000025 2.9000041 1.0998917]\n",
+      "[2.9000025 2.9000049 1.0999191]\n",
       "loss = \n",
-      "1.8542798\n",
+      "1.842192\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.16773301,  0.08943623, -0.00293276], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.17958276,  0.07446898, -0.00394023], dtype=float32)>]\n",
       "epoch idx = 0\n",
       "batch idx = 1\n",
       "loss idx = 1loss idx = 1\n",
       "param estimates vec = \n",
-      "[2.799869  2.8062766 1.1990938]\n",
+      "[2.799888  2.8002446 1.1998558]\n",
       "loss = \n",
-      "1.8106003\n",
+      "1.8236434\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.18633094,  0.0463278 , -0.00427562], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19167222,  0.06955977, -0.00393211], dtype=float32)>]\n",
       "epoch idx = 0\n",
       "batch idx = 2\n",
       "loss idx = 1loss idx = 1loss idx = 1\n",
       "param estimates vec = \n",
-      "[2.6995242 2.712989  1.2988633]\n",
+      "[2.6996505 2.7006023 1.2986425]\n",
       "loss = \n",
-      "1.7864225\n",
+      "1.8083805\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.1995734 ,  0.04989492, -0.00452022], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19662091,  0.06852128, -0.0031915 ], dtype=float32)>]\n",
       "epoch idx = 0\n",
       "batch idx = 3\n",
       "loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
       "param estimates vec = \n",
-      "[2.599233  2.6170754 1.3972341]\n",
+      "[2.599317  2.6033454 1.3970671]\n",
       "loss = \n",
-      "1.7573079\n",
+      "1.7607116\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.18729582,  0.0780474 , -0.00309442], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19837826,  0.04842849, -0.00323941], dtype=float32)>]\n",
       "epoch idx = 0\n",
       "batch idx = 4\n",
       "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
       "param estimates vec = \n",
-      "[2.4984465 2.520298  1.4947301]\n",
+      "[2.4987133 2.5062532 1.4957827]\n",
       "loss = \n",
-      "1.747625\n",
+      "1.729641\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.22910105,  0.06666841, -0.00303283], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.21125045,  0.05691139, -0.00352802], dtype=float32)>]\n",
       "epoch idx = 0\n",
       "batch idx = 5\n",
       "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
       "param estimates vec = \n",
-      "[2.3974323 2.4239147 1.5907309]\n",
+      "[2.3981245 2.4076552 1.5923631]\n",
       "loss = \n",
-      "1.709598\n",
+      "1.7086257\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.21575327,  0.05656858, -0.00262011], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.20089568,  0.08428677, -0.00242907], dtype=float32)>]\n",
       "epoch idx = 0\n",
       "batch idx = 6\n",
       "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
       "param estimates vec = \n",
-      "[2.2964296 2.326414  1.6849146]\n",
+      "[2.2981992 2.307796  1.6861664]\n",
       "loss = \n",
-      "1.6834306\n",
+      "1.6796335\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.20689684,  0.07232871, -0.00231828], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.17688504,  0.09221584, -0.00199667], dtype=float32)>]\n",
       "epoch idx = 0\n",
       "batch idx = 7\n",
       "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
       "param estimates vec = \n",
-      "[2.1955712 2.229299  1.7786318]\n",
+      "[2.1989205 2.207567  1.7781724]\n",
       "loss = \n",
-      "1.6540613\n",
+      "1.6723717\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.20119974,  0.05812022, -0.00262817], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.17165783,  0.07743218, -0.00206914], dtype=float32)>]\n",
       "epoch idx = 0\n",
       "batch idx = 8\n",
       "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
       "param estimates vec = \n",
-      "[2.0962844 2.1316028 1.8718178]\n",
+      "[2.1005316 2.1073053 1.8696551]\n",
       "loss = \n",
-      "1.6329801\n",
+      "1.6561368\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.15516864,  0.06749016, -0.00253692], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.16000824,  0.07370939, -0.00234353], dtype=float32)>]\n",
       "epoch idx = 0\n",
       "batch idx = 9\n",
       "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
       "param estimates vec = \n",
-      "[1.9989424 2.0324278 1.964124 ]\n",
+      "[2.0035422 2.006688  1.961708 ]\n",
       "loss = \n",
-      "1.6269236\n",
+      "1.61352\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.13748883,  0.0838896 , -0.00234049], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.14190692,  0.07871971, -0.00270886], dtype=float32)>]\n",
       "epoch idx = 1\n",
       "batch idx = 0\n",
       "loss idx = 2\n",
       "param estimates vec = \n",
-      "[1.9037818 1.9325936 2.0566897]\n",
+      "[1.9083072 1.9061228 2.054336 ]\n",
       "loss = \n",
-      "1.6056341\n",
+      "1.5810052\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.12357073,  0.07559266, -0.00271131], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.12730442,  0.07395212, -0.00274613], dtype=float32)>]\n",
       "epoch idx = 1\n",
       "batch idx = 1\n",
       "loss idx = 2loss idx = 2\n",
       "param estimates vec = \n",
-      "[1.8106493 1.8335708 2.1486576]\n",
+      "[1.8153732 1.8066419 2.1458778]\n",
       "loss = \n",
-      "1.5867333\n",
+      "1.5688558\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.11827952,  0.05961064, -0.00236891], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.1093511 ,  0.06160902, -0.00212804], dtype=float32)>]\n",
       "epoch idx = 1\n",
       "batch idx = 2\n",
       "loss idx = 2loss idx = 2loss idx = 2\n",
       "param estimates vec = \n",
-      "[1.7188463 1.7334707 2.2390122]\n",
+      "[1.7236977 1.7076926 2.2364676]\n",
       "loss = \n",
-      "1.5620642\n",
+      "1.5598991\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.12527607,  0.08114556, -0.00195494], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.12207814,  0.06540862, -0.00209805], dtype=float32)>]\n",
       "epoch idx = 1\n",
       "batch idx = 3\n",
       "loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
       "param estimates vec = \n",
-      "[1.6284885 1.6337031 2.3272076]\n",
+      "[1.6337197 1.6084582 2.3253818]\n",
       "loss = \n",
-      "1.5565135\n",
+      "1.5470736\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.11787256,  0.06559747, -0.00168642], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.10881519,  0.07389895, -0.00179646], dtype=float32)>]\n",
       "epoch idx = 1\n",
       "batch idx = 4\n",
       "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
       "param estimates vec = \n",
-      "[1.5400777 1.5346786 2.413988 ]\n",
+      "[1.545393  1.5090497 2.4130933]\n",
       "loss = \n",
-      "1.5332825\n",
+      "1.5377227\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.10324349,  0.06061659, -0.00181304], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.10437928,  0.07311903, -0.00185756], dtype=float32)>]\n",
       "epoch idx = 1\n",
       "batch idx = 5\n",
       "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
       "param estimates vec = \n",
-      "[1.4547741 1.4357916 2.4987955]\n",
+      "[1.4591417 1.4094998 2.4997888]\n",
       "loss = \n",
-      "1.5085909\n",
+      "1.5218382\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.07725767,  0.06565627, -0.00156867], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.09200424,  0.07306314, -0.00185085], dtype=float32)>]\n",
       "epoch idx = 1\n",
       "batch idx = 6\n",
       "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
       "param estimates vec = \n",
-      "[1.3733443 1.3367829 2.5823998]\n",
+      "[1.3757746 1.3093805 2.5852606]\n",
       "loss = \n",
-      "1.4925483\n",
+      "1.493098\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.05816011,  0.06820349, -0.00171251], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.07339107,  0.07868449, -0.00173159], dtype=float32)>]\n",
       "epoch idx = 1\n",
       "batch idx = 7\n",
       "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
       "param estimates vec = \n",
-      "[1.29687   1.2370442 2.665029 ]\n",
+      "[1.2970629 1.2114276 2.6696327]\n",
       "loss = \n",
-      "1.4805127\n",
+      "1.4758615\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.03499013,  0.07542612, -0.00172254], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.04089103,  0.05057912, -0.00170977], dtype=float32)>]\n",
       "epoch idx = 1\n",
       "batch idx = 8\n",
       "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
       "param estimates vec = \n",
-      "[1.2254297 1.1380087 2.7458746]\n",
+      "[1.2237631 1.1143844 2.7522388]\n",
       "loss = \n",
-      "1.480488\n",
+      "1.4738764\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02629118,  0.06108572, -0.00143832], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02324354,  0.05958575, -0.00146456], dtype=float32)>]\n",
       "epoch idx = 1\n",
       "batch idx = 9\n",
       "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
       "param estimates vec = \n",
-      "[1.158645  1.0402969 2.8243454]\n",
+      "[1.1557505 1.018653  2.832692 ]\n",
       "loss = \n",
-      "1.4686989\n",
+      "1.4623235\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02430945,  0.05424135, -0.00120815], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01752051,  0.05452095, -0.0012908 ], dtype=float32)>]\n",
       "epoch idx = 2\n",
       "batch idx = 0\n",
       "loss idx = 3\n",
       "param estimates vec = \n",
-      "[1.0972079 0.9443041 2.899747 ]\n",
+      "[1.0925932 0.9246962 2.9104476]\n",
       "loss = \n",
-      "1.4541671\n",
+      "1.4505153\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00875971,  0.04918629, -0.00095053], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01629195,  0.04881333, -0.00107566], dtype=float32)>]\n",
       "epoch idx = 2\n",
       "batch idx = 1\n",
       "loss idx = 3loss idx = 3\n",
       "param estimates vec = \n",
-      "[1.0407723 0.8502906 2.9720314]\n",
+      "[1.0356227 0.8334024 2.9851775]\n",
       "loss = \n",
-      "1.4601344\n",
+      "1.4470191\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00660503,  0.04517424, -0.00085177], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00639804,  0.0396426 , -0.00091386], dtype=float32)>]\n",
       "epoch idx = 2\n",
       "batch idx = 2\n",
       "loss idx = 3loss idx = 3loss idx = 3\n",
       "param estimates vec = \n",
-      "[0.98962766 0.75827956 3.042186  ]\n",
+      "[0.9846718  0.74347967 3.0569613 ]\n",
       "loss = \n",
-      "1.4488623\n",
+      "1.4486709\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00303373,  0.04301031, -0.00101053], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01123504,  0.04742689, -0.00085114], dtype=float32)>]\n",
       "epoch idx = 2\n",
       "batch idx = 3\n",
       "loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
       "param estimates vec = \n",
-      "[0.9442081  0.66976756 3.1090963 ]\n",
+      "[0.9397962  0.65534556 3.1259131 ]\n",
       "loss = \n",
-      "1.4425358\n",
+      "1.4449323\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01434643,  0.03014727, -0.00068178], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01833931,  0.04263168, -0.00080159], dtype=float32)>]\n",
       "epoch idx = 2\n",
       "batch idx = 4\n",
       "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
       "param estimates vec = \n",
-      "[0.9049285 0.5852264 3.1727233]\n",
+      "[0.9016608  0.57070667 3.19153   ]\n",
       "loss = \n",
-      "1.4531754\n",
+      "1.4487878\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02547108,  0.02434298, -0.00059476], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03235657,  0.02838082, -0.00061536], dtype=float32)>]\n",
       "epoch idx = 2\n",
       "batch idx = 5\n",
       "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
       "param estimates vec = \n",
-      "[0.87221426 0.5068562  3.233435  ]\n",
+      "[0.8698655  0.49141037 3.2539175 ]\n",
       "loss = \n",
-      "1.4376698\n",
+      "1.445109\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03683884,  0.00796583, -0.00060242], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03447305,  0.01357921, -0.00056298], dtype=float32)>]\n",
       "epoch idx = 2\n",
       "batch idx = 6\n",
       "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
       "param estimates vec = \n",
-      "[0.84571105 0.4334282  3.2906876 ]\n",
+      "[0.8435799  0.41644228 3.3125002 ]\n",
       "loss = \n",
-      "1.4406862\n",
+      "1.4470602\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0393015 ,  0.01187786, -0.00041627], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03117552,  0.01677602, -0.00036749], dtype=float32)>]\n",
       "epoch idx = 2\n",
       "batch idx = 7\n",
       "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
       "param estimates vec = \n",
-      "[0.8252318 0.3666724 3.344758 ]\n",
+      "[0.82223934 0.34924883 3.3672285 ]\n",
       "loss = \n",
-      "1.445512\n",
+      "1.4425766\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04330137, -0.00082285, -0.0004037 ], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03006091, -0.00645812, -0.0002804 ], dtype=float32)>]\n",
       "epoch idx = 2\n",
       "batch idx = 8\n",
       "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
       "param estimates vec = \n",
-      "[0.8112218 0.308637  3.3951893]\n",
+      "[0.8071481  0.29143378 3.4188473 ]\n",
       "loss = \n",
-      "1.4478825\n",
+      "1.4469512\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05442389, -0.01517084, -0.00024219], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04953224, -0.01920903, -0.00036029], dtype=float32)>]\n",
       "epoch idx = 2\n",
       "batch idx = 9\n",
       "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
       "param estimates vec = \n",
-      "[0.8027636  0.25908732 3.4422653 ]\n",
+      "[0.7975068  0.24065925 3.4674718 ]\n",
       "loss = \n",
-      "1.4502373\n",
+      "1.4483721\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05019284, -0.01790779, -0.00023111], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04651206, -0.01077145, -0.00032277], dtype=float32)>]\n",
       "epoch idx = 3\n",
       "batch idx = 0\n",
       "loss idx = 4\n",
       "param estimates vec = \n",
-      "[0.7994737  0.21839374 3.4863567 ]\n",
+      "[0.7934272 0.1989816 3.5129714]\n",
       "loss = \n",
-      "1.4457085\n",
+      "1.4583516\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05116305, -0.02365818, -0.00024332], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05291445, -0.02532014, -0.00023899], dtype=float32)>]\n",
       "epoch idx = 3\n",
       "batch idx = 1\n",
       "loss idx = 4loss idx = 4\n",
       "param estimates vec = \n",
-      "[0.8019115  0.18874976 3.5272894 ]\n",
+      "[0.7951882 0.168268  3.555098 ]\n",
       "loss = \n",
-      "1.453366\n",
+      "1.450285\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06264719, -0.03894603, -0.00015247], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06133314, -0.03958578, -0.00013258], dtype=float32)>]\n",
       "epoch idx = 3\n",
       "batch idx = 2\n",
       "loss idx = 4loss idx = 4loss idx = 4\n",
       "param estimates vec = \n",
-      "[0.8090788  0.16882834 3.565827  ]\n",
+      "[0.802463   0.14781263 3.594179  ]\n",
       "loss = \n",
-      "1.4492255\n",
+      "1.4521937\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05676201, -0.03738511, -0.00024688], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0633002 , -0.04125423, -0.00013674], dtype=float32)>]\n",
       "epoch idx = 3\n",
       "batch idx = 3\n",
       "loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
       "param estimates vec = \n",
-      "[0.82172215 0.15814538 3.60142   ]\n",
+      "[0.8148231  0.13747549 3.630944  ]\n",
       "loss = \n",
-      "1.4448808\n",
+      "1.4536517\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-6.9991276e-02, -3.9534312e-02, -9.5345131e-05], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06380248, -0.04573262, -0.00022409], dtype=float32)>]\n",
       "epoch idx = 3\n",
       "batch idx = 4\n",
       "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
       "param estimates vec = \n",
-      "[0.8385643  0.15638521 3.634842  ]\n",
+      "[0.83220524 0.1379679  3.665462  ]\n",
       "loss = \n",
-      "1.447959\n",
+      "1.4534432\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0606694 , -0.04242504, -0.00019341], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06799801, -0.05517248, -0.00019579], dtype=float32)>]\n",
       "epoch idx = 3\n",
       "batch idx = 5\n",
       "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
       "param estimates vec = \n",
-      "[0.85843027 0.16371782 3.6659951 ]\n",
+      "[0.85327095 0.1471979  3.697747  ]\n",
       "loss = \n",
-      "1.4496356\n",
+      "1.4548279\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05116434, -0.04793705, -0.00013618], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05777238, -0.04949598, -0.00015911], dtype=float32)>]\n",
       "epoch idx = 3\n",
       "batch idx = 6\n",
       "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
       "param estimates vec = \n",
-      "[0.8814618  0.17607728 3.6953337 ]\n",
+      "[0.8768133 0.1629495 3.7281516]\n",
       "loss = \n",
-      "1.4490722\n",
+      "1.452676\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05551864, -0.03047688, -0.00018258], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04767168, -0.04155282, -0.00018634], dtype=float32)>]\n",
       "epoch idx = 3\n",
       "batch idx = 7\n",
       "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
       "param estimates vec = \n",
-      "[0.905829   0.19350295 3.7235162 ]\n",
+      "[0.9023623  0.18509561 3.7569134 ]\n",
       "loss = \n",
-      "1.4475743\n",
+      "1.4521537\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03769421, -0.03302765, -0.0002738 ], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04458722, -0.04428627, -0.00019758], dtype=float32)>]\n",
       "epoch idx = 3\n",
       "batch idx = 8\n",
       "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
       "param estimates vec = \n",
-      "[0.93219465 0.2158641  3.7502794 ]\n",
+      "[0.9285128  0.21183234 3.7841148 ]\n",
       "loss = \n",
-      "1.4416944\n",
+      "1.4497753\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04610473, -0.03470998, -0.0002036 ], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03093106, -0.03719429, -0.00018419], dtype=float32)>]\n",
       "epoch idx = 3\n",
       "batch idx = 9\n",
       "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
       "param estimates vec = \n",
-      "[0.9596169 0.2391893 3.7759218]\n",
+      "[0.95514625 0.2389763  3.8103368 ]\n",
       "loss = \n",
-      "1.4428248\n",
+      "1.4476804\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03731237, -0.01562711, -0.00023311], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02996248, -0.01558652, -0.0002619 ], dtype=float32)>]\n",
       "epoch idx = 4\n",
       "batch idx = 0\n",
       "loss idx = 5\n",
       "param estimates vec = \n",
-      "[0.98732203 0.26394328 3.8003218 ]\n",
+      "[0.98284054 0.26766473 3.8354344 ]\n",
       "loss = \n",
-      "1.4413815\n",
+      "1.440324\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02959614, -0.01839505, -0.00019111], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0362909 , -0.02199792, -0.00021823], dtype=float32)>]\n",
       "epoch idx = 4\n",
       "batch idx = 1\n",
       "loss idx = 5loss idx = 5\n",
       "param estimates vec = \n",
-      "[1.0144882  0.29028043 3.8239458 ]\n",
+      "[1.0107948  0.29471922 3.8596227 ]\n",
       "loss = \n",
-      "1.4403428\n",
+      "1.4389327\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02079152, -0.01972847, -0.00025278], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0285775 , -0.00514138, -0.00023646], dtype=float32)>]\n",
       "epoch idx = 4\n",
       "batch idx = 2\n",
       "loss idx = 5loss idx = 5loss idx = 5\n",
       "param estimates vec = \n",
-      "[1.0400971  0.31489325 3.8471987 ]\n",
+      "[1.0375991  0.32115778 3.8833475 ]\n",
       "loss = \n",
-      "1.4419827\n",
+      "1.4450333\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00936251, -0.00330799, -0.0003099 ], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01402356, -0.00979149, -0.00029777], dtype=float32)>]\n",
       "epoch idx = 4\n",
       "batch idx = 3\n",
       "loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
       "param estimates vec = \n",
-      "[1.0648285  0.33957437 3.8699245 ]\n",
+      "[1.0639434 0.3475969 3.9063044]\n",
       "loss = \n",
-      "1.4498895\n",
+      "1.4459312\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01491034, -0.01156354, -0.00027403], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01987683, -0.01270972, -0.00023561], dtype=float32)>]\n",
       "epoch idx = 4\n",
       "batch idx = 4\n",
       "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
       "param estimates vec = \n",
-      "[1.087991   0.36353722 3.8920097 ]\n",
+      "[1.0880895  0.37248653 3.9287438 ]\n",
       "loss = \n",
-      "1.4409277\n",
+      "1.4422848\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00685828, -0.00754976, -0.00024389], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00185204, -0.0044181 , -0.00026452], dtype=float32)>]\n",
       "epoch idx = 4\n",
       "batch idx = 5\n",
       "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
       "param estimates vec = \n",
-      "[1.1088734 0.3868912 3.9136603]\n",
+      "[1.1112101  0.39394766 3.9504275 ]\n",
       "loss = \n",
-      "1.4433501\n",
+      "1.4337175\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00183127, -0.00770823, -0.00026721], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01150758,  0.00605297, -0.00021433], dtype=float32)>]\n",
       "epoch idx = 4\n",
       "batch idx = 6\n",
       "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
       "param estimates vec = \n",
-      "[1.1287394 0.4046704 3.9349144]\n",
+      "[1.1316968  0.41228497 3.971874  ]\n",
       "loss = \n",
-      "1.4363635\n",
+      "1.4485946\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00882687,  0.01669663, -0.00026483], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0052239 ,  0.00601129, -0.00028764], dtype=float32)>]\n",
       "epoch idx = 4\n",
       "batch idx = 7\n",
       "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
       "param estimates vec = \n",
-      "[1.1464884  0.41847834 3.9560318 ]\n",
+      "[1.1492966 0.4286675 3.993079 ]\n",
       "loss = \n",
-      "1.4461224\n",
+      "1.4374295\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00308733,  0.01131699, -0.00030025], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00989177,  0.00146055, -0.00028105], dtype=float32)>]\n",
       "epoch idx = 4\n",
       "batch idx = 8\n",
       "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
       "param estimates vec = \n",
-      "[1.1611129  0.42825916 3.9771044 ]\n",
+      "[1.1637803  0.44111693 4.014187  ]\n",
       "loss = \n",
-      "1.4489844\n",
+      "1.4491439\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0147959 ,  0.01319544, -0.00031104], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01446706,  0.01222664, -0.00029883], dtype=float32)>]\n",
       "epoch idx = 4\n",
       "batch idx = 9\n",
       "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
       "param estimates vec = \n",
-      "[1.1721175  0.43563142 3.998192  ]\n",
+      "[1.1744717  0.44987336 4.035012  ]\n",
       "loss = \n",
-      "1.442042\n",
+      "1.4431133\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02220261,  0.00717293, -0.00031775], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02338649,  0.01270325, -0.0002649 ], dtype=float32)>]\n",
       "epoch idx = 5\n",
       "batch idx = 0\n",
       "loss idx = 6\n",
       "param estimates vec = \n",
-      "[1.1795458  0.44114628 4.0191336 ]\n",
+      "[1.1833935  0.45439652 4.0555754 ]\n",
       "loss = \n",
-      "1.4479225\n",
+      "1.4366727\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02475947,  0.00555085, -0.00028918], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00745552,  0.016912  , -0.00026216], dtype=float32)>]\n",
       "epoch idx = 5\n",
       "batch idx = 1\n",
       "loss idx = 6loss idx = 6\n",
       "param estimates vec = \n",
-      "[1.1850702 0.4432441 4.0397005]\n",
+      "[1.1888099  0.45567632 4.0760813 ]\n",
       "loss = \n",
-      "1.4426982\n",
+      "1.4386952\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01170299,  0.01352488, -0.00024786], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02501199,  0.01383792, -0.00028872], dtype=float32)>]\n",
       "epoch idx = 5\n",
       "batch idx = 2\n",
       "loss idx = 6loss idx = 6loss idx = 6\n",
       "param estimates vec = \n",
-      "[1.1877538  0.44279945 4.0603046 ]\n",
+      "[1.1923411  0.45134878 4.096587  ]\n",
       "loss = \n",
-      "1.4490587\n",
+      "1.446478\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02210822,  0.01082812, -0.00030643], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01282045,  0.02668777, -0.00029474], dtype=float32)>]\n",
       "epoch idx = 5\n",
       "batch idx = 3\n",
       "loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
       "param estimates vec = \n",
-      "[1.1887773 0.440985  4.0804477]\n",
+      "[1.1932838  0.44618145 4.116813  ]\n",
       "loss = \n",
-      "1.4321401\n",
+      "1.4452852\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01327815,  0.00644303, -0.00022558], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02071659,  0.00596254, -0.00024882], dtype=float32)>]\n",
       "epoch idx = 5\n",
       "batch idx = 4\n",
       "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
       "param estimates vec = \n",
-      "[1.1876335  0.43729928 4.10024   ]\n",
+      "[1.1914343  0.43974406 4.1369996 ]\n",
       "loss = \n",
-      "1.4393805\n",
+      "1.4334708\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01930386,  0.0092297 , -0.00023475], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02455385,  0.00834037, -0.00028027], dtype=float32)>]\n",
       "epoch idx = 5\n",
       "batch idx = 5\n",
       "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
       "param estimates vec = \n",
-      "[1.1841393  0.43293226 4.119914  ]\n",
+      "[1.1875178  0.43252608 4.156955  ]\n",
       "loss = \n",
-      "1.4383972\n",
+      "1.4383688\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02267957,  0.00457919, -0.00026406], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0201391 ,  0.00650353, -0.0002482 ], dtype=float32)>]\n",
       "epoch idx = 5\n",
       "batch idx = 6\n",
       "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
       "param estimates vec = \n",
-      "[1.1787736  0.42761812 4.1393576 ]\n",
+      "[1.1832361  0.42348263 4.176452  ]\n",
       "loss = \n",
-      "1.4450212\n",
+      "1.4382118\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0200928 ,  0.00600473, -0.00024326], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00647444,  0.01171121, -0.00020874], dtype=float32)>]\n",
