4.Model Experiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   instant    dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
      "0        1  1/1/2011       1   0     1        0        6           0   \n",
      "1        2  1/2/2011       1   0     1        0        0           0   \n",
      "2        3  1/3/2011       1   0     1        0        1           1   \n",
      "3        4  1/4/2011       1   0     1        0        2           1   \n",
      "4        5  1/5/2011       1   0     1        0        3           1   \n",
      "\n",
      "   weathersit      temp     atemp       hum  windspeed  rentals  day  \n",
      "0           2  0.344167  0.363625  0.805833   0.160446      331    1  \n",
      "1           2  0.363478  0.353739  0.696087   0.248539      131    2  \n",
      "2           1  0.196364  0.189405  0.437273   0.248309      120    3  \n",
      "3           1  0.200000  0.212122  0.590435   0.160296      108    4  \n",
      "4           1  0.226957  0.229270  0.436957   0.186900       82    5  \n",
      "Training Set: 511 rows\n",
      "Test Set: 220 rows\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2023-11-21 17:23:40--  https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/daily-bike-share.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 48800 (48K) [text/plain]\n",
      "Saving to: 'daily-bike-share.csv.4'\n",
      "\n",
      "     0K .......... .......... .......... .......... .......   100%  231K=0.2s\n",
      "\n",
      "2023-11-21 17:23:41 (231 KB/s) - 'daily-bike-share.csv.4' saved [48800/48800]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget  https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/daily-bike-share.csv\n",
    "bike_data = pd.read_csv('daily-bike-share.csv')\n",
    "bike_data['day'] = pd.DatetimeIndex(bike_data['dteday']).day\n",
    "numeric_features = ['temp', 'atemp', 'hum', 'windspeed']\n",
    "categorical_features = ['season','mnth','holiday','weekday','workingday','weathersit', 'day']\n",
    "bike_data[numeric_features + ['rentals']].describe()\n",
    "print(bike_data.head())\n",
    "\n",
    "X, y = bike_data[['season','mnth', 'holiday','weekday','workingday','weathersit','temp', 'atemp', 'hum', 'windspeed']].values, bike_data['rentals'].values\n",
    "\n",
    "\n",
    "X_train,X_val,y_train,y_val = train_test_split(X,y,test_size=0.3,random_state=0)\n",
    "\n",
    "print ('Training Set: %d rows\\nTest Set: %d rows' % (X_train.shape[0], X_val.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try Another Linear Algorithm\n",
    "\n",
    "Let's try training our regression model by using a **Lasso** algorithm. We can do this by just changing the estimator in the training code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 201155.70593338404\n",
      "rmse 448.5038527519959\n",
      "r score: 0.6056468637824488\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# estimator\n",
    "model = Lasso()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "#prediction\n",
    "predictions = model.predict(X_val)\n",
    "\n",
    "#metrics\n",
    "mse = mean_squared_error(y_val,predictions)\n",
    "print('mse:',mse)\n",
    "\n",
    "rmse = np.sqrt(mse)\n",
    "print('rmse',rmse)\n",
    "\n",
    "r2 = r2_score(y_val,predictions)\n",
    "print('r score:',r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_val,predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_val,predictions,1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_val,p(y_val),color='cyan')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying with a Decision Tree Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso() \n",
      "\n",
      "|--- feature_6 <= 0.45\n",
      "|   |--- feature_4 <= 0.50\n",
      "|   |   |--- feature_7 <= 0.32\n",
      "|   |   |   |--- feature_8 <= 0.41\n",
      "|   |   |   |   |--- feature_1 <= 2.50\n",
      "|   |   |   |   |   |--- feature_7 <= 0.26\n",
      "|   |   |   |   |   |   |--- value: [317.00]\n",
      "|   |   |   |   |   |--- feature_7 >  0.26\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.36\n",
      "|   |   |   |   |   |   |   |--- value: [558.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.36\n",
      "|   |   |   |   |   |   |   |--- value: [515.00]\n",
      "|   |   |   |   |--- feature_1 >  2.50\n",
      "|   |   |   |   |   |--- feature_8 <= 0.40\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.28\n",
      "|   |   |   |   |   |   |   |--- value: [981.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.28\n",
      "|   |   |   |   |   |   |   |--- value: [968.00]\n",
      "|   |   |   |   |   |--- feature_8 >  0.40\n",
      "|   |   |   |   |   |   |--- feature_3 <= 3.00\n",
      "|   |   |   |   |   |   |   |--- value: [710.00]\n",
      "|   |   |   |   |   |   |--- feature_3 >  3.00\n",
      "|   |   |   |   |   |   |   |--- value: [532.00]\n",
      "|   |   |   |--- feature_8 >  0.41\n",
      "|   |   |   |   |--- feature_7 <= 0.25\n",
      "|   |   |   |   |   |--- feature_6 <= 0.18\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.43\n",
      "|   |   |   |   |   |   |   |--- value: [284.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.43\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.11\n",
      "|   |   |   |   |   |   |   |   |--- value: [150.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.11\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.18\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.34\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [68.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.17\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [67.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.34\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [73.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.18\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [117.00]\n",
      "|   |   |   |   |   |--- feature_6 >  0.18\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.69\n",
      "|   |   |   |   |   |   |   |   |--- value: [123.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.69\n",
      "|   |   |   |   |   |   |   |   |--- value: [140.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.19\n",
      "|   |   |   |   |   |   |   |   |--- value: [333.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.19\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.53\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [251.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [217.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [205.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.53\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [288.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [275.00]\n",
      "|   |   |   |   |--- feature_7 >  0.25\n",
      "|   |   |   |   |   |--- feature_9 <= 0.11\n",
      "|   |   |   |   |   |   |--- value: [706.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.11\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.54\n",
      "|   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [309.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.26\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [408.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.30\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [502.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.30\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [440.00]\n",
      "|   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [618.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.54\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.18\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.63\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [354.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.63\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [318.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.18\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.57\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [155.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.57\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [195.00]\n",
      "|   |   |--- feature_7 >  0.32\n",
      "|   |   |   |--- feature_9 <= 0.25\n",
      "|   |   |   |   |--- feature_7 <= 0.38\n",
      "|   |   |   |   |   |--- feature_7 <= 0.36\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.36\n",
      "|   |   |   |   |   |   |   |--- feature_1 <= 10.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.34\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1047.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [724.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.34\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.83\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.35\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.35\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [694.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.83\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [879.00]\n",
      "|   |   |   |   |   |   |   |--- feature_1 >  10.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.57\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.53\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1156.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.53\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.22\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [943.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.57\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.06\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [955.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.06\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 11.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [922.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  11.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [767.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.36\n",
      "|   |   |   |   |   |   |   |--- value: [1658.00]\n",
      "|   |   |   |   |   |--- feature_7 >  0.36\n",
      "|   |   |   |   |   |   |--- feature_1 <= 6.00\n",
      "|   |   |   |   |   |   |   |--- value: [331.00]\n",
      "|   |   |   |   |   |   |--- feature_1 >  6.00\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.37\n",
      "|   |   |   |   |   |   |   |   |--- value: [538.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.37\n",
      "|   |   |   |   |   |   |   |   |--- value: [560.00]\n",
      "|   |   |   |   |--- feature_7 >  0.38\n",
      "|   |   |   |   |   |--- feature_9 <= 0.24\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.15\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.67\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.41\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2252.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.41\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2290.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.67\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 10.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1619.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  10.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.38\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1249.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.38\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1153.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.15\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.38\n",
