FYS-STK4155/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision trees, overarching aims\n",
    "\n",
    "\n",
    "We start here with the most basic algorithm, the so-called decision\n",
    "tree. With this basic algorithm we can in turn build more complex\n",
    "networks, spanning from homogeneous and heterogenous forests (bagging,\n",
    "random forests and more) to one of the most popular supervised\n",
    "algorithms nowadays, the extreme gradient boosting, or just\n",
    "XGBoost. But let us start with the simplest possible ingredient.\n",
    "\n",
    "Decision trees are supervised learning algorithms used for both,\n",
    "classification and regression tasks.\n",
    "\n",
    "\n",
    "The main idea of decision trees\n",
    "is to find those descriptive features which contain the most\n",
    "**information** regarding the target feature and then split the dataset\n",
    "along the values of these features such that the target feature values\n",
    "for the resulting underlying datasets are as pure as possible.\n",
    "\n",
    "The descriptive features which reproduce best the target/output features are normally  said\n",
    "to be the most informative ones. The process of finding the **most\n",
    "informative** feature is done until we accomplish a stopping criteria\n",
    "where we then finally end up in so called **leaf nodes**. \n",
    "\n",
    "## Basics of a tree\n",
    "\n",
    "A decision tree is typically divided into a **root node**, the **interior nodes**,\n",
    "and the final **leaf nodes** or just **leaves**. These entities are then connected by so-called **branches**.\n",
    "\n",
    "The leaf nodes\n",
    "contain the predictions we will make for new query instances presented\n",
    "to our trained model. This is possible since the model has \n",
    "learned the underlying structure of the training data and hence can,\n",
    "given some assumptions, make predictions about the target feature value\n",
    "(class) of unseen query instances.\n",
    "\n",
    "\n",
    "## General Features\n",
    "\n",
    "The overarching approach to decision trees is a top-down approach.\n",
    "\n",
    "* A leaf provides the classification of a given instance.\n",
    "\n",
    "* A node specifies a test of some attribute of the instance.\n",
    "\n",
    "* A branch corresponds to a possible values of an attribute.\n",
    "\n",
    "* An instance is classified by starting at the root node of the tree, testing the attribute specified by this node, then moving down the tree branch corresponding to the value of the attribute in the given example.\n",
    "\n",
    "This process is then repeated for the subtree rooted at the new\n",
    "node.\n",
    "\n",
    "\n",
    "\n",
    "In simplified terms, the process of training a decision tree and\n",
    "predicting the target features of query instances is as follows:\n",
    "\n",
    "1. Present a dataset containing of a number of training instances characterized by a number of descriptive features and a target feature\n",