       "epoch idx = 5\n",
       "batch idx = 7\n",
       "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
       "param estimates vec = \n",
-      "[1.1718378  0.42159498 4.1584473 ]\n",
+      "[1.1765053  0.41288206 4.1957498 ]\n",
       "loss = \n",
-      "1.4391196\n",
+      "1.4452323\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01876439,  0.00528995, -0.00021942], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0251914 ,  0.01115507, -0.00023986], dtype=float32)>]\n",
       "epoch idx = 5\n",
       "batch idx = 8\n",
       "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
       "param estimates vec = \n",
-      "[1.1634625  0.41306695 4.177364  ]\n",
+      "[1.1679971 0.4008849 4.2147465]\n",
       "loss = \n",
-      "1.4494781\n",
+      "1.4429992\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01874916,  0.01341558, -0.00024035], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02108202,  0.01099462, -0.00022027], dtype=float32)>]\n",
       "epoch idx = 5\n",
       "batch idx = 9\n",
       "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
       "param estimates vec = \n",
-      "[1.1543602  0.40409064 4.195902  ]\n",
+      "[1.1584523  0.38807756 4.233438  ]\n",
       "loss = \n",
-      "1.4432405\n",
+      "1.4429775\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01341613,  0.00536929, -0.00020532], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01588852,  0.00880811, -0.00021405], dtype=float32)>]\n",
       "epoch idx = 6\n",
       "batch idx = 0\n",
       "loss idx = 7\n",
       "param estimates vec = \n",
-      "[1.1446416  0.39412606 4.2143517 ]\n",
+      "[1.1485958  0.37564033 4.2517805 ]\n",
       "loss = \n",
-      "1.4478772\n",
+      "1.4357446\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01293007,  0.00783917, -0.00024206], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01032232,  0.00370391, -0.00020194], dtype=float32)>]\n",
       "epoch idx = 6\n",
       "batch idx = 1\n",
       "loss idx = 7loss idx = 7\n",
       "param estimates vec = \n",
-      "[1.1356127  0.37936834 4.2327814 ]\n",
+      "[1.139312   0.35905427 4.27008   ]\n",
       "loss = \n",
-      "1.439957\n",
+      "1.4395021\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00184615,  0.02458238, -0.00024927], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00291093,  0.02407301, -0.00024052], dtype=float32)>]\n",
       "epoch idx = 6\n",
       "batch idx = 2\n",
       "loss idx = 7loss idx = 7loss idx = 7\n",
       "param estimates vec = \n",
-      "[1.1262296 0.3646304 4.2509656]\n",
+      "[1.1296252  0.34453222 4.28805   ]\n",
       "loss = \n",
-      "1.4485915\n",
+      "1.4375937\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01038933,  0.00573483, -0.00021423], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01074547, -0.00236056, -0.00019692], dtype=float32)>]\n",
       "epoch idx = 6\n",
       "batch idx = 3\n",
       "loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
       "param estimates vec = \n",
-      "[1.1179028  0.34733748 4.268984  ]\n",
+      "[1.1207823 0.3318469 4.305574 ]\n",
       "loss = \n",
-      "1.4436961\n",
+      "1.4405926\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00160565,  0.01662212, -0.00022111], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00046233, -0.00217559, -0.00017456], dtype=float32)>]\n",
       "epoch idx = 6\n",
       "batch idx = 4\n",
       "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
       "param estimates vec = \n",
-      "[1.1099538  0.33150983 4.2868834 ]\n",
+      "[1.1126881  0.32245648 4.3226776 ]\n",
       "loss = \n",
-      "1.4439816\n",
+      "1.4392704\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00339416,  0.00058956, -0.00022421], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00060603, -0.00932529, -0.0001712 ], dtype=float32)>]\n",
       "epoch idx = 6\n",
       "batch idx = 5\n",
       "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
       "param estimates vec = \n",
-      "[1.1024853 0.3209647 4.304304 ]\n",
+      "[1.1061152  0.31639445 4.339199  ]\n",
       "loss = \n",
-      "1.4475049\n",
+      "1.4316002\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00220122, -0.01578271, -0.00016964], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00637821, -0.01074126, -0.00014183], dtype=float32)>]\n",
       "epoch idx = 6\n",
       "batch idx = 6\n",
       "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
       "param estimates vec = \n",
-      "[1.0967247  0.31093088 4.3215146 ]\n",
+      "[1.1009691  0.31413436 4.355367  ]\n",
       "loss = \n",
-      "1.4433697\n",
+      "1.4352622\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00855398,  0.00194071, -0.00020033], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00670968, -0.0140708 , -0.00016537], dtype=float32)>]\n",
       "epoch idx = 6\n",
       "batch idx = 7\n",
       "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
       "param estimates vec = \n",
-      "[1.0915626  0.30356205 4.338632  ]\n",
+      "[1.0963562  0.31563038 4.3712034 ]\n",
       "loss = \n",
-      "1.4467583\n",
+      "1.4431243\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00051757, -0.00708049, -0.00021291], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00044027, -0.01531911, -0.00016278], dtype=float32)>]\n",
       "epoch idx = 6\n",
       "batch idx = 8\n",
       "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
       "param estimates vec = \n",
-      "[1.086672   0.30091318 4.3554583 ]\n",
+      "[1.0941831  0.31885317 4.3869133 ]\n",
       "loss = \n",
-      "1.4442953\n",
+      "1.4383785\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00174739, -0.01638662, -0.00018259], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01629994, -0.00801161, -0.00018586], dtype=float32)>]\n",
       "epoch idx = 6\n",
       "batch idx = 9\n",
       "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
       "param estimates vec = \n",
-      "[1.0832942  0.30342916 4.372121  ]\n",
+      "[1.0921764 0.3247177 4.402421 ]\n",
       "loss = \n",
-      "1.4424853\n",
+      "1.442446\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0087261 , -0.01989289, -0.00019544], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00029344, -0.01255248, -0.00017266], dtype=float32)>]\n",
       "epoch idx = 7\n",
       "batch idx = 0\n",
       "loss idx = 8\n",
       "param estimates vec = \n",
-      "[1.0812957 0.3101258 4.3885174]\n",
+      "[1.0906609  0.33380148 4.417589  ]\n",
       "loss = \n",
-      "1.4381754\n",
+      "1.439075\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00872504, -0.01778691, -0.00017797], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00242848, -0.01598314, -0.00015042], dtype=float32)>]\n",
       "epoch idx = 7\n",
       "batch idx = 1\n",
       "loss idx = 8loss idx = 8\n",
       "param estimates vec = \n",
-      "[1.0808198  0.31594047 4.4048615 ]\n",
+      "[1.0894346  0.34302503 4.4327664 ]\n",
       "loss = \n",
-      "1.4391034\n",
+      "1.4470668\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01087773,  0.00102212, -0.00020278], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00117162, -0.00416337, -0.00019224], dtype=float32)>]\n",
       "epoch idx = 7\n",
       "batch idx = 2\n",
       "loss idx = 8loss idx = 8loss idx = 8\n",
       "param estimates vec = \n",
-      "[1.0817597  0.32517597 4.4208617 ]\n",
+      "[1.0869502 0.3525718 4.4478645]\n",
       "loss = \n",
-      "1.4432172\n",
+      "1.4433887\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01109107, -0.01577439, -0.00016123], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01082762, -0.00495951, -0.00017937], dtype=float32)>]\n",
       "epoch idx = 7\n",
       "batch idx = 3\n",
       "loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
       "param estimates vec = \n",
-      "[1.0837673  0.33366182 4.436654  ]\n",
+      "[1.0843853  0.36318544 4.462837  ]\n",
       "loss = \n",
-      "1.4404781\n",
+      "1.4384784\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00928558, -0.00045538, -0.00017426], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00245179, -0.00814662, -0.00017114], dtype=float32)>]\n",
       "epoch idx = 7\n",
       "batch idx = 4\n",
       "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
       "param estimates vec = \n",
-      "[1.0863196 0.3439123 4.4521465]\n",
+      "[1.0835631  0.37384227 4.4773154 ]\n",
       "loss = \n",
-      "1.4397323\n",
+      "1.4315205\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00584762, -0.01004762, -0.00015831], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01168082, -0.00428121, -0.00011989], dtype=float32)>]\n",
       "epoch idx = 7\n",
       "batch idx = 5\n",
       "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
       "param estimates vec = \n",
-      "[1.089426  0.3530099 4.467668 ]\n",
+      "[1.0842719  0.38367778 4.491507  ]\n",
       "loss = \n",
-      "1.4380817\n",
+      "1.4380674\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00629036,  0.0007439 , -0.00019755], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01123098, -0.00073078, -0.0001404 ], dtype=float32)>]\n",
       "epoch idx = 7\n",
       "batch idx = 6\n",
       "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
       "param estimates vec = \n",
-      "[1.0930592 0.3608603 4.4829483]\n",
+      "[1.085438   0.39336017 4.5056567 ]\n",
       "loss = \n",
-      "1.4351432\n",
+      "1.433667\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00644218,  0.00151912, -0.00016092], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00402009, -0.00313354, -0.00016791], dtype=float32)>]\n",
       "epoch idx = 7\n",
       "batch idx = 7\n",
       "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
       "param estimates vec = \n",
-      "[1.0968163  0.36757573 4.4980264 ]\n",
+      "[1.0857435  0.40166748 4.519708  ]\n",
       "loss = \n",
-      "1.4404207\n",
+      "1.44164\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0036394 ,  0.00152523, -0.00016214], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00572438,  0.00187825, -0.00015916], dtype=float32)>]\n",
       "epoch idx = 7\n",
       "batch idx = 8\n",
       "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
       "param estimates vec = \n",
-      "[1.1003006  0.37630227 4.5130086 ]\n",
+      "[1.0860194 0.4078472 4.5334926]\n",
       "loss = \n",
-      "1.4425391\n",
+      "1.4473066\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00062544, -0.01009827, -0.0001727 ], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 7.4390368e-06,  5.3982227e-03, -1.3540038e-04], dtype=float32)>]\n",
       "epoch idx = 7\n",
       "batch idx = 9\n",
       "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
       "param estimates vec = \n",
-      "[1.1039852  0.38215378 4.527873  ]\n",
+      "[1.0861707  0.41147858 4.5473948 ]\n",
       "loss = \n",
-      "1.4351695\n",
+      "1.443031\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00407679,  0.00778261, -0.00016767], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00074206,  0.00783633, -0.00018063], dtype=float32)>]\n",
       "epoch idx = 8\n",
       "batch idx = 0\n",
       "loss idx = 9\n",
       "param estimates vec = \n",
-      "[1.108554   0.38644925 4.542696  ]\n",
+      "[1.0865843 0.4135923 4.5610366]\n",
       "loss = \n",
-      "1.4380676\n",
+      "1.4426723\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00944455,  0.00378663, -0.00017517], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00206831,  0.0046567 , -0.00013298], dtype=float32)>]\n",
       "epoch idx = 8\n",
       "batch idx = 1\n",
       "loss idx = 9loss idx = 9\n",
       "param estimates vec = \n",
-      "[1.1127751  0.38757885 4.5574913 ]\n",
+      "[1.086962   0.41516757 4.5744743 ]\n",
       "loss = \n",
-      "1.4414084\n",
+      "1.4391497\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00063106,  0.01033926, -0.0001755 ], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-2.2409678e-05,  1.3354786e-03, -1.3624398e-04], dtype=float32)>]\n",
       "epoch idx = 8\n",
       "batch idx = 2\n",
       "loss idx = 9loss idx = 9loss idx = 9\n",
       "param estimates vec = \n",
-      "[1.1151948 0.3885092 4.5722904]\n",
+      "[1.089566   0.41517997 4.5874853 ]\n",
       "loss = \n",
-      "1.4410145\n",
+      "1.4338611\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01062315,  0.00034508, -0.00017819], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01670188,  0.00553294, -0.0001052 ], dtype=float32)>]\n",
       "epoch idx = 8\n",
       "batch idx = 3\n",
       "loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
       "param estimates vec = \n",
-      "[1.1160829 0.3889015 4.5869   ]\n",
+      "[1.0929747  0.41109115 4.600365  ]\n",
       "loss = \n",
-      "1.4418608\n",
+      "1.4347651\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00980707,  0.00166633, -0.00015279], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0077092 ,  0.01595904, -0.00013646], dtype=float32)>]\n",
       "epoch idx = 8\n",
       "batch idx = 4\n",
       "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
       "param estimates vec = \n",
-      "[1.1151407  0.38874596 4.601293  ]\n",
+      "[1.0958776  0.40398166 4.6131945 ]\n",
       "loss = \n",
-      "1.449048\n",
+      "1.4527373\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0130653 ,  0.00187863, -0.00014627], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00133967,  0.01319903, -0.00014419], dtype=float32)>]\n",
       "epoch idx = 8\n",
       "batch idx = 5\n",
       "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
       "param estimates vec = \n",
-      "[1.1132163  0.38996303 4.6155877 ]\n",
+      "[1.0989645  0.39788467 4.625984  ]\n",
       "loss = \n",
-      "1.4390029\n",
+      "1.433577\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00796717, -0.00496458, -0.00015778], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00332123, -0.00131052, -0.00014414], dtype=float32)>]\n",
       "epoch idx = 8\n",
       "batch idx = 6\n",
       "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
       "param estimates vec = \n",
-      "[1.1107329  0.39109832 4.6296782 ]\n",
+      "[1.1025059  0.38908994 4.6386113 ]\n",
       "loss = \n",
-      "1.4422728\n",
+      "1.4415176\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00547815, -0.00012111, -0.00014261], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00538006,  0.01254487, -0.00012795], dtype=float32)>]\n",
       "epoch idx = 8\n",
       "batch idx = 7\n",
       "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
       "param estimates vec = \n",
-      "[1.1081474  0.39224786 4.643607  ]\n",
+      "[1.1044495  0.38172662 4.651297  ]\n",
       "loss = \n",
-      "1.4373249\n",
+      "1.4436734\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00247728, -0.00043956, -0.00014476], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00904359, -0.00227912, -0.00015223], dtype=float32)>]\n",
       "epoch idx = 8\n",
       "batch idx = 8\n",
       "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
       "param estimates vec = \n",
-      "[1.1070322  0.38874733 4.6572657 ]\n",
+      "[1.1069456 0.3701836 4.664143 ]\n",
       "loss = \n",
-      "1.4363204\n",
+      "1.4396455\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00895177,  0.01632423, -0.00012877], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00524557,  0.01850639, -0.00016446], dtype=float32)>]\n",
       "epoch idx = 8\n",
       "batch idx = 9\n",
       "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
       "param estimates vec = \n",
-      "[1.1066437  0.38428232 4.6705694 ]\n",
+      "[1.108262   0.36057305 4.6769705 ]\n",
       "loss = \n",
-      "1.4314362\n",
+      "1.4383945\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00451128,  0.0046349 , -0.00011466], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00667872, -0.00315757, -0.0001439 ], dtype=float32)>]\n",
       "epoch idx = 9\n",
       "batch idx = 0\n",
       "loss idx = 10\n",
       "param estimates vec = \n",
-      "[1.1050621 0.377372  4.6839914]\n",
+      "[1.1098832 0.3504471 4.6898117]\n",
       "loss = \n",
-      "1.4425762\n",
+      "1.4454774\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00888315,  0.01020803, -0.00016755], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00302575,  0.00534486, -0.00014687], dtype=float32)>]\n",
       "epoch idx = 9\n",
       "batch idx = 1\n",
       "loss idx = 10loss idx = 10\n",
       "param estimates vec = \n",
-      "[1.1028092  0.37159202 4.6973195 ]\n",
+      "[1.1115258  0.34209138 4.7024536 ]\n",
       "loss = \n",
-      "1.4383688\n",
+      "1.4405192\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00589911, -0.0016791 , -0.00014236], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00122528, -0.00301396, -0.00012089], dtype=float32)>]\n",
       "epoch idx = 9\n",
       "batch idx = 2\n",
       "loss idx = 10loss idx = 10loss idx = 10\n",
       "param estimates vec = \n",
-      "[1.1006745  0.36682868 4.7105193 ]\n",
+      "[1.1119335  0.33700937 4.714936  ]\n",
       "loss = \n",
-      "1.4325638\n",
+      "1.4491068\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00068273, -0.00164583, -0.00013646], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00751026, -0.00917777, -0.00012279], dtype=float32)>]\n",
       "epoch idx = 9\n",
       "batch idx = 3\n",
       "loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
       "param estimates vec = \n",
-      "[1.0988505  0.36037257 4.7236466 ]\n",
+      "[1.1127315  0.33444378 4.727139  ]\n",
       "loss = \n",
-      "1.4521855\n",
+      "1.4446946\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00076689,  0.00750601, -0.00014098], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00296978, -0.00748824, -0.00010637], dtype=float32)>]\n",
       "epoch idx = 9\n",
       "batch idx = 4\n",
       "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
       "param estimates vec = \n",
-      "[1.0984527  0.35366836 4.7365246 ]\n",
+      "[1.1133637 0.3352313 4.739198 ]\n",
       "loss = \n",
-      "1.4386785\n",
+      "1.4422052\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00884843,  0.00299711, -0.0001185 ], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00061911, -0.01139222, -0.00011853], dtype=float32)>]\n",
       "epoch idx = 9\n",
       "batch idx = 5\n",
       "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
       "param estimates vec = \n",
-      "[1.0999657  0.34849286 4.7493324 ]\n",
+      "[1.1135654  0.33966425 4.7511787 ]\n",
       "loss = \n",
-      "1.4380639\n",
+      "1.438652\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.013158  , -0.00308303, -0.00013625], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0025362 , -0.01357338, -0.00012398], dtype=float32)>]\n",
       "epoch idx = 9\n",
       "batch idx = 6\n",
       "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
       "param estimates vec = \n",
-      "[1.1019045 0.347699  4.7620068]\n",
+      "[1.114643   0.34629375 4.7629676 ]\n",
       "loss = \n",
-      "1.4466885\n",
+      "1.4348353\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00398146, -0.01337982, -0.0001274 ], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.006095  , -0.00950801, -0.00010942], dtype=float32)>]\n",
       "epoch idx = 9\n",
       "batch idx = 7\n",
       "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
       "param estimates vec = \n",
-      "[1.1036462  0.34625834 4.77473   ]\n",
+      "[1.1149516 0.3518664 4.7748837]\n",
       "loss = \n",
-      "1.4426162\n",
+      "1.447805\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 8.8380511e-05,  2.4739732e-03, -1.4666056e-04], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00451319,  0.00153804, -0.00014353], dtype=float32)>]\n",
       "epoch idx = 9\n",
       "batch idx = 8\n",
       "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
       "param estimates vec = \n",
-      "[1.1051816  0.34838104 4.7873063 ]\n",
+      "[1.1145345  0.35975656 4.7867503 ]\n",
       "loss = \n",
-      "1.433877\n",
+      "1.4338326\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00027952, -0.01167708, -0.00012369], dtype=float32)>]\n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00469216, -0.01023915, -0.00012396], dtype=float32)>]\n",
       "epoch idx = 9\n",
       "batch idx = 9\n",
       "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
       "param estimates vec = \n",
-      "[1.1067883  0.35351214 4.79964   ]\n",
+      "[1.1138574  0.36365664 4.798547  ]\n",
       "loss = \n",
-      "1.4425683\n",
+      "1.4366425\n",
       "grads = \n",
-      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00149583, -0.01089652, -0.00011046], dtype=float32)>]\n"
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00200888,  0.01156584, -0.00012051], dtype=float32)>]\n"
      ]
     }
    ],
@@ -1054,12 +1054,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1075,7 +1075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1088,12 +1088,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
diff --git a/docs/demo.ipynb b/docs/demo.ipynb
new file mode 100644
index 0000000..2efc594
--- /dev/null
+++ b/docs/demo.ipynb
@@ -0,0 +1,3156 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "from jacques import kcqe\n",
+    "\n",
+    "plt.style.use('ggplot')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data generation adapted from https://matplotlib.org/stable/gallery/mplot3d/scatter3d.html\n",
+    "# Fix random state for reproducibility\n",
+    "np.random.seed(9731)\n",
+    "\n",
+    "def randrange(n, vmin, vmax):\n",
+    "    \"\"\"\n",
+    "    Helper function to make an array of random numbers having shape (n, )\n",
+    "    with each number distributed Uniform(vmin, vmax).\n",
+    "    \"\"\"\n",
+    "    return (vmax - vmin)*np.random.rand(n) + vmin\n",
+    "\n",
+    "n = 500\n",
+    "\n",
+    "#Create random predictors x0 and x1\n",
+    "x0 = randrange(n, -2. * np.pi, 2. * np.pi)\n",
+    "x1 = randrange(n, -2. * np.pi, 2. * np.pi)\n",
+    "\n",
+    "#Create response variable y\n",
+    "y = 10. * np.cos(x0) + 0.5 * x1**2 + np.random.normal(loc=0., scale=5., size=n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Combine x0 and x1 into a single data array of predictors \n",
+    "\n",
+    "\n",
+    "x = np.concatenate([x0[:, np.newaxis], x1[:, np.newaxis]], axis=1)\n",
+    "\n",
+    "grid_size = 120\n",
+    "\n",
+    "#Creates a grid of x values for test set\n",
+    "x_test = np.concatenate(\n",
+    "    [np.tile(np.linspace(-2. * np.pi, 2. * np.pi, num = grid_size), grid_size)[:, np.newaxis],\n",
+    "        np.repeat(np.linspace(-2. * np.pi, 2. * np.pi, num = grid_size), grid_size)[:, np.newaxis]],\n",
+    "    axis=1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# x_train_val\n",
+    "x = np.reshape(x, (1, 500, 2))\n",
+    "# y_train_val\n",
+    "y = np.reshape(y, (1, 500))\n",
+    "\n",
+    "x_test = np.reshape(x_test, (1, 14400, 2))\n",
+    "\n",
+    "x = tf.constant(x, dtype=tf.float32)\n",
+    "y = tf.constant(y, dtype=tf.float32)\n",
+    "x_test = tf.constant(x_test, dtype=tf.float32)\n",
+    "    \n",
+    "block_size = 50\n",
+    "num_blocks = math.floor(y.shape[1]/block_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kcqe_obj = kcqe.KCQE(p=2)\n",
+    "generator = kcqe_obj.generator(x, y, batch_size = 10, block_size = block_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(500, 2)\n",
+      "(500,)\n",
+      "(3500, 2)\n",
+      "(3500,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_val, x_train, y_val, y_train = next(generator)\n",
+    "print(x_val.shape)\n",
+    "print(y_val.shape)\n",
+    "print(x_train.shape)\n",
+    "print(y_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch idx = 0\n",
+      "batch idx = 0\n",
+      "loss idx = 1\n",
+      "param estimates vec = \n",
+      "[3.0999959 3.0999956 0.9000329]\n",
+      "loss = \n",
+      "0.4340877\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.09047129, -0.08324867,  0.0098167 ], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 1\n",
+      "loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[3.199856   3.1997824  0.79993284]\n",
+      "loss = \n",
+      "0.41570568\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.08663627, -0.07824451,  0.01055256], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 2\n",
+      "loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[3.2994022 3.2993462 0.7002404]\n",
+      "loss = \n",
+      "0.39937627\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.08152357, -0.07530656,  0.00937018], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 3\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[3.3985517  3.3984368  0.60091174]\n",
+      "loss = \n",
+      "0.3819919\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.07755921, -0.07013773,  0.00904217], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 4\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[3.4973433 3.4974148 0.5018099]\n",
+      "loss = \n",
+      "0.3666924\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.07546826, -0.07184812,  0.00898133], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 5\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[3.595891  3.5961401 0.4026683]\n",
+      "loss = \n",
+      "0.35161352\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.07499909, -0.06933736,  0.0092812 ], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 6\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[3.6941018 3.6946476 0.3030563]\n",
+      "loss = \n",
+      "0.3367726\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.07261316, -0.06862752,  0.01017664], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 7\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[3.7918015  3.792903   0.20341837]\n",
+      "loss = \n",
+      "0.32252237\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06918541, -0.06733802,  0.00954178], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 8\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[3.8889618  3.8909302  0.10318676]\n",
+      "loss = \n",
+      "0.30863085\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06721225, -0.06673731,  0.0107368 ], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 9\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[3.9854839e+00 3.9885240e+00 2.3397878e-03]\n",
+      "loss = \n",
+      "0.29379937\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06459757, -0.06372651,  0.01112682], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 0\n",
+      "loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 4.0811944   4.0855007  -0.09842206]\n",
+      "loss = \n",
+      "0.28246936\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06122424, -0.06071583,  0.00996225], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 1\n",
+      "loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 4.1756215  4.1814218 -0.1969113]\n",
+      "loss = \n",
+      "0.27405998\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05500582, -0.05519107,  0.00678986], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 2\n",