      "|   |   |   |   |   |   |   |   |--- value: [1651.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.38\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.20\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.78\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1070.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.78\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1188.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.20\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 5.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [642.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  5.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [665.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.24\n",
      "|   |   |   |   |   |   |--- value: [2301.00]\n",
      "|   |   |   |--- feature_9 >  0.25\n",
      "|   |   |   |   |--- feature_8 <= 0.81\n",
      "|   |   |   |   |   |--- feature_7 <= 0.35\n",
      "|   |   |   |   |   |   |--- value: [397.00]\n",
      "|   |   |   |   |   |--- feature_7 >  0.35\n",
      "|   |   |   |   |   |   |--- feature_3 <= 3.00\n",
      "|   |   |   |   |   |   |   |--- value: [982.00]\n",
      "|   |   |   |   |   |   |--- feature_3 >  3.00\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.27\n",
      "|   |   |   |   |   |   |   |   |--- value: [640.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.27\n",
      "|   |   |   |   |   |   |   |   |--- value: [480.00]\n",
      "|   |   |   |   |--- feature_8 >  0.81\n",
      "|   |   |   |   |   |--- feature_3 <= 3.00\n",
      "|   |   |   |   |   |   |--- value: [120.00]\n",
      "|   |   |   |   |   |--- feature_3 >  3.00\n",
      "|   |   |   |   |   |   |--- value: [121.00]\n",
      "|   |--- feature_4 >  0.50\n",
      "|   |   |--- feature_6 <= 0.34\n",
      "|   |   |   |--- feature_1 <= 2.50\n",
      "|   |   |   |   |--- feature_7 <= 0.29\n",
      "|   |   |   |   |   |--- feature_7 <= 0.19\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.14\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.43\n",
      "|   |   |   |   |   |   |   |   |--- value: [95.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.43\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.12\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [86.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.12\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [89.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.14\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.46\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [75.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.17\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [61.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.46\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.30\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.49\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [38.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [41.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.49\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.57\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.57\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [42.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.30\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [25.00]\n",
      "|   |   |   |   |   |--- feature_7 >  0.19\n",
      "|   |   |   |   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.60\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.19\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.19\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.60\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.24\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [186.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.24\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.22\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.22\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [82.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.23\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [15.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.23\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.26\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [64.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [72.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [34.00]\n",
      "|   |   |   |   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |--- value: [115.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.47\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.23\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [148.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [149.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.23\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [174.00]\n",
      "|   |   |   |   |--- feature_7 >  0.29\n",
      "|   |   |   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.32\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.29\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [206.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.29\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.31\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [163.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.31\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [173.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [218.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.32\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.63\n",
      "|   |   |   |   |   |   |   |   |--- value: [135.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.63\n",
      "|   |   |   |   |   |   |   |   |--- value: [74.00]\n",
      "|   |   |   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.14\n",
      "|   |   |   |   |   |   |   |--- value: [227.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.14\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.32\n",
      "|   |   |   |   |   |   |   |   |--- value: [310.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.32\n",
      "|   |   |   |   |   |   |   |   |--- value: [307.00]\n",
      "|   |   |   |--- feature_1 >  2.50\n",
      "|   |   |   |   |--- feature_9 <= 0.20\n",
      "|   |   |   |   |   |--- feature_9 <= 0.12\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.06\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.05\n",
      "|   |   |   |   |   |   |   |   |--- value: [337.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.05\n",
      "|   |   |   |   |   |   |   |   |--- value: [362.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.06\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [143.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [174.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [178.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  3.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.11\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.09\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [261.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.09\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [268.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.11\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 11.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [243.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  11.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [254.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.12\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.64\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.45\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.14\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [316.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.14\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [245.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.45\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.30\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.30\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [359.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [429.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [491.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.64\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.31\n",
      "|   |   |   |   |   |   |   |   |--- value: [168.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.31\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.72\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.66\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [289.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.66\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [314.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.72\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [349.00]\n",
      "|   |   |   |   |--- feature_9 >  0.20\n",
      "|   |   |   |   |   |--- feature_8 <= 0.78\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.23\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.21\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 7.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [221.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [222.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  7.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [198.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.21\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.23\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [123.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.23\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [137.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [191.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [177.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.23\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.56\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [203.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.26\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.30\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [326.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.30\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [317.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.56\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.62\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.58\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [139.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.58\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [150.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.62\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [247.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [195.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 7.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.29\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.59\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [300.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.59\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [307.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.29\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [247.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  7.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [456.00]\n",
      "|   |   |   |   |   |--- feature_8 >  0.78\n",
      "|   |   |   |   |   |   |--- feature_0 <= 1.50\n",
      "|   |   |   |   |   |   |   |--- value: [9.00]\n",