    "\n",
    "2. Train the decision tree model by continuously splitting the target feature along the values of the descriptive features using a measure of information gain during the training process\n",
    "\n",
    "3. Grow the tree until we accomplish a stopping criteria create leaf nodes which represent the *predictions* we want to make for new query instances\n",
    "\n",
    "4. Show query instances to the tree and run down the tree until we arrive at leaf nodes\n",
    "\n",
    "Then we are essentially done!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd degree coefficients:\n",
      "zero power:  -4.655365720948307\n",
      "first power:  0.09332315952999948\n",
      "second power:  -0.0004298347114567659\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAHHCAYAAAC/R1LgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADI2klEQVR4nOydZ3gUVReA391N74UUAoTee0eRKtKliCBK/xSQIkqxgIIgIDZAEakqRQGxgCDSm/QivdeEEggJ6b3szvdjnGE32YSUTRaS+/LsQ2bmzr1nZmdnzpx7ikaSJAmBQCAQCASCYoDW2gIIBAKBQCAQFBZC8REIBAKBQFBsEIqPQCAQCASCYoNQfAQCgUAgEBQbhOIjEAgEAoGg2CAUH4FAIBAIBMUGofgIBAKBQCAoNgjFRyAQCAQCQbFBKD4CgUAgEAiKDULxEQiKEK1bt6Z169bWFsMiLF++HI1GQ3BwcK73HTx4MOXKlbO4TEWVcuXKMXjwYGuLIRAUCkLxEQisiPJwVz4ODg5UqVKF0aNH8+DBA2uLV+Rp3bq1yfl3dHSkTp06fP311xgMBmuLJxAICgAbawsgEAjgk08+oXz58iQnJ3PgwAEWLlzI5s2bOX/+PE5OTtYWzyoMGDCAvn37Ym9vX6DjlC5dmlmzZgHw8OFDVq9ezdixYwkPD2fmzJkFOvaTwpUrV9BqxXuwoHggFB+B4AmgU6dONGrUCIA33ngDb29v5syZw4YNG3j11VetLJ110Ol06HS6Ah/H3d2d/v37q8tvvvkm1apV49tvv+WTTz4pFBkUkpOTsbOzK3QlpKCVS4HgSUKo+ALBE0jbtm0BCAoKAiA9PZ3p06dTsWJF7O3tKVeuHJMmTSIlJSXLPuLj43F2dubtt9/OtO3u3bvodDrV0qFMuR08eJBx48bh4+ODs7MzPXv2JDw8PNP+CxYsoGbNmtjb2xMQEMCoUaOIjo42adO6dWtq1arF2bNnadWqFU5OTlSqVInff/8dgH/++YemTZvi6OhI1apV2blzp8n+5nx8NmzYQJcuXQgICMDe3p6KFSsyffp09Hr9409qDnFwcKBx48bExcURFhZmsu3nn3+mYcOGODo64uXlRd++fblz506mPr777jsqVKiAo6MjTZo0Yf/+/Zn8r/bu3YtGo+GXX37ho48+olSpUjg5OREbGwvA0aNH6dixI+7u7jg5OdGqVSsOHjxoMk5cXBzvvPMO5cqVw97eHl9fX1544QVOnjyptrl27Rq9evXC398fBwcHSpcuTd++fYmJiVHbmPPxuXnzJr1798bLywsnJyeaNWvG33//bdJGOYZff/2VmTNnUrp0aRwcHHj++ee5fv16rs67QFBYCMVHIHgCuXHjBgDe3t6AbAWaMmUKDRo0YO7cubRq1YpZs2bRt2/fLPtwcXGhZ8+erF27NpNisGbNGiRJol+/fibr33rrLc6cOcPHH3/MiBEj+Ouvvxg9erRJm6lTpzJq1CgCAgKYPXs2vXr1YvHixbRv3560tDSTtlFRUXTt2pWmTZvyxRdfYG9vT9++fVm7di19+/alc+fOfPbZZyQkJPDyyy8TFxeX7XlZvnw5Li4ujBs3jm+++YaGDRsyZcoUPvjgg+xPaC4JDg5Go9Hg4eGhrps5cyYDBw6kcuXKzJkzh3feeYddu3bRsmVLE6Vv4cKFjB49mtKlS/PFF1/QokULevTowd27d82ONX36dP7++28mTJjAp59+ip2dHbt376Zly5bExsby8ccf8+mnnxIdHU3btm05duyYuu+bb77JwoUL6dWrFwsWLGDChAk4Ojpy6dIlAFJTU+nQoQNHjhzhrbfe4rvvvmPYsGHcvHkzk6JqzIMHD3j22WfZtm0bI0eOZObMmSQnJ9OtWzfWr1+fqf1nn33G+vXrmTBhAhMnTuTIkSOZri2B4IlBEggEVmPZsmUSIO3cuVMKDw+X7ty5I/3yyy+St7e35OjoKN29e1c6ffq0BEhvvPGGyb4TJkyQAGn37t3qulatWkmtWrVSl7dt2yYB0pYtW0z2rVOnjkk7RY527dpJBoNBXT927FhJp9NJ0dHRkiRJUlhYmGRnZye1b99e0uv1arv58+dLgPTjjz+ayAJIq1evVtddvnxZAiStVisdOXIkk5zLli3LJFNQUJC6LjExMdM5HD58uOTk5CQlJyer6wYNGiSVLVs2U9uMtGrVSqpWrZoUHh4uhYeHS5cvX5beffddCZC6dOmitgsODpZ0Op00c+ZMk/3PnTsn2djYqOtTUlIkb29vqXHjxlJaWprabvny5RJgcs737NkjAVKFChVMjstgMEiVK1eWOnToYPJdJCYmSuXLl5deeOEFdZ27u7s0atSoLI/v1KlTEiD99ttv2Z6HsmXLSoMGDVKX33nnHQmQ9u/fr66Li4uTypcvL5UrV0797pVjqF69upSSkqK2/eabbyRAOnfuXLbjCgTWQFh8BIIngHbt2uHj40OZMmXo27cvLi4urF+/nlKlSrF582YAxo0bZ7LP+PHjATJNP2TsNyAggFWrVqnrzp8/z9mzZ038WhSGDRuGRqNRl1u0aIFer+fWrVsA7Ny5k9TUVN555x0TP5ShQ4fi5uaWSRYXFxcTq1TVqlXx8PCgevXqNG3aVF2v/H3z5s0sjwXA0dFR/TsuLo6HDx/SokULEhMTuXz5crb7ZsXly5fx8fHBx8eHatWq8eWXX9KtWzeWL1+utlm3bh0Gg4E+ffrw8OFD9ePv70/lypXZs2cPAP/++y8REREMHToUG5tHLpT9+vXD09PT7PiDBg0yOa7Tp09z7do1XnvtNSIiItSxEhISeP7559m3b58acebh4cHRo0e5d++e2b7d3d0B2LZtG4mJiTk+J5s3b6ZJkyY899xz6joXFxeGDRtGcHAwFy9eNGk/ZMgQ7Ozs1OUWLVoAj/8+BQJrIJybBYIngO+++44qVapgY2ODn58fVatWVRWLW7duodVqqVSpksk+/v7+eHh4qEqJObRaLf369WPhwoUkJibi5OTEqlWrcHBwoHfv3pnaBwYGmiwrD+uoqChVFpAVGGPs7OyoUKFCJllKly5tokiB/DAuU6ZMpnXG42TFhQsX+Oijj9i9e7fqC6Ng7LOSG8qVK8fSpUsxGAzcuHGDmTNnEh4ejoODg9rm2rVrSJJE5cqVzfZha2sLPDo/Gb8rGxubLPMKlS9f3mT52rVrgKwQZUVMTAyenp588cUXDBo0iDJlytCwYUM6d+7MwIEDqVChgtr3uHHjmDNnDqtWraJFixZ069aN/v37q+fcHLdu3TJRTBWqV6+ubq9Vq5a6/nHXjUDwJCEUH4HgCaBJkyZqVFdWZFQgcsrAgQP58ssv+fPPP3n11VdZvXo1Xbt2NfvgyyqCSZKkPI2dVX95GSc6OppWrVrh5ubGJ598QsWKFXFwcODkyZO8//77ec674+zsTLt27dTl5s2b06BBAyZNmsS8efMAMBgMaDQatmzZYlZ2FxeXPI0NplYsZSyAL7/8knr16pndRxmvT58+tGjRgvXr17N9+3a+/PJLPv/8c9atW0enTp0AmD17NoMHD2bDhg1s376dMWPGMGvWLI4cOULp0qXzLLcxlr5uBIKCRCg+AsETTtmyZTEYDFy7dk194wbZATU6OpqyZctmu3+tWrWoX78+q1atonTp0ty+fZtvv/02z7KAnPdFsSqA7EQbFBRkokBYmr179xIREcG6deto2bKlul6JfLMUderUoX///ixevJgJEyYQGBhIxYoVkSSJ8uXLU6VKlSz3Vc7P9evXadOmjbo+PT2d4OBg6tSp89jxK1asCICbm1uOzmfJkiUZOXIkI0eOJCwsjAYNGjBz5kxV8QGoXbs2tWvX5qOPPuLQoUM0b96cRYsWMWPGjCyP48qVK5nWK9OJj7vmBIInGeHjIxA84XTu3BmAr7/+2mT9nDlzAOjSpctj+xgwYADbt2/n66+/xtvb2+ShmBvatWuHnZ0d8+bNM3mb/+GHH4iJicmRLHlFsSoYj5uamsqCBQssPtZ7771HWlqaeo5feukldDod06ZNy2TFkCSJiIgIABo1aoS3tzdLly4lPT1dbbNq1aocT/s0bNiQihUr8tVXXxEfH59pu5JeQK/XZ5re8/X1JSAgQE1zEBsbayIHyEqQVqvNNhVC586dOXbsGIcPH1bXJSQksGTJEsqVK0eNGjVydCwCwZOIsPgIBE84devWZdCgQSxZskSd7jl27BgrVqygR48eJpaFrHjttdd47733WL9+PSNGjFB9UnKLj48PEydOZNq0aXTs2JFu3bpx5coVFixYQOPGjc06TFuKZ599Fk9PTwYNGsSYMWPQaDT89NNPBTKdUqNGDTp37sz333/P5MmTqVixIjNmzGDixIkEBwfTo0cPXF1dCQoKYv369QwbNowJEyZgZ2fH1KlTeeutt2jbti19+vQhODiY5cuXU7FixRxNV2q1Wr7//ns6depEzZo1GTJkCKVKlSIkJIQ9e/bg5ubGX3/9RVxcHKVLl+bll1+mbt26uLi4sHPnTo4fP87s2bMB2L17N6NHj6Z3795UqVKF9PR0fvrpJ3Q6Hb169cpShg8++IA1a9bQqVMnxowZg5eXFytWrCAoKIg//vhDZHkWPNUIxUcgeAr4/vvvqVChAsuXL2f9+vX4+/szceJEPv744xzt7+fnR/v27dm8eTMDBgzIlyxTp07Fx8eH+fPnM3bsWLy8vBg2bBiffvppnhWqnODt7c2mTZsYP348H330EZ6envTv35/nn3+eDh06WHy8d999l7///ptvv/2WqVOn8sEHH1ClShXmzp3LtGnTAChTpgzt27enW7du6n6jR49GkiRmz57NhAkTqFu3Lhs3bmTMmDEmDtPZ0bp1aw4fPsz06dOZP38+8fHx+Pv707RpU4YPHw6Ak5MTI0eOZPv27WrUWaVKlViwYAEjRowAZKW5Q4cO/PXXX4SEhODk5ETdunXZsmULzZo1y3J8Pz8/Dh06xPvvv8+3335LcnIyderU4a+//ipQq55AUBhoJOF9JhAUC3r27Mm5c+dERl0rYDAY8PHx4aWXXmLp0qXWFkcgKNYIe6VAUAy4f/8+f//9d76tPYLHk5ycnGn6beXKlURGRpqUrBAIBNZBWHwEgiJMUFAQBw8e5Pvvv+f48ePcuHEDf39/a4tVpNm7dy9jx46ld+/eeHt7c/LkSX744QeqV6/OiRMnTBL9CQSCwkf4+AgERZh//vmHIUOGEBgYyIoVK4TSUwiUK1eOMmXKMG/ePCIjI/Hy8mLgwIF