+      "loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 4.268919    4.276417   -0.29549962]\n",
+      "loss = \n",
+      "0.25533882\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05416697, -0.05450184,  0.00950411], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 3\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 4.3609185   4.370388   -0.39378908]\n",
+      "loss = \n",
+      "0.24520996\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05082754, -0.05208125,  0.00890636], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 4\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 4.4514413   4.46302    -0.49144515]\n",
+      "loss = \n",
+      "0.23602378\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0474291 , -0.04788806,  0.0083439 ], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 5\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 4.5404906   4.55435    -0.58856815]\n",
+      "loss = \n",
+      "0.22558987\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04550537, -0.04641562,  0.00833094], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 6\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 4.6277394  4.644008  -0.6835995]\n",
+      "loss = \n",
+      "0.21969283\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04116281, -0.04195452,  0.00628893], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 7\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 4.7130346   4.7321057  -0.77843916]\n",
+      "loss = \n",
+      "0.20754728\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03808317, -0.04095031,  0.00823064], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 8\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 4.7967606  4.818595  -0.8721204]\n",
+      "loss = \n",
+      "0.20486857\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03890415, -0.03896853,  0.00698192], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 9\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 4.878986   4.9036536 -0.9655547]\n",
+      "loss = \n",
+      "0.19276465\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03779875, -0.03862039,  0.00787362], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 0\n",
+      "loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 4.9598184  4.9874167 -1.0597819]\n",
+      "loss = \n",
+      "0.18611147\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03707707, -0.0381521 ,  0.00915468], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 1\n",
+      "loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 5.039706   5.069919  -1.1552566]\n",
+      "loss = \n",
+      "0.17834263\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03884636, -0.03713228,  0.00992965], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 2\n",
+      "loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 5.118558   5.1509433 -1.2507145]\n",
+      "loss = \n",
+      "0.17207992\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03721558, -0.03451128,  0.00844712], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 3\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 5.196028   5.23017   -1.3463507]\n",
+      "loss = \n",
+      "0.17714319\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0338779 , -0.0311991 ,  0.00868054], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 4\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 5.272312   5.307709  -1.4399177]\n",
+      "loss = \n",
+      "0.16643275\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03395461, -0.03049473,  0.00604484], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 5\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 5.3473663  5.383646  -1.5354514]\n",
+      "loss = \n",
+      "0.15401198\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03263132, -0.02972618,  0.0107067 ], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 6\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 5.4208794  5.4576707 -1.6329474]\n",
+      "loss = \n",
+      "0.14617164\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02963778, -0.02668136,  0.0111    ], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 7\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 5.492739   5.5296254 -1.7319256]\n",
+      "loss = \n",
+      "0.14109774\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02778094, -0.02446125,  0.01080523], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 8\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 5.5636444  5.5998025 -1.8301619]\n",
+      "loss = \n",
+      "0.14126992\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03082748, -0.0247898 ,  0.00812277], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 9\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 5.6338468  5.668663  -1.9270192]\n",
+      "loss = \n",
+      "0.13906966\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03155376, -0.02623895,  0.00725388], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 0\n",
+      "loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 5.7029963  5.7361298 -2.0225542]\n",
+      "loss = \n",
+      "0.13578936\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02882227, -0.02488827,  0.00712991], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 1\n",
+      "loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 5.769996  5.801443 -2.115954]\n",
+      "loss = \n",
+      "0.12776574\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02168905, -0.01983597,  0.00606149], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 2\n",
+      "loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 5.834852   5.8644032 -2.2058945]\n",
+      "loss = \n",
+      "0.12050984\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02042891, -0.01755857,  0.00445892], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 3\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 5.898888   5.9264145 -2.2907815]\n",
+      "loss = \n",
+      "0.1254972\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02652235, -0.0236512 ,  0.00261195], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 4\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 5.962038   5.9873443 -2.3704436]\n",
+      "loss = \n",
+      "0.12218558\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02552432, -0.02230183,  0.00202747], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 5\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 6.0236034  6.0475044 -2.4443555]\n",
+      "loss = \n",
+      "0.11412597\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02107397, -0.02322092,  0.00115154], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 6\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 6.083282  6.105881 -2.512604]\n",
+      "loss = \n",
+      "0.1128903\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01852372, -0.01752764,  0.00078552], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 7\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 6.1415405  6.1619997 -2.57529  ]\n",
+      "loss = \n",
+      "0.11720057\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01991348, -0.01423455,  0.00044925], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 8\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 6.1983876  6.216029  -2.6337907]\n",
+      "loss = \n",
+      "0.108098745\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01912536, -0.01394496,  0.00116669], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 9\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 6.254628  6.268377 -2.686642]\n",
+      "loss = \n",
+      "0.109484516\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02244778, -0.01487482, -0.00031273], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 0\n",
+      "loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 6.310241   6.319488  -2.7342913]\n",
+      "loss = \n",
+      "0.10742861\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0218899 , -0.01614296, -0.00035055], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 1\n",
+      "loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 6.3638864  6.3681803 -2.7783322]\n",
+      "loss = \n",
+      "0.10059788\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01473247, -0.00997245,  0.00053338], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 2\n",
+      "loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 6.4172125  6.4166627 -2.8176882]\n",
+      "loss = \n",
+      "0.10731679\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0218851 , -0.0191124 , -0.00054507], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 3\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 6.468494   6.4631815 -2.8547506]\n",
+      "loss = \n",
+      "0.09340275\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01315152, -0.01082676,  0.00096522], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 4\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 6.517154   6.50865   -2.8871949]\n",
+      "loss = \n",
+      "0.099775046\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00942962, -0.01405858, -0.00094722], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 5\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 6.5641727  6.5549264 -2.917521 ]\n",
+      "loss = \n",
+      "0.09983172\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01284493, -0.02193999,  0.0006142 ], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 6\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 6.6096077  6.6002116 -2.945929 ]\n",
+      "loss = \n",
+      "0.099487975\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01232379, -0.01403601,  0.00061684], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 7\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 6.6537642  6.6449146 -2.9726846]\n",
+      "loss = \n",
+      "0.09563699\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0129744 , -0.0153389 ,  0.00068054], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 8\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 6.697048   6.6899347 -2.9968665]\n",
+      "loss = \n",
+      "0.09620985\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-1.4199249e-02, -1.8952260e-02, -9.9959267e-05], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 9\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 6.740657   6.7365165 -3.0193014]\n",
+      "loss = \n",
+      "0.094205156\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01920061, -0.02445577,  0.00032784], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 0\n",
+      "loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 6.782441   6.7815924 -3.039304 ]\n",
+      "loss = \n",
+      "0.09101444\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00952346, -0.01160324, -0.00027242], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 1\n",
+      "loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 6.8244295  6.828236  -3.0590806]\n",
+      "loss = \n",
+      "0.08958374\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01774773, -0.02417039,  0.00112914], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 2\n",
+      "loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 6.8656425  6.873665  -3.07746  ]\n",
+      "loss = \n",
+      "0.089447364\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01336752, -0.01273013,  0.00028776], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 3\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 6.906254  6.918332 -3.095522]\n",
+      "loss = \n",
+      "0.08547422\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01372232, -0.01407947,  0.00094508], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 4\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 6.9460006  6.961589  -3.1116207]\n",
+      "loss = \n",
+      "0.08631629\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01225072, -0.01099682, -0.00020863], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 5\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 6.9852214  7.004135  -3.125169 ]\n",
+      "loss = \n",
+      "0.0857819\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01327842, -0.0132684 , -0.0007245 ], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 6\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 7.0240107  7.045644  -3.1374524]\n",
+      "loss = \n",
+      "0.083528206\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-1.3385864e-02, -1.1578526e-02, -1.2241659e-05], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 7\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 7.062295   7.086417  -3.1467273]\n",
+      "loss = \n",
+      "0.08225488\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01282051, -0.01233627, -0.00124738], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 8\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 7.099523   7.125743  -3.1556613]\n",
+      "loss = \n",
+      "0.07843824\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01023536, -0.00913865,  0.00033983], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 9\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 7.135659   7.1623907 -3.1627004]\n",
+      "loss = \n",
+      "0.08408284\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00961699, -0.00371938, -0.00069993], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 0\n",
+      "loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 7.1699     7.1969705 -3.1691723]\n",
+      "loss = \n",
+      "0.08039943\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-5.8631045e-03, -5.1111807e-03,  5.4374006e-05], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 1\n",
+      "loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 7.2032013  7.230555  -3.1733062]\n",
+      "loss = \n",
+      "0.08063931\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00898727, -0.00848802, -0.00111919], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 2\n",
+      "loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 7.2368817  7.2655845 -3.1738768]\n",
+      "loss = \n",
+      "0.08243103\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01390606, -0.01752613, -0.00203178], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 3\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 7.2702847  7.2992077 -3.1732795]\n",
+      "loss = \n",
+      "0.079363406\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01132351, -0.00698824, -0.00070726], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 4\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 7.3033934  7.33274   -3.1729462]\n",
+      "loss = \n",
+      "0.07477369\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0110848 , -0.0114388 ,  0.00013118], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 5\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 7.336214   7.366417  -3.1730962]\n",
+      "loss = \n",
+      "0.0742717\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01093223, -0.01223066,  0.00028249], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 6\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 7.368678   7.3999043 -3.1724353]\n",
+      "loss = \n",
+      "0.0744591\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01049402, -0.01095087, -0.00049398], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 7\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 7.4007554  7.434195  -3.1704984]\n",
+      "loss = \n",
+      "0.07568657\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01018335, -0.01453011, -0.00082294], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 8\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 7.432286   7.467856  -3.1678607]\n",
+      "loss = \n",
+      "0.073922284\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00936451, -0.00941514, -0.0005384 ], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 9\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 7.4641037  7.502553  -3.1639245]\n",
+      "loss = \n",
+      "0.07201635\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01231463, -0.01527985, -0.00093698], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 0\n",
+      "loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 7.496179   7.5372972 -3.159275 ]\n",
+      "loss = \n",
+      "0.07255843\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01223353, -0.01192675, -0.00065086], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 1\n",
+      "loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 7.527835   7.5721035 -3.1535478]\n",
+      "loss = \n",
+      "0.07386011\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00969125, -0.01193007, -0.00090456], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 2\n",
+      "loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 7.5590296  7.6070356 -3.1471584]\n",
+      "loss = \n",
+      "0.07313626\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00933171, -0.01211584, -0.00071155], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 3\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 7.589823   7.6427093 -3.1425176]\n",
+      "loss = \n",
+      "0.06999305\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00934089, -0.01431   ,  0.00067773], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 4\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 7.6200643  7.6771126 -3.1387346]\n",
+      "loss = \n",
+      "0.06882052\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00859319, -0.00731352,  0.00024741], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 5\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 7.6493015  7.7115154 -3.1363287]\n",
+      "loss = \n",
+      "0.06687779\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00669458, -0.01133636,  0.00059431], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 6\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 7.6774883  7.7457523 -3.1365387]\n",
+      "loss = \n",
+      "0.06609851\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00614659, -0.01069358,  0.00138045], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 7\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 7.7049956  7.7795606 -3.1394255]\n",
+      "loss = \n",
+      "0.06540257\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00709643, -0.00966503,  0.00154854], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 8\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 7.7319713  7.812249  -3.1438448]\n",
+      "loss = \n",
+      "0.06555256\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00736145, -0.00708333,  0.00102975], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 9\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 7.7583423  7.843409  -3.1491308]\n",
+      "loss = \n",
+      "0.06451465\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0068827 , -0.00528116,  0.00072735], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 0\n",
+      "loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 7.7847185  7.8729424 -3.1537852]\n",
+      "loss = \n",
+      "0.06427418\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-8.8092117e-03, -4.4335346e-03, -7.5314943e-05], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 1\n",
+      "loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 7.8110886  7.90162   -3.15715  ]\n",
+      "loss = \n",
+      "0.065365374\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00872454, -0.00649869, -0.00047617], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 2\n",
+      "loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 7.8355513  7.9287558 -3.1603155]\n",
+      "loss = \n",
+      "0.0634719\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-2.0215895e-03, -3.8912250e-03,  6.5634391e-05], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 3\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 7.859979   7.954614  -3.1627107]\n",
+      "loss = \n",
+      "0.06384216\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00789744, -0.00426926, -0.00026073], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 4\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 7.884041  7.979665 -3.165081]\n",
+      "loss = \n",
+      "0.06418406\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00669811, -0.00540519,  0.00011068], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 5\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 7.9064665  8.00341   -3.1670315]\n",
+      "loss = \n",
+      "0.06305346\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00218379, -0.00348833, -0.00010692], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 6\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 7.9298835  8.02707   -3.16994  ]\n",
+      "loss = \n",
+      "0.06311504\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01063075, -0.00706219,  0.0006202 ], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 7\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 7.9527235  8.050717  -3.173607 ]\n",
+      "loss = \n",
+      "0.063240275\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00555464, -0.00723714,  0.00055806], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 8\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 7.974506  8.073307 -3.176525]\n",
+      "loss = \n",
+      "0.0625969\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00372126, -0.00381589, -0.00021567], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 9\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 7.995193   8.094777  -3.1792755]\n",
+      "loss = \n",
+      "0.062173024\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-3.2408470e-03, -3.2739022e-03,  5.8047452e-05], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 0\n",
+      "loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 8.016094   8.115664  -3.1825366]\n",
+      "loss = \n",
+      "0.062243287\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00725166, -0.00463277,  0.00040918], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 1\n",
+      "loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 8.036437  8.135622 -3.185853]\n",
+      "loss = \n",
+      "0.061846115\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00471525, -0.00334055,  0.00019224], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 2\n",
+      "loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 8.057288   8.155077  -3.1892369]\n",
+      "loss = \n",
+      "0.062022477\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00804406, -0.00438942,  0.00020045], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 3\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 8.078469  8.174714 -3.193116]\n",
+      "loss = \n",
+      "0.06132816\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00757713, -0.00637373,  0.00042575], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 4\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 8.0990925  8.194614  -3.197605 ]\n",
+      "loss = \n",
+      "0.062482804\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00473958, -0.00665132,  0.00050752], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 5\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 8.120217   8.213727  -3.2013662]\n",
+      "loss = \n",
+      "0.061571416\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00799035, -0.00342593, -0.00015525], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 6\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 8.140355   8.232463  -3.2050688]\n",
+      "loss = \n",
+      "0.061046638\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00329544, -0.00445019,  0.00015337], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 7\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 8.160669   8.25024   -3.2082288]\n",
+      "loss = \n",
+      "0.06086245\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-6.7184810e-03, -2.5310533e-03, -9.7062046e-05], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 8\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 8.1803255  8.268321  -3.211258 ]\n",
+      "loss = \n",
+      "0.060137905\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-4.0698745e-03, -6.1031892e-03,  8.6250249e-05], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 9\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 8.199786   8.286788  -3.2142801]\n",
+      "loss = \n",
+      "0.05985846\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0053239 , -0.00641284,  0.00014213], dtype=float32)>]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# plug in generator instead of raw data to get away from calculating 7-day avg\n",
+    "# initialize parameters at some bad values; otherwise, convergence is instantaneous\n",
+    "param_vec = kcqe_obj.fit(xval_batch_gen = generator,\n",
+    "    num_blocks = num_blocks, \n",
+    "    tau=tf.constant(np.array([0.1, 0.5, 0.9]), dtype=tf.float32),\n",
+    "    optim_method=\"adam\", \n",
+    "    num_epochs=10, \n",
+    "    learning_rate=0.1,\n",
+    "    init_param_vec=tf.constant(np.array([3.0, 3.0, 1.0]), dtype=tf.float32),\n",