      "|   |   |   |   |   |   |--- feature_0 >  1.50\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.28\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [179.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_7 >  0.26\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [166.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.28\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.81\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [123.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.81\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [87.00]\n",
      "|   |   |--- feature_6 >  0.34\n",
      "|   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |--- feature_8 <= 0.48\n",
      "|   |   |   |   |   |--- feature_6 <= 0.42\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.44\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.40\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.23\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [229.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.23\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.37\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [340.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.37\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [324.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.40\n",
      "|   |   |   |   |   |   |   |   |--- value: [208.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.44\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [518.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [482.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.47\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [413.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  3.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [663.00]\n",
      "|   |   |   |   |   |--- feature_6 >  0.42\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.40\n",
      "|   |   |   |   |   |   |   |--- value: [1192.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.40\n",
      "|   |   |   |   |   |   |   |--- feature_1 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [795.00]\n",
      "|   |   |   |   |   |   |   |--- feature_1 >  3.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.44\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [834.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.44\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [819.00]\n",
      "|   |   |   |   |--- feature_8 >  0.48\n",
      "|   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.55\n",
      "|   |   |   |   |   |   |   |--- feature_1 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.21\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.35\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [141.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.35\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [199.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.21\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.40\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [253.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.40\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [259.00]\n",
      "|   |   |   |   |   |   |   |--- feature_1 >  2.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 7.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [432.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  7.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [331.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.55\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.65\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.19\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [615.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [571.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.19\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.42\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [394.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.42\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [460.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.65\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.10\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.07\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [305.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.07\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [376.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [370.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.10\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.76\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.40\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [410.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.40\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [433.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.76\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [439.00]\n",
      "|   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.39\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.38\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.38\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [466.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.38\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [534.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.38\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [330.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.35\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.82\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 2.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [269.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  2.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.82\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [203.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.35\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [127.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.39\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 3.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [190.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  3.00\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.40\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [233.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.40\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.42\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.42\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.26\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.75\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 3.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [167.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  3.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [181.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [255.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.75\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_5 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.31\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [81.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.31\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [112.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_5 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.93\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.93\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [50.00]\n",
      "|   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |--- feature_8 <= 0.39\n",
      "|   |   |   |   |   |--- value: [1807.00]\n",
      "|   |   |   |   |--- feature_8 >  0.39\n",
      "|   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.37\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.35\n",
      "|   |   |   |   |   |   |   |   |--- value: [484.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.35\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.24\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [709.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.24\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [618.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.37\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.37\n",
      "|   |   |   |   |   |   |   |   |--- value: [1603.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.37\n",
      "|   |   |   |   |   |   |   |   |--- value: [1095.00]\n",
      "|   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.37\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.20\n",
      "|   |   |   |   |   |   |   |   |--- value: [246.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.20\n",
      "|   |   |   |   |   |   |   |   |--- value: [178.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.37\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.39\n",
      "|   |   |   |   |   |   |   |   |--- value: [796.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.39\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.42\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.34\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [470.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.34\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [447.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.42\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [548.00]\n",
      "|--- feature_6 >  0.45\n",
      "|   |--- feature_4 <= 0.50\n",
      "|   |   |--- feature_8 <= 0.83\n",
      "|   |   |   |--- feature_1 <= 10.50\n",
      "|   |   |   |   |--- feature_9 <= 0.28\n",
      "|   |   |   |   |   |--- feature_6 <= 0.81\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.67\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.09\n",
      "|   |   |   |   |   |   |   |   |--- value: [3065.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.09\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.66\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.16\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.16\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.66\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3031.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.67\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.67\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.51\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [2207.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 7.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1138.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  7.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.51\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [3155.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.67\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.74\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 <= 0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.73\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1249.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.73\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1298.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_3 >  0.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1521.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1549.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.74\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1920.00]\n",
      "|   |   |   |   |   |--- feature_6 >  0.81\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.84\n",
      "|   |   |   |   |   |   |   |--- value: [1203.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.84\n",