+    "    verbose = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8eS4hpCqFhYV49NFHTeCRgPPwww+je/fuFYaD+vXrh+bNm5u5Lm+99RamTZtmhprk/MvJnJglS5a4nyclJWH69OmIjY2Fr0jvkLedTO2Pwg1rELnnEzS+YYDXrx+ofFFrqhxrbR3W2jqsdXDW2pKJIvXq1cPMmTNRVFSE3bt3mzkwLVq0MMNFIi3NNTwi2rRpY4aMHnvsMezZs8f03lxuxIgRGDp0qPt5WcLLzc01ocib5NryByJByttDN06ZfLthDc5t+i/ODx0D5aiDUObLWlNFrLV1WGvrsNaBV2uZDlLdzgaPAotMlJUeD1kdVJ48v7zXpTz5TFkKa9u2LY4ePYply5a5A8vlJMzIcJAUorLAEh4ebh6V8dU3qVzX69fu0hOo3wA4cwo6cw/Q8crfayjySa2pUqy1dVhr67DWwVlrh6dJqF27dmYeShkZIpLnHTp0qPZ15DPl56Bc7uTJkzh37hwaN26MYKbCwqGuvd4cy4ohIiIi8tIqIRmKWb9+PTZu3IgjR45g3rx5KC4uRv/+/c37c+bMwcKFCyvMN/niiy9w/Phxc77s37J582bceOON5n0ZJlqwYIGZtJuTk2OGjGbMmGF6ZGQvlmCn+rrqoD/ZCl1aandziIiI/JLHc1hSU1PN5NvFixeboSAZ4pk8ebJ7SOjEiRMVZg1LmJFQI70msvy5VatWZn6KXKdsuOjQoUNm47iCggI0adLETMi95557qhz2CSoduwONooGzZ4C9XwBdr7W7RURERH5H6SAa6JNJt9831FQTEr7i4+ORlZXls3E655t/g964BiptEBwPPoFQZUWtyYW1tg5rbR3WOvBqLR0T1Z10y3sJ+QHV+9Kw0GcfQXs5cBEREQUDBhZ/0L4zENMEKCwAMj6zuzVERER+h4HFD8j+K6rXpa36uVqIiIjoCgwsfkL1uTQstOtj6AvFdjeHiIjIrzCw+It2HYGmzYHi88DunXa3hoiIyK8wsPjRjOuyXhbn9k12N4eIiMivMLD4EdXvZtfB7p3QBefsbg4REZHfYGDxIyqhLdAqESgpgf5ki93NISIi8hsMLH5G9XPd4kBzWIiIiMiNgcXPqL43uQ4y06FP5drdHCIiIr/AwOJnVNNYoEM3c6w//sDu5hAREfkFBhY/nnzLYSEiIiIXBhY/ZHa9DQsDjnwDffRbu5tDRERkOwYWP6QaNAS69TbHevtGu5tDRERkOwYWP+X4Qdmw0AfQTqfdzSEiIrIVA4u/6t4HiKwPyEqh/Rl2t4aIiMhWDCx+SoVHQF13vTnm5FsiIgp1DCyBsInczi3QFy/a3RwiIiLbMLD4s47dgJimQOE5YPcOu1tDRERkGwYWP6Ycddx7sji3cbUQERGFLgYWP6euH/DdHZzP5dvdHCIiIlswsPg5JXdvbtMOKC2B3vGh3c0hIiKyBQNLAPWy6G3v290UIiIiWzCwBModnB0O4GAmdPZRu5tDRERkOQaWAKCiGgNdrzPH+qMNdjeHiIjIcgwsAUL94NKeLB9t5Fb9REQUchhYAoTq2c+1Vf/JHG7VT0REIYeBJUCoiLpQ16W6e1mIiIhCCQNLAFHXDzRf9c4PoS8U290cIiIiyzCwBJL2XYAmscD5QujPP7a7NURERJZhYAkgyuH4bvLtxx/Y3RwiIiLLMLAEGNXnRtdB+ifQhQV2N4eIiMgSDCyBRrbqj28NlJRA7/rI7tYQERFZgoElwCil3L0svLcQERGFirCafGjt2rVYsWIF8vLykJiYiHHjxiE5ObnSc7dv346lS5ciOzsbpaWliIuLw1133YWbbrrJfY7WGosXL8b69etRUFCATp06Yfz48YiPj6/57yyIqT43QL+7EPhyl7mDs2oYZXeTiIiI/KuHZevWrZg/fz5GjRqF6dOnm8AydepUnDlzptLzGzZsiJEjR+L555/HzJkzMWDAALz88svYtWuX+5zly5djzZo1mDBhAqZNm4a6deuaa164cKF2v7sgpeISgNZJQGkp9Kfb7G4OERGR/wWWlStXYtCgQSZ4JCQkmJARERGBDRsqv8dN165d0bdvX3Ou9K4MGTLEhJy9e/e6e1dWr15tQk2fPn3Me5MmTcLp06exY8eO2v8Og5Tq4+qh0js2290UIiIi/xoSKikpwYEDBzB8+HD3aw6HAykpKcjMzLzq5yWcpKen49ixY7j33nvNazk5OWZoqXv37u7z6tevb4aY5JppaWlXXOfixYvmUX5eR2RkpPvYm8qu5+3r1pajzw0ofed1YF86kJ8HFd0Ygc5fax2MWGvrsNbWYa2Du9YeBZb8/Hw4nU7ExMRUeF2eSwipSmFhIR599FETeCTgPPzww+6AImFFREdHV/iMPC9773IyJ2bJkiXu50lJSWZ4KjY2Fr4ivUN+JT4exzt2w4V96Wj01W40uuseBAu/q3UQY62tw1pbh7UOzlrXaNKtp+rVq2fmrxQVFWH37t1mDkyLFi3McFFNjBgxAkOHDnU/L0t4ubm5JhR5k1xb/kBk0rD0EPkTZ49+poclb91KnOv93STmQOXPtQ42rLV1WGvrsNaBV+uwsLBqdzZ4FFiioqJMD8nlPR/y/PJel/LkM2UprG3btjh69CiWLVtmAkvZ52TSbuPG3w1ryHM5tzLh4eHmURlffZPKdf3uL0DvG4B/vQrs/xLOkzlQsm1/EPDLWgcp1to6rLV1WOvgrLXD0yTUrl07Mw+ljAwRyfMOHTpU+zrymbI5KM2bNzehRXpeyg8h7d+/36NrhiLVuCmQ3Nl9Q0QiIqJg5fEqIRmKkf1SNm7ciCNHjmDevHkoLi5G//6ue9zMmTMHCxcurDDf5IsvvsDx48fN+bJ/y+bNm3HjjTe6u5Vk5dA777yDnTt34tChQ+Ya0tsiq4aouquFGFiIiCh4eTyHJTU11Uy+lY3eZChIhm0mT57sHto5ceJEhVnDEmYk1Jw8edIsf27VqhUee+wxc50yw4YNM+e98sorpndFNo6Ta8r59P1Ur1Tot/8OfPMV9OGDULI/CxERUZBROogG+mTSbfnlzt4g4Ut23M3KyvLbMVHn32ea/VjU9QPhGPcLBKpAqHWwYK2tw1pbh7UOvFrLfNTqTrrlvYSCgLp1mPmqP/4AOu+k3c0hIiLyOgaWIKCSOgDJXYDSEuj3V9ndHCIiIq9jYAkSjrJelk1roYuL7G4OERGRVzGwBIuefYHYOKDwHPTW9Xa3hoiIyKsYWIKEctT5bi7LunehnaV2N4mIiMhrGFiCiEodBNRvCORkAZ/zTtdERBQ8GFiCiKpbD+rmwebY+d9ldjeHiIjIaxhYgowaeCdQJwzYnwF9MNPu5hAREXkFA0uQUTFNofq6bnvgXP4mN08iIqKgwMAShNSd98idKoE9nwGffWR3c4iIiGqNgSUIqRYtoW4faY6di/6P+7IQEVHAY2AJUuqOHwFNmwOnTkCvWmx3c4iIiGqFgSVIqbp14RgzwRzr/y6Dzjpid5OIiIhqjIElmPXoC6T0NvcYcr71CifgEhFRwGJgCfLbf5telrBw4MvPoXdusbtJRERENcLAEuRU83ioO0aZY714HvSFYrubRERE5DEGlhCgBo8EYpoAeaeAfbvtbg4REZHHGFhCgIqoC9W9jznWsjcLERFRgGFgCRGqS0/zVWfssrspREREHmNgCRWdusssXCDrMPTpk3a3hoiIyCMMLCFCNWgEtG1vjtnLQkREgYaBJYSozq5hITCwEBFRgGFgCcV5LF/ugnY67W4OERFRtTGwhJJrOgJ16wFnzwBHvrG7NURERNXGwBJClOx426Gbu5eFiIgoUDCwhOqwEPdjISKiAMLAEmJU12tdB19lcJt+IiIKGAwsoSYuAYhpCpRcBPZn2N0aIiKiamFgCcE7OKuuZcNCnMdCRESBgYElFF3aj4UbyBERUaBgYAlBqnMP18GRg9D5p+1uDhER0VUxsIQgFRUDtGlnjnXG53Y3h4iI6KoYWEJ9m/7dO+1uChER0VUxsIQodd315qv+ZAt0brbdzSEiIvpeYaiBtWvXYsWKFcjLy0NiYiLGjRuH5OTkSs9dt24dPvjgAxw+fNg8b9euHX784x9XOH/u3LnYtGlThc/16NEDTz/9dE2aR9Wg2nUEZBO5jF3Qa5ZA3T/J7iYRERF5L7Bs3boV8+fPx4QJE9C+fXusWrUKU6dOxaxZsxAdHX3F+RkZGUhLS0PHjh0RHh6O5cuX4/nnn8dLL72EJk2auM/r2bMnJk6c+F3DwmqUpcgDjrvGwCmBZet66DtGQcXG2d0kIiIi7wwJrVy5EoMGDcKAAQOQkJBggktERAQ2bNhQ6fmPP/44br/9drRt2xatWrXCT3/6U2itsXv37grnSUCJiYlxPxo2bOhp08hDKrmLq5eltNT0shAREfkrj7oxSkpKcODAAQwfPtz9msPhQEpKCjIzM6t1jeLiYnOdywOJ9MSMHz8eDRo0QLdu3TBmzBg0atSo0mtcvHjRPMpvhhYZGek+9qay63n7uv6izg/HovRSLwuG/MjWXpZgr7U/Ya2tw1pbh7UO7lp7FFjy8/PhdDpND0h58vzYsWPVusabb75phoIk5JQfDurXrx+aN2+O7OxsvPXWW5g2bZoZapJAdLmlS5diyZLvegSSkpIwffp0xMbGwlfi4oJ0uCQ+Hrn//QGKPv0I9TasRJMnptjdouCttR9ira3DWluHtQ7OWls6UWTZsmXYsmULnnvuOTOMVEbmuJRp06aNmcj72GOPYc+ePRWCTZkRI0Zg6NCh7udlCS83N9f03niTXFv+QCRIyVBWMNK3jQQ+/QgF61eiaMBQ23pZQqHW/oK1tg5rbR3WOvBqLdNBqtvZ4FFgiYqKMj0esjqoPHl+ea/L5d59910TWKZMmWICyfdp0aKFGQ6SQlQWWGTyrjwq46tvUrlu0P4FuKYT0OVaIOMzOFcthuOBx2xtTlDX2s+w1tZhra3DWgdnrR2eJiFZlpyenu5+TYaI5HmHDh2q/JysDPr3v/+NyZMn45prrrnqr3Py5EmcO3cOjRs39qR5VMsVQ8KsGMrJsrs5REREtVslJEMx69evx8aNG3HkyBHMmzfPTKTt37+/eX/OnDlYuHCh+3zpVVm0aBF+9rOfmTkq0hsjj6KiIvO+fF2wYIGZtJuTk2NWD82YMcN0NcleLGQNldwZ6HadJFDopQvsbg4REVHt5rCkpqaaybeLFy82wUOWK0vPSdmQ0IkTJyrMGn7vvffMvBLZd6W8UaNGYfTo0WaI6dChQ2bjuIKCAjMht3v37rjnnnuqHPYh33Dc/QCcez6D3vkh9K3DXJvLERER+QGlg2igTybdll/u7A0SvuLj45GVlRUSY6LOf86G3rIeSO4Cx5N/tHTJWqjV2k6stXVYa+uw1oFXa+mYqO6kW95LiCpQw34CyAqu/RnAZx/Z3RwiIiKDgYUqUI2bQt3i2hjQ+e/Xob28TJyIiKgmGFjoCuqOkUCjaCDnGPQHa+1uDhEREQMLXUnVqw/1wx+bY73ibejCArubREREIY6BhSqlbrgNiEsAzuVDr/233c0hIqIQx8BClVJhYWaZs9DrV0Cfzbe7SUREFMIYWKhqPfoCicnAhWLodcvtbg0REYUwBhb63nX2jjtHm2P9/krognN2N4mIiEIUAwtdvZelVSJQdN6EFiIiIjswsND3Ug4HVFkvy7p3oYsK7W4SERGFIAYWuirVKxWIawUUnoPesMbu5hARUQhiYKGrUo46UHf8yBzr95ZBFxfb3SQiIgoxDCxULarfzUCzFsDZM9Cb/2N3c4iIKMQwsFC1qDrSyzLKHOv/vAN98YLdTSIiohDCwELVplIHAo2bAXmnoN+Zb3dziIgohDCwULWpsHA4xox3rxhyblhld5OIiChEMLCQR9R1qVAj7zfH+q3/g/5ih91NIiKiEMDAQh5Tg++GuuFWQDvh/PtM6EMH7G4SEREFOQYWqtGW/erenwGdewDFRXD+9Q/Qp07Y3SwiIgpiDCxU87s5//S3QHxrMwnX+cp0aK3tbhYREQUpBhaqMVW/ARxPPAtE1AUO7AO+yrC7SUREFKQYWKhWVNPmrk3lZBLuxtV2N4eIiIIUAwvVmhpwp/mqP90KnXfK7uYQEVEQYmChWlOtk4DkzkBpKfTm/9rdHCIiCkIMLOQVqv8Q81V/sBa6pMTu5hARUZBhYCGvUL1SgagYs2IIn2+3uzlERBRkGFjIa9v2qxtvM8fODZx8S0RE3sXAQl6jbrodUA5g327oo4fsbg4REQURBhbyGtUkFujZ1xxziTMREXkTAwt5laNsifO2DdDnC+1uDhERBQkGFvKuTt2BuASg+Dycf/kD9Nd77W4REREFAQYW8vqNER13PwCEhQP7M+D805MonfM89NFv7W4aEREFMAYW8jrVsx8cU/8X6oZbXZNwP/8Yzt8/DueiebxBIhER1UhYTT60du1arFixAnl5eUhMTMS4ceOQnJxc6bnr1q3DBx98gMOHD5vn7dq1w49//OMK58s/YosXL8b69etRUFCATp06Yfz48YiPj6/Z74r8YgKueuAx6NtGwLn8DeCTrdDr3oXqfQNwTSe7m0dERMHew7J161bMnz8fo0aNwvTp001gmTp1Ks6cOVPp+RkZGUhLS8Ozzz6L559/Hk2bNjVfT5367p4zy5cvx5o1azBhwgRMmzYNdevWNde8cOFC7X53ZDsVn4A6P/0tVOog81xvWGV3k4iIKBQCy8qVKzFo0CAMGDAACQkJJmRERERgw4YNlZ7/+OOP4/bbb0fbtm3RqlUr/PSnPzU9Krt37zbvy/Hq1asxcuRI9OnTxwSgSZMm4fTp09ixY0ftf4fkF9SAS1v3f7IFOj/P7uYQEVEwB5aSkhIcOHAAKSkp313A4TDPMzMzq3WN4uJic52GDRua5zk5OWZoqXv37u5z6tevb4aMqntN8n+qbXsgqYN8E/EGiURE5Ns5LPn5+XA6nYiJianwujw/duxYta7x5ptvokmTJu7QI2FFREdHVzhPnpe9d7mLFy+aR/mVKZGRke5jbyq7nrevG4ocA++E8x+Z0JvWAneMgqpTp8L7rLV1WGvrsNbWYa2Du9Y1mnRbU8uWLcOWLVvw3HPPmWGkmlq6dCmWLFnifp6UlGTm08TGxsJX4uLifHbtUKGHjsKxf70G5+kTiDn0FeqnDqj0PNbaOqy1dVhr67DWwVlrjwJLVFSUGQK6vOdDnl/e63K5d9991wSWKVOmmHkqZco+J5N2Gzdu7H5dnsu8l8qMGDECQ4cOdT8vS3i5ublmuMmb5NryB5Kdnc0luV6g024B1izBqX8vwJmkiquFWGvrsNbWYa2tw1oHXq3DwsKq3dkQ5umFZVlyeno6+vZ13TNGhojk+eDBg6v8nKwCeuedd/D000/jmmuuqfBe8+bNTWiRSbhlAaWwsBD79+/Hbbe57v57ufDwcPOojK++SeW6/AtQe+rmwdBr34He+wWcxw5Bxbe+4hzW2jqstXVYa+uw1sFZa49XCUnPhuyXsnHjRhw5cgTz5s0zE2n79+9v3p8zZw4WLlzoPl96VRYtWoSf/exnJpxIb4w8ioqK3CltyJAhJtDs3LkThw4dMteQ3hZZNUTBRTVtDvRw/bnqDbxBIhER+WgOS2pqqpl8Kxu9SfCQXpHJkye7h3ZOnDhRYRLOe++9Z4ZpXnrppQrXkX1cRo8ebY6HDRtmQs8rr7xieldk4zi5Zm3muZD/cgwYAueu7dDb3oceeR9Uvfp2N4mIiPyc0kHUbyZzWMqvHvIGCV+y425WVha7GL1EO51wPvNz4PhRqNEPw3HrMPM6a20d1to6rLV1WOvAq7VM76juHBbeS4gspxwOqFvuMsd62QLoI9/Y3SQiIvJzDCxkC3XTYKDLtcCFC3D+73To84V2N4mIiPwYAwvZ1sviGP8roHEzMzSk589hFy4REVWJgYVsoxpFw/Hok0CdOtA7P+SNEYmIqEoMLGQrdU0nqLsfNMfORf9A8b50u5tERER+iIGFbKdu+SFw3fVAaQlO/vG30IUFdjeJiIj8DAML+cXyOMcDjwOxcSjNzYZzyWt2N4mIiPwMAwv5BVW/Aeo89IQ51h/8B/rLz+1uEhER+REGFvIbqkM3NBgyyhw7ZdVQsev2DURERAws5FdiHpoENGkGnDgOvewNu5tDRER+goGF/IqjfkM47p9kjvX6FdBf77W7SURE5AcYWMjvOLr1grp+oNy3HM7X/wp98YLdTSIiIpsxsJBfUvc8DETFAFmHoZe/aXdziIjIZgws5JdUg0Zw/GSiOdb/WQrnjg8rPU/nHEPpzMlw/mepxS0kIiIrMbCQ31LX/gDqthHmWP9zNvThg1eEFefMp4HMdOilC6BPnbCppURE5GsMLOTX1N33X7qrczGcc6dCn803r+vjl8JK3knXiaUl0O8tt7exRETkMwws5NeUow4cj/wGaB4PnMyB85Xp0FmH4XzhUliJbw310C/MuXrzf6DPuQINEREFFwYW8nuqQUM4Jj4N1I0E9u2G8/dPuMOK49dToa4fALRpBxQXQb+/0u7mEhGRDzCwUEBQrdrAMf6XrielJd+FlagY172I7nDtkKvXr4QuKrS3sURE5HUMLBQwVM8fmOEflTrIHVbc5G7PLVoBhefMvYiIiCi4MLBQQHGkDoTjoScqhpVLc13U7ZdWFL23HPriRZtaSEREvsDAQkHDzGWJaQrknYLe9r7dzSEiIi9iYKGgocLCoW4fbo712n9Dl5ba3SQiIvISBhYKKurG24GGjYDcbOCzbXY3h4iIvISBhYKKqlsP6uY7zLFz4xq7m0NERF7CwEJBR910u8zCNXu26KwjdjeHiIi8gIGFgo5qEgt0722O9Qdr7W4OERF5AQMLBSXHpWEhvXU9dHGx3c0hIqJaYmCh4NT1WqBpc6CwAHrnh3a3hoiIaomBhYKScjigbh5sjvUmTr4lIgp0DCwUtFTaLUCdMOBgJvS3X9vdHCIiqgUGFgpa5saIco8h9rIQEQU8BhYKaqr/pcm32zdBFxbY3RwiIqohBhYKbu27AvGtgQvF0B9tsLs1RERUQ2E1+dDatWuxYsUK5OXlITExEePGjUNycnKl5x4+fBiLFi3CwYMHkZubiwceeAB33nlnhXMWL16MJUuWVHitZcuWmDVrVk2aR+SmlDI73+q3/w79/irom2439xwiIqIgDyxbt27F/PnzMWHCBLRv3x6rVq3C1KlTTbiIjo6+4vzi4mK0aNEC119/PV5//fUqr9u6dWtMmTLF/dzhYOcPee8uznrlW8Dxo9Br34Eaeo/dTSIiIg95nApWrlyJQYMGYcCAAUhISDDBJSIiAhs2VN7dLj0v9913H9LS0hAeXvX/bCWgxMTEuB9RUVGeNo2oUqp+A6gxj5hjvWoR9LFDdjeJiIh8GVhKSkpw4MABpKSkfHcBh8M8z8zMRG1kZ2fj0UcfxaRJk/CXv/wFJ06cqNX1iMpTfW8CUnrLNzGc8+dAO0vtbhIREflqSCg/Px9Op9P0gJQnz48dO4aakqGliRMnmnkrp0+fNvNZnnnmGbz44ouIjIy84vyLFy+aR/l5CmXnybE3lV3P29cla2tt5rLcNxGlU34OfL0X2LgGatBdCFX8vrYOa20d1jq4a12jSbfedu2117qPZRJvWYDZtm0bBg4ceMX5S5curTBJNykpCdOnT0dsbKzP2hgXF+eza5NFtY6Px7mHn8Dpl/8EvXQBYm+7C2HN4xHK+H1tHdbaOqx1cNbao8Ai80pkCEhWB5Unzy/vdamNBg0amN4WGSaqzIgRIzB06FD387KEJ6uQZNjKm+Ta8gcibdFae/XaZH2tdc/rzVJn/dUeZL/wLBy/eC4k/zfG72vrsNbWYa0Dr9ZhYWHV7mwI8/TC7dq1Q3p6Ovr27WtekyEieT54sOu+Ld5QVFRkinDjjTdW+r5M3q1qAq/P/qHTmn8BLOLTWisFx/0/h/P3T0Dv+RTO/y6F47YRCFX8vrYOa20d1jo4a+3xKiHp2Vi/fj02btyII0eOYN68eWbpcv/+/c37c+bMwcKFC93nS4/HN998Yx5yfOrUKXNcvvdElklnZGQgJycH+/btw8yZM01Pzg033OCt3yeRm4pLgBpxnznW/3oNznXv2t0kIiLy9hyW1NRUM/lWNnuToaC2bdti8uTJ7iEhWd1TvotdAsqTTz7pfi4bzsmjS5cueO6559znzJ49G2fPnjXDTp06dTJ7u3BpM/mKunUYUHAOevVi6EXz4JT0fssP7W4WERFVQekg6jeTOSzlVw95g4Sv+Ph4ZGVlsYvRx6yutenKXPamCS3m179nfMiEFn5fW4e1tg5rHXi1lukdPpnDQhRMTE/g8HvNsbun5cg3QGR94MIF4OIFSTVASi+o61KhwvjXhYjILvwJTCHtitCyZd2VJ320ATqmqbnzs7ppMFQjDlUSEVmNgYVCnju0tGwNHMwEIiKAsAjX18Jz0B+uA/JOQi97A3rVYqjUgWbSrmrQyO6mExGFDAYWorKdcPvdDMjjMvqusdA7NkOvXwEc+hp601roT7fBMfZRoFdaSO7jQkRkNQYWoqtQ4eGmV0VfPwDITIfzzf8Fsg7D+coMoGc/OO79KVRMU7ubSUQU1Dzeh4UopHthOqbAMWUW1NAxQJ06wK7tcD4zCfrzHXY3j4goqDGwENWgx8UxbCwcv/sz0LY9cL4Azlf/DJ1f8ZYVRETkPQwsRDWkEtrC8dR0oHWSa3Luon/Y3SQioqDFwEJUC7I3i+O+SYByQH+8CTr9E7ubREQUlBhYiGpJJbWHGuS6e7jzjb9BFxfZ3SQioqDDwELkBWrYvUCTWOBkDvS7b9ndHCKioMPAQuQFql6kWd4s9Lrl0Ie+trtJRERBhYGFyEtU9z5QvW8AnE4458+FLi21u0lEREGDgYXIi9SYCUBkA+Db/dBr/213c4iIggYDC5EXqejGUGPGm2O94i3ob/dXep4+fgzOjWugiwotbiERUWBiYCHyMnX9QOC6VKC0FM55L0EXF1d4X0KMc9qvod/8G5zPTuJSaCKiamBgIfLBFv6O+yYC0U2A7CPQ//6n+z19MBPOF6eYjebM1v6nTsA5+/dwvjoLuuCsre0mIvJnDCxEPqAaRsHx0BPmWG9YBZ3+KfTXe+H88zNmK38kd4Zj+qtQg+6ShAO97X04n/k59O6ddjediMgvMbAQ+Yjqei3UwEsbyr36Zzj//CxwvhDo0BWOJ54z810cYybA8eSfgLgEID8Pzr/9CTony+6mExH5HQYWIh9Sdz8AxLcGzp4Bis8Dcrfnx581+7a4z5Helmdmmfdw8QKcb/4NWmtb201E5G8YWIh8SEXUhWP8r4DoxkDPH8Dx2DNQdetdeV54BBz3/RwICwcydkFv32RLe4mI/BUDC5GPqTbXwDHjNdT5+WSounWrPq9FS6g7R5tjvfgf1Z6Ey94YIgoFDCxEFlCO6v1VU4NHuoeQ9L9eu2pQca58G85fjIVzyzovtZSIyD8xsBD5ERUW7hoakkCyZR30vvRKz9MXL0DLHi/LFwKFBdCL/gF9Nt/i1hIRWYeBhcjPqPZdoG4abI6db8yFPlcxiOizZ+B88XfQH29y7eUS09QsldYr37apxUREvhdmwa9BRB5Sd98P/fl2IPsonL+6D2iVCNW+K5CY7AomJ46bexY5fvZbc77zpSnQm9ZAD7gTKq6V3c0nIvI69rAQ+SFVvyEcE37jms8ik2qPfOPagO6fs11hJTYOjv+ZAdW5h3kgpbfrVgD/ft3uphMR+QR7WIj8lOrYDXX+MBc6/zTwVQZ05h7or/aYISDHQ7+AahTlPtfxo4fg3PMpsOsjM+9FPktEFEwYWIj8nIpqDPRKg+qVVvU58a2hbrwNetNaOP/1KhyTX6iwMknLDrt161V7tRIRkb9hYCEKEuqHY10bzn27H3rzf4GYJtB7PoPO2AUcPwo0jzeTeVXqIKioaLubS0TkEQYWoiChomKg7hgFvXQB9Bsv44rt5HKyoJe8Br1sAXSvNBSPfhCIblbl9bTTCZzLN9clIrIb+4eJgoi65YdA85auJ7FxUP3vgGPiZDhmvgZ1/ySzygglJaYnJufX4+D87KMqw4rz5Wlw/vpBM3eGiMhu7GEhCrZ7F015yWwmp5rEVnzvxtsAmeciQ0arFkN/9hGcr82G45nZUE0rnqvXLAE+/9h1vHE1VIeulv4+iIguxx4WoiCj6tW/IqxUeD8xGY5Hn0SEhJDCc3D+30zokhL3+7LKyOygW/Z813bXpF0iokDrYVm7di1WrFiBvLw8JCYmYty4cUhOTq703MOHD2PRokU4ePAgcnNz8cADD+DOO++s1TWJqPa3AGj61DRkTRoLfL0X+t03oUY+YJZQO//vBRkTgrp+APTBr4DsI9CfbIG64Va7m01EIczjHpatW7di/vz5GDVqFKZPn27CxdSpU3HmzJlKzy8uLkaLFi0wduxYxMTEeOWaRFR7YXGt4HjwMXOs1/wbevcncM57CThzymxYp+79GdQP+rve/2ijza0lolDncWBZuXIlBg0ahAEDBiAhIQETJkxAREQENmzYUOn50kty3333IS0tDeHh4V65JhF5h0P2dxkwxBw75z4PfPk5IPNgfvoUlOzbcimwYN9u6JM59jaWiEKaR0NCJSUlOHDgAIYPH+5+zeFwICUlBZmZmTVqQE2uefHiRfMoo5RCZGSk+9ibyq7n7evSlVhre2rtGP0wSvfvBQ4fMK857psIR6tE1/vNWkB3TIHetxvYvgnqztEVrqOLzkN/ug0oOg84SwFZCi23EpD/nNRvaB6qQUNAllzHxiEU8fvaOqx1cNfao8CSn58Pp9N5xdCOPD927FiNGlCTay5duhRLlixxP09KSjJDSbGxVU80rK24uND8YWsH1tr6Wl+c8gJOvTAF9XqlInrkvRXOOTd4OE7v2w3Hjs2Ie/hx9w8offECcp56BBf2pVfr14pMG4gmTzwDhwSYEMTva+uw1sFZ64Bc1jxixAgMHTrU/bzsB6hM6pUeG2+Sa8sfSHZ2NrT8z5F8hrW2sdaOcODJP0HWAhVmZVU4Vyd3A8IjUHLkG2R9tBmqbXvzeun8OWZFkdw1WnXpKV2j3z0uXgAKzkEXnAMKzwKnTuD8lvdxNPNL1PnZU1BtrkGo4Pe1dVjrwKt1WFhYtTsbPAosUVFRZrhGVvKUJ8+rmlDri2vKXJiq5sP46ptUrsu/ANZgrf2s1vUioXr2g96xGc6t78ORmAznB/+B/uA/8lMLjkd+DdWt1/f/Ogcz4XxlBpCbhdJpv4H68QSoG283P/TMr38uH8g/47p9QBV/twMdv6+tw1oHZ63DPE1C7dq1Q3p6Ovr27Wtek+EceT548OCaNcAH1yQi7zJLnHdsdj163wD91iuu14fde9WwYs5L6gDHlD/D+eos4Isd0AteNjdqNHNfTp909ciIhlFQqQPNJncqLsHXvy0iCiAeDwnJUMzcuXNNyJAVQKtXrzZLl/v3d60mmDNnDpo0aWKWMQsZojly5Ij7+NSpU/jmm29Qr14999jX1a5JRDbrci3QKBo4ewbOPz9jtvfHdddDDflRtS+hGjSC4+dPQ7+3DPqd+cAh1yRft7r1TE+L/u8y80D7LlA33wHV50beZZqIPA8sqampZqLs4sWLzbBN27ZtMXnyZPfwzYkTJyrMGpaA8uSTT7qfy+Zw8ujSpQuee+65al2TiOyl6tSB6nsT9PoVrt6Q+NZwPPSExysEJHio20dCp/SGPnwQqnFToHEzQL4qB5D+KZyb/wN8sRP4KgNaHhtXw/GTn0O1auOz3x8R+T+lg2igTybdll/u7A3yAzk+Ph5ZWVkcE/Ux1tq/a60PHYDz+V+aOS2OyS9CxbXyWfv06ZPQm/8L/d+lQHERUCfMBB01dDRUeAQCCb+vrcNaB16tZT6qTybdElHoUm3awfHraa49VXwYVsyv1bgp1A9/DH3DLXAufMXciFGvXgy9czPUkNFQ3a6Dim7s0zYQkX9hYCGiarP6rs1yE0eZ94LPtsH51t+BnCzof86G+f9cm3ZQXa9ztalefddmdWHhrq/NWkA56ljaViLyLQYWIvJrZp7MdalwdOoBve5d6M8/Bg59bSbtyjCVXvPdJpJuLVrB8bPfQl3asZeIAh8DCxEFBFW/gRkmggwV5Z+G3rPLTNLVckuBkotyzw7XV1kqffwonNN+DXX/JDj63Wx304nICxhYiCjgqKjGZm8YyOMy+mw+nP8309zIUc97Ec6DmVCjHoIKC3NNDszPA44dArQTiJZVSk1cu/Xy/jNEfo2BhYiCimoUBccvnoNe9qYZLpKl2PqrPUBEPSDrMFBw9soPyR4wjZtB9b4BasAdJhARkX9hYCGioCMTbtXI+6GTOsD56p8rblInPSly52iZoJt3EigscC2dzj4CvfJt6LVLoPr1h7p1WI3mwGiZX9O0hesu1UTkNQwsRBS01LU/gGPKLOhdHwEyjNSyDRDXCiqirvscXVxsgov+dr9rY7wD+6C3rDMPJHcxN3ZUHbsBSR2vep8j53/egV7yT7MrsNlYL6W3Bb9LotDAwEJEQU3JDRVvG1H1+3XrAi1aQrVoCchuvl/vhX5vOfSn24D9GdDykBNlw7prOsFx+4hK75/k3LbBFVaE3MLgL3+AuuWHcNz9YJW/tplTIwFp+ybo40ehUgeZHYU5n4boSgwsRETlqGs6mYc+mQO9+xMgMx16327XZN29X8C59wuowXe7bvwY5voRqmW10ut/cX1+0F2u12TuzLp3UbovHRcn/wn6Qil0UaFrFVPhOXNt/fEHwInj7l9bZ+yC/vA9OMY+ChXf2qYKEPknbs1/Fdzq2TqstXVYa8+YGmUfhX5/pbm3kZHcGY4JvwHyT8P5wtNmHozqezPUw78090zSn++A85+zgHOVTPItr26kGbqS+ylJwDH3apJ7N90yDGroPVD1Ii35PQYDfl8H99b8DCxXwb8A1mGtrcNa15z+ZAucr/8VOF8INIxyTeI9ewbo3AOOx5+Bksm8ZefK3JjXZpueE0Peq1fPtWKpdRJUv5uhuvd1DUvJ+bnZcC6aZ25FYMTGmRVPqnlLW36vgYbf19bhvYSIiPyc6pUGR+skOF+Z8d3qozbXwDHxfyqEFXNuTFM4fvX/o0V0FLJPnjQ3cfzea8fGoc6k35ndfJ0L/xeQAPOnp+B44jmoxGt8+dsi8nsOuxtARBRopMfD8dsZULePAHr0heOJZ6DkfkZVcMguvZeFme+9vlxz8oumF8ZM4J05+bteGqIQxR4WIqIaUOERZgddn10/ujEcv/kjnC9Pc032lVVHD/8Sjj43VnPOzRHozD1A5h7oC0VQPfuZuTKqPveHocDEwEJE5KdUZH04Hn8W+h8vmbkz+u8zUfrOfCC6MRDTBCq6CSDzXy5ccE3WvVAMLRvhHcx0zaspR+/aDv3Gy0C33q6l0z36VNiP5mq0s9S1UqpRDFQd3gmbrMfAQkTkx8xmdY/8GljcxLWxnSyDvrQU+nunOsq+Me06QnXoCsiqpY83u25NsOsjs5GejqwP1edGs/eLOe+yvV9ML82xQ9B7v4DeuxvI3O3aFVjm4TRrAcj+NvLo3BPo3pt7x5DPMbAQEQXCrQbGTICWDfBO5QBnTkPnnQbOnHT1rkREANJbEl7X9LiohCQgMbnCzrz6znuAo9+YvV9MeJF9Zj74j3mgRSvXrrznz0GfPgmYxwnXnjGXKy0xd8OWhwQmE6K69XLtHSO3PCDyEQYWIqIAoZo0A+Qhx55+VnpAEpJMmNHD7wO+2gO9Zb0ZajLhQ0LI5SQIye0JOnU3DzMJ+EwekHMMOicLOHLQbHSH9E/gfHaSa9+Y24Z7NMGYqLoYWIiIQoxsbIeOKVAdU6DHPgL9yVbXEu2oGLOBnSzHlq9oFnfl/ZOaxpqH6tzDPNWD7oLzjb8B+3ZDL10A/dFGqOsHQrXvfKmXJ8Ke3yQFHQYWIqIQJsuxVdotQFoNPx+XAMf/97wJKvpfr5p5Mvqd113za+TWBRJaOveAun4AN8CjWmFgISKiWpHhJgkkuntv6K3vQ3+1B9j/pWulktxMUh4rFwEdukKl3QrVKxWqbj27m00BhoGFiIi8QjVoBHXrMODWYa5VRrlZ0F99Cb1zM7DnM9eeMPJ46xVXz0tcKyCuFVSLBPMVTWK5ZJqqxMBCREReZyb5Nm/pGgZKGwR9Khd62wboLevMLQfMnBe5C3b55dkSVprEArHxULEtXPdekv1l5B5xJRfN8my0bgfVtj2Q2O57dxfWF4rNvBz97X6gQUOonj/gjSQDHAMLERH5nJLekztHQ98xCjh8AProIdfqpOyjZldeyKojCSUSZnKzq95jZvsm13sSiOISXJOA69aDqhsJ1IvEqboRKMn4wqxgQmmp+2NazrkuFSptENC+q2visbwugUjuqF1wFig8Zx664BxQVATVoiXQrgN3B/YTDCxERGQZExRkOCgxucLr2ukE8k65wkpulmtzPBlWklVK0tMiq42KzkMf+hr45ivg1AnXRnjyKNdLU1D+oo2iAemNkSXbOVnQ2943D8gOwdKOgnzXPjZVKB+M1DWdgPgE14vSVtn5VzuBkpJLj4uuPWqUw7WKSjbXa9rctcmeDJVxY71aY2AhIiL/CDKX9plRHbtd9Xydfxr49gC0TOwtPm96RHChCA3r10dBTCyQ1N4EBgkKZj6NTPzduh5654fAmVMVLya/tvSiNGgE1G9ghpBMQDryjavHR1Y+XQpG1VWhh0hWSzWMBhpFuW5tINfX2nW7AxN+nCaYqYZRQNlD2iE9ROa2C8WuYbGIuq6hsth4QDbpk4nLp05Af/2lmeQsk5txvgBo2QaqVVsgIREqoa0JTcGwvJyBhYiIAo6Kagyk9KqwgZ6Ek5j4eJzPynKFlHKvI7kzVHJn6DETXD00sitww0aukCK3KaiiB0TL/ZMO7IM+sBc4mQs46rgCTtlDblUggUR6geSr9LaczIU+KbdQkF2JT7leyzvpenzPLRW+91YLlZ0jc3Iq241Yeqk+/7ji+fL7jGniugeV7LdTtjOyfJUNAuV1CUIyDBYV45c9QgwsREQUMswNHzt0q/758o+73Om6Z78a/XpmjoyEHukJOpsPfTbPdU8mGTqqcyn0yLGZS5N/6XHWNY8mLMzVMyKBIrwuUFQIbeb4ZLnm3UhYKZuInNwZuKYzVFQ09LFDpndIHz5o7geF4iLXHB15HP326oFJem5kKEt6eWSfnsj6JtSZ45H3u+f/WI2BhYiIyEdM4JC5LPKowS0VqqIl9Mg9n5o1v2JPG9nB2H2e9DTJuaaH5xS0zBOS0CRDTbKSSoJScRG03Dvq+DHX3CAJOEe//e4aZQdh4XCMehB2YWAhIiIKMEp6P+RxtfNkaEfmzMijVeJVA5OWuTIy4flkjqtH53yhqydHvsqcGxsxsBAREZFh7h0lq6Hk4cUeIW+wZyCKiIiIyAMMLEREROT3ajQktHbtWqxYsQJ5eXlITEzEuHHjkJxccROg8rZt24ZFixYhNzcXcXFxuPfee3Hddde53587dy42bdpU4TM9evTA008/XZPmERERUagHlq1bt2L+/PmYMGEC2rdvj1WrVmHq1KmYNWsWoqOjrzh/3759mD17NsaOHWtCyocffoiZM2di+vTpaNOmjfu8nj17YuLEid81TNazExEREdVkSGjlypUYNGgQBgwYgISEBBNcIiIisGHDhkrPX716tQkjP/zhD835Y8aMQbt27UwvTXkSUGJiYtyPhg157wYiIiJy8agbo6SkBAcOHMDw4cPdrzkcDqSkpCAzM7PSz8jrQ4cOvWK4Z8eOHRVey8jIwPjx49GgQQN069bNBJtGjRpVes2LFy+aR/llW5GRrrtwent3vrLr+eOuf8GGtbYOa20d1to6rHVw19qjwJKfnw+n02l6QMqT58eOHav0MzLP5fKhInkur5eRHph+/fqhefPmyM7OxltvvYVp06aZoSYJRJdbunQplixZ4n6elJRkhphiY2PhKzL3hqzBWluHtbYOa20d1jo4a+0XE0XS0tLcxzKvRSbyPvbYY9izZ4/pvbnciBEjKvTalCU8mdQrvUDeJNeWPxAJUuXvTUHex1pbh7W2DmttHdY68Got00Gq29ngUWCJiooyPR7le0eEPL+816WMvH7mzJkKr8nzqs4XLVq0MMNBUojKAkt4eLh5VMZX36RyXf4FsAZrbR3W2jqstXVY6+CstcPTJCQTZtPT092vyRCRPO/QoUOln5HXd+/eXeG1L774wqwwqsrJkydx7tw5NG7c2JPmERERUZDyeJWQDMWsX78eGzduxJEjRzBv3jwUFxejf//+5v05c+Zg4cKF7vOHDBmCzz//3OzbcvToUSxevBhff/01Bg8ebN4vKirCggULzOTcnJwcE25mzJhhuppkci4RERGRx3NYUlNTzeRbCR4yFNS2bVtMnjzZPcRz4sSJCrOGO3bsiMcffxxvv/22mUwbHx+P3/zmN+49WGSI6dChQ2bjuIKCAjRp0gTdu3fHPffcU+WwDxEREYUWpYNooE8m3ZZf7uwNEr4kZGVlZXFM1MdYa+uw1tZhra3DWgderaVjwieTbv2dL3fH5c671mGtrcNaW4e1tg5rHTi19uTzQdXDQkRERMGJd2u+ivPnz+Opp54yX8m3WGvrsNbWYa2tw1oHd60ZWK5COqAOHjzI8VALsNbWYa2tw1pbh7UO7lozsBAREZHfY2AhIiIiv8fAUo0lV6NGjeKeMBZgra3DWluHtbYOax3cteYqISIiIvJ77GEhIiIiv8fAQkRERH6PgYWIiIj8HgMLERER+T3ecOEq1q5dixUrVpg7UycmJmLcuHFITk62u1kBa+nSpfj4449x9OhRREREoEOHDvjJT36Cli1bus+5cOEC5s+fj61bt5qbWfbo0QPjx4933xGcambZsmVYuHAhhgwZggcffNC8xlp7z6lTp/DGG29g165dKC4uRlxcHCZOnIhrrrnGvC/rG+Qu9+vXrzd3pu/UqZOptdxAjjzjdDpNLTdv3mx+Njdp0gQ333wz7r77bnNTPsF610xGRgbeffddsync6dOn8etf/xp9+/Z1v1+dup47dw6vvvoqPvnkE/Pn0a9fPzz00EOoV68eaoM9LN9DfojLD3NZujV9+nQTWKZOnYozZ87Y3bSA/stw++23mzr+7ne/Q2lpKZ5//nkUFRW5z3n99dfNN/qvfvUr/P73vzd/aV588UVb2x3o9u/fj/fee898D5fHWnuH/ICeMmWKuZHb5MmT8ec//xn3338/GjRo4D5n+fLlWLNmDSZMmIBp06ahbt265u+BhEbyPHzL9/PDDz9san3vvfeaf2SlvmVY75qRsN22bVtT28pUp65/+ctfcPjwYfMz/re//S2+/PJLvPLKK6g1WdZMlfuf//kfPW/ePPfz0tJS/cgjj+ilS5fa2q5gcubMGf2jH/1I79mzxzwvKCjQY8aM0du2bXOfc+TIEXPOvn37bGxp4Dp//rx+/PHH9eeff66fffZZ/dprr5nXWWvveeONN/SUKVOqfN/pdOoJEybo5cuXu1+T+o8dO1Z/+OGHFrUyePzxj3/UL7/8coXXZs6cqWfPnm2OWW/vkJ8F27dvdz+vTl0PHz5sPrd//373OZ999pkePXq0PnnyZK3awx6WKpSUlODAgQNISUlxv+ZwOMzzzMxMW9sWTAoLC83Xhg0bmq9Sc+l1KV/3Vq1aoVmzZqx7Dc2bNw/XXnstunfvXuF11tp7du7ciXbt2uGll14y3eNPPvkk1q1b534/JyfHDF2U/zOoX7++GV5mrT0nQ8np6ek4duyYef7NN99g37595vtcsN6+UZ26ylfpWSwbChXyM0aGhqSntzY4h6UK+fn5Zpz08rF8eV72l4RqR+r7z3/+Ex07dkSbNm3Ma/KXQbrVy3eli+joaPMeeWbLli1mLPqPf/zjFe+x1t79QS5DFHfeeSdGjBiBr7/+Gq+99pqpb//+/d31lNqWx1rXzPDhw81dgn/5y1+a/0jKz5IxY8bgxhtvNO+z3r5RnbrK16ioqArv16lTx/yntLa1Z2Ah2/zjH/8w45x/+MMf7G5KUDpx4oQJhDKOLBOcyXfkH0z5H+XYsWPN86SkJBw6dMiEGAks5F3btm3Dhx9+iMcffxytW7c2PSzyvd64cWPWO4gxsFRBEqIk98sToTznCgrvhJVPP/3UTPRs2rSp+3WprQzHyezz8v/zl4nOrLtnZMhH6vbUU09V+IdVJsDJ6renn36atfYS+YcyISGhwmvyfPv27ea4rJ5SWzm3jDyXCY7kGVmNNWzYMKSlpZnn0kObm5trJuNKYGG9faM6dZVzZISiPBl6lonptf25wjksVZCuXBmTlnHS8j/s5bmMn1LNyJI4CSuytPmZZ55B8+bNK7wvNZfuw927d7tfkyE46S1g3T0j48YvvPACZsyY4X5IL8ANN9zgPmatvUOGNS8fKpbnsbGx5li+z+WHdflay/wtGdNnrWu2kkX+Q1mePC+7NR7r7RvVqat8lf8EyX+Yysi/m/JnU9stQdjD8j2GDh2KuXPnmn9EpdCrV682f1HY5VhzElakK1cmJUZGRrp7sGTilgxbyNeBAwea5eQy5inPZT2//CXgDxrPSH3L5gaVkSWIjRo1cr/OWnuHzF2RZc3vvPMOUlNTzQ9w2afikUceMe/LhEPZ/0bel/0q5Af/22+/bf6X2qdPH7ubH3B69eplaikTxKUnS4aEVq5ciQEDBpj3We+aky0msrOzK8zPkvrKzwip99XqKn8ePXv2NMuYZemz9OLKzxX5eyH75dQG79Z8FdJ1Luv75R9W6fKSzW/at29vd7MC1ujRoyt9XTbYKguCZZuZyYRR+WbnZmbe89xzz5nv48s3jmOta0/2s5GN+eSHvfwglxBzyy23XLHhlqwekv+VyoZbstdF+U0TqXpkwu2iRYtMT60MR8g/hDI8JHtmSe+4YL1rZs+ePWao/nKyMd/Pf/7zatVVhn/kP6flN46TTVdru3EcAwsRERH5Pc5hISIiIr/HwEJERER+j4GFiIiI/B4DCxEREfk9BhYiIiLyewwsRERE5PcYWIiIiMjvMbAQERGR32NgISIiIr/HwEJERER+j4GFiIiI/B4DCxEREcHf/T+fnOGNpLnTzAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.arange(len(kcqe_obj.loss_trace)), kcqe_obj.loss_trace)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q_hat = kcqe_obj.predict(param_vec,\n",
+    "                         x_train=x,\n",
+    "                         y_train=y,\n",
+    "                         x_test=x_test,\n",
+    "                         tau=tf.constant(np.array([0.1, 0.5, 0.9]), dtype=tf.float32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(8,8))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "#ax = Axes3D(fig)\n",
+    "\n",
+    "ax.scatter(x0, x1, y)\n",
+    "\n",
+    "x0_grid = x_test[..., 0].numpy().reshape((grid_size, grid_size))\n",
+    "x1_grid = x_test[..., 1].numpy().reshape((grid_size, grid_size))\n",
+    "q_grid_q10 = q_hat[..., 0].numpy().reshape((grid_size, grid_size))\n",
+    "q_grid_median = q_hat[..., 1].numpy().reshape((grid_size, grid_size))\n",
+    "q_grid_q90 = q_hat[..., 2].numpy().reshape((grid_size, grid_size))\n",
+    "\n",
+    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_q10, rstride=10, cstride=10, color=\"red\")\n",
+    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_median, rstride=10, cstride=10)\n",
+    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_q90, rstride=10, cstride=10, color=\"orange\")\n",
+    "\n",
+    "ax.set_xlabel('X_0')\n",
+    "ax.set_ylabel('X_1')\n",
+    "ax.set_zlabel('Y')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kcqe_obj = kcqe.KCQE(x_kernel='gaussian_full', p=2)\n",
+    "generator = kcqe_obj.generator(x, y, batch_size = 10, block_size = block_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch idx = 0\n",
+      "batch idx = 0\n",
+      "loss idx = 1\n",
+      "param estimates vec = \n",
+      "[ 0.09999859  0.09999733  0.09998348 -0.09996673]\n",
+      "loss = \n",
+      "1.409389\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.41964015, -0.15804687, -0.019936  ,  0.00969788], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 1\n",
+      "loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[ 0.19960712  0.19981268  0.06152826 -0.19974244]\n",
+      "loss = \n",
+      "1.353905\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.37931693, -0.14954597,  0.04193939,  0.00913671], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 2\n",
+      "loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[ 0.29874092  0.29961312  0.00872738 -0.29987463]\n",
+      "loss = \n",
+      "1.3012924\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.35225403, -0.15052633,  0.02026094,  0.01135916], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 3\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[ 0.39714307  0.39919865 -0.02096948 -0.40038955]\n",
+      "loss = \n",
+      "1.2494416\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.32182658, -0.1438985 , -0.01118219,  0.0126293 ], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 4\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[ 0.49461117  0.49854285 -0.01983659 -0.50132376]\n",
+      "loss = \n",
+      "1.2048392\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.29499397, -0.14039136, -0.0250363 ,  0.01316192], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 5\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[ 5.9102130e-01  5.9763294e-01  2.6381016e-04 -6.0277504e-01]\n",
+      "loss = \n",
+      "1.1625265\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.27317622, -0.13755594, -0.02273353,  0.01480571], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 6\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[ 0.686416    0.69666624  0.02996209 -0.70470953]\n",
+      "loss = \n",
+      "1.1210687\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.2598306 , -0.14035279, -0.0160234 ,  0.01495201], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 7\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[ 0.7808517   0.7958175   0.06206988 -0.80695593]\n",
+      "loss = \n",
+      "1.0811888\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.24953552, -0.14444786, -0.00795009,  0.01458106], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 8\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[ 0.87417704  0.89503676  0.08942112 -0.909284  ]\n",
+      "loss = \n",
+      "1.0418584\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.23348244, -0.14379591,  0.00150534,  0.01402713], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 9\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[ 0.9662537   0.99429613  0.10416637 -1.011095  ]\n",
+      "loss = \n",
+      "1.0051059\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.21840578, -0.14337854,  0.01245922,  0.01248935], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 0\n",
+      "loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 1.0570061   1.09363     0.10406005 -1.1118416 ]\n",
+      "loss = \n",
+      "0.971062\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.20599331, -0.14441882,  0.01808471,  0.01110983], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 1\n",
+      "loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 1.1465725   1.1932626   0.09053116 -1.2128935 ]\n",
+      "loss = \n",
+      "0.93517303\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.20130217, -0.15071572,  0.01972287,  0.01369971], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 2\n",
+      "loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 1.2348399   1.2931674   0.06829683 -1.314561  ]\n",
+      "loss = \n",
+      "0.9009958\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.1897544 , -0.15155648,  0.01548282,  0.01471543], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 3\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 1.3220093   1.3932673   0.04663283 -1.4153634 ]\n",
+      "loss = \n",
+      "0.86895245\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.18839274, -0.1508683 ,  0.00237442,  0.01155932], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 4\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 1.4082406   1.4937472   0.03226115 -1.5149976 ]\n",
+      "loss = \n",
+      "0.8355062\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.1869731 , -0.15671171, -0.00766308,  0.01071288], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 5\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 1.4933274   1.5944754   0.01934377 -1.6138843 ]\n",
+      "loss = \n",
+      "0.80258596\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=\n",
+      "array([-1.7513682e-01, -1.5503499e-01, -1.5882961e-04,  1.1142525e-02],\n",
+      "      dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 6\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 1.5772055   1.6955163   0.00494434 -1.7104542 ]\n",
+      "loss = \n",
+      "0.7728414\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.16734865, -0.1578681 ,  0.00397914,  0.00833005], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 7\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 1.6595396   1.7967504  -0.00686724 -1.8012117 ]\n",
+      "loss = \n",
+      "0.74515325\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.15191172, -0.15619363, -0.00184433,  0.00333169], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 8\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 1.7403765   1.898569   -0.02357305 -1.8850738 ]\n",
+      "loss = \n",
+      "0.7173369\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.14684722, -0.16731745,  0.00882579,  0.00143444], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 9\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[ 1.819818    2.000237   -0.03629423 -1.9659388 ]\n",
+      "loss = \n",
+      "0.6859819\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.14370884, -0.15117976, -0.00354662,  0.00502473], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 0\n",
+      "loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 1.8978463   2.1018224  -0.03815849 -2.043059  ]\n",
+      "loss = \n",
+      "0.6570889\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.13782065, -0.15238373, -0.0138377 ,  0.00375278], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 1\n",
+      "loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 1.9750584   2.2023394  -0.02383587 -2.1138232 ]\n",
+      "loss = \n",
+      "0.635595\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.14858809, -0.1302307 , -0.02410283,  0.00056673], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 2\n",
+      "loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 2.0524056e+00  2.3019304e+00 -1.3665874e-03 -2.1820583e+00]\n",
+      "loss = \n",
+      "0.61164725\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.17107621, -0.1302645 , -0.01468088,  0.00392503], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 3\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 2.12879     2.4001346   0.00729959 -2.2456512 ]\n",
+      "loss = \n",
+      "0.5827753\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.14062226, -0.11859177,  0.01742362,  0.00143218], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 4\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 2.2040246   2.4965556   0.00566971 -2.3043013 ]\n",
+      "loss = \n",
+      "0.56507957\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.1321012 , -0.10779253,  0.01451829,  0.00070037], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 5\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 2.2783911   2.591595   -0.01172426 -2.359277  ]\n",
+      "loss = \n",
+      "0.5443064\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.13525264, -0.11115728,  0.02550661,  0.00148806], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 6\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 2.351019    2.684417   -0.02822491 -2.4098785 ]\n",
+      "loss = \n",
+      "0.52323216\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.11043361, -0.09335585,  0.0010323 ,  0.00048426], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 7\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 2.4222186   2.7742472  -0.03407981 -2.4591024 ]\n",
+      "loss = \n",
+      "0.5020498\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.11292464, -0.07637033, -0.01417992,  0.00296669], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 8\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 2.4921274   2.8621166  -0.02974603 -2.501961  ]\n",
+      "loss = \n",
+      "0.4948654\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.11197098, -0.08769239, -0.01518001, -0.00180088], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 9\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[ 2.5597117   2.9454453  -0.02206853 -2.543014  ]\n",
+      "loss = \n",
+      "0.46941847\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.08429437, -0.04497679, -0.00585669,  0.00183963], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 0\n",
+      "loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 2.626192    3.027458   -0.01598692 -2.5806463 ]\n",
+      "loss = \n",
+      "0.46059138\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.10636163, -0.086395  ,  0.00141233,  0.00020604], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 1\n",
+      "loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 2.6905415   3.107804   -0.02179513 -2.6141376 ]\n",
+      "loss = \n",
+      "0.45123258\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.08014926, -0.07851788,  0.01752808, -0.00069812], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 2\n",
+      "loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 2.7539525   3.1877375  -0.01534702 -2.6439157 ]\n",
+      "loss = \n",
+      "0.44235072\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.10139666, -0.0946982 , -0.01828019, -0.00063709], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 3\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 2.8164499   3.265227   -0.02112898 -2.6731    ]\n",
+      "loss = \n",
+      "0.42742717\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.09919685, -0.06335051,  0.01826849,  0.00179567], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 4\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 2.877456    3.340685   -0.02267553 -2.6989934 ]\n",
+      "loss = \n",
+      "0.4134921\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.08409949, -0.06549207, -0.00581209, -0.00058507], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 5\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 2.938098    3.4162397  -0.02587572 -2.7230275 ]\n",
+      "loss = \n",
+      "0.40378645\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.1045806 , -0.0930947 ,  0.00277679,  0.00038391], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 6\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 2.9976854  3.49092   -0.0411775 -2.7456236]\n",
+      "loss = \n",
+      "0.39021003\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.08842133, -0.07860894,  0.01946341,  0.00059552], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 7\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 3.0566194   3.5644236  -0.04742771 -2.7672877 ]\n",
+      "loss = \n",
+      "0.38006577\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.09407446, -0.07268364, -0.01179963,  0.00090369], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 8\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 3.116149    3.636994   -0.02972762 -2.7881331 ]\n",
+      "loss = \n",
+      "0.3669596\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.11829576, -0.07406846, -0.03803867,  0.00092017], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 9\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[ 3.1749289e+00  3.7081487e+00 -1.5213154e-03 -2.8095808e+00]\n",
+      "loss = \n",
+      "0.35553658\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.09075679, -0.06579412, -0.02068964,  0.00200973], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 0\n",
+      "loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 3.2319427   3.7768595   0.02931897 -2.8290653 ]\n",
+      "loss = \n",
+      "0.34530827\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=\n",
+      "array([-6.8256535e-02, -4.9949411e-02, -8.7002860e-03, -2.6846472e-05],\n",
+      "      dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 1\n",
+      "loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 3.2879128   3.8436928   0.06104978 -2.8461838 ]\n",
+      "loss = \n",
+      "0.33765516\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.07882778, -0.05402971, -0.00609059, -0.00048189], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 2\n",
+      "loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 3.343962    3.9106305   0.08934847 -2.8621917 ]\n",
+      "loss = \n",
+      "0.32290754\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.09860324, -0.07738795,  0.00092169,  0.00033932], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 3\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 3.3982952   3.975231    0.11181985 -2.8784616 ]\n",
+      "loss = \n",
+      "0.31525815\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.06302711, -0.0454906 ,  0.00520497,  0.00132506], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 4\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 3.4513257   4.0395126   0.10046826 -2.8947978 ]\n",
+      "loss = \n",
+      "0.3081906\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.06745028, -0.06808108,  0.05262861,  0.00118069], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 5\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 3.5030875   4.1035194   0.07854303 -2.9095292 ]\n",
+      "loss = \n",
+      "0.30388528\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=\n",
+      "array([-6.5349028e-02, -6.7855895e-02,  2.0963093e-02, -9.4030751e-05],\n",
+      "      dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 6\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 3.555038   4.167919   0.0407449 -2.9243288]\n",
+      "loss = \n",
+      "0.28640494\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.09017408, -0.0756693 ,  0.03414775,  0.00105596], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 7\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 3.6065702   4.2313747  -0.00961155 -2.9382944 ]\n",
+      "loss = \n",
+      "0.28437033\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.07850923, -0.05888878,  0.03197856,  0.00038002], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 8\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 3.6581204   4.2948403  -0.06972658 -2.9530954 ]\n",
+      "loss = \n",
+      "0.26497498\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.08537532, -0.06930573,  0.0294859 ,  0.00155387], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 9\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[ 3.707881    4.35709    -0.13354811 -2.9676237 ]\n",
+      "loss = \n",
+      "0.26683545\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.05179493, -0.05385634,  0.01848201,  0.00078532], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 0\n",
+      "loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 3.7573972   4.4197783  -0.15219705 -2.9815454 ]\n",
+      "loss = \n",
+      "0.26175368\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.07636718, -0.07236565, -0.06966754,  0.00051984], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 1\n",
+      "loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 3.8057947   4.4803185  -0.15920006 -2.9977546 ]\n",
+      "loss = \n",
+      "0.2551861\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.05977342, -0.04134246, -0.02079394,  0.00255897], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 2\n",
+      "loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 3.8529851   4.5390444  -0.15002379 -3.0133264 ]\n",
+      "loss = \n",
+      "0.24081942\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.05598368, -0.04266251, -0.03317864,  0.00060087], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 3\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 3.8987305   4.5982428  -0.13463524 -3.0325358 ]\n",
+      "loss = \n",
+      "0.23553902\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.04951133, -0.06734995, -0.01522215,  0.00359138], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 4\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 3.9442036   4.6561465  -0.10644131 -3.050839  ]\n",
+      "loss = \n",
+      "0.22888497\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.06722172, -0.04679903, -0.03144041,  0.00059873], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 5\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 3.9891708   4.7145705  -0.06118064 -3.0683517 ]\n",
+      "loss = \n",
+      "0.23432934\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.0623193 , -0.06621005, -0.04629936,  0.00061771], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 6\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 4.0323358   4.771055   -0.01366359 -3.086777  ]\n",
+      "loss = \n",
+      "0.21983965\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03908372, -0.03817228, -0.01466428,  0.00173791], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 7\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 4.0737653   4.825362    0.04209578 -3.103617  ]\n",
+      "loss = \n",
+      "0.21542963\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=\n",
+      "array([-3.7170991e-02, -3.3255685e-02, -2.9846065e-02,  7.6437245e-05],\n",
+      "      dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 8\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 4.1135855   4.8780107   0.09374955 -3.1217015 ]\n",
+      "loss = \n",
+      "0.21311706\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03636591, -0.0365732 , -0.00232053,  0.00189507], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 9\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[ 4.152737    4.930398    0.12735091 -3.1411908 ]\n",
+      "loss = \n",
+      "0.20851938\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.04919384, -0.05011932,  0.02827242,  0.00206801], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 0\n",
+      "loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 4.1904445   4.982146    0.15378332 -3.1605558 ]\n",
+      "loss = \n",
+      "0.20295672\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03519342, -0.04539749,  0.00893229,  0.00111734], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 1\n",
+      "loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 4.2262945   5.032405    0.16486137 -3.180253  ]\n",
+      "loss = \n",
+      "0.20606495\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.02615217, -0.03523801,  0.02811454,  0.00140469], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 2\n",
+      "loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 4.2616      5.0827284   0.16678748 -3.2013555 ]\n",
+      "loss = \n",
+      "0.19878536\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.04427313, -0.05033552,  0.01797476,  0.00212383], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 3\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 4.296591    5.1321225   0.16663264 -3.2229924 ]\n",
+      "loss = \n",
+      "0.19520305\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.04686373, -0.03943434,  0.00420247,  0.00162475], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 4\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 4.3303194   5.1799912   0.15461361 -3.2447345 ]\n",
+      "loss = \n",
+      "0.19856948\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03113034, -0.03195976,  0.02634279,  0.00136747], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 5\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 4.362934    5.226547    0.14821668 -3.2662187 ]\n",
+      "loss = \n",
+      "0.18695325\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03143645, -0.03251204, -0.00988899,  0.00113102], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 6\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 4.395349    5.273032    0.12126026 -3.2862315 ]\n",
+      "loss = \n",
+      "0.19541618\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.04384287, -0.04404736,  0.04844857,  0.00033557], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 7\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 4.4262486  5.318236   0.0977855 -3.3050728]\n",
+      "loss = \n",
+      "0.18188107\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.0230196 , -0.03115672, -0.0021806 ,  0.00043681], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 8\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 4.4570837   5.363651    0.09103847 -3.3215165 ]\n",
+      "loss = \n",
+      "0.18411393\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.04291843, -0.04514467, -0.03224958, -0.00040033], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 9\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[ 4.487633    5.40807     0.08331718 -3.3345695 ]\n",
+      "loss = \n",
+      "0.17812157\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03919737, -0.03273558,  0.00361571, -0.00114998], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 0\n",
+      "loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 4.5187125   5.451077    0.07698825 -3.3475597 ]\n",
+      "loss = \n",
+      "0.17897856\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.05092388, -0.02738941, -0.0015001 ,  0.00071379], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 1\n",
+      "loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 4.549533    5.492935    0.08937559 -3.360251  ]\n",
+      "loss = \n",
+      "0.17987783\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03950789, -0.02855868, -0.04090399,  0.00056096], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 2\n",
+      "loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 4.5802755   5.533464    0.10921647 -3.3718953 ]\n",
+      "loss = \n",
+      "0.17627572\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=\n",
+      "array([-4.1625608e-02, -2.5525665e-02, -1.9686043e-02,  8.5517051e-05],\n",
+      "      dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 3\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 4.611045    5.5726905   0.13307966 -3.3839452 ]\n",
+      "loss = \n",
+      "0.1609605\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.04283613, -0.02442982, -0.01338785,  0.00090426], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 4\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 4.641241    5.610182    0.14346226 -3.3931928 ]\n",
+      "loss = \n",
+      "0.16424192\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03379956, -0.01886207,  0.02503033, -0.00100173], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 5\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 4.671273   5.647105   0.1363068 -3.4011612]\n",
+      "loss = \n",
+      "0.16676486\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03880528, -0.02853293,  0.03753451, -0.00024496], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 6\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 4.7024875   5.6838374   0.12525252 -3.4067776 ]\n",
+      "loss = \n",
+      "0.16162877\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.05799295, -0.03151587,  0.01043263, -0.000948  ], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 7\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 4.734852    5.7202725   0.10250181 -3.4092338 ]\n",
+      "loss = \n",
+      "0.1584607\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.05886944, -0.03015547,  0.02939174, -0.0015463 ], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 8\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 4.767609    5.7557793   0.06937666 -3.412578  ]\n",
+      "loss = \n",
+      "0.15113996\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.04920368, -0.02369189,  0.02926033,  0.00065371], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 9\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[ 4.8001475   5.790519    0.01771035 -3.4149394 ]\n",
+      "loss = \n",
+      "0.1585053\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.0407194 , -0.02427388,  0.05359101, -0.00038767], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 0\n",
+      "loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 4.8319507   5.824961   -0.01833939 -3.417809  ]\n",
+      "loss = \n",
+      "0.14481218\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03287198, -0.02789038, -0.02464403,  0.00042138], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 1\n",
+      "loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 4.862296    5.858252   -0.04838233 -3.4197128 ]\n",
+      "loss = \n",
+      "0.1511284\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.02156219, -0.01949084, -0.00609969, -0.0003984 ], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 2\n",
+      "loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 4.8939633   5.8927665  -0.05158214 -3.4218926 ]\n",
+      "loss = \n",
+      "0.14548452\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.05855059, -0.04055905, -0.05581503,  0.00025946], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 3\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 4.924504    5.9259214  -0.05296526 -3.4244187 ]\n",
+      "loss = \n",
+      "0.14298718\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.02579568, -0.01745139, -0.00363913,  0.00031224], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 4\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 4.9535255   5.957814   -0.04841848 -3.4295144 ]\n",
+      "loss = \n",
+      "0.15182978\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.01878959, -0.01708326, -0.01387376,  0.00158071], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 5\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 4.9825826   5.9909234  -0.04036465 -3.4363897 ]\n",
+      "loss = \n",
+      "0.13767052\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03808122, -0.03862914, -0.00938304,  0.00126573], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 6\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 5.010853    6.022563   -0.03210349 -3.4441433 ]\n",
+      "loss = \n",
+      "0.13167807\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.02669432, -0.01501388, -0.00229435,  0.00085064], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 7\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 5.039377    6.0545297  -0.01929486 -3.4513233 ]\n",
+      "loss = \n",
+      "0.14640199\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=\n",
+      "array([-3.9708868e-02, -2.9929290e-02, -1.2590049e-02,  8.8455585e-05],\n",
+      "      dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 8\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 5.0678954e+00  6.0864248e+00 -3.3189822e-03 -3.4587049e+00]\n",
+      "loss = \n",
+      "0.13845903\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03632194, -0.02647942, -0.01030481,  0.00048467], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 9\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[ 5.0959725   6.1181674   0.01192447 -3.4673586 ]\n",
+      "loss = \n",
+      "0.12967475\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03027736, -0.02556127, -0.00182273,  0.00107816], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 0\n",
+      "loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 5.1242685   6.1497974   0.03751232 -3.477529  ]\n",
+      "loss = \n",
+      "0.12888822\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03840997, -0.02566834, -0.02782483,  0.00127175], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 1\n",
+      "loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 5.1518984   6.181125    0.06155404 -3.4896603 ]\n",
+      "loss = \n",
+      "0.1285064\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.02672164, -0.02377138, -0.00204977,  0.00158425], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 2\n",
+      "loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 5.178829    6.211113    0.07297467 -3.4999883 ]\n",
+      "loss = \n",
+      "0.13301225\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.02532193, -0.01428632,  0.02374361, -0.00035038], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 3\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 5.2058163   6.2412186   0.06585572 -3.5087552 ]\n",
+      "loss = \n",
+      "0.13558541\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03426778, -0.02585645,  0.04064076, -0.00031032], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 4\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 5.234104    6.271183    0.04579275 -3.5170274 ]\n",
+      "loss = \n",
+      "0.12878063\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.05052149, -0.02357828,  0.03234565,  0.00017987], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 5\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 5.2630496   6.299651    0.02746219 -3.5243149 ]\n",
+      "loss = \n",
+      "0.1265182\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.0435137 , -0.01163494,  0.00041035, -0.00010498], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 6\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 5.2918797   6.3268495   0.01276483 -3.5313895 ]\n",
+      "loss = \n",
+      "0.123860985\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03408676, -0.01230628, -0.00441132,  0.00025256], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 7\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 5.320539   6.353758  -0.013386  -3.5396888]\n",
+      "loss = \n",
+      "0.1255579\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03307474, -0.01959365,  0.03044175,  0.00099374], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 8\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 5.348364    6.3791456  -0.04798635 -3.5468566 ]\n",
+      "loss = \n",
+      "0.12436943\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.02419855, -0.00881345,  0.02606606, -0.00017768], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 9\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[ 5.3759923   6.4046144  -0.07521569 -3.555701  ]\n",
+      "loss = \n",
+      "0.12388037\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03121982, -0.02109133, -0.00947112,  0.00122283], dtype=float32)>]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# plug in generator instead of raw data to get away from calculating 7-day avg\n",
+    "# initialize parameters at some bad values; otherwise, convergence is instantaneous\n",
+    "param_vec = kcqe_obj.fit(xval_batch_gen = generator,\n",
+    "    num_blocks = num_blocks, \n",
+    "    tau=tf.constant(np.array([0.1, 0.5, 0.9]), dtype=tf.float32),\n",
+    "    optim_method=\"adam\", \n",
+    "    num_epochs=10, \n",
+    "    learning_rate=0.1,\n",
+    "    verbose = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q_hat = kcqe_obj.predict(param_vec,\n",
+    "                         x_train=x,\n",
+    "                         y_train=y,\n",
+    "                         x_test=x_test,\n",
+    "                         tau=tf.constant(np.array([0.1, 0.5, 0.9]), dtype=tf.float32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(8,8))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "#ax = Axes3D(fig)\n",
+    "\n",
+    "ax.scatter(x0, x1, y)\n",
+    "\n",
+    "x0_grid = x_test[..., 0].numpy().reshape((grid_size, grid_size))\n",
+    "x1_grid = x_test[..., 1].numpy().reshape((grid_size, grid_size))\n",
+    "q_grid_q10 = q_hat[..., 0].numpy().reshape((grid_size, grid_size))\n",
+    "q_grid_median = q_hat[..., 1].numpy().reshape((grid_size, grid_size))\n",
+    "q_grid_q90 = q_hat[..., 2].numpy().reshape((grid_size, grid_size))\n",
+    "\n",
+    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_q10, rstride=10, cstride=10, color=\"red\")\n",
+    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_median, rstride=10, cstride=10)\n",
+    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_q90, rstride=10, cstride=10, color=\"orange\")\n",
+    "\n",
+    "ax.set_xlabel('X_0')\n",
+    "ax.set_ylabel('X_1')\n",
+    "ax.set_zlabel('Y')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Missing Data\n",
+    "\n",
+    "Testing to see if method still works with missing values added."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing = random.sample(range(1, 501), 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# generate 50 random indices\n",
+    "random_indices = tf.random.shuffle(tf.range(500))[:50]\n",
+    "\n",
+    "# scatter NaN values at the random indices\n",
+    "y_miss = 10. * np.cos(x0) + 0.5 * x1**2 + np.random.normal(loc=0., scale=5., size=n)\n",
+    "\n",
+    "y_miss [random_indices] = np.nan\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# y_train_val\n",
+    "y_miss = np.reshape(y, (1, 500))\n",
+    "\n",
+    "y_miss = tf.constant(y_miss, dtype=tf.float32)\n",
+    "\n",
+    "    \n",
+    "block_size = 50\n",
+    "num_blocks = math.floor(y.shape[1]/block_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kcqe_obj = kcqe.KCQE(p=2)\n",
+    "generator = kcqe_obj.generator(x, y_miss, batch_size = 10, block_size = block_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch idx = 0\n",
+      "batch idx = 0\n",
+      "loss idx = 1\n",
+      "param estimates vec = \n",
+      "[2.9000022 2.9000053 1.0999295]\n",
+      "loss = \n",
+      "1.8559208\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19440739,  0.06930105, -0.00452088], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 1\n",
+      "loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[2.8000438 2.8       1.199272 ]\n",
+      "loss = \n",
+      "1.8236434\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19167238,  0.06955975, -0.00393209], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 2\n",
+      "loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[2.7000172 2.700042  1.2976269]\n",
+      "loss = \n",
+      "1.7839864\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19608937,  0.06858149, -0.00341937], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 3\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[2.5998323 2.602054  1.396668 ]\n",
+      "loss = \n",
+      "1.7598867\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.20315905,  0.05021626, -0.00421176], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 4\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[2.4995885 2.5045018 1.4941242]\n",
+      "loss = \n",
+      "1.7497969\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.20135477,  0.05505765, -0.00299582], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 5\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[2.3989813 2.4076326 1.59027  ]\n",