      "|   |   |   |   |   |   |   |--- value: [987.00]\n",
      "|   |   |   |   |--- feature_9 >  0.28\n",
      "|   |   |   |   |   |--- feature_7 <= 0.46\n",
      "|   |   |   |   |   |   |--- value: [1558.00]\n",
      "|   |   |   |   |   |--- feature_7 >  0.46\n",
      "|   |   |   |   |   |   |--- feature_2 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- value: [998.00]\n",
      "|   |   |   |   |   |   |--- feature_2 >  0.50\n",
      "|   |   |   |   |   |   |   |--- value: [1198.00]\n",
      "|   |   |   |--- feature_1 >  10.50\n",
      "|   |   |   |   |--- feature_9 <= 0.18\n",
      "|   |   |   |   |   |--- value: [1097.00]\n",
      "|   |   |   |   |--- feature_9 >  0.18\n",
      "|   |   |   |   |   |--- value: [787.00]\n",
      "|   |   |--- feature_8 >  0.83\n",
      "|   |   |   |--- feature_7 <= 0.62\n",
      "|   |   |   |   |--- feature_8 <= 0.88\n",
      "|   |   |   |   |   |--- feature_7 <= 0.57\n",
      "|   |   |   |   |   |   |--- value: [1582.00]\n",
      "|   |   |   |   |   |--- feature_7 >  0.57\n",
      "|   |   |   |   |   |   |--- value: [1483.00]\n",
      "|   |   |   |   |--- feature_8 >  0.88\n",
      "|   |   |   |   |   |--- feature_9 <= 0.21\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.90\n",
      "|   |   |   |   |   |   |   |--- value: [1033.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.90\n",
      "|   |   |   |   |   |   |   |--- value: [902.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.21\n",
      "|   |   |   |   |   |   |--- value: [1462.00]\n",
      "|   |   |   |--- feature_7 >  0.62\n",
      "|   |   |   |   |--- value: [226.00]\n",
      "|   |--- feature_4 >  0.50\n",
      "|   |   |--- feature_8 <= 0.83\n",
      "|   |   |   |--- feature_3 <= 4.50\n",
      "|   |   |   |   |--- feature_7 <= 0.53\n",
      "|   |   |   |   |   |--- feature_9 <= 0.10\n",
      "|   |   |   |   |   |   |--- feature_6 <= 0.48\n",
      "|   |   |   |   |   |   |   |--- value: [1348.00]\n",
      "|   |   |   |   |   |   |--- feature_6 >  0.48\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.74\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.63\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.49\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [830.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.49\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [846.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.63\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [763.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.74\n",
      "|   |   |   |   |   |   |   |   |--- value: [1122.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.10\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.23\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.64\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.46\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [838.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.47\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [922.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.46\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 7.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  7.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.64\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [699.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [637.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [486.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.48\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [409.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.51\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [655.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.47\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.45\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [516.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.45\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.51\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.54\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.52\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [735.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.52\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [695.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.54\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [550.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [559.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.23\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.37\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [745.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  4.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [614.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.37\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [471.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.47\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.55\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.34\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [834.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.34\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [905.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1008.00]\n",
      "|   |   |   |   |--- feature_7 >  0.53\n",
      "|   |   |   |   |   |--- feature_9 <= 0.13\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.72\n",
      "|   |   |   |   |   |   |   |--- feature_6 <= 0.68\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.82\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.09\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.07\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.07\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.09\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.82\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1334.00]\n",
      "|   |   |   |   |   |   |   |--- feature_6 >  0.68\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.12\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1177.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1363.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.12\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.73\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [989.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.73\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1233.00]\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.72\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.61\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.12\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.74\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [872.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_7 >  0.74\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [921.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.12\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [778.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.61\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 6.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [673.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  6.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [568.00]\n",
      "|   |   |   |   |   |--- feature_9 >  0.13\n",
      "|   |   |   |   |   |   |--- feature_8 <= 0.53\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.73\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.15\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1281.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.15\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 6.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  6.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.52\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1242.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.52\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1032.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.73\n",
      "|   |   |   |   |   |   |   |   |--- value: [1405.00]\n",
      "|   |   |   |   |   |   |--- feature_8 >  0.53\n",
      "|   |   |   |   |   |   |   |--- feature_5 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1128.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1198.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_9 >  0.17\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.78\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.66\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.66\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.78\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 3.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [662.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  3.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [606.00]\n",
      "|   |   |   |   |   |   |   |--- feature_5 >  1.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.76\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.55\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.55\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.76\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.56\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [653.00]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.56\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [630.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.19\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.19\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 8.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  8.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [428.00]\n",
      "|   |   |   |--- feature_3 >  4.50\n",
      "|   |   |   |   |--- feature_1 <= 4.00\n",
      "|   |   |   |   |   |--- value: [2469.00]\n",
      "|   |   |   |   |--- feature_1 >  4.00\n",
      "|   |   |   |   |   |--- feature_8 <= 0.72\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.12\n",
      "|   |   |   |   |   |   |   |--- feature_9 <= 0.08\n",
      "|   |   |   |   |   |   |   |   |--- value: [1325.00]\n",
      "|   |   |   |   |   |   |   |--- feature_9 >  0.08\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.65\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 8.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1511.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  8.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1516.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1379.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.12\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.77\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 <= 6.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.52\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.25\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.25\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [898.00]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_8 >  0.52\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 <= 0.58\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_8 >  0.58\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_1 >  6.50\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 <= 0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_6 >  0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_6 >  0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 <= 0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_7 >  0.72\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1366.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.77\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.52\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [829.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.52\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [670.00]\n",