+      "loss = \n",
+      "1.7066048\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.21908772,  0.05129457, -0.00284991], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 6\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[2.2990868 2.3090844 1.6828065]\n",
+      "loss = \n",
+      "1.6970944\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.17948543,  0.08364669, -0.00189128], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 7\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[2.2001014 2.2090614 1.772595 ]\n",
+      "loss = \n",
+      "1.6672314\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.1675583 ,  0.09621327, -0.00187299], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 8\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[2.1019576 2.1083338 1.8623364]\n",
+      "loss = \n",
+      "1.6460686\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.16293514,  0.08118437, -0.00256723], dtype=float32)>]\n",
+      "epoch idx = 0\n",
+      "batch idx = 9\n",
+      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
+      "param estimates vec = \n",
+      "[2.004904  2.00799   1.9519942]\n",
+      "loss = \n",
+      "1.6058226\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.15114632,  0.06751331, -0.00252381], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 0\n",
+      "loss idx = 2\n",
+      "param estimates vec = \n",
+      "[1.9096098 1.9064658 2.0420508]\n",
+      "loss = \n",
+      "1.6036257\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.12997659,  0.09212349, -0.00269014], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 1\n",
+      "loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[1.8165389 1.8050964 2.1315641]\n",
+      "loss = \n",
+      "1.5859874\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.11326034,  0.07368275, -0.00232928], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 2\n",
+      "loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[1.724881  1.7045035 2.2200985]\n",
+      "loss = \n",
+      "1.55997\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.12176181,  0.06573528, -0.00212138], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 3\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[1.6354887 1.6036618 2.3074188]\n",
+      "loss = \n",
+      "1.53492\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.10016322,  0.07671296, -0.00197278], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 4\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[1.548937  1.5031002 2.392799 ]\n",
+      "loss = \n",
+      "1.5166188\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.08365919,  0.07061405, -0.00166669], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 5\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[1.4654448 1.4024062 2.4759827]\n",
+      "loss = \n",
+      "1.502093\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.07330681,  0.07511549, -0.00149781], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 6\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[1.3851527 1.301945  2.5574634]\n",
+      "loss = \n",
+      "1.4913731\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.06460683,  0.07098984, -0.00155042], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 7\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[1.3089576 1.2023017 2.6381624]\n",
+      "loss = \n",
+      "1.4874972\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0450785 ,  0.06412175, -0.00175205], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 8\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[1.237444  1.1043955 2.7170825]\n",
+      "loss = \n",
+      "1.4676135\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.03026615,  0.053858  , -0.00141577], dtype=float32)>]\n",
+      "epoch idx = 1\n",
+      "batch idx = 9\n",
+      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
+      "param estimates vec = \n",
+      "[1.1706498 1.0075078 2.7931123]\n",
+      "loss = \n",
+      "1.4639068\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02306372,  0.05839595, -0.0010446 ], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 0\n",
+      "loss idx = 3\n",
+      "param estimates vec = \n",
+      "[1.1084764  0.91276044 2.8658946 ]\n",
+      "loss = \n",
+      "1.4530964\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01804207,  0.04698691, -0.00086463], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 1\n",
+      "loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[1.0517025 0.8210188 2.935904 ]\n",
+      "loss = \n",
+      "1.464254\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00228592,  0.03776612, -0.00089324], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 2\n",
+      "loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[1.0001569 0.7318968 3.0038497]\n",
+      "loss = \n",
+      "1.450712\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00182817,  0.03803513, -0.00099266], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 3\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[0.95418423 0.6437734  3.0689113 ]\n",
+      "loss = \n",
+      "1.444152\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01180253,  0.04837034, -0.00073255], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 4\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[0.9155319 0.5612134 3.130532 ]\n",
+      "loss = \n",
+      "1.4371657\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03819613,  0.0144155 , -0.00053033], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 5\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[0.8822253  0.48449677 3.1895068 ]\n",
+      "loss = \n",
+      "1.4463148\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02258179,  0.00923126, -0.00063375], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 6\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[0.8543499 0.4119088 3.2454674]\n",
+      "loss = \n",
+      "1.4519398\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0289036 ,  0.01656612, -0.00048858], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 7\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[0.8321453  0.34784347 3.2979848 ]\n",
+      "loss = \n",
+      "1.4470874\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03694937, -0.01192567, -0.00032886], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 8\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[0.81688684 0.29154918 3.3471909 ]\n",
+      "loss = \n",
+      "1.4471983\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05690408, -0.01185824, -0.00028712], dtype=float32)>]\n",
+      "epoch idx = 2\n",
+      "batch idx = 9\n",
+      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
+      "param estimates vec = \n",
+      "[0.80550313 0.24211183 3.3933575 ]\n",
+      "loss = \n",
+      "1.4535428\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02878779, -0.01049848, -0.00027894], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 0\n",
+      "loss idx = 4\n",
+      "param estimates vec = \n",
+      "[0.79996103 0.20118912 3.4366474 ]\n",
+      "loss = \n",
+      "1.4492233\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05460704, -0.02281621, -0.00025339], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 1\n",
+      "loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[0.8004906  0.16852792 3.476867  ]\n",
+      "loss = \n",
+      "1.4492558\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06281266, -0.02542387, -0.00015878], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 2\n",
+      "loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[0.8070037  0.14676112 3.5141025 ]\n",
+      "loss = \n",
+      "1.4506102\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06775291, -0.04343731, -0.00011977], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 3\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[0.81906927 0.13645472 3.5488958 ]\n",
+      "loss = \n",
+      "1.4559287\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06877872, -0.0521648 , -0.00017384], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 4\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[0.83569235 0.1358833  3.581444  ]\n",
+      "loss = \n",
+      "1.4593924\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06293649, -0.04894633, -0.00016829], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 5\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[0.85626286 0.14438191 3.611901  ]\n",
+      "loss = \n",
+      "1.4534637\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06037865, -0.05057959, -0.00015802], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 6\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[0.87988895 0.15880553 3.6405165 ]\n",
+      "loss = \n",
+      "1.4477147\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05401319, -0.03756427, -0.00016882], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 7\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[0.9050867  0.17941949 3.6678402 ]\n",
+      "loss = \n",
+      "1.453794\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04021679, -0.04212832, -0.00024062], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 8\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[0.9316176  0.20455824 3.6940472 ]\n",
+      "loss = \n",
+      "1.4419829\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03881332, -0.03576044, -0.00024964], dtype=float32)>]\n",
+      "epoch idx = 3\n",
+      "batch idx = 9\n",
+      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
+      "param estimates vec = \n",
+      "[0.95852613 0.2316449  3.7192802 ]\n",
+      "loss = \n",
+      "1.4485679\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02944825, -0.02330769, -0.00025542], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 0\n",
+      "loss idx = 5\n",
+      "param estimates vec = \n",
+      "[0.9863718  0.25973713 3.7435422 ]\n",
+      "loss = \n",
+      "1.444761\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03553405, -0.01886015, -0.00023764], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 1\n",
+      "loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[1.0140066 0.2859389 3.7672126]\n",
+      "loss = \n",
+      "1.4425075\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02399032, -0.00344075, -0.00028875], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 2\n",
+      "loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[1.0401615  0.31041712 3.7905743 ]\n",
+      "loss = \n",
+      "1.4420202\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01042622, -0.00340413, -0.00032761], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 3\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[1.0661138  0.33569008 3.81343   ]\n",
+      "loss = \n",
+      "1.43649\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02207221, -0.01592234, -0.00028469], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 4\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[1.0915865 0.3600539 3.8359523]\n",
+      "loss = \n",
+      "1.438874\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01887969, -0.00719224, -0.0003046 ], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 5\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[1.1149976  0.38278204 3.858116  ]\n",
+      "loss = \n",
+      "1.4407051\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00245952, -0.00295044, -0.0002924 ], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 6\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[1.1353183 0.4014608 3.8804445]\n",
+      "loss = \n",
+      "1.442847\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00949064,  0.01008283, -0.00037498], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 7\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[1.1524001  0.41801113 3.9024217 ]\n",
+      "loss = \n",
+      "1.442622\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01353705,  0.00217182, -0.00028598], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 8\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[1.1671234 0.4315588 3.924501 ]\n",
+      "loss = \n",
+      "1.4386582\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00775092,  0.00746074, -0.00035518], dtype=float32)>]\n",
+      "epoch idx = 4\n",
+      "batch idx = 9\n",
+      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
+      "param estimates vec = \n",
+      "[1.1798738  0.43836367 3.9465914 ]\n",
+      "loss = \n",
+      "1.441081\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0060271 ,  0.02715849, -0.00033863], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 0\n",
+      "loss idx = 6\n",
+      "param estimates vec = \n",
+      "[1.1889488  0.44380313 3.9686806 ]\n",
+      "loss = \n",
+      "1.4426184\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02373386,  0.00365785, -0.00033409], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 1\n",
+      "loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[1.1949495  0.44726977 3.9905963 ]\n",
+      "loss = \n",
+      "1.4333929\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02104502,  0.00718385, -0.00030305], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 2\n",
+      "loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[1.1985656  0.44524533 4.012247  ]\n",
+      "loss = \n",
+      "1.441034\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0170673 ,  0.02499608, -0.00028323], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 3\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[1.1992948  0.44096854 4.0336485 ]\n",
+      "loss = \n",
+      "1.4439518\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02356364,  0.01170017, -0.00027971], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 4\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[1.1966887 0.434133  4.054998 ]\n",
+      "loss = \n",
+      "1.4389583\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02991313,  0.01405628, -0.00030563], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 5\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[1.1927644  0.42415828 4.076297  ]\n",
+      "loss = \n",
+      "1.4397972\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01413841,  0.01782531, -0.00030294], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 6\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[1.1863697  0.41325137 4.0972033 ]\n",
+      "loss = \n",
+      "1.4446416\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02551033,  0.00867383, -0.00024604], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 7\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[1.1776865  0.40128598 4.117822  ]\n",
+      "loss = \n",
+      "1.4463412\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02573439,  0.00959656, -0.00025509], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 8\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[1.167838  0.3901589 4.138179 ]\n",
+      "loss = \n",
+      "1.4399837\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01743715,  0.00122226, -0.00025315], dtype=float32)>]\n",
+      "epoch idx = 5\n",
+      "batch idx = 9\n",
+      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
+      "param estimates vec = \n",
+      "[1.1577613  0.37860078 4.1578574 ]\n",
+      "loss = \n",
+      "1.4366187\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01000121,  0.00666728, -0.00018382], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 0\n",
+      "loss idx = 7\n",
+      "param estimates vec = \n",
+      "[1.1465868  0.36812165 4.177256  ]\n",
+      "loss = \n",
+      "1.4445062\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 1.7711593e-02, -4.6406407e-05, -2.3371859e-04], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 1\n",
+      "loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[1.1351373 0.3582987 4.1962585]\n",
+      "loss = \n",
+      "1.442611\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01132894,  0.00140971, -0.00021067], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 2\n",
+      "loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[1.1251526 0.349082  4.214697 ]\n",
+      "loss = \n",
+      "1.4378071\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00345947,  0.00135463, -0.00017868], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 3\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[1.1152713  0.34203127 4.232848  ]\n",
+      "loss = \n",
+      "1.4372504\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00700283, -0.00578776, -0.00021087], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 4\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[1.1057912  0.33778358 4.2509656 ]\n",
+      "loss = \n",
+      "1.4413385\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00436127, -0.00941142, -0.00024297], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 5\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[1.0972017 0.3347281 4.268706 ]\n",
+      "loss = \n",
+      "1.439222\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-7.7326884e-05, -3.4690390e-03, -1.9064292e-04], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 6\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[1.0893782  0.33556113 4.2863555 ]\n",
+      "loss = \n",
+      "1.4413978\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00027638, -0.01556928, -0.00022595], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 7\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[1.083191  0.3410916 4.3036447]\n",
+      "loss = \n",
+      "1.4431176\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00747986, -0.02052842, -0.00018442], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 8\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[1.0785092 0.3492683 4.320729 ]\n",
+      "loss = \n",
+      "1.4393232\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00759681, -0.01351472, -0.00020077], dtype=float32)>]\n",
+      "epoch idx = 6\n",
+      "batch idx = 9\n",
+      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
+      "param estimates vec = \n",
+      "[1.0753169  0.35477516 4.337476  ]\n",
+      "loss = \n",
+      "1.436825\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00855085,  0.00807359, -0.00017798], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 0\n",
+      "loss idx = 8\n",
+      "param estimates vec = \n",
+      "[1.0737627  0.35662022 4.3541045 ]\n",
+      "loss = \n",
+      "1.4395348\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01080782,  0.01323894, -0.00020335], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 1\n",
+      "loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[1.0722647 0.3605856 4.370283 ]\n",
+      "loss = \n",
+      "1.4396706\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0007131 , -0.00959263, -0.0001542 ], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 2\n",
+      "loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[1.0712063  0.36567798 4.3861065 ]\n",
+      "loss = \n",
+      "1.4449698\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00238191, -0.00622507, -0.0001606 ], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 3\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[1.0713689 0.3715807 4.4014378]\n",
+      "loss = \n",
+      "1.440836\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0088608 , -0.00531256, -0.00013619], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 4\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[1.0714346 0.3774383 4.416542 ]\n",
+      "loss = \n",
+      "1.4369131\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00064116, -0.00208263, -0.00016539], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 5\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[1.0733511 0.3803406 4.4314203]\n",
+      "loss = \n",
+      "1.4371399\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01447204,  0.00978383, -0.00016172], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 6\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[1.0760314 0.3829049 4.4460254]\n",
+      "loss = \n",
+      "1.4395671\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00730673,  0.00026478, -0.00015152], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 7\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[1.0789249  0.38234797 4.4606657 ]\n",
+      "loss = \n",
+      "1.4414686\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00357632,  0.01156854, -0.00018862], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 8\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[1.0824571 0.3834414 4.475298 ]\n",
+      "loss = \n",
+      "1.4413307\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00695959, -0.00637969, -0.00018219], dtype=float32)>]\n",
+      "epoch idx = 7\n",
+      "batch idx = 9\n",
+      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
+      "param estimates vec = \n",
+      "[1.0851073  0.38370383 4.4898157 ]\n",
+      "loss = \n",
+      "1.4369284\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00417044,  0.00288797, -0.000167  ], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 0\n",
+      "loss idx = 9\n",
+      "param estimates vec = \n",
+      "[1.0880408  0.38543057 4.5043797 ]\n",
+      "loss = \n",
+      "1.4412868\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00401904, -0.00586963, -0.0001858 ], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 1\n",
+      "loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[1.0908641  0.38741627 4.518667  ]\n",
+      "loss = \n",
+      "1.4384803\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00124609, -0.00165183, -0.00014314], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 2\n",
+      "loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[1.0939311  0.38878882 4.5328    ]\n",
+      "loss = \n",
+      "1.4422288\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00379818,  0.00165761, -0.00015478], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 3\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[1.0971068  0.39152417 4.546935  ]\n",
+      "loss = \n",
+      "1.4412123\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00294688, -0.0057741 , -0.00017231], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 4\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[1.100317   0.39493263 4.561067  ]\n",
+      "loss = \n",
+      "1.4411871\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00246   , -0.00358211, -0.00017078], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 5\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[1.1036817  0.39168406 4.5751867 ]\n",
+      "loss = \n",
+      "1.4443262\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0033477 ,  0.02424129, -0.00016864], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 6\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[1.1055071  0.38627672 4.5893316 ]\n",
+      "loss = \n",
+      "1.4469988\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00887258,  0.00939817, -0.00017234], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 7\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[1.106778   0.37997663 4.6032124 ]\n",
+      "loss = \n",
+      "1.4472324\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00275847,  0.00532113, -0.00013531], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 8\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[1.106654  0.3746618 4.617041 ]\n",
+      "loss = \n",
+      "1.443735\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00915337, -0.0014635 , -0.000158  ], dtype=float32)>]\n",
+      "epoch idx = 8\n",
+      "batch idx = 9\n",
+      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
+      "param estimates vec = \n",
+      "[1.1066027 0.3720279 4.630771 ]\n",
+      "loss = \n",
+      "1.4424518\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00043422, -0.00814789, -0.00015081], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 0\n",
+      "loss idx = 10\n",
+      "param estimates vec = \n",
+      "[1.1044961 0.3693011 4.64445  ]\n",
+      "loss = \n",
+      "1.4434372\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01462935,  0.00127526, -0.0001548 ], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 1\n",
+      "loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[1.1031853  0.36209315 4.6580863 ]\n",
+      "loss = \n",
+      "1.4437724\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00420734,  0.01757874, -0.00015438], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 2\n",
+      "loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[1.1024231 0.3595044 4.6716294]\n",
+      "loss = \n",
+      "1.4425727\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00297887, -0.0144944 , -0.000147  ], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 3\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[1.1021637  0.36038035 4.6852064 ]\n",
+      "loss = \n",
+      "1.441389\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00300559, -0.01180881, -0.00016066], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 4\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[1.1019517 0.363261  4.6985316]\n",
+      "loss = \n",
+      "1.4385345\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00015816, -0.00762316, -0.00012563], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 5\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[1.102833   0.36467758 4.711683  ]\n",
+      "loss = \n",
+      "1.4386714\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00741098,  0.00432125, -0.00013156], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 6\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[1.1031005  0.36458832 4.724749  ]\n",
+      "loss = \n",
+      "1.448237\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00364006,  0.00495168, -0.00013962], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 7\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[1.1026801  0.36609784 4.73786   ]\n",
+      "loss = \n",
+      "1.4409873\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0045265 , -0.00571362, -0.00015343], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 8\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[1.10248    0.36436862 4.750865  ]\n",
+      "loss = \n",
+      "1.4367217\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00122608,  0.01106403, -0.00013526], dtype=float32)>]\n",
+      "epoch idx = 9\n",
+      "batch idx = 9\n",
+      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
+      "param estimates vec = \n",
+      "[1.102398   0.36562392 4.7637753 ]\n",
+      "loss = \n",
+      "1.442529\n",
+      "grads = \n",
+      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00066888, -0.01003128, -0.00013487], dtype=float32)>]\n"
+     ]
+    }
+   ],
+   "source": [
+    "param_vec = kcqe_obj.fit(xval_batch_gen = generator,\n",
+    "    num_blocks = num_blocks, \n",
+    "    tau=tf.constant(np.array([0.1, 0.5, 0.9]), dtype=tf.float32),\n",
+    "    optim_method=\"adam\", \n",
+    "    num_epochs=10, \n",
+    "    learning_rate=0.1,\n",
+    "    init_param_vec=tf.constant(np.array([3.0, 3.0, 1.0]), dtype=tf.float32),\n",
+    "    verbose = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.arange(len(kcqe_obj.loss_trace)), kcqe_obj.loss_trace)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index 0ef2171..2281ad9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,15 +11,23 @@ readme = "README.md"
 license = {text = "MIT"}
 
 dependencies = [
-    "pandas>=2.2.2",
-    "numpy>=1.26.1",
-    "tensorflow>=2.16.1,<3",
-    "tensorflow-probability>=0.24.0",
+    "pandas",
+    "numpy",
+    "tensorflow",
+    "tensorflow-probability",
+    "pytest",
+    "polars",
+    "matplotlib>=3.10.0",
+    "tf-keras>=2.18.0",
+    "ipykernel>=6.29.5",
 ]
 
 [project.optional-dependencies]
-doc = [
-     "matplotlib>=3.9.0",
+dev = [
+    'mypy',
+    'pytest',
+    'pytest-mock',
+    'ruff',
 ]
 
 
@@ -27,3 +35,9 @@ doc = [
 requires = ["setuptools>=61", "wheel"]
 build-backend = "setuptools.build_meta"
 
+[tool.ruff]
+line-length = 120
+lint.extend-select = ['I']
+
+[tools.setuptools]
+packages = ['jacques']
diff --git a/requirements/requirements-doc.txt b/requirements/requirements-dev.txt
similarity index 64%
rename from requirements/requirements-doc.txt
rename to requirements/requirements-dev.txt
index f1e831d..34dbf32 100644
--- a/requirements/requirements-doc.txt
+++ b/requirements/requirements-dev.txt
@@ -1,5 +1,5 @@
 # This file was autogenerated by uv via the following command:
-#    uv pip compile pyproject.toml --extra doc -o requirements/requirements-doc.txt
+#    uv pip compile pyproject.toml --extra dev -o requirements/requirements-dev.txt
 absl-py==2.1.0
     # via
     #   keras
@@ -8,110 +8,111 @@ absl-py==2.1.0
     #   tensorflow-probability
 astunparse==1.6.3
     # via tensorflow
-certifi==2024.6.2
+certifi==2024.12.14
     # via requests
-charset-normalizer==3.3.2
+charset-normalizer==3.4.1
     # via requests
-cloudpickle==3.0.0
+cloudpickle==3.1.0
     # via tensorflow-probability
-contourpy==1.2.1
-    # via matplotlib
-cycler==0.12.1
-    # via matplotlib
 decorator==5.1.1
     # via tensorflow-probability
 dm-tree==0.1.8
     # via tensorflow-probability
-flatbuffers==24.3.25
+flatbuffers==24.12.23
     # via tensorflow
-fonttools==4.53.0
-    # via matplotlib
-gast==0.5.4
+gast==0.6.0
     # via
     #   tensorflow
     #   tensorflow-probability
 google-pasta==0.2.0
     # via tensorflow
-grpcio==1.64.1
+grpcio==1.69.0
     # via
     #   tensorboard
     #   tensorflow
-h5py==3.11.0
+h5py==3.12.1
     # via
     #   keras
     #   tensorflow
-idna==3.7
+idna==3.10
     # via requests
-keras==3.3.3
+iniconfig==2.0.0
+    # via pytest
+keras==3.7.0
     # via tensorflow
-kiwisolver==1.4.5
-    # via matplotlib
 libclang==18.1.1
     # via tensorflow
-markdown==3.6
+markdown==3.7
     # via tensorboard
 markdown-it-py==3.0.0
     # via rich
-markupsafe==2.1.5
+markupsafe==3.0.2
     # via werkzeug
-matplotlib==3.9.0
-    # via jacques (pyproject.toml)
 mdurl==0.1.2
     # via markdown-it-py
-ml-dtypes==0.3.2
+ml-dtypes==0.4.1
     # via
     #   keras
     #   tensorflow
+mypy==1.14.1
+    # via jacques (pyproject.toml)
+mypy-extensions==1.0.0
+    # via mypy
 namex==0.0.8
     # via keras
-numpy==1.26.4
+numpy==2.0.2
     # via
     #   jacques (pyproject.toml)
-    #   contourpy
     #   h5py
     #   keras
-    #   matplotlib
     #   ml-dtypes
-    #   opt-einsum
     #   pandas
     #   tensorboard
     #   tensorflow
     #   tensorflow-probability
-opt-einsum==3.3.0
+opt-einsum==3.4.0
     # via tensorflow
-optree==0.11.0
+optree==0.13.1
     # via keras
-packaging==24.1
+packaging==24.2
     # via
-    #   matplotlib
+    #   keras
+    #   pytest
+    #   tensorboard
     #   tensorflow
-pandas==2.2.2
+pandas==2.2.3
+    # via jacques (pyproject.toml)
+pluggy==1.5.0
+    # via pytest
+polars==1.19.0
     # via jacques (pyproject.toml)
-pillow==10.3.0
-    # via matplotlib
-protobuf==4.25.3
+protobuf==5.29.2
     # via
     #   tensorboard
     #   tensorflow
-pygments==2.18.0
+pygments==2.19.1
     # via rich
-pyparsing==3.1.2
-    # via matplotlib
-python-dateutil==2.9.0.post0
+pytest==8.3.4
     # via
-    #   matplotlib
-    #   pandas
-pytz==2024.1
+    #   jacques (pyproject.toml)
+    #   pytest-mock
+pytest-mock==3.14.0
+    # via jacques (pyproject.toml)
+python-dateutil==2.9.0.post0
+    # via pandas
+pytz==2024.2
     # via pandas
 requests==2.32.3
     # via tensorflow
-rich==13.7.1
+rich==13.9.4
     # via keras
-setuptools==70.0.0
+ruff==0.8.6
+    # via jacques (pyproject.toml)
+setuptools==75.7.0
     # via
     #   tensorboard
     #   tensorflow
-six==1.16.0
+six==1.17.0
     # via
     #   astunparse
     #   google-pasta
@@ -119,31 +120,32 @@ six==1.16.0
     #   tensorboard
     #   tensorflow
     #   tensorflow-probability
-tensorboard==2.16.2
+tensorboard==2.18.0
     # via tensorflow
 tensorboard-data-server==0.7.2
     # via tensorboard
-tensorflow==2.16.1
+tensorflow==2.18.0
     # via
     #   jacques (pyproject.toml)
     #   tf-keras
-tensorflow-probability==0.24.0
+tensorflow-probability==0.25.0
     # via jacques (pyproject.toml)
-termcolor==2.4.0
+termcolor==2.5.0
     # via tensorflow
-tf-keras==2.16.0
+tf-keras==2.18.0
     # via jacques (pyproject.toml)
 typing-extensions==4.12.2
     # via
+    #   mypy
     #   optree
     #   tensorflow
-tzdata==2024.1
+tzdata==2024.2
     # via pandas
-urllib3==2.2.2
+urllib3==2.3.0
     # via requests
-werkzeug==3.0.3
+werkzeug==3.1.3
     # via tensorboard
-wheel==0.43.0
+wheel==0.45.1
     # via astunparse
-wrapt==1.16.0
+wrapt==1.17.0
     # via tensorflow
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 7b3b857..4954629 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 # This file was autogenerated by uv via the following command:
-#    uv pip compile pyproject.toml -o requirements/requirements.txt
+#    uv pip compile pyproject.toml --extra dev -o requirements/requirements.txt
 absl-py==2.1.0
     # via
     #   keras
@@ -8,90 +8,111 @@ absl-py==2.1.0
     #   tensorflow-probability
 astunparse==1.6.3
     # via tensorflow
-certifi==2024.2.2
+certifi==2024.12.14
     # via requests
-charset-normalizer==3.3.2
+charset-normalizer==3.4.1
     # via requests
-cloudpickle==3.0.0
+cloudpickle==3.1.0
     # via tensorflow-probability
 decorator==5.1.1
     # via tensorflow-probability
 dm-tree==0.1.8
     # via tensorflow-probability
-flatbuffers==24.3.25
+flatbuffers==24.12.23
     # via tensorflow
-gast==0.5.4
+gast==0.6.0
     # via
     #   tensorflow
     #   tensorflow-probability
 google-pasta==0.2.0
     # via tensorflow
-grpcio==1.64.0
+grpcio==1.69.0
     # via
     #   tensorboard
     #   tensorflow
-h5py==3.11.0
+h5py==3.12.1
     # via
     #   keras
     #   tensorflow
-idna==3.7
+idna==3.10
     # via requests
-keras==3.3.3
+iniconfig==2.0.0
+    # via pytest
+keras==3.7.0
     # via tensorflow
 libclang==18.1.1
     # via tensorflow
-markdown==3.6
+markdown==3.7
     # via tensorboard
 markdown-it-py==3.0.0
     # via rich
-markupsafe==2.1.5
+markupsafe==3.0.2
     # via werkzeug
 mdurl==0.1.2
     # via markdown-it-py
-ml-dtypes==0.3.2
+ml-dtypes==0.4.1
     # via
     #   keras
     #   tensorflow
+mypy==1.14.1
+    # via jacques (pyproject.toml)
+mypy-extensions==1.0.0
+    # via mypy
 namex==0.0.8
     # via keras
-numpy==1.26.4
+numpy==2.0.2
     # via
     #   jacques (pyproject.toml)
     #   h5py
     #   keras
     #   ml-dtypes
-    #   opt-einsum
     #   pandas
     #   tensorboard
     #   tensorflow
     #   tensorflow-probability
-opt-einsum==3.3.0
+opt-einsum==3.4.0
     # via tensorflow
-optree==0.11.0
+optree==0.13.1
     # via keras
-packaging==24.0
-    # via tensorflow
-pandas==2.2.2
+packaging==24.2
+    # via
+    #   keras
+    #   pytest
+    #   tensorboard
+    #   tensorflow
+pandas==2.2.3
     # via jacques (pyproject.toml)
-protobuf==4.25.3
+pluggy==1.5.0
+    # via pytest
+polars==1.19.0
+    # via jacques (pyproject.toml)
+protobuf==5.29.2
     # via
     #   tensorboard
     #   tensorflow
-pygments==2.18.0
+pygments==2.19.1
     # via rich
+pytest==8.3.4
+    # via
+    #   jacques (pyproject.toml)
+    #   pytest-mock
+pytest-mock==3.14.0
+    # via jacques (pyproject.toml)
 python-dateutil==2.9.0.post0
     # via pandas
-pytz==2024.1
+pytz==2024.2
     # via pandas
-requests==2.32.2
+requests==2.32.3
     # via tensorflow
-rich==13.7.1
+rich==13.9.4
     # via keras
-setuptools==70.0.0
+ruff==0.8.6
+    # via jacques (pyproject.toml)
+setuptools==75.7.0
     # via
     #   tensorboard
     #   tensorflow
-six==1.16.0
+six==1.17.0
     # via
     #   astunparse
     #   google-pasta
@@ -99,31 +120,32 @@ six==1.16.0
     #   tensorboard
     #   tensorflow
     #   tensorflow-probability
-tensorboard==2.16.2
+tensorboard==2.18.0
     # via tensorflow
 tensorboard-data-server==0.7.2
     # via tensorboard
-tensorflow==2.16.1
+tensorflow==2.18.0
     # via
     #   jacques (pyproject.toml)
     #   tf-keras
-tensorflow-probability==0.24.0
+tensorflow-probability==0.25.0
     # via jacques (pyproject.toml)
-termcolor==2.4.0
+termcolor==2.5.0
     # via tensorflow
-tf-keras==2.16.0
+tf-keras==2.18.0
     # via jacques (pyproject.toml)
-typing-extensions==4.11.0
+typing-extensions==4.12.2
     # via
+    #   mypy
     #   optree
     #   tensorflow
-tzdata==2024.1
+tzdata==2024.2
     # via pandas
-urllib3==2.2.1
+urllib3==2.3.0
     # via requests
-werkzeug==3.0.3
+werkzeug==3.1.3
     # via tensorboard
-wheel==0.43.0
+wheel==0.45.1
     # via astunparse
-wrapt==1.16.0
+wrapt==1.17.0
     # via tensorflow
diff --git a/setup.py b/setup.py
deleted file mode 100644
index ce9feeb..0000000
--- a/setup.py
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/usr/bin/env python
-
-from distutils.core import setup
-
-setup(name='jacques',
-    version='0.0.1',
-    description='Just another conditional quantile estimator',
-    author='Serena Wang, Evan L. Ray',
-    author_email='elray@umass.edu',
-    url='https://github.com/reichlab/jacques',
-    packages=['jacques'],
-)
diff --git a/jacques/__init__.py b/src/jacques/__init__.py
similarity index 100%
rename from jacques/__init__.py
rename to src/jacques/__init__.py
diff --git a/src/jacques/data_processing.py b/src/jacques/data_processing.py
new file mode 100644
index 0000000..3ac4a32
--- /dev/null
+++ b/src/jacques/data_processing.py
@@ -0,0 +1,135 @@
+import random
+
+import numpy as np
+import polars as pl
+import tensorflow as tf
+
+from jacques.kernels import diff_x_pairs
+
+
+def date_block_map(df, time_var, num_blocks):
+    """
+    Parameters
+    __________
+
+    df: pandas dataframe
+    time_var: str
+        Name of the time variable
+    num_blocks: integer
+        Total number of blocks to be created with given dataset
+    batch_size: integer
+        Number of blocks in each batch. Each block has size of block_size. Default to 1.
+        This means each gradient descent iteration sees forecasts for only one time block.
+
+    Returns
+    _______________
+    block_map: dict
+        Dictionary with time points as keys and block assignments as values
+    """
+    if df.empty:
+        raise ValueError("Input dataframe is empty.")
+
+    unique_times = df[time_var].unique()
+    time_points = len(unique_times)
+
+    if num_blocks <= 0:
+        raise ValueError("Number of blocks must be greater than zero.")
+
+    block_size = time_points // num_blocks
+
+    block_assignments = np.repeat(np.arange(num_blocks), block_size)
+
+    # if there are remaining observations, assign them to the first block
+    leftover = time_points % num_blocks
+
+    #All of the leftover time points get assigned to the first block
+    if leftover > 0:
+        block_assignments = np.concatenate((np.repeat(0, leftover), block_assignments))
+
+    
+    #Create a dictionary of time points and their corresponding block assignments
+    block_map = dict(zip(unique_times, block_assignments))
+    
+    return block_map
+
+
+def assign_blocks(df, time_var, features, target, num_blocks):
+    """
+    Assigns each time point in the dataset to a block
+    
+    Parameters
+    __________
+    df: pandas dataframe
+    time_var: str
+        Name of the time variable
+    block_map: dict
+        Dictionary with time points as keys and block assignments as values
+
+    Returns
+    _______
+    block_list: list of dictionaries
+        Each dictionary contains the features and target values for a given block
+    """
+
+    block_map = date_block_map(df, time_var, num_blocks)
+
+    df['block'] = df[time_var].map(block_map).astype(int)
+    
+    block_list = []
+    for i in range(num_blocks):
+        data_dict = {
+            'features' : tf.constant(pl.from_pandas(df).filter(pl.col("block") == i).select(features), dtype = tf.float32),
+            'target': tf.constant(pl.from_pandas(df).filter(pl.col("block") == i).select(target), dtype = tf.float32)
+        }
+        block_list.append(data_dict)
+
+    return block_list
+
+
+def validation_training_pairings(num_blocks):
+    """
+    Parameters
+    __________
+    num_blocks: integer
+        Total number of blocks to be created with given dataset
+
+    Returns
+    __________
+    matrix: 2D numpy array
+        A matrix identifying which training blocks to use for each validation block.
+        Entry i,j is 1 if the jth block is used for training when the ith block is used for validation, and 0 otherwise.
+    """
+    if(num_blocks < 4):
+        raise ValueError("Number of blocks must be greater than 3")
+    # Create an n x n matrix filled with 1s
+    matrix = np.ones((num_blocks, num_blocks), dtype=int)
+    
+    # Set the main diagonal and the two adjacent diagonals to 0
+    np.fill_diagonal(matrix, 0)
+    np.fill_diagonal(matrix[1:], 0)
+    np.fill_diagonal(matrix[:, 1:], 0)
+    
+    #For the first block, we remove the following block, and one random block from the rest
+    matrix[0, random.randint(2, num_blocks - 1)] = 0 
+
+    # For the last block, we remove the previous block, and one random block from the rest
+    matrix[num_blocks - 1, random.randint(0, num_blocks - 3)] = 0
+
+    return matrix
+
+def calc_diffs_one_validation_block(list_of_tensors, num_blocks):
+    """
+    Calculate the differences between the test block and all the training blocks
+    """
+
+    # We only need to go up to num_blocks-2 because at that point we've calculated all relevant differences
+    for i in range(num_blocks):
+        diffs = []
+        for j in range(num_blocks):
+            if(j <= (i + 1)):
+                diffs.append(None)
+            elif(j > (i+1)):
+                # Get pairwised differences between each row in the training block and the test block
+                diffs.append(diff_x_pairs(list_of_tensors[i], list_of_tensors[j]))
+        yield diffs
+
diff --git a/jacques/jacques.py b/src/jacques/jacques.py
similarity index 97%
rename from jacques/jacques.py
rename to src/jacques/jacques.py
index 9feb716..385f4c4 100644
--- a/jacques/jacques.py
+++ b/src/jacques/jacques.py
@@ -1,10 +1,10 @@
-import pandas as pd
-import numpy as np
-import tensorflow as tf
 import abc
 import math
-import random
 import pickle
+import random
+
+import numpy as np
+import tensorflow as tf
 