      "|   |   |   |   |   |--- feature_8 >  0.72\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.20\n",
      "|   |   |   |   |   |   |   |--- feature_1 <= 5.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 <= 0.73\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [909.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_8 >  0.73\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1417.00]\n",
      "|   |   |   |   |   |   |   |--- feature_1 >  5.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1182.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.55\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 <= 0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_9 >  0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_9 >  0.16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [1045.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.20\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.57\n",
      "|   |   |   |   |   |   |   |   |--- value: [529.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.57\n",
      "|   |   |   |   |   |   |   |   |--- value: [533.00]\n",
      "|   |   |--- feature_8 >  0.83\n",
      "|   |   |   |--- feature_5 <= 2.50\n",
      "|   |   |   |   |--- feature_8 <= 0.94\n",
      "|   |   |   |   |   |--- feature_1 <= 5.50\n",
      "|   |   |   |   |   |   |--- feature_3 <= 4.00\n",
      "|   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |--- value: [678.00]\n",
      "|   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 <= 0.59\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [536.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_6 >  0.59\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [547.00]\n",
      "|   |   |   |   |   |   |--- feature_3 >  4.00\n",
      "|   |   |   |   |   |   |   |--- value: [692.00]\n",
      "|   |   |   |   |   |--- feature_1 >  5.50\n",
      "|   |   |   |   |   |   |--- feature_3 <= 3.50\n",
      "|   |   |   |   |   |   |   |--- feature_7 <= 0.55\n",
      "|   |   |   |   |   |   |   |   |--- value: [438.00]\n",
      "|   |   |   |   |   |   |   |--- feature_7 >  0.55\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [477.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_3 >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [480.00]\n",
      "|   |   |   |   |   |   |--- feature_3 >  3.50\n",
      "|   |   |   |   |   |   |   |--- value: [555.00]\n",
      "|   |   |   |   |--- feature_8 >  0.94\n",
      "|   |   |   |   |   |--- value: [258.00]\n",
      "|   |   |   |--- feature_5 >  2.50\n",
      "|   |   |   |   |--- feature_1 <= 11.00\n",
      "|   |   |   |   |   |--- feature_6 <= 0.57\n",
      "|   |   |   |   |   |   |--- feature_9 <= 0.25\n",
      "|   |   |   |   |   |   |   |--- value: [254.00]\n",
      "|   |   |   |   |   |   |--- feature_9 >  0.25\n",
      "|   |   |   |   |   |   |   |--- feature_8 <= 0.90\n",
      "|   |   |   |   |   |   |   |   |--- value: [204.00]\n",
      "|   |   |   |   |   |   |   |--- feature_8 >  0.90\n",
      "|   |   |   |   |   |   |   |   |--- value: [217.00]\n",
      "|   |   |   |   |   |--- feature_6 >  0.57\n",
      "|   |   |   |   |   |   |--- value: [315.00]\n",
      "|   |   |   |   |--- feature_1 >  11.00\n",
      "|   |   |   |   |   |--- value: [126.00]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import export_text\n",
    "\n",
    "# Train the model\n",
    "model2 = DecisionTreeRegressor().fit(X_train, y_train)\n",
    "print (model, \"\\n\")\n",
    "\n",
    "# Visualize the model tree\n",
    "tree = export_text(model2)\n",
    "print(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 255175.4909090909\n",
      "rmse 505.1489789251196\n",
      "r score: 0.4997444658160569\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = model2.predict(X_val)\n",
    "\n",
    "#metrics\n",
    "mse = mean_squared_error(y_val,predictions)\n",
    "print('mse:',mse)\n",
    "\n",
    "rmse = np.sqrt(mse)\n",
    "print('rmse',rmse)\n",
    "\n",
    "r2 = r2_score(y_val,predictions)\n",
    "print('r score:',r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_val,predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_val,predictions,1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_val,p(y_val),color='cyan')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try an Ensemble Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 105290.01411227272\n",
      "rmse 324.4842278328374\n",
      "r score: 0.7935855357177118\n"
     ]
    },
    {
     "data": {
      "image/png": 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tapc1pkyZgu+//x733XcfXnzxRbRq1QqVlZW4cOECtm3bhmnTpqFjx47o3bs37rvvPsyYMQNFRUVo37499u/fjzVr1lg8R61atfDOO+/g6aefRs+ePTFu3DhER0fjzJkz+OOPP7Bs2TIA0AwxL168GP369YOvry9atWpldIjyxRdfxFdffYUBAwbgjTfeQHx8PLZs2YLly5dj/PjxaNq0qVX34ejRo5g4cSIeeeQRNGnSBP7+/ti5cyeOHj2q03NLZA8MloiqSJ0w0d/fH7Vr10bz5s3x8ssv4+mnnzZI9Ldu3TpMnjwZM2bMQHl5Oe655x5s377d6mX6+h599FGsWLEC48ePN5p/yJTi4mIkJSVpvo+KikLz5s2xceNGPPDAA1a1oWHDhpqA6d5778Uvv/yCFi1a4PDhw3jzzTfx2muvITc3F7Vr10aTJk3Qv39/s8fr1KkTnnzySezatQtr1qzB1atXERQUhBYtWuDDDz/E+PHjrWofoBoK2rJlC6ZNm4ZRo0YhODgYQ4YMwbfffou2bdtKOoavry82bdqE999/H2vWrMGiRYtQo0YN1KtXD127dtWZo+YIwcHB2Lt3L9566y18+umnyMzMRFBQEOrXr4+ePXtqepZ8fHywadMmTJ06FUuWLEFpaSnuuecebN26FXfccYfF8zz11FOIjY3F4sWL8fTTT0MIgQYNGmDMmDGaOiNHjsT+/fuxfPlyvPHGGxBCIDMz0+jcpDp16iAlJQWzZs3CrFmzUFBQgIYNG2LJkiWYOnWq1fdBoVCgUaNGWL58ObKysiCTydCwYUO88847mDRpktXHIzJHJsz1sRKRR5g2bRpWrFiBrKwss0NbRERkPfYsEXmwtLQ0/PXXX1i+fDmeffZZBkpERA7AniUiDyaTyVCzZk30798fq1atkpRbiYiIrMOeJSIPxr91iIgcj6kDiIiIiMxgsERERERkBoMlIiIiIjM4Z0miyspKXLp0CSEhIXbbWJKIiIgcSwiBwsJCxMbGWpWHTv8gLrN8+XLRsmVLERISIkJCQkSnTp3E1q1bNa9XVlaKOXPmiJiYGBEYGCi6du0qMjIydI5x69YtMXHiRBERESFq1qwpBg0aJLKysnTqXLt2TYwaNUqEhoaK0NBQMWrUKJGfn29VW7OysgQAfvGLX/ziF7/45YFf+rGBNVyaOmDz5s3w9fVF48aNAQBffvkl3n77bRw5cgR33nknFi9ejAULFmD16tVo2rQp5s+fjz179uDUqVOazSrHjx+PzZs3Y/Xq1YiIiMC0adNw7do1HDp0CL6+vgCAfv364eLFi/j0008BAM888wwaNGiAzZs3S26rUqlE7dq1kZWVZbALOREREbmngoICxMXF4fr165DL5bYdxOYwy0HCwsLE559/LiorK4VCoRBvvfWW5rVbt24JuVwuPv74YyGEENevXxd+fn5i/fr1mjr//POP8PHxEcnJyUIIIU6cOCEAiLS0NE2d1NRUAUD8+eefktulVCoFAKFUKqt6iUREROQk9nh+u80E74qKCqxfvx5FRUVISkpCZmYmcnJy0Lt3b02dgIAAdO3aFSkpKQCAQ4cOoaysTKdObGwsEhMTNXVSU1Mhl8vRsWNHTZ1OnTpBLpdr6hARERGZ4vIJ3seOHUNSUhJu3bqFWrVqYePGjWjRooUmkImOjtapHx0djfPnzwMAcnJy4O/vj7CwMIM6OTk5mjpRUVEG542KitLUMaakpAQlJSWa7wsKCmy7QCIiIvJoLu9ZatasGdLT05GWlobx48djzJgxOHHihOZ1/ZVnQgiLq9H06xirb+k4ixYtglwu13zFxcVJvSQiIiKqRlweLPn7+6Nx48Zo3749Fi1ahLvuugvvv/8+FAoFABj0/uTm5mp6mxQKBUpLS5Gfn2+2zuXLlw3Oe+XKFYNeK22zZs2CUqnUfGVlZVXpOomIiMgzuTxY0ieEQElJCRISEqBQKLB9+3bNa6Wlpdi9ezc6d+4MAGjXrh38/Px06mRnZyMjI0NTJykpCUqlEgcOHNDU+e2336BUKjV1jAkICEBoaKjOFxEREXkfl85ZeuWVV9CvXz/ExcWhsLAQ69evx6+//ork5GTIZDJMmTIFCxcuRJMmTdCkSRMsXLgQNWvWxMiRIwEAcrkcTz31FKZNm4aIiAiEh4dj+vTpaNmyJXr27AkAaN68Ofr27Ytx48bhk08+AaBKHTBw4EA0a9bMZddOREREnsGlwdLly5cxevRoZGdnQy6Xo1WrVkhOTkavXr0AADNmzEBxcTGef/555Ofno2PHjti2bZsmxxIAvPfee6hRowaGDRuG4uJi9OjRA6tXr9bkWAKAr7/+Gi+88IJm1dzgwYOxbNky514sEREReSSXJqX0JAUFBZDL5VAqlRySIyIi8hD2eH673ZwlIiIiInfCYImIiIjIDAZLRERERGYwWCIiIiIyg8ESERERuZQAkG+xluswWCIiIiKXWQdVMBIOINXFbTHF5RvpEhERkfcpB1AfQLZWWZiL2mIJe5aIiIjIqfYB8INuoPQHgDtc0xyLGCwRERGRUwgAfQHcq1XWHkAlgFYuaZE0HIYjIiIihzsDoIle2c9QBU/ujj1LRERE5FDToRso+QO4Bc8IlAD2LBEREZGD5AGI1CtbDmC8C9pSFQyWiIiIyO4+A/CMXtkVGAZPnoDDcERERGQ3JQBqQjdQmgLV5G5PDJQA9iwRERGRnWwH0Fuv7BSApi5oiz2xZ4mIiIiqRADoBN1AqRdUKQE8PVAC2LNEREREVZABoKVe2W4A97mgLY7CniUiIiKyyTPQDZSiAZSiegVKAHuWiIiIyEo5AGL0ytYAGOWCtjgDe5aIiIhIsndhGChdR/UNlAAGS0RERCTBTQAyANO0yuZANblb7pIWOQ+H4YiIiMisjQCG6pWdAxDv/Ka4BHuWiIiIyKhKAHdAN1B6GKreJG8JlAD2LBEREZERBwF00Cs7AOBuF7TF1dizRERERDqGQTdQagagHN4ZKAHsWSIiIqJ/nQfQQK9sI4AHnN4S98KeJSIiIsJcGAZKN8BACWDPEhERkVdTAqitV/YOgKnOb4rbYrBERETkpdYCGK1Xlg1A4YK2uDMOwxEREXmZMqj2cdMOlJ6GKiUAAyVD7FkiIiLyInsAdNUrOwYg0QVt8RTsWSIiIvICAkAv6AZKHaFKPMlAyTz2LBEREVVzf0GVK0nb/wD0dkFbPBF7loiIiKqxF6EbKAUBuAUGStZgzxIREVE1lAcgUq/sEwDPuKAtno49S0RERNXMxzAMlK6CgZKtGCwRERFVEyUAAgCM1yqbBtXk7giXtKh64DAcERFRNZAMoJ9e2WkAjV3QluqGPUtEREQeTAC4G7qBUl+oUgIwULIP9iwRERF5qKMA7tIr2wugiwvaUp2xZ4mIiMgDPQndQCkWqm1MGCjZH3uWiIiIPEg2VIGRtq8BjHRBW7wFgyUiIgsqKgUOZF5DbuEtRIUEokNCOHx9ZK5uFnmhtwHM0CtTAgh1QVu8CYMlIiIzkjOyMW/zCWQrb2nKYuSBmDOoBfomxriwZeRNigDU0iubB+B1F7TFG3HOEhGRCckZ2Ri/9rBOoAQAOcpbGL/2MJIzsl3UMvIm38MwULoABkrOxGCJiMiIikqBeZtPQBh5TV02b/MJVFQaq0FUdRVQLf1/WKtsOFSfvziXtMh7MVgiIjLiQOY1gx4lbQJAtvIWDmRec16jyGscgGqezFmtst8BfOOa5ng9BktEREbkFpoOlGypRyTVUAAdtb5vAVUvUzvXNIfg4mBp0aJFuPvuuxESEoKoqCg88MADOHXqlE6dsWPHQiaT6Xx16tRJp05JSQkmTZqEyMhIBAcHY/Dgwbh48aJOnfz8fIwePRpyuRxyuRyjR4/G9evXHX2JROShokIC7VqPyJJzAGQANmqV/QDgONiz4Wouvf+7d+/GhAkTkJaWhu3bt6O8vBy9e/dGUVGRTr2+ffsiOztb87V161ad16dMmYKNGzdi/fr12LdvH27cuIGBAweioqJCU2fkyJFIT09HcnIykpOTkZ6ejtGjRzvlOonI83RICEeMPBCmEgTIoFoV1yEh3JnNompqNoAEvbIiAENc0BYyJBNCuM3sxCtXriAqKgq7d+/GfffdB0DVs3T9+nX88MMPRt+jVCpRp04drFmzBo8++igA4NKlS4iLi8PWrVvRp08fnDx5Ei1atEBaWho6dlR1bqalpSEpKQl//vknmjVrZrFtBQUFkMvlUCqVCA1lRgsib6BeDQdAZ6K3OoBaMaot0wdQlVwHEKZX9h6AKU5vSfVlj+e3W/XsKZVKAEB4uO5far/++iuioqLQtGlTjBs3Drm5uZrXDh06hLKyMvTu3VtTFhsbi8TERKSkpAAAUlNTIZfLNYESAHTq1AlyuVxTh4hIX9/EGKwY1RYKue5Qm0IeyECJquwrGAZKOWCg5I7cJimlEAJTp05Fly5dkJiYqCnv168fHnnkEcTHxyMzMxOzZ89G9+7dcejQIQQEBCAnJwf+/v4IC9P9yEVHRyMnJwcAkJOTg6ioKINzRkVFaeroKykpQUlJieb7goICe1wmEXmYvokx6NVCwQzeZDdlAGIA5GmVPQvgY9c0hyRwm2Bp4sSJOHr0KPbt26dTrh5aA4DExES0b98e8fHx2LJlC4YOHWryeEIIyGS3f5lp/9tUHW2LFi3CvHnzrL0MIqqGfH1kSGoU4epmUDXwK4D79cqOQ7XijdyXWwzDTZo0CZs2bcKuXbtQr149s3VjYmIQHx+P06dPAwAUCgVKS0uRn5+vUy83NxfR0dGaOpcvXzY41pUrVzR19M2aNQtKpVLzlZWVZculERERQQDoDt1A6R4AlWCg5AlcGiwJITBx4kRs2LABO3fuREKC/loAQ3l5ecjKykJMjGquQLt27eDn54ft27dr6mRnZyMjIwOdO3cGACQlJUGpVOLAgQOaOr/99huUSqWmjr6AgACEhobqfBERVQcVlQKpZ/PwY/o/SD2bxyzkDqB9j7+5kA8fALu0Xt8OYB9gcrUluReXroZ7/vnnsW7dOvz44486K9LkcjmCgoJw48YNzJ07Fw899BBiYmJw7tw5vPLKK7hw4QJOnjyJkJAQAMD48ePx008/YfXq1QgPD8f06dORl5eHQ4cOwdfXF4Bq7tOlS5fwySefAACeeeYZxMfHY/PmzZLaytVwRFQdcGNgx9O+x9cGtUDhPbc7AkIAXAXg77LWeR97PL9dGiyZmi+0atUqjB07FsXFxXjggQdw5MgRXL9+HTExMbj//vvx5ptvIi7u9s44t27dwksvvYR169ahuLgYPXr0wPLly3XqXLt2DS+88AI2bdoEABg8eDCWLVuG2rVrS2orgyUi8nTqVAj6v/SZCsF+1Pe4PNgfF2f30nkt4vujWNusDu+xk3l8sORJGCwRkSerqBTosninyf3uZFClRNj3cneu9LOR+h7/1Twa1x5I1Hmt3rxtqFFcxnvsAvZ4frvNajgiInIcazYG9uaVfxWVwuY0EXvPXUPajPsB39vTgUN/PYuw5D8B8B6bUpV77iwMloiIvAA3BrasKvO5tgIY0FA3AIpdsgt+124a1HWne+zqQMVT5tAxWCIi8gLcGNg8U/O5cpS3MH7tYZPzuSoBtAdwRKss6ORlRH35u8lzucs9dnWgYus9dwW3yLNERNUTl6i7D24MbFpFpcC8zScMHtrA7T0B520+YfD5TQfgC91A6c6vfke0iUDJne6xOlDRH5pVByrJGdkOPb+t99xVGCwRkUMkZ2Sjy+KdGPFZGiavT8eIz9LQZfFOh/8SJuN8fWSYM0iV/lA/YFJ/P2dQC7ebK+IM1sznUhsLoI1WnTiotjH5T9u6ANz7HrtDoGLLPXclBktEZHeu/quVjLNlY2BH9Q66U6+jNfO5/oEq8PlSq3w9gAtQzWvxhM2X3SFQ8bQ5dJyzRER2ZemvVhlUf7X2aqFw+V/Y3siajYEdNafF1XNl9EmdQ5QcVxsP6JXlVwqczLyGH7XupbtvvuwOgYqnzaFjsEREdsUl6u5PysbAjpp8646TetXzuXKUt4wH+f6+uPBGX3ysVbYAQNuMbPQzE/S56+fbHQIVS/dcnffLHeZ3ARyGIyI7c4e/WqlqHDWnxR3myhhjbj7XzZYKXHijr05ZFlSBkqcONbvDZH9Pm0PHYImI7Mod/mqlqnHUnBZ3mCtjiv5cIyEDLs64H1cea6ep89i/bYxx06BPKncJVDxhfpcah+GIyK48rXudDDmqd9Ddex3Vc42+uFSAZ+rJdV47jNur36rDULM6UNGfO6Zw8twxd5/fpcZgiYjsSv1X6/i1hyEDdAImd+xeJ0OO6h30hF7HoT4ybNIKlFpClU9JexjG3YM+qdwlUJEyh87VOAxHRHbnSd3rZMhRc1rcYa6MKX//e/5NWmWbARyF4YPSE4I+qdSBypDWdZHUKIJ/xJjAniUicgh3+auVrOeo3kFzx8W/3/dPVH1mnPlZeQXAIr2ymwCCTNTnULP3kQkh3HMGmpspKCiAXC6HUqlEaGioq5tDRORwzsyz5CMDtOdDOyPvUj4A/XDmAwCTJLxXnQIBMB5MqntQXb1RLdnn+c1gSSIGS0TkjRz1sFcfd/uJHHyx/5zB6/pBh72tAvCkXtllAFFWHMNSMFmVYJNBlv0wWHIiBktERPZVUSnQZfFOkyvL1MNZ+17