 
 class jacques(abc.ABC):
@@ -130,17 +130,6 @@ def single_batch_generator(self, x_train_val, y_train_val, block_size):
             y_train = tf.reshape(y_train, [-1])
             y_train = tf.expand_dims(y_train, axis=0)
 
-            # Drop any entries with missing data in either target or features
-            mask = tf.math.isfinite(x_val).all(axis=1) & tf.math.isfinite(y_val)
-
-            x_val = tf.boolean_mask(x_val, mask)
-            y_val = tf.boolean_mask(y_val, mask)
-
-            mask = tf.math.isfinite(x_train).all(axis=1) & tf.math.isfinite(y_train)
-            x_train = tf.boolean_mask(x_train, mask)
-            y_train = tf.boolean_mask(y_train, mask)
-
-
             i += 1
 
             yield x_val, x_train, y_val, y_train
@@ -347,7 +336,7 @@ def fit(
             Defaults to None. Path to save parameter estimation snapshots.
         """
         # initialize init_param_vec
-        if init_param_vec == None:
+        if init_param_vec is None:
             # all zeros
             init_param_vec = tf.constant(np.zeros(self.n_param), dtype=np.float32)
 
diff --git a/jacques/kcqe.py b/src/jacques/kcqe.py
similarity index 97%
rename from jacques/kcqe.py
rename to src/jacques/kcqe.py
index 708554e..aa27233 100644
--- a/jacques/kcqe.py
+++ b/src/jacques/kcqe.py
@@ -1,9 +1,5 @@
-import numpy as np
-import tensorflow as tf
-from .jacques import jacques
-import tensorflow_probability as tfp
-
 from . import kernels
+from .jacques import jacques
 
 
 class KCQE(jacques):
diff --git a/jacques/kernels.py b/src/jacques/kernels.py
similarity index 99%
rename from jacques/kernels.py
rename to src/jacques/kernels.py
index 6029243..c5a5464 100644
--- a/jacques/kernels.py
+++ b/src/jacques/kernels.py
@@ -68,8 +68,7 @@ def transform_chol(theta_raw, d):
     """
     diag_raw = theta_raw[:d]
     corr_chol_raw = theta_raw[d:]
-    scale_bijector = tfp.bijectors.ScaleMatvecDiag(
-        scale_diag=softplus_bijector.forward(diag_raw))
+    scale_bijector = tfp.bijectors.ScaleMatvecDiag(scale_diag=softplus_bijector.forward(diag_raw))
     return scale_bijector.forward(x=corr_chol_bijector.forward(corr_chol_raw))
 
 
diff --git a/test/__init__.py b/tests/__init__.py
similarity index 100%
rename from test/__init__.py
rename to tests/__init__.py
diff --git a/tests/jacques/data_processing/test_assign_blocks.py b/tests/jacques/data_processing/test_assign_blocks.py
new file mode 100644
index 0000000..cfc643c
--- /dev/null
+++ b/tests/jacques/data_processing/test_assign_blocks.py
@@ -0,0 +1,115 @@
+import pandas as pd
+import pytest
+
+from jacques.data_processing import assign_blocks, date_block_map
+
+
+@pytest.fixture
+def sample_df():
+    """Fixture to create a sample DataFrame for testing."""
+    return pd.DataFrame({
+        'time': pd.date_range(start='2023-01-01', periods=10, freq='D')
+    })
+
+def test_equal_block_size(sample_df):
+    """Test when num_blocks divides the time points evenly."""
+    block_map = date_block_map(sample_df, 'time', 2)  # 10 time points, 2 blocks
+    assert len(block_map) == 10  # Should have 10 time points
+
+    block_values = list(block_map.values())
+    # Check that the blocks are divided evenly
+    assert block_values.count(0) == 5
+    assert block_values.count(1) == 5
+
+def test_with_leftovers(sample_df):
+    """Test when there are leftover time points."""
+    block_map = date_block_map(sample_df, 'time', 3)  # 10 time points, 3 blocks
+    assert len(block_map) == 10
+
+    block_values = list(block_map.values())
+    # With 10 points and 3 blocks, each block should have 3 or 4 points
+    assert block_values.count(0) == 4  # First block takes the leftovers
+    assert block_values.count(1) == 3
+    assert block_values.count(2) == 3
+
+
+def test_single_block(sample_df):
+    """Test with a single block."""
+    block_map = date_block_map(sample_df, 'time', 1)  # All points should go to block 0
+    assert len(block_map) == 10
+
+    block_values = list(block_map.values())
+    assert block_values == [0] * 10
+
+
+def test_no_blocks(sample_df):
+    """Test with num_blocks = 0, which should raise an error."""
+    with pytest.raises(ValueError, match="Number of blocks must be greater than zero."):
+        date_block_map(sample_df, 'time', 0)
+
+
+def test_empty_df():
+    """Test with an empty dataframe."""
+    empty_df = pd.DataFrame({'time': []})
+    with pytest.raises(ValueError, match="Input dataframe is empty."):
+        date_block_map(empty_df, 'time', 3)
+
+
+def test_df_with_duplicate_times():
+    """Test with a dataframe containing duplicate time entries."""
+    df_with_duplicates = pd.DataFrame({
+        'time': pd.to_datetime(['2023-01-01', '2023-01-01', '2023-01-02', '2023-01-03'])
+    })
+    block_map = date_block_map(df_with_duplicates, 'time', 2)  # 3 time points, 2 blocks
+    assert len(block_map) == 3
+    block_values = list(block_map.values())
+    assert block_values.count(0) == 2
+    assert block_values.count(1) == 1
+
+
+# def test_assign_blocks():
+#     # Create test data
+#     df = pd.DataFrame({'date': ['2022-01-06', '2022-01-13', '2022-01-20', '2022-01-27', '2022-02-03'],
+#                        'location': [1, 2, 3, 4, 1],
+#                        'x0': [0, 1, 0, 1, 1],
+#                        'x1': [0, 1, 0, 1, 1],
+#                        'target': [10.0, 10.5, 10.0, 10.5, 12]})
+    
+#     features = ['x0', 'x1']
+#     target = 'target'
+#     num_blocks = 2
+#     time_var = 'date'
+
+#     # Assign blocks
+#     block_list = assign_blocks(df, time_var, features, target, num_blocks)
+
+#     # Check the output
+#     assert len(block_list) == 2
+#     assert block_list[0]['features'].shape == (3, 2)
+#     assert block_list[0]['target'].shape == (3,1)
+#     assert block_list[1]['features'].shape == (2, 2)
+#     assert block_list[1]['target'].shape == (2,1)
+
+
+def test_assign_blocks():
+    # Create test data
+    df = pd.DataFrame({'date': ['2022-01-06', '2022-01-13', '2022-01-20', '2022-01-27', '2022-02-03'],
+                       'location': [1, 2, 3, 4, 1],
+                       'x0': [0, 1, 0, 1, 1],
+                       'x1': [0, 1, 0, 1, 1],
+                       'target': [10.0, 10.5, 10.0, 10.5, 12]})
+    
+    features = ['x0', 'x1']
+    target = 'target'
+    num_blocks = 2
+    time_var = 'date'
+
+    # Assign blocks
+    block_list = assign_blocks(df, time_var, features, target, num_blocks)
+
+    # Check the output
+    assert len(block_list) == 2
+    assert block_list[0]['features'].shape == (3, 2)
+    assert block_list[0]['target'].shape == (3, 1)
+    assert block_list[1]['features'].shape == (2, 2)
+    assert block_list[1]['target'].shape == (2, 1)
\ No newline at end of file
diff --git a/test/test_gaussian_kernel.py b/tests/jacques/kernels/test_gaussian_kernel.py
similarity index 99%
rename from test/test_gaussian_kernel.py
rename to tests/jacques/kernels/test_gaussian_kernel.py
index 4fa86e4..21be1ac 100644
--- a/test/test_gaussian_kernel.py
+++ b/tests/jacques/kernels/test_gaussian_kernel.py
@@ -1,16 +1,15 @@
-import sys
-import os
-sys.path.append(os.path.join(os.path.dirname(__file__), '..')) 
 import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+import sys
+import unittest
 
 import numpy as np
 import tensorflow as tf
 import tensorflow_probability as tfp
-import unittest
 
 from jacques import kernels
 
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+sys.path.append(os.path.join(os.path.dirname(__file__), '..')) 
 
 class Test_Gaussian_Kernel(unittest.TestCase):
     def test_diff_x_pairs(self):
diff --git a/tests/jacques/kernels/test_helper_functions.py b/tests/jacques/kernels/test_helper_functions.py
new file mode 100644
index 0000000..759cc9d
--- /dev/null
+++ b/tests/jacques/kernels/test_helper_functions.py
@@ -0,0 +1,16 @@
+import tensorflow as tf
+
+from jacques.kernels import diff_x_pairs
+
+
+# Test for diff_x_pairs
+def test_diff_x_pairs():
+    x1 = tf.constant([[1.0, 2.0], [3.0, 4.0]])
+    x2 = tf.constant([[5.0, 6.0], [7.0, 8.0]])
+    expected_result = tf.constant([
+        [[-4.0, -4.0], [-6.0, -6.0]],
+        [[-2.0, -2.0], [-4.0, -4.0]]
+    ])
+    
+    result = diff_x_pairs(x1, x2)
+    tf.debugging.assert_equal(result, expected_result)
\ No newline at end of file
diff --git a/test/test_kernel_smooth_quantile_fn.py b/tests/jacques/kernels/test_kernel_smooth_quantile_fn.py
similarity index 98%
rename from test/test_kernel_smooth_quantile_fn.py
rename to tests/jacques/kernels/test_kernel_smooth_quantile_fn.py
index f766d98..f4b89ba 100644
--- a/test/test_kernel_smooth_quantile_fn.py
+++ b/tests/jacques/kernels/test_kernel_smooth_quantile_fn.py
@@ -1,13 +1,12 @@
 import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+import unittest
 
 import numpy as np
 import tensorflow as tf
-import tensorflow_probability as tfp
-import unittest
 
 from jacques import kernels
 
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 
 class Test_Kernel_Smooth_Quantile_Fn(unittest.TestCase):
     def test_quantile_smooth_bw(self):
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..3e3f502
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1741 @@
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+requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version < '3.12'",
+    "python_full_version == '3.12.*'",
+    "python_full_version >= '3.13'",
+]
+
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