ubrdAoRRANIDrWmUTACyz8XimghpTyTelBIHuluHc09nj+c0J3kRE5BLOzru0E0AAdAOlE7A9UAKMT5CuSvJN7qvonhgsERGRSzhrCb4A0BVAD62yrgAqATSv0pGNszUIdNcM58RgiYiIXMQZS/BPQvWg26NV9guAX2GYvdpebA0C3TnDubdj6gAiInKJqizBlzIBegKA5Vrf14ZqEre/3a7AOFuDwOqS7LI6YrBEREQuYWs+J0sToHOhmsSt7QsAT9j/EoyyNQisTskuqxsOwxERkctYm+3d0gTo8ZeUBoFSPpwXKAG2b1TrzhnOvR1TB0jE1AFERI4jZVjNXKqByho+yJrfT6dsFoCFjmy0BbakAJCa7JKkY54lJ2KwRETkWqln8zDiszSD8pvNo3BlzN06ZX8DSHBSu8yxJbkk8yzZlz2e35yzREREHkF/YrOQAdkv3IuymNsPwKATOfimtAIJres6u3lGqfMwWYP7KrofBktEROQRtCc2l8aGIvuFe3VeV3y0HwFZ1xE1rpOzm2Z3tgRZ5DgMloiIyCOoJ0Af63sHitrc7jmqkXcTsf/ZBR9hOtUAUVUwWCIiIo+Q7SND2qweOmWRXx9C8LEcs6vMiKqKqQOIiMjtLQAQp1cW93oygo/lADCdaoDIHtizREREbqsQgP76pUUAXqoUODDmbrebAG3L6jdyfwyWiIg8nNQHtKc9yNcDGKFXdhFAXQBwwwnQXPJffTFYIiLyYFIf0J70IC+HKkfSRa2yxwF86ZrmSKJOJqmfuFCdWZxDhJ6Nc5aIiDyUpa0/kjOyrarnDlIA+EE3UEqH6UCpolIg9Wwefkz/B6ln81BR6fw8yxWVAvM2nzC6D5y6bN7mEy5pG9kHe5aIiDyQpQe0DKoHdPc7oiXV69VC4dIhOQFgEIAtWmVtARyE6b/q3aW37EDmNaNbsKgJANnKWziQec3thg5JGvYsEXkhd/hrnKpG6gN6Teo5yQ9yVzkL1cNIO1DaAuAQzAdK7tJbpp9ZvKr1yP2wZ4nIy7jLX+Ou5GkTnY2R+uA9f+2mXY9nby8DWKL1fQ2oVsAFGq8OQHqvmrN6y7Qzi9ujHrkfBktEXoSTUKtPsCj1wRsfXtOux7OXS/h3VZuWZQAmSHivuw17qTOL5yhvGQ3gZGBmcU/HYTgiL8FJqO41dFNV6ge0qX4TGVRB4OikBpLqOfNBPgGGgVIupAVKgPsNe/n6yDBnUAsAMLjPzCxePTBYIvIS1vw1Xh1Vt2BR6gPav4aP2zzIi/8953KtsqZQ3f86VhzHHYe9+ibGYMWotlDIdc/JzOLVA4fhiLyEu/017mzuNnRjD+oHtP6wokJvWFFqPUf6FMCzemX7ANxjw7Hcddirb2IMerVQePx8ODLEYInIS7jjX+POJDUIzCnwrGBR6gPaVQ9yAeNDGJUw7OmSSt2rNn7tYcgAnYDJ1cNevm6YWZyqjsESkZdw17/GnUVqEPjmT8cR5OfjUcMmUh/Qzn6Q/wrgfr2y1QDG2OHY7tBbRt5DJoTwjAF6FysoKIBcLodSqURoqP62jkSeQT3BGTD+13h1nltRUSnQZfFOk8GiNhkccy+qQ8oCqeoDyNIruwUgwM7n8aZ7Sraxx/ObwZJEDJaouqguS+dtYSpY1KfuZdv3cne7PXi95b6fBdBYr2wqgHdc0BYigMGSUzFYourEm/8aT87IxisbM3CtqNRi3W/GdbLLsJWp/FbVrUdvBID1emU5AKJd0BYiNXs8vzlnicgLefIk1KoGen0TY1BcWoEXv/vDYl17rAx0t2zTjlAAQK5Xdg9Uq92IqgMGS0TkUtYEP/YaylLIgyTVs8fKwOqYskDb2wBm6JUdAdDa+U0hchiXJqVctGgR7r77boSEhCAqKgoPPPAATp06pVNHCIG5c+ciNjYWQUFB6NatG44fP65Tp6SkBJMmTUJkZCSCg4MxePBgXLx4UadOfn4+Ro8eDblcDrlcjtGjR+P69euOvkQiMiM5IxtdFu/EiM/SMHl9OkZ8loYui3dqMmlrb/j7/o6/8Jydsm9LzX5tj5WB1TW/VQVU90k/UBJgoETVj0uDpd27d2PChAlIS0vD9u3bUV5ejt69e6OoqEhTZ8mSJXj33XexbNkyHDx4EAqFAr169UJhYaGmzpQpU7Bx40asX78e+/btw40bNzBw4EBUVFRo6owcORLp6elITk5GcnIy0tPTMXr0aKdeLxHdZmnrkUVbT+gEUu/tOG30OLZk33bm9hTVMb/VTzAcltgA85PmiTyZW03wvnLlCqKiorB7927cd999EEIgNjYWU6ZMwcsvvwxA1YsUHR2NxYsX49lnn4VSqUSdOnWwZs0aPProowCAS5cuIS4uDlu3bkWfPn1w8uRJtGjRAmlpaejYsSMAIC0tDUlJSfjzzz/RrFkzi23jBG8i+1Ev4zc3PGULaydkO2OFmqWUBY5YeedIfgDK9crKwDkd5L7s8fx2q73hlEolACA8XNX1nZmZiZycHPTu3VtTJyAgAF27dkVKSgoA4NChQygrK9OpExsbi8TERE2d1NRUyOVyTaAEAJ06dYJcLtfUISLnsTSPx1bWDmX1TYzBvpe745txnfD+8Nb4Zlwn7Hu5O3q1UGiG/1LP5lVpv7jqsslqBlTt1Q6U3oSqN4mBElV3bvMZF0Jg6tSp6NKlCxITEwEAOTk5AIDoaN2Fp9HR0Th//rymjr+/P8LCwgzqqN+fk5ODqKgog3NGRUVp6ugrKSlBSUmJ5vuCggIbr4yI9Dlqfo4tQ1n6KwMd0dvk6dmmewL4Ra8sH0Bt5zeFyCXcJliaOHEijh49in37DBebymS6f3EJIQzK9OnXMVbf3HEWLVqEefPmSWk6EVnJ3vNz7LVVi6l8SOp5VFXJh+SJm6xeBVBHr+wBABud3xQil3KLYbhJkyZh06ZN2LVrF+rVq6cpVygUAGDQ+5Obm6vpbVIoFCgtLUV+fr7ZOpcvXzY475UrVwx6rdRmzZoFpVKp+crK0k/cT0S2srQazRr2GsqylA8JsG4SuTHqXqwhresiqVGEWwdKr8AwUPoLDJTIO7k0WBJCYOLEidiwYQN27tyJhIQEndcTEhKgUCiwfft2TVlpaSl2796Nzp07AwDatWsHPz8/nTrZ2dnIyMjQ1ElKSoJSqcSBAwc0dX777TcolUpNHX0BAQEIDQ3V+SIi+zA3j8daCnmgXTJgW5MPqTorhepnskirLBKq62/ikhYRuZ5Lh+EmTJiAdevW4ccff0RISIimB0kulyMoKAgymQxTpkzBwoUL0aRJEzRp0gQLFy5EzZo1MXLkSE3dp556CtOmTUNERATCw8Mxffp0tGzZEj179gQANG/eHH379sW4cePwySefAACeeeYZDBw4UNJKOCKyP1PzeGLkgRh8Vww+3ZMJwPiGv1N6NkWDyJp2HcqqrvmQrLEOwGN6ZduhmrNE5M1cGiytWLECANCtWzed8lWrVmHs2LEAgBkzZqC4uBjPP/888vPz0bFjR2zbtg0hISGa+u+99x5q1KiBYcOGobi4GD169MDq1avh6+urqfP111/jhRde0KyaGzx4MJYtW+bYCyQis8zN42lTP8ypE6KrYz4kqQSMDzNUmCgn8jZulWfJnTHPEpHzOXPD3+qWD0mqAwA66pUtAzDBBW0hcgSX5Fn68ssvsWXLFs33M2bMQO3atdG5c2fNcn4iIntw5oTo6pIPyRqtYBgo3QADJSJ9VgdLCxcuRFCQahPK1NRULFu2DEuWLEFkZCRefPFFuzeQiMhZ1POoFHLdoTZ7TSJ3FxehCgCPaZU9BdVwXLBLWkTk3qyes5SVlYXGjRsDAH744Qc8/PDDeOaZZ3DPPfcYzD0iIvI0npgPyRrPAvhUr+wCgDgXtIXIU1gdLNWqVQt5eXmoX78+tm3bpulNCgwMRHFxsd0bSETkbPpZvauDmzDsNWoB4LgL2kLkaawOlnr16oWnn34abdq0wV9//YUBAwYAAI4fP44GDRrYu31ERFRFKwA8r1eWCqCTC9pC5ImsDpY++ugjvPbaa8jKysL333+PiAjVX1+HDh3CiBEj7N5AIiKyjamUAJWQlgzUmasRidwZUwdIxNQBRORJdgLooVe2BsAoie93xIbCRK5gj+e3pJ6lo0ePSj5gq1atbGoIERHZhwKA/m6YtwAESHy/IzcUJvJEkoKl1q1bQyaTwVQnlPo1mUyGiooKuzaQiIikOQPD/dtmAFhsxTEsbSgsg2pD4V4tFBySI68hKVjKzMx0dDuIiKgKHgbwvV5ZLoA6Vh7Hmg2Fq9uKQSJTJAVL8fHxjm4HERHZQAmgtl5ZNwC7bDweNxQmMmTTHolr1qzBPffcg9jYWM0WJ0uXLsWPP/5o18YRkfepqBRIPZuHH9P/QerZPFRUcg2KKYtgGCgdhe2BEuDdGwoTmWJ16oAVK1bg9ddfx5QpU7BgwQLNHKXatWtj6dKlGDJkiN0bSUTegSuwpCkH4KdXJoMqJUBVdUgIR4w80OKGwh0Swu1wNiLPYHXP0ocffojPPvsMr776Knx9fTXl7du3x7Fjx8y8k4i8lZTeIvUKLP35MuoVWMkZ2c5qrlv7EYaB0o+wT6AEeOeGwkSWWN2zlJmZiTZt2hiUBwQEoKioyC6NIqLqQ0pvEVdgSWPsyssB+Boprwr1hsL6PzcFe/nIS1kdLCUkJCA9Pd1g0vfPP/+MFi1a2K1hROT5pObr4Qos844CuEuvbBGAmQ48Z3XfUJjIGlYHSy+99BImTJiAW7duQQiBAwcO4JtvvsGiRYvw+eefO6KNROSBrOkt4gos07oB2K1Xdh2A3Annro4bChPZwupg6YknnkB5eTlmzJiBmzdvYuTIkahbty7ef/99DB8+3BFtJCIPZE1vEVdgGcoFEK1X9jCA/3NBW4i8ndXBEgCMGzcO48aNw9WrV1FZWYmoqCh7t4uIPJw1vUUDW8VyBZaWGQDe1is7DaCxC9pCRDbmWQKA3NxcnDx5En/99ReuXLlizzYRUTVgTW8RV2CplEB1vdqBkgKqXjgGSkSuY3WwVFBQgNGjRyM2NhZdu3bFfffdh9jYWIwaNQpKpdIRbSQiD6TO12MuvKld00/TW6RegaWQ6wZZCnmgV2zcugaAfni5EwATJhC5nkyY2h3XhGHDhiE9PR0ffvghkpKSIJPJkJKSgsmTJ6NVq1b47rvvHNVWlyooKIBcLodSqURoaKirm0NkVxWVwiGrnpIzsvHc2sNm63ysFwg5qi3uSsD4X62VMJ4qgIisY4/nt9XBUnBwMP73v/+hS5cuOuV79+5F3759q22uJQZLVF05Mmt2RaVAu/nbcf1mmdHX1XOR9r3cvVoHRKakAuisV7YCwHMuaAtRdWWP57fVw3ARERGQyw0XrcrlcoSFhdnUCCJyDUdnzT6Qec1koAToroizpLrtGdcchoFSERgoEbkjq1fDvfbaa5g6dSq++uorxMSo/urMycnBSy+9hNmzZ9u9gUTkGM7Imm2v/EnVac+4CwDi9cqeg6pHiYjck6RgqU2bNpDJbv+yPH36NOLj41G/fn0AwIULFxAQEIArV67g2WefdUxLiciunJE12x75k6RmAbeFs+dHPQXgC72yiwDqOuyMRGQPkoKlBx54wMHNICJnc0bW7KruYO/I3i9n9lYVAailV3YXgHS7noWIHEVSsDRnzhxHt4OInMwZWbPV+ZPGrz0MGaAT9EjJn+So3i9H9lbpWwZgkl7ZbwA62OXoROQMNielJCLPZikPkgyqnpaqZs2uSv4kR/R+WeqtAlS9VVWdQK5e+q8fKFWCgRKRp7F6gndFRQXee+89fPfdd7hw4QJKS0t1Xr92zfKqFiJyvar2+ljD1h3sHdH75Yy5WtsB9NYrWwdghE1HIyJXs7pnad68eXj33XcxbNgwKJVKTJ06FUOHDoWPjw/mzp3rgCYSkaM4M2u2egf7Ia3rIqlRhKQgzBG9X46eqxUBw0CpBAyUiDyZ1T1LX3/9NT777DMMGDAA8+bNw4gRI9CoUSO0atUKaWlpeOGFFxzRTiJyEFt7fZzBEb1fjpqr9ReAZnplrwBYYNVRiMgdWd2zlJOTg5YtWwIAatWqpdkPbuDAgdiyZYt9W0dETmFLr4+z2Lv3yxG9VQ/AMFC6AgZKRNWF1T1L9erVQ3Z2NurXr4/GjRtj27ZtaNu2LQ4ePIiAgABHtJGIvJw9e7/s2VuVD0A/pOoFYJvVrSIid2Z1z9KDDz6IX375BQAwefJkzJ49G02aNMHjjz+OJ5980u4NJCIC7Nv7ZY/eqjdhGChlgIESUXVk9Ua6+n777Tfs378fjRs3xuDBg+3VLrfDjXSJqsbZ2bId1aZyAH56Zf5QTeImIvdjj+d3lYMltcuXL+OTTz7B66+/bo/DuR0GS0S2qy57u20A8JBe2U8ABrigLUQkjVsFS3/88Qfatm2LiooKexzO7TBYIrKNqWzZ6v4be6cocBRj/U3lAHyd3RAisoo9nt/M4E1EDuOsbNmOlA7DQOltqNrPQInIO1i9Go6ISCpnZMt2pHsApOiVKQGwb5m8lTvOPXQGBktE5DCOzpbtKJcBKPTKRkC1ZQmRt6oucw9tITlYmjp1qtnXr1y5UuXGEFH14qhs2Y40FcB7emVnATR0QVuI3IWpuYc5ylsYv/awx8w9tJXkYOnIkSMW69x3331VagwRVS/qbNk5yltG5y3JoMptZE22bEcpAaAfssUBuOCCthC5E0tzD2VQzT3s1UJRbYfkJAdLu3btcmQ7iDyet47lm+OIvd0cYTWAJ/TKfgXQ1ektIXI/nj730B44Z4nIDpIzsjF303HkFNxOTagIDcDcwXdW665pKdTZsvXnOijcYK6DgPElwZUwniqAyBt56txDe2KwRFRFyRnZeG7tYYPynIISPLf2MD6u5mP5Uthzbzd72Q+gi17ZpwDGGanLXkPyZp4499DeGCwRVUFFpcDMDcfM1pm54Vi1HsuXSr23mztoAuCMXtlNAEFG6nrzCiAiwLPmHjoKk1ISVUHa2Txcv1lmts71m2VIO5vnpBaROeeh+sWuHShNgGo4zlSgNH7tYYP5GuoVQMkZ2Q5rK5G7UM89BAyHp91p7qEjuTRY2rNnDwYNGoTY2FjIZDL88MMPOq+PHTsWMplM56tTp046dUpKSjBp0iRERkYiODgYgwcPxsWLF3Xq5OfnY/To0ZDL5ZDL5Rg9ejSuX7/u4Ksjb5D691W71iPHGQOggV7ZPwCWmahfHbKPE9mLeu6hQq471KaQB1b7tAGAxGG4o0ePSj5gq1atJNctKirCXXfdhSeeeAIPPaS/PaVK3759sWrVKs33/v7+Oq9PmTIFmzdvxvr16xEREYFp06Zh4MCBOHToEHx9VZsRjBw5EhcvXkRycjIA4JlnnsHo0aOxefNmyW0lMk7qX1LV9y8ud3cDQIheWXsABy28jyuAiHS549xDZ5EULLVu3RoymQxCCMhk5m+KNRvp9uvXD/369TNbJyAgAAqFfi5dFaVSiZUrV2LNmjXo2bMnAGDt2rWIi4vDjh070KdPH5w8eRLJyclIS0tDx44dAQCfffYZkpKScOrUKTRr1kxye4n0JTWKwLJd+rNfjNdzNE5CNvQ+gCl6Zb8DaCfhvVwBRGTIneYeOpOkYCkzM1Pz7yNHjmD69Ol46aWXkJSUBABITU3FO++8gyVLlti9gb/++iuioqJQu3ZtdO3aFQsWLEBUVBQA4NChQygrK0Pv3r019WNjY5GYmIiUlBT06dMHqampkMvlmkAJADp16gS5XI6UlBQGS1QlnRpGoHZNP7PzlsJq+qFTQ8f+cuEkZF2VML7JrTUDZlwBRERqkoKl+Ph4zb8feeQRfPDBB+jfv7+mrFWrVoiLi8Ps2bPxwAMP2K1x/fr1wyOPPIL4+HhkZmZi9uzZ6N69Ow4dOoSAgADk5OTA398fYWFhOu+Ljo5GTk4OACAnJ0cTXGmLiorS1DGmpKQEJSW3c+YUFBTY6aqoOvH1keGtoS2Npg5QWzS0paQeHlt7hly5DYE79mb9D0BfvbJvAQyz8jhcAUREalanDjh27BgSEhIMyhMSEnDixAm7NErt0Ucf1fw7MTER7du3R3x8PLZs2YKhQ4eafJ/+cKGxoUNLQ4qLFi3CvHnzbGw5eZO+iTH4eFRbzN10AjkFtvXs2Noz5MptCNyxNysEqjlK2koB+NlwLE/JPk5Ejmf1arjmzZtj/vz5uHXr9i/IkpISzJ8/H82bN7dr4/TFxMQgPj4ep0+fBgAoFAqUlpYiPz9fp15ubi6io6M1dS5fvmxwrCtXrmjqGDNr1iwolUrNV1ZWlh2vhKqbvokx2D+zO74Z1wnvD2+Nb8Z1wr6Xu0sOlGxdnm7NJGR7crcl9X9CFcBoB0qzobp+WwIlNW9fAUREKlb3LH388ccYNGgQ4uLicNdddwEA/vjjD8hkMvz00092b6C2vLw8ZGVlISZG9QuqXbt28PPzw/bt2zFsmKqTPTs7GxkZGZr5U0lJSVAqlThw4AA6dOgAAPjtt9+gVCrRuXNnk+cKCAhAQECAQ6+HqhdbJj5WtWfIFZOQ3W1TzQEAtuqV5QGw1+CYN68AIiIVq4OlDh06IDMzE2vXrsWff/4JIQQeffRRjBw5EsHBwVYd68aNGzhz5vZKoszMTKSnpyM8PBzh4eGYO3cuHnroIcTExODcuXN45ZVXEBkZiQcffBAAIJfL8dRTT2HatGmIiIhAeHg4pk+fjpYtW2pWxzVv3hx9+/bFuHHj8MknnwBQpQ4YOHAgJ3eTy1V1eborJiG7y5L6awD0j94XwM8OOJe3rgAiIhWbtjupWbMmnnnmmSqf/Pfff8f999+v+X7q1KkAgDFjxmDFihU4duwYvvrqK1y/fh0xMTG4//778e233yIk5HbWlPfeew81atTAsGHDUFxcjB49emD16tWaHEsA8PXXX+OFF17QrJobPHgwli0zlYqOyHmq2jPkqEnI5iZuu8OS+rkA9GcUngDg2IkAROStbAqW1qxZg08++QR///03UlNTER8fj/feew8NGzbEkCFDJB+nW7duEML0Yt7//e9/Fo8RGBiIDz/8EB9++KHJOuHh4Vi7dq3kdpF7cseVV1VV1Z4hR0xCtjRx25VL6ssA+OuVBcNwUrc3qo7/fxC5C6uDpRUrVuD111/HlClTMH/+fE0SyrCwMCxdutSqYIlIKndceWUP9ugZUk9C1r8/Chvuj5Q0BL1aKFyypP6/AB7RK9sKwHxaW+9QXf//IHIXMmGua8eIFi1aYOHChXjggQcQEhKCP/74Aw0bNkRGRga6deuGq1er5x5YBQUFkMvlUCqVCA0NdXVzvIqpB7j6b2ZPX5Wkvj7AeM+Q1Ouras9CRaVAl8U7Tc5HUgdB+17uju0ncuzSZqmMXUUFuBM4UP3//yCqKns8v63+XZOZmYk2bdoYlAcEBKCoqMimRhCZ4qrNTCsqBVLP5uHH9H+QejbPoZul2mt5unoS8pDWdZHUKMLqIRhrJm47a0n9YRgGSu/+2xYGStzsl8hZrB6GS0hIQHp6uk5WbwD4+eef0aJFC7s1jAhwzcorVwxpuMPydGsnbju6zR1guNltAQw3xfVm7rIykai6szpYeumllzBhwgTcunULQggcOHAA33zzDRYtWoTPP//cEW0kL+bslVeu3DrE1cvTbZm47Yg2ZwOI1SsbDeAru56lenCHlYlE3sDqYOmJJ55AeXk5ZsyYgZs3b2LkyJGoW7cu3n//fQwfPtwRbSQv5syVV+6WbNHZ3GEvtBcA6K9rzQTQwGFn9Gzc7JfIOWwa9h83bhzOnz+P3Nxc5OTkICsrC0899ZS920akeYCbCk1kUA2R2eMB7qqtQyxx1vwpdRoCwHCekKP3Qrv17zm0A6WGUN3zBnY/W/XhzP8/iLyZ1cFS9+7dcf36dQBAZGQkoqKiAKhmm3fv3t2ujSNy5gPcHYc0kjOy0WXxToz4LA2T16djxGdp6LJ4p8P2XnPFXmgrAQTple0FcNbuZ6p+XBngEnkTq1MH+Pj4ICcnRxMkqeXm5qJu3booKyuzawPdBVMHuJYzJl2nns3DiM/SLNb7Zlwnh84tUqcA2H4iB1/sP2fwujOWhDsjwaGpFW2VMJ4qgExjniUi0+zx/JY8Z+no0aOaf584cQI5OTma7ysqKpCcnIy6deva1AgiS5yxWswd5uwYe+jpc8b8KUdPNt8L4D69spUAnnTYGas3d1hNSVSdSQ6WWrduDZlMBplMZnS4LSgoyOyWI0RV5egHuCO2DrGGqZV4xnjykvAEAOf0yooBcApy1bh6NSVRdSY5WMrMzIQQAg0bNsSBAwdQp04dzWv+/v6IiorS2byWyBPZc+sQa5hbiWeOJy0Jz4Rq0ra2yQCWOr8pRERWkRwsqZNQVlZWOqwxRO7AFUMallbimeIpS8IfA7BOrywbgMIFbSEispbVeZYWLVqE6OhoPPmk7uyCL774AleuXMHLL79st8YRuYr+kIZ6+b6jgidre4icMX/KHgoB6E+n7ARg378TyH/j/Boi8gBWB0uffPIJ1q3T/xsRuPPOOzF8+HAGS1TtOGOlkTU9RJ6yJPwdANP1yg4DuJyRjS5cuUVEHsTqPEs5OTmIiTH8hVanTh1kZzsm9wuRq6gnXesPkam3P7FXviNLyQW1OTLnkT1UQBXQ6QdKAqpAyRn3k4jInqwOluLi4rB//36D8v379yM2Vn9HJyLP5cwd3c0lF1R76p4G+GZcJ+x7ubvbBkpbYdhd/V+o7pcz7ye5jrMyzhM5k9XDcE8//TSmTJmCsrIyTQqBX375BTNmzMC0adPs3kAiV3H2ju6mVuJ5yhBVTahSAGgrw+1fMs6+n+R8TI5J1ZXVwdKMGTNw7do1PP/88ygtLQUABAYG4uWXX8asWbPs3kAiV3HF9ieemFzwBIA79crmAXhdr8wdt5Mh+zGVJ0w9xOrOQ8dEllgdLMlkMixevBizZ8/GyZMnERQUhCZNmiAgIMAR7SNyGVft6O6q5IK2bHHSF8D/9MquAQgzUtdV95Mcz9IQq6MzzhM5mtXBklqtWrVw991327MtRG7FHbY/cRZrh0/yAETqlQ0CsMnMObzpfnobDrFSdScpWBo6dChWr16N0NBQDB061GzdDRs22KVhRK7m6u1PnMXa4ZPXACzQq/sngGYWzuMt99MbcYiVqjtJq+HkcjlkMpnm3+a+iKoT9aRrhVx3aMjdl+9LZc0KtTKoghrtQEn+bz1LgZJadb+f3opDrFTdyYQQXNcpQUFBAeRyOZRKJUJD9XMSU3Vny3wed2Kq/aln8zDiszSL73/yxfswJzpEp+x/AHrbuT3kmSoqBbos3mlxiHXfy935cyans8fz2+Y5S0TexJN3dDc3H6mk3PxejwLAhbcGYI5eeQVsSNKmxZPvJxniECtVd5KCpTZt2miG4Sw5fPhwlRpERPZjaT7SlJ5NTL63pK4cOZO66JS9D+AF+zeTqgFTecIUzLNE1YCkYOmBBx7Q/PvWrVtYvnw5WrRogaSkJABAWloajh8/jueff94hjSQi60lZzv3NgQtQhAbicoHu8En2pC4oras7B7EQQC3HNZeqAU/ME0YkhaRgac6c253wTz/9NF544QW8+eabBnWysrLs2zoispmU5dw5BSWY0qMJlv5yGgBQHhqAf17pqVMv+Pcs/DewBmqxZ4Ak4BArVUdWz1n6v//7P/z+++8G5aNGjUL79u3xxRdf2KVhRFQ1Updpr9qfiWB/X1zo1xw3kuJ1Xqu76Bf4KW9hnjyQCQWJyGtZHSwFBQVh3759aNJEd67Dvn37EBjIZaFE7kLqMu388kpkze+nU1Yj9wbqvrsbgGsTClZUCqT9nYfUs3kABJIaRqJTowgGbUTkVFYHS1OmTMH48eNx6NAhdOrUCYBqztIXX3yB11/X3w2KiLQ5c8m8pYzZAFDYIQ7XhrbSKYtenoLAC/kGdZ2dUDA5IxszNxzD9ZtlmrJlu86idk0/vDW0JScME5HT2JRn6bvvvsP777+PkydPAgCaN2+OyZMnY9iwYXZvoLtgniWqKlfsyG5qNZw6JYC++jO3wFTo9s24Tk7rWUrOyMZza82vrP2YSSyJSAJ7PL+ZlFIiBktUFaaCFnVg4sjs1ckZ2Zj5/TFcL1b10NxqGI7LzyTp1In4Lh21Dv9j9P3OTihYUSlwz1s7kVNgvidLERqA/TN7cEiOiMyyx/Pbprxy169fx+eff45XXnkF165dA6DKr/TPP8Z/2RJ5M0tL+AWAmRuOYf/pq6iotP/fLn0TY/DRY20BABdn9TAIlOq/+rPZQAlwbkLBA5nXLAZKgGol3+r9mQ65Z0RE2qyes3T06FH07NkTcrkc586dw9NPP43w8HBs3LgR58+fx1dffeWIdhJ5LEtL+AHg+s0yPLbyN4cNy9VpGIHzesNuoXv+RthW1VC6DIC8ph8Ca/jqBCquSChozdyoN7ecxOf7Mpn0kIgcyupgaerUqRg7diyWLFmCkJDbe0X169cPI0eOtGvjiKoDax7+6sza9hyWexTAd3q9QvXe3A7folIAt3uP3hra0i0SClq72aoj7hkRkTarg6WDBw/ik08+MSivW7cucnJy7NIoourEmoe/OrP2vM0nNHmNbF1BVwBArld2Z1EpQj7Yi+x/AyXAsPfI1QkFOySEQxEaKGkoDjB+z4iI7MnqYCkwMBAFBQUG5adOnUKdOnXs0igie3L1DvdSlvBr085rpCwutWkF3RIAL+uVpQO4K9gfFS93d3nvkTm+PjLMHdzC4mo4ba7MBUVE1Z/VwdKQIUPwxhtv4LvvvgMAyGQyXLhwATNnzsRDDz1k9wYSVYUrluvrM7cjuznbT+Rg1f5zJjfBNTbsVAHj/1NrH8MTtqPomxiDj0e1NcizZImzc0ERkXewejXcf/7zH1y5cgVRUVEoLi5G165d0bhxY4SEhGDBggWOaCORTdTL9fUnV6uDjeSMbKe1Rb0ju0IufUjuh/RLJlfQAaphJ+2VYD/BMFDaAOnBmbvpmxiDQ6/1wtdPd8SDrWMlvcfa+U5ERFLYnGdp586dOHz4MCorK9G2bVv07NnT8ps8GPMseZaKSoEui3eaXIXm7NxB2u1K+zsPE74+rMl7ZKxtYcF+uFZkuUdFnSiyBlS9StrKYEPXsZtS/zxNDWW66udJRO7P6XmWysvLUaNGDWRkZKB79+6YPn06ZsyYUe0DJfI8lpbra89xcSZfHxnuaRyJtx5qCRlgkC1b/f2DretKOt7BsnLIoBsozYfq+mwNlCoqBVLP5uHH9H+QejbPLfIYqYcyAdP3zJm5oIjIu1j1+7RGjRqIj49HRYX+37BE7kXq3BVXzXFRD8vpz6dSr0yTB/lj5f5zZo9x+emOmNw4UqcsH0DtKrTLHeZ4mWLpnrm6fURUfVn9x+drr72GWbNmYe3atQgPD3dEm4iqTOrcFan1HLGirm9ijMm8RhWVwuQKuopgf1yc3Uun7EGo5idVhaktWazNY+TI1Yfm7hkRkaNYPWepTZs2OHPmDMrKyhAfH4/g4GCd1w8flr7c15NwzpJnseccF1f1tqiDF+D2JO38Ps1QcH9jnXp/AWhSxXPZa46XO/dMEZF3ssfz26bUATIZ/4ojx7FHz4S55frm5rjonzu/qAQT1h2pcm+LLbSHnS7dKMGFBf11Xq8DINdO57JmjpeptAP26pkiInI3VgdLc+fOdUAziFTs2TNh7RwXY+f2kRlfeu+srNF9E2NwpYUCj+sdf96Zq+glk6HCTkNQ1s7x0g8q28WHmd0smBm2iciTSQ6Wbt68iZdeegk//PADysrK0LNnT3zwwQeIjIy0/GYiCRzRMyF1joupc5tbCOborNEC/y5X1Wtr/Vlb8IUAvoD9hrismeNlLKgMt5DqgBm2iciTSU4dMGfOHKxevRoDBgzA8OHDsX37dowfP75KJ9+zZw8GDRqE2NhYyGQy/PDDDzqvCyEwd+5cxMbGIigoCN26dcPx48d16pSUlGDSpEmIjIxEcHAwBg8ejIsXL+rUyc/Px+jRoyGXyyGXyzF69Ghcv369Sm0n+6qoFGZ7JgDDJIxSqTNWD2ldF0mNIowOvZk6txSOWFH3Gwz/5wz/IQPxM7dAptVQeyXYVG/JYqrPRwZVYJZfVGo00aeUnFAAM2wTkWeSHCxt2LABK1euxKeffooPPvgAW7ZswQ8//FClNAJFRUW46667sGzZMqOvL1myBO+++y6WLVuGgwcPQqFQoFevXigsLNTUmTJlCjZu3Ij169dj3759uHHjBgYOHKjTrpEjRyI9PR3JyclITk5Geno6Ro8ebXO7yf5cmRfJ0rktsXfW6FYAOumV3f32LoSknTeoW9VAUk1KHqPZA5rjzS22B5UAM2wTkWeSHCxlZWXh3nvv1XzfoUMH1KhRA5cuXbL55P369cP8+fMxdOhQg9eEEFi6dCleffVVDB06FImJifjyyy9x8+ZNrFu3DgCgVCqxcuVKvPPOO+jZsyfatGmDtWvX4tixY9ixYwcA4OTJk0hOTsbnn3+OpKQkJCUl4bPPPsNPP/2EU6dO2dx2si9X5kWqyjF9ZEB+UYld2nERqsDkmFbZ0wBSzuYhN++myffZK5A0tSWLQh6IFaPaIiw4wOagUt0z1SGB6UaIyPNInrNUUVEBf39/3TfXqIHy8nK7NwoAMjMzkZOTg969e2vKAgIC0LVrV6SkpODZZ5/FoUOHUFZWplMnNjYWiYmJSElJQZ8+fZCamgq5XI6OHTtq6nTq1AlyuRwpKSlo1qyZQ9pP1rF3XiRHnNuYSgFMWHcEK3xkVZo39CyAT/XKsgDUA/CjEwNJc3O8fkz/x6ZjMsM2EXk6ycGSEAJjx45FQECApuzWrVt47rnndHItbdhQ1dR4Kjk5OQCA6OhonfLo6GicP39eU8ff3x9hYWEGddTvz8nJQVRUlMHxo6KiNHWMKSkpQUnJ7R6DgoIC2y6EJFHPmbGUF8kRPRMdEsKhCA1EToHtwYYtK70qKgX2nLuG7g11JzwnQrd3ydmBpHqOl63HrxVQAzdKbv8RxQzbROTpJAdLY8aMMSgbNWqUXRtjjH5OJyGExTxP+nWM1bd0nEWLFmHevHlWtpZsZWteJHvYfiIHt8ptn3tny0qv5IxsjL+oxLm+d+iUv3f2KqY0ur3CtKJSoLJSoHaQn9mNdx0VSGqzFNCq3Sgpx4s9m6BBZDAzbBNRtSA5WFq1apUj22FAoVAAUPUMxcTc/os0NzdX09ukUChQWlqK/Px8nd6l3NxcdO7cWVPn8uXLBse/cuWKQa+VtlmzZmHq1Kma7wsKChAXF1e1iyKzXLH3l6mUAbaQOgy2NSMbAxJjAL3riZ+5Be8DuOPfFAnGlujrc+YQlzqgfW6t+Sz9MgDrD2ZJyo5OROQJbN2Y3OESEhKgUCiwfft2tGnTBgBQWlqK3bt3Y/HixQCAdu3awc/PD9u3b8ewYcMAANnZ2cjIyMCSJUsAAElJSVAqlThw4AA6dOgAAPjtt9+gVCo1AZUxAQEBOkOO5BzO3PurqikD9EkZptpWKVSBkpaI9UdQK/32Qol5m0+gslIYzRyuz9lDXH0TY/BizyZ4b8dpk3WYU4mIqhuXBks3btzAmTNnNN9nZmYiPT0d4eHhqF+/PqZMmYKFCxeiSZMmaNKkCRYuXIiaNWti5MiRAAC5XI6nnnoK06ZNQ0REBMLDwzF9+nS0bNkSPXv2BAA0b94cffv2xbhx4/DJJ58AAJ555hkMHDiQk7vdlKk5M/aWdjavSikD1LSHwcxt1fI0gJX6CSZf3QpZxe2QSB1ovPZjhtlAqXaQHz56rC06NTTMG+VoDSKDLVcCcyoRUfXh0mDp999/x/3336/5Xj3sNWbMGKxevRozZsxAcXExnn/+eeTn56Njx47Ytm0bQkJCNO957733UKNGDQwbNgzFxcXo0aMHVq9eDV9fX02dr7/+Gi+88IJm1dzgwYNN5nYix3LkjvTWSM7Ixszvj1muaIH2MNj2EzlGt2qZ9GAiHrtDd8g3dNcZhP3PdOoKS0kerxeXwUcmc8m9c+XKRSIiV5AJIew1ClGt2WPXYm/nLjvS23Oekrr9AIwes+DeBOQPaKFTFjfnf/ApqXrKjfeHt8aQ1nWrfBxrVVQKdFm80+LKRc5ZIiJ3YI/nt+SklERVoQ5Q9Ie97LVdh1T2mqdUu6Yfvn6qI/a93B29WigMjlnp54Pzbw3QCZTmVAqUVwrUDaxhdluRiGB/E6/qigy2fU5dRaVA6tk8/Jj+D1LP5lmV/VtKtm/mVCKi6sRtJ3hT9VFaXolXNh5zix3pq7q1ibp1bw1tiXuaqJb4p+rNfbp5ZzSujG6v8766i35B2N314duzicUUCUPb1sVnezMttmXa//2BuYNVvXLaw5uRwQGADLh6o8ToUKc9evhsXbnoLsOwRETWYLBEDpWckY1XNma4ZEd6Yw/mqk46rl3TD4uGttQJBtTHFDLg0tSuKK9TS/NazT8uoc43RwAA7+34C80UtcwGGqr9105KasvlAlWv3DP3JWDTH9kmg0DtQMjUEKS6h2/Fv2kLpLB25aK7DMMSEVmLwRI5jLVzg+yxekodIO04kYON6f/oBGnhwX54vFN8lY4fUMMHvVoodMqiQgJRUk+OnIlddMoVH+5FwD+6md/VPWimAg1rer7U9/WTPeZ7odSB0Ecj2+DNLSft2sMndeWiPYM0IiJn45wlcghb5gZVdfVUckY2uizeiRGfpWHl/nMGvVnXisqw9JczCPb3NTlnyJKcghKDDWvfaRiuEyj5XS5E/VlbDAIlQHfDW18fGTokhCMqJBC5hbeQdjYP+85csbFlpql/Bq/9mGE2ELPXhrz6zH0W1GXzNp+wat4UEZEzsWeJHMKaHhJL23VImediTS9WUantW5sAt3vAzgNoAABa2+bU+ep31DxhmDHe2PulZOi2FwHL6QjU7J0fydJngUksicjdMVgih7D2gWtq9ZSUeS629GIF1vDBrfJKq9qoFhUSiLkA9HcOXLLzND6yECip32/P9AX2Zu/8SFI/C0xiSUTuisNw5BBSH7jhwX4m56tITTdgywo3WwIlGYA6UcHo3ChCJ1B6F6rekandGkMRavq6ZVAFeu3iw+y6zYo1woP9zaYtiHHAhrxMYklEno7BEjmEeod6c3ODIoL9kTarp9FAyZp5LjtO5NijyWbJANxoXRe/T+2mU54D4EXcHirs31Khqa//fkDVg3bofL5Tht70zx8jD8T8IYk67dF+HXBMfiRLnwVHBWlERPbCYIkcwlLiQhmABQ8mwr+G8Y+g1Hkuy3aewcr95+zRZNPn8pHh4uxeuDq8tabs2X/bEA3dieVf/NsWmd5FK+SBmh40Vww3CQDD745DWWUlpvRsimi9HjDt9tkbk1gSkafjnCVyGFsSF6p7aH6WmNF71X7LyRttVSvAF8NHtcPsJnV0yjMA3Pnvv03NPVIv7Hrqngbo2UKhmZReUSlwtbDEYW02RiYDgvx88d6O05oyRWgAXuzZBA0ig52SHNLWJJZERO6Ae8NJxL3hbCc1a7MzV4dZIgCEv9YT6bVubynSGcA+3O4NUe+RZqq9+nukOfr6agXUwA0r95z72Mn5jZjBm4iczR7Pb/YskcNJSVzoTqvDyiKDcWl6N1zQKtsGoJdePWuWxCuLSx1+fTV8rQ86Zm445pRtZtSkJrEkInInnLNUzVRlg1RXsdfmtvZwbWALXJreTfN9LQAlMAyUAOlL3XOUxXa7vmB/HwTU0A1sYuSBeLFnE1y/KS2PkrbrN8uwbOcZO7SMiKj6Ys9SNeKpe29VdXNbe6io6YeLr/fWKRt/IR/L64eZfI/Upe7XikqrfH39EhX4LfMarhWVaspqB/nhiXsSMLF7Y/x09JLNx16VkomJ3RtzOIyIyAT2LFUTUnMSuSNHrA5TP/abx4RYrFvYsb5BoFRv3jakfXnQ7H2TuiQ+XGvek61+zsjRCZQAQFlchqU7/sL2EzlVylF0/WaZ3bc4ISKqThgseTD1kNvGI//glY3HPHbvLUckI1TIA/HxqLa4M8b0ZD5RwwfnF/TDtQdbaspCd59F/Mwt8C0uw7WiMrOBptQl8eYSVVaF9s+2XXyYxbxW5jB7NhGRaQyWPJR2bp8Xv003u++XozZItRcpPTThwX6SjxcR7I/dL92Pykrgv4f/MVqnuGkdXJjfD/C9/b9A7JJdCPv5T4O65gJN9ZJ4hdx03iIpCTptpf7ZHjqfbzJwk4LZs4mITOOcJQ9k68oxd+09UPfQjF97GDJA57rUD/75QxLx5paTkub+5BWVYtaGo9hgJFASAHIm3oPSerU1ZUEnL6POl78bDTKkbPLaNzEGvVooTC6Jt3R9wsi/jX1vTm7hLQxpXddoLiOZDDCVIMTSJsZERMRgyeNUZeWYO/ceSEla6OMjw3NrD0s63vdGAqVSRQiyp9ynU9Zm3SFcO2p5uxRLgaZ6Sbw6j9BPRy/pBE2Wrg+A0deG3x2nk0zSFPXP1ljgll9UignrDINrZs8mIpKGwZKHsWXlmKf0HljqoembGIMXezbFezv+svrYVx9uhaL2cZrvfa8Xo+6SXSj185X0fimBpqXViJauz9hrALD+YBZylLeMBsjGfrbGchmt8GH2bCIiWzFY8jDWDqW5c++BqWzO5pIWTuzeGN8cOI+cAmlbhpSHBOCfV3vqlEWuO4zgo6pJ2zdKyhES6IvCWxUmj+EjA/KLzJ/P1NCoejWiev6SpR4o9bVr35vhd9fH0h1/mRyilPKztRSoERGRaQyWPIy1Q2nu2ntga04oXx8Z5g6+E+P/HY4zNxypvK8hrvdvrlMW93oyfEp1AyNzgRKg2udtwrojWPHvcJo+c0OjAqqgZt7mE5pM2Zau3djrtWuqJrhrJ5609mfL7NlERLbh3nASucvecOr9yMwNy4QH++O1Ac2hkAfZtffAXvt6meqFUR9phYT9yszts1bp54usN/vqlMm3nULtKmaqDg/2Q9qsnvCvobuINPVsHkZ8lmbx/d+M62Ry2xP1tT9zXwI+3ZNp9HUBOHXzWyKi6oB7w3khKSvHFjyYaPeeJHtlB7e2F8aUXi0UCAnwQ+rfVwHIIA/yw4KtJ1GUqMDVUe106tZd+AtqFFR9JeC1ojJ0WvQLFurdX8nbnhTcwpLkP81e+2d7DQMl7dfXH8zSbMxLRETOwWDJA0lZOWZPUufjSGHN5rOmhoyMBW7R8kBkz7gfpeE1NWU1j/yDOt+mS2qXVNeKSg2uWfK2JzdKLF67uX5eKfeGiIjsj8GSh3LWhF179QSph/B+lrjtiqneGmOBW0lcbRyYcI9OPcUHexFwqUDSuWyhfc3qpJOWVqyFB/vb5dw7TuQwWCIiciIGSx7MGRN2HdUTZImx3hpjgVvuqHYoTlRovg/IKUT0+3sgc+BMPP1rljI0OmdQC8iD7BMsbUz/B68McL/VjURE1RW3OyGzpM7HMVVv69FsPGdkg19T1JvPGssJpR24lYUF4fxbA3QCpTpfHoRiqbRAKTzYr8rbj2hfszO3PblWxI1viYiciT1LZJbU+TjG6m09egkTvzki+VyW8gapg5PrvZpC2aOJzmtxr/0Mn/JKAEDtID8oi8vMDonNHtAcE9YdsWpLEX2nL99A6tk8nSzdUrc9qSp33bqGiKg6Ys8SmSVlk1tjPUHJGdl4ft0RmNh/1ijtXhhjguSq3iTtQCls03HEz9yiCZQAIKlRhMlACVAFY/1bxRrtCbLGsl1nMOKzNHRZvBPJ/87FUg+NDmldVzNEp61vYgw+GtkWVR1Bc+eta4iIqhv2LHk5S7mTpM7H0X6Pem6RVI8nxaPfv8NUpubhfAlgrF5AVu/N7fAtKjWo+3OGaq83Hxl0gjX91YL6PUGnLxdi2a6zktutZu2qwLBgf6uCSG2esnUNEVF1wmDJi0nNnWRtqgJr96/rlxhjcnL4rUqBKCFQ6Hu7EzQk9RwifjxucfhMvQz/yXsaoFcLhdFgTHuSfOrZPJuCJWtWBQK2D6G589Y1RETVGYMlL2Vt7iRrUhVYEwyYmswNAEsy8/ByQgSgNQh416epeLSeHJvkgRYDMnUQ83NGDl6VsHrMUgoAS+eSmgNJ6hBaeLAfrhXZvr0JERHZB4MlL2Rr7iSpqQqsmU9jrJdEAGh9owRHE26fK+DvPER/mgYlgE//voaPRrZBWHAA9p+5YrY3yJogxtyQo1RSAkWpeZl2v3Q/Dp3P58a3REQuxgneXsia3Em2kLJE3kcGLB9pOMfnT6g+lEdrBWjKoj5Lg+LTNJ0A5s0tJ9EhIRxNokMktUlqb5epFAAREhNKSgkU1UEZAIN7pD3U5l/Dx+xkcSIicg72LHmhquZOskRKD82yEW3Qv5VuoDQJwDKt72XFZYibvx2yCt0jaAdzVUltYIqxIcd28WHo+vYui71BUideO3vLGiIish2DJS8UGRxguZIV9YwxFQwYm0B+BUCU3vsj/u8P1Dp00ew5cgtvYWCrWElDWtauHjM25GjtqkBLnLVlDRERVQ2DJW8k8Vl88Nw13NMk0ubTSAkGlkHVo6QtOfManrUQKAGq3iJbUhvos5Q+Qft67N0b5Iwta4iIqGoYLHmhqzdKJNVb+stp3BETUqUhIVPBwC0AwQAqtcpmAVgIoCI+zKreoqoEMVLTJ6ixN4iIyPvIhBC27vbgVQoKCiCXy6FUKhEaGurq5lRJ6tk8jPgsTVLdGHkg9r3c3a7BwBYAA/XKzgJoqPW9OrUBYLy3yFgCSFM9RKbKTaVPMHcOIiLyLPZ4frNnyQt1SAiHIjQQOQWWJ3BLXXavzVRwUgmgHYB0rbqDAGwycgzVtiBt8NqPGZJzDWn3YqnbsP1EDn5Iv4RrWpm+Y/7dG+7NLSetTp9ARETeh8GSF9p+Ige3yisk17dmVZypYa1Rj7TCxMZ1dOqmAEgyc5w3t5zUCZTCg/0xe4DpYTV1gLTjRA42pv+j815tOcpbeH6d+Q1+rcnPRERE1RuDJS9jaujJHKnL7k0d+1ifZjqBUgMAZwD4Wnmc/KJSTFh3GCt8DIfHjAVpplhz7bamTyAiouqDSSm9iLnM3cbIYH47EkvHLg8NwPm3BqCobT1N2TeVApkwHShZyi4OqIbHKrR2olUHV9bsRyeVNfmZiIioemKw5EWs2eDW2txB+sdWdmuEf17pqVMn7vVkxFvICm5tdnFrA0CprAkUiYioeuMwnBexZkjJ2txB6mNX+vsi642+Oq/V/vlPyHefldQGa7OLWxMAmmKvJJNERFQ9MVjyIlKHlGYPaI6x9yRYDBS0V71dLSxBUcsYXH2srU6dugt3oEbB7bxOltpg7fYlts4pUudqmj2gBd7cwi1HiIjINLcehps7dy5kMpnOl0Kh0LwuhMDcuXMRGxuLoKAgdOvWDcePH9c5RklJCSZNmoTIyEgEBwdj8ODBuHjRcnbo6sjSBrfqoScpgVJyRja6LN6JEZ+l4YVv0/FsokInUAo+fBHxM7doAiWpw1odEsJRu6afydf1j2PLnCLtnqP+rWKw7+Xu+GZcJ7w/vDW+GdcJ+17uzkCJiIg03DpYAoA777wT2dnZmq9jx45pXluyZAneffddLFu2DAcPHoRCoUCvXr1QWFioqTNlyhRs3LgR69evx759+3Djxg0MHDgQFRXSl85XF9q73ZsiZehJe0J1Sf3auLBoACrCampej3l/LyK/+0PzvTXDWttP5OD6TeNL/gHVcJn2cSwFgMYo5IE6CSfV+ZmGtK6LpEYRHHojIiIdbj8MV6NGDZ3eJDUhBJYuXYpXX30VQ4cOBQB8+eWXiI6Oxrp16/Dss89CqVRi5cqVWLNmDXr2VE02Xrt2LeLi4rBjxw706dPHqdcihdR9ymzVNzEGz9yXgM/2ZkJrQRl8ZMC4exMs9qhoT6jOfbw9iltEa17z/0cJxbJ98IXuNiZSh7XUxzYnrKYferW4/XkwtzectvBgPzzYui56tlBwexI4/nNGRFSduH2wdPr0acTGxiIgIAAdO3bEwoUL0bBhQ2RmZiInJwe9e/fW1A0ICEDXrl2RkpKCZ599FocOHUJZWZlOndjYWCQmJiIlJcXtgiWp+5RV5UGXnJGNT/dkGgQVQgCf7slEm/phZvc+O5B5DRd8Zbj01gCd99dZdRA1T+UCUAVKswc0R2RIgFXtkzJZO/9mmUGiSFN7w0UE+2NI61j0YoCkw9r98IiIvJ1bB0sdO3bEV199haZNm+Ly5cuYP38+OnfujOPHjyMnJwcAEB0drfOe6OhonD9/HgCQk5MDf39/hIWFGdRRv9+UkpISlJTcnphcUFBgj0syyVQixhzlLYxfe1gzbFSVB52lHEYyADM3HMPcTSd0tkLR7pX5T7AfLs3ofvuNlQL1X0+GrLxS53iRIQEY0rqutIv/l7Ur4bRxg1tppH7OiIjoNrcOlvr166f5d8uWLZGUlIRGjRrhyy+/RKdOnQAAMpnuw1AIYVCmT0qdRYsWYd68eTa23DpSgph5m0+gshKYsM72B52UHEaq+UK6c4auFZXh08MX8fqgO3XKw37MQGjqeaPHsmbitbqn7PTlG5Lqmzq29t5wZEjq54z74RER6XL7Cd7agoOD0bJlS5w+fVozj0m/hyg3N1fT26RQKFBaWor8/HyTdUyZNWsWlEql5isrK8uOV6JLaiLG137MsCqztT5bl9nfaF8PF+foDlnWe3O70UDJ2mSO2qvqlu06Y7YuE0VWjbUJP4mISMWjgqWSkhKcPHkSMTExSEhIgEKhwPbt2zWvl5aWYvfu3ejcuTMAoF27dvDz89Opk52djYyMDE0dUwICAhAaGqrz5ShSg5hrRaUmX5PyoLN2mb3wleHC3N7Ie/guTVnI/kzEz9wCXyNtsTaZozXblDBRZNVVZZiTiMibufUw3PTp0zFo0CDUr18fubm5mD9/PgoKCjBmzBjIZDJMmTIFCxcuRJMmTdCkSRMsXLgQNWvWxMiRIwEAcrkcTz31FKZNm4aIiAiEh4dj+vTpaNmypWZ1nDuw5/5j5h506mX2OcpbFrcHKW4UgdxxnXTKYv/zK/yuFmm+Dw/2w7Wi20N21iRztHabEiaKrDprE34SEZGKWwdLFy9exIgRI3D16lXUqVMHnTp1QlpaGuLj4wEAM2bMQHFxMZ5//nnk5+ejY8eO2LZtG0JCQjTHeO+991CjRg0MGzYMxcXF6NGjB1avXg1fX1NbuTqfNUGMJeYedFKW2QsAl5/thJKE23N/As5eRfRnvxnkMpo98E4oQgNtmlAtdZuSifc3wj2N63Cyth1Y+pyps5pzmJOISJdMCGHvPUirpYKCAsjlciiVSocMyamHpADDfcqk/IDUD7p9L3eXlFRy5oZjBskfy6Jq4dLUrjpl0Z+mIvBv40N734zrZPOE6h/T/8Hk9ekW670/vLXVq+rINHOfMwBcDUdE1Y49nt8eNWepOlPnClLIdXuGwoP9Jb1fAGgfXxs/Hb2E1LN5Zid6AzAIlPKG3KkTKPkUlaL+K1uNBkpSJlpXVAqkns3Dj+n/GG0Ph4Rcw9TnTD+rORER3caeJYkc3bOkpp9wMkdZjBe1tg6RylTupYpKgS6Ld2qGwCqC/XFxdi+dOpH/l47gQ/8YPa6UHggpuaDU7bA0JCSlp4ysxwzeROQt2LNUDenvU6aQB9l0HHXupeSMbJ1y7blCBZ0bGARKcXP/h+BD/yAk0Ph0NnUPRK8WCqM9R6ZWuOm3R3ufOv1HNFe+OR73wyMiks6tJ3iT7ZO/TSUZzC28hcoaPsia30+nfujO0wjb9pfm+8Jb5UaPO3tAcwDQ6Z0CVD1Hswe0wJtbLCc97H5HNA6dz0dJeSWm9GyKbw5c0MkYLmXlG3tGiIjIWTgMJ5GzhuGMMbVFhVTaE7Hfzi7AjBjd9tddvBM18ostHkcGoHZNP+TrzXdSvya1feHB/jo5oxShARjRoT4aRAYjKiQQ7eLDcOh8vslAiHubERGRVPZ4fjNYksiVwRKgChBe2XhMJ6+RVO8Pb41BreviLgAZWuVBGTmIWnvIbm20lfY8KABmAyFTgSNXcxERkTEMlpzI1cESAJSWV6LTol/MZvI2Zu6Ee/BEXG2dspiP9iMg63qV8zrZiwyAvKYflDfLTAZCH41size3nDCZn4mTwomISB8neHsZ/xo+WPhgImQwnBRtSsFjbXUCpcYAygF80bWhwfLxYH/XJepUb+Jrbu+72T9mcG8zIiJyOgZLHsZUnhx95fJAnH9rAPJb3h6S+i+A0wB8/z3Ovpe748WeTVE7yA8AUFRa4biGV5EAkCexR417mxERkT1xNZwH6psYg14tFDiQeQ05ymLM/vE4bpTcXr2mvL8xrvdppvMeZaVAqN7Q1PYTOVi64y+3GYqzFyayJCIie2Kw5KHUeXJSz+ZpAqVKf19kvdFXp17trSch3/M3jo/rhA4J4Zrl9pG1AjB303HJgZKPDLCQFNwpwoP9kF9kfLiOe5sREZEjMFjycOohp6JWMbg6sq3Oa3UX7ECNwhIAwHvbT+HCtWKdfEZSTLy/McJq+uHNLSft02AzapuY4A3cDoRmD2iOCeuOGKQqUPeZzR7QnPmXiIjIrhgseSh1UsY/cwtxcVZ3VGhl+g7+PQuR/z2qU//AuXybztMkulaV2ilFeLAfFj7YEgAwfu1hk4GQOn3ACh+ZQXoBhTwQg++KwZtbTjL/EhER2RWDJQ+hnbH63NUifHPgAs6F1cTl8Z116sUs3QP/nEK7ndfR838igv2ROqsH/Guo1hqsGNXWaCCkHfBoz9lS9yDlF5ViwjrD/EvqbVaYf4mIiGzFYMkD6GesFgCujL0bxXdEaer4Z12H4qP9klMKWKI//8eWLVcsHR8AFjyYqAmUAOOBkLGhNPWcLeD2pryWtlnR3vaFiIhIKgZLbk4/Y3VZeE1cmnG/Tp2oLw4g6K8rdjunsY1s5wxqYXSIzJxRHePQLj4c+89cxfaTuVAW384+bm7/N+1ASArtzYGN0c6/ZM1xiYiIAAZLbq2iUmDe5tsb0+b3uwMFXRvdrlBegfpztkFWUWnX8xoLZNT5nWZuOIbrRvaGM6ZdfDgebFsPD7at59CNb6XmVWL+JSIisgWDJTem7jGpCPLDxTm9dV4L33gMIb9dsNu5+rSIQv9WsWYDmV4tFJi76bjkY0aF3p7vZG1vkTWkzqti/iUiIrIFM3i7sdzCWyi8O84gUKr3xja7BkoAsO1ELvx8fJDUKMJkj8+BzGvIKSiRfMxp36UjOSPbXk00qUNCOGLkgSbna8mgmnPF/EtERGQLBktuqhTAiFaxuPZQK01ZyL6/ET9zC3wlDoNZQwB4ft1hs8GNtcNYlwtKMH6t+WPag6+PDHMGtQBguGeesflXRERE1mCw5IZ2AAgAUKz1cI/9z68I/8lyYsiqboY7b/MJVJhI1W3tMJb6KOaOaS+m9sxTyAOZNoCIiKqEc5bciABwD4BUrbJGV26g7J3dklICDGgZgy3HqtaLY2rVWEWlQKUQqB3kh+vF0nu2nLkSTWraASIiImswWHITxwEk6pVFf5KK8sxrkgKlAF8ZtlYxUFLTH27Tz/NUlWM6clUc4NiJ5ERE5J0YLLmBZwF8qvW9T+Et1Fu0EzIrhq5KKuw3zKU93Kaf56kqxzQWdHE7EiIicnecs+Rib0A3UGq06TjiFvxiVaBkL/qrxvTzPBlTO8gPYTVNx9zqY+YXlWL82sMGvVPq7UicsWqOiIjIFgyWXKxU69/bMq+hPOWcq5oCQHfVmKXM2ABwvbgMYzsnQAbTK9FmD2iON7cYD7qcOQmciIjIFgyWXOxNAIVQBQ03lcUua4ciNMBg1ZjUVAENIoPNrkQLCw6QvB0JERGRu+GcJReTAaj1779dlWH6xZ5NMbF7Y4OJ1tZkxk5qFGFyJdqP6f9IOg63IyEiInfEYMmNdEgIR0igLwpvVTjlfJYmV6szY+cobxkdQpNB1XuknuNkaiUatyMhIiJPxmE4N+LrI8PDbes55Vwv9myCfS93N7sKzV6ZsbkdCREReTIGS26m952OX0IvA7D+YJakuvbIjM3tSIiIyJNxGM6NVFQKVFYKyIP8oLQiS7a1rM2qbY/M2OqgSz/PkoJ5loiIyM0xWHIT9siSbS1rJlTbIzM2tyMhIiJPxGDJhdRbf+w4kYOV+885/fyumFDN7UiIiMjTMFhyEVf0JKnpr2IjIiIi0xgsuYC99luzBSdUExERWYfBkpNJ2W/NkWQyYNy9CZxQTUREJBFTBziZlP3WrBUWJD3mrRTAp3syuXEtERGRRAyWnMyeW3qokzn+9movvNizqVXv5ca1RERE0jBYcjJ7r0CbM6gF/Gv4YHLPJvh4VFvEyC0fnxvXEhERScdgycksbf0hVYyRDNp9E2Ow7+XumHh/I0nH4Ma1RERElnGCt5Opt/4Yv/YwZIDkid4tYmqhXXw42sSFIaZ2kMlkjr4+MtzTuA6W7Tpr8ZjcuJaIiMgy9iy5gKn91sw5kX0Da9Iu4O1tp6AsLjW77J8b1xIREdkPgyUXUQ+ZfTOuEybe31jy+3KUtzB+7WGzq9m4cS0REZH9MFhyIfXWH02ia0l+j3rYztJqNlO9Vwojc52IiIjINM5ZcgORwQFW1ddezWZunzVuXEtERFR1DJbcgY2xi5TVbNy4loiIqGo4DOcGrt4osel9XM1GRETkeF4VLC1fvhwJCQkIDAxEu3btsHfvXlc3CYD1QQ9XsxERETmP1wRL3377LaZMmYJXX30VR44cwb333ot+/frhwoULrm6aVYkquZqNiIjIubwmWHr33Xfx1FNP4emnn0bz5s2xdOlSxMXFYcWKFa5umtml/vq4mo2IiMi5vGKCd2lpKQ4dOoSZM2fqlPfu3RspKSkuapUu9VL/eZtPIFt5e+K2IjQAIzrUR4PIYK5mIyIicgGvCJauXr2KiooKREdH65RHR0cjJyfH6HtKSkpQUnJ74nVBQYFD2whwqT8REZE78opgSU0m0w06hBAGZWqLFi3CvHnznNEsHVzqT0RE5F68Ys5SZGQkfH19DXqRcnNzDXqb1GbNmgWlUqn5ysrKckZTiYiIyM14RbDk7++Pdu3aYfv27Trl27dvR+fOnY2+JyAgAKGhoTpfRERE5H28Zhhu6tSpGD16NNq3b4+kpCR8+umnuHDhAp577jlXN42IiIjcmNcES48++ijy8vLwxhtvIDs7G4mJidi6dSvi4+Nd3TQiIiJyYzIhhOmt60mjoKAAcrkcSqWSQ3JEREQewh7Pb6+Ys0RERERkKwZLRERERGYwWCIiIiIyg8ESERERkRlesxquqtTz4J2x7QkRERHZh/q5XZX1bAyWJCosLAQAxMXFubglREREZK3CwkLI5XKb3svUARJVVlbi0qVLCAkJMbmfnLUKCgoQFxeHrKwsr05HwPtwG++FCu+DCu/DbbwXKrwPKtbcByEECgsLERsbCx8f22YfsWdJIh8fH9SrV88hx+Z2Kiq8D7fxXqjwPqjwPtzGe6HC+6Ai9T7Y2qOkxgneRERERGYwWCIiIiIyg8GSCwUEBGDOnDkICAhwdVNcivfhNt4LFd4HFd6H23gvVHgfVJx9HzjBm4iIiMgM9iwRERERmcFgiYiIiMgMBktEREREZjBYIiIiIjKDwZILLV++HAkJCQgMDES7du2wd+9eVzfJbubOnQuZTKbzpVAoNK8LITB37lzExsYiKCgI3bp1w/Hjx3WOUVJSgkmTJiEyMhLBwcEYPHgwLl686OxLsdqePXswaNAgxMbGQiaT4YcfftB53V7Xnp+fj9GjR0Mul0Mul2P06NG4fv26g69OOkv3YezYsQafkU6dOunUqQ73YdGiRbj77rsREhKCqKgoPPDAAzh16pROHW/4TEi5D97wmVixYgVatWqlSaaYlJSEn3/+WfO6N3wWAMv3we0+C4JcYv369cLPz0989tln4sSJE2Ly5MkiODhYnD9/3tVNs4s5c+aIO++8U2RnZ2u+cnNzNa+/9dZbIiQkRHz//ffi2LFj4tFHHxUxMTGioKBAU+e5554TdevWFdu3bxeHDx8W999/v7jrrrtEeXm5Ky5Jsq1bt4pXX31VfP/99wKA2Lhxo87r9rr2vn37isTERJGSkiJSUlJEYmKiGDhwoLMu0yJL92HMmDGib9++Op+RvLw8nTrV4T706dNHrFq1SmRkZIj09HQxYMAAUb9+fXHjxg1NHW/4TEi5D97wmdi0aZPYsmWLOHXqlDh16pR45ZVXhJ+fn8jIyBBCeMdnQQjL98HdPgsMllykQ4cO4rnnntMpu+OOO8TMmTNd1CL7mjNnjrjrrruMvlZZWSkUCoV46623NGW3bt0ScrlcfPzxx0IIIa5fvy78/PzE+vXrNXX++ecf4ePjI5KTkx3adnvSDxLsde0nTpwQAERaWpqmTmpqqgAg/vzzTwdflfVMBUtDhgwx+Z7qeB+EECI3N1cAELt37xZCeO9nQv8+COG9n4mwsDDx+eefe+1nQU19H4Rwv88Ch+FcoLS0FIcOHULv3r11ynv37o2UlBQXtcr+Tp8+jdjYWCQkJGD48OH4+++/AQCZmZnIycnRuf6AgAB07dpVc/2HDh1CWVmZTp3Y2FgkJiZ69D2y17WnpqZCLpejY8eOmjqdOnWCXC73qPvz66+/IioqCk2bNsW4ceOQm5urea263gelUgkACA8PB+C9nwn9+6DmTZ+JiooKrF+/HkVFRUhKSvLaz4L+fVBzp88CN9J1gatXr6KiogLR0dE65dHR0cjJyXFRq+yrY8eO+Oqrr9C0aVNcvnwZ8+fPR+fOnXH8+HHNNRq7/vPnzwMAcnJy4O/vj7CwMIM6nnyP7HXtOTk5iIqKMjh+VFSUx9yffv364ZFHHkF8fDwyMzMxe/ZsdO/eHYcOHUJAQEC1vA9CCEydOhVdunRBYmIiAO/8TBi7D4D3fCaOHTuGpKQk3Lp1C7Vq1cLGjRvRokULzQPcWz4Lpu4D4H6fBQZLLiSTyXS+F0IYlHmqfv36af7dsmVLJCUloVGjRvjyyy81k/Rsuf7qco/sce3G6nvS/Xn00Uc1/05MTET79u0RHx+PLVu2YOjQoSbf58n3YeLEiTh69Cj27dtn8Jo3fSZM3Qdv+Uw0a9YM6enpuH79Or7//nuMGTMGu3fv1rzuLZ8FU/ehRYsWbvdZ4DCcC0RGRsLX19cgss3NzTX4i6K6CA4ORsuWLXH69GnNqjhz169QKFBaWor8/HyTdTyRva5doVDg8uXLBse/cuWKx96fmJgYxMfH4/Tp0wCq332YNGkSNm3ahF27dqFevXqacm/7TJi6D8ZU18+Ev78/GjdujPbt22PRokW466678P7773vdZ8HUfTDG1Z8FBksu4O/vj3bt2mH79u065du3b0fnzp1d1CrHKikpwcmTJxETE4OEhAQoFAqd6y8tLcXu3bs119+uXTv4+fnp1MnOzkZGRoZH3yN7XXtSUhKUSiUOHDigqfPbb79BqVR67P3Jy8tDVlYWYmJiAFSf+yCEwMSJE7Fhwwbs3LkTCQkJOq97y2fC0n0wprp+JvQJIVBSUuI1nwVT1PfBGJd/FqyaDk52o04dsHLlSnHixAkxZcoUERwcLM6dO+fqptnFtGnTxK+//ir+/vtvkZaWJgYOHChCQkI01/fWW28JuVwuNmzYII4dOyZGjBhhdHlsvXr1xI4dO8Thw4dF9+7dPSJ1QGFhoThy5Ig4cuSIACDeffddceTIEU1aCHtde9++fUWrVq1EamqqSE1NFS1btnSrpcHm7kNhYaGYNm2aSElJEZmZmWLXrl0iKSlJ1K1bt9rdh/Hjxwu5XC5+/fVXnWXQN2/e1NTxhs+EpfvgLZ+JWbNmiT179ojMzExx9OhR8corrwgfHx+xbds2IYR3fBaEMH8f3PGzwGDJhT766CMRHx8v/P39Rdu2bXWW0Ho6dW4QPz8/ERsbK4YOHSqOHz+ueb2yslLMmTNHKBQKERAQIO677z5x7NgxnWMUFxeLiRMnivDwcBEUFCQGDhwoLly44OxLsdquXbsEAIOvMWPGCCHsd+15eXniscceEyEhISIkJEQ89thjIj8/30lXaZm5+3Dz5k3Ru3dvUadOHeHn5yfq168vxowZY3CN1eE+GLsHAMSqVas0dbzhM2HpPnjLZ+LJJ5/U/N6vU6eO6NGjhyZQEsI7PgtCmL8P7vhZkAkhhHV9UURERETeg3OWiIiIiMxgsERERERkBoMlIiIiIjMYLBERERGZwWCJiIiIyAwGS0RERERmMFgiIiIiMoPBEhF5FZlMhh9++MFhx1+9ejVq165d5eM4up1EJB2DJSJyiJSUFPj6+qJv375Wv7dBgwZYunSp/RslwdixY/HAAw+45NxE5J4YLBGRQ3zxxReYNGkS9u3bhwsXLri6OURENmOwRER2V1RUhO+++w7jx4/HwIEDsXr1aoM6mzZtQvv27REYGIjIyEgMHToUANCtWzecP38eL774ImQyGWQyGQBg7ty5aN26tc4xli5digYNGmi+P3jwIHr16oXIyEjI5XJ07doVhw8ftuu1vfvuu2jZsiWCg4MRFxeH559/Hjdu3DCo98MPP6Bp06YIDAxEr169kJWVpfP65s2b0a5dOwQGBqJhw4aYN28eysvLjZ6ztLQUEydORExMDAIDA9GgQQMsWrTIrtdFRKYxWCIiu/v222/RrFkzNGvWDKNGjcKqVaugvQ3lli1bMHToUAwYMABHjhzBL7/8gvbt2wMANmzYgHr16uGNN95AdnY2srOzJZ+3sLAQY8aMwd69e5GWloYmTZqgf//+KCwstNu1+fj44IMPPkBGRga+/PJL7Ny5EzNmzNCpc/PmTSxYsABffvkl9u/fj4KCAgwfPlzz+v/+9z+MGjUKL7zwAk6cOIFPPvkEq1evxoIFC4ye84MPPsCmTZvw3Xff4dSpU1i7dq1OkEhEDmbbfsFERKZ17txZLF26VAghRFlZmYiMjBTbt2/XvJ6UlCQee+wxk++Pj48X7733nk7ZnDlzxF133aVT9t5774n4+HiTxykvLxchISFi8+bNmjIAYuPGjSbfM2bMGDFkyBCTr+v77rvvREREhOb7VatWCQAiLS1NU3by5EkBQPz2229CCCHuvfdesXDhQp3jrFmzRsTExBht56RJk0T37t1FZWWl5HYRkf2wZ4mI7OrUqVM4cOCApielRo0aePTRR/HFF19o6qSnp6NHjx52P3dubi6ee+45NG3aFHK5HHK5HDdu3LDrnKldu3ahV69eqFu3LkJCQvD4448jLy8PRUVFmjo1atTQ9JQBwB133IHatWvj5MmTAIBDhw7hjTfeQK1atTRf48aNQ3Z2Nm7evGlwzrFjxyI9PR3NmjXDCy+8gG3bttnteojIshqubgARVS8rV65EeXk56tatqykTQsDPzw/5+fkICwtDUFCQ1cf18fHRGcoDgLKyMp3vx44diytXrmDp0qWIj49HQEAAkpKSUFpaatvF6Dl//jz69++P5557Dm+++SbCw8Oxb98+PPXUUwZtUc+1MlZWWVmJefPmaeZpaQsMDDQoa9u2LTIzM/Hzzz9jx44dGDZsGHr27In//ve/drkuIjKPwRIR2U15eTm++uorvPPOO+jdu7fOaw899BC+/vprTJw4Ea1atcIvv/yCJ554wuhx/P39UVFRoVNWp04d5OTkQAihCTrS09N16uzduxfLly9H//79AQBZWVm4evWqna4O+P3331FeXo533nkHPj6qjvnvvvvOoF55eTl+//13dOjQAYCqt+369eu44447AKiCn1OnTqFx48aSzx0aGopHH30Ujz76KB5++GH07dsX165dQ3h4uB2ujIjMYbBERHbz008/IT8/H0899RTkcrnOaw8//DBWrlyJiRMnYs6cOejRowcaNWqE4cOHo7y8HD///LNmonSDBg2wZ88eDB8+HAEBAYiMjES3bt1w5coVLFmyBA8//DCSk5Px888/IzQ0VHOOxo0bY82aNWjfvj0KCgrw0ksv2dSLpVQqDQKx8PBwNGrUCOXl5fjwww8xaNAg7N+/Hx9//LHB+/38/DBp0iR88MEH8PPzw8SJE9GpUydN8PT6669j4MCBiIuLwyOPPAIfHx8cPXoUx44dw/z58w2O99577yEmJgatW7eGj48P/u///g8KhcIuyS+JSAIXz5kiompk4MCBon///kZfO3TokAAgDh06JIQQ4vvvvxetW7cW/v7+IjIyUgwdOlRTNzU1VbRq1UoEBAQI7V9TK1asEHFxcSI4OFg8/vjjYsGCBToTvA8fPizat28vAgICRJMmTcT//d//GUwWh4QJ3gAMvsaMGSOEEOLdd98VMTExIigoSPTp00d89dVXAoDIz88XQqgmeMvlcvH999+Lhg0bCn9/f9G9e3dx7tw5nfMkJyeLzp07i6CgIBEaGio6dOggPv30U6Pt/PTTT0Xr1q1FcHCwCA0NFT169BCHDx82eQ1EZF8yIfQmARARERGRBlfDEREREZnBYImIiIjIDAZLRERERGYwWCIiIiIyg8ESERERkRkMloiIiIjMYLBEREREZAaDJSIiIiIzGCwRERERmcFgiYiIiMgMBktEREREZjBYIiIiIjLj/wGGmczoQq0LyAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# estimator\n",
    "model3 = RandomForestRegressor()\n",
    "model3.fit(X_train,y_train)\n",
    "\n",
    "#prediction\n",
    "predictions = model3.predict(X_val)\n",
    "\n",
    "#metrics\n",
    "mse = mean_squared_error(y_val,predictions)\n",
    "print('mse:',mse)\n",
    "\n",
    "rmse = np.sqrt(mse)\n",
    "print('rmse',rmse)\n",
    "\n",
    "r2 = r2_score(y_val,predictions)\n",
    "print('r score:',r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_val,predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_val,predictions,1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_val,p(y_val),color='cyan')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For good measure, let's also try a *boosting* ensemble algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 104204.13033119454\n",
      "rmse 322.80664542601124\n",
      "r score: 0.795714342716494\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# estimator\n",
    "model4 = GradientBoostingRegressor()\n",
    "model4.fit(X_train,y_train)\n",
    "\n",
    "#prediction\n",
    "predictions = model4.predict(X_val)\n",
    "\n",
    "#metrics\n",
    "mse = mean_squared_error(y_val,predictions)\n",
    "print('mse:',mse)\n",
    "\n",
    "rmse = np.sqrt(mse)\n",
    "print('rmse',rmse)\n",
    "\n",
    "r2 = r2_score(y_val,predictions)\n",
    "print('r score:',r2)\n",
    "\n",
    "# Plot predicted vs actual\n",
    "plt.scatter(y_val,predictions)\n",
    "plt.xlabel('Actual Labels')\n",
    "plt.ylabel('Predicted Labels')\n",
    "plt.title('Daily Bike Share Predictions')\n",
    "\n",
    "# overlay the regression line\n",
    "z = np.polyfit(y_val,predictions,1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(y_val,p(y_val),color='cyan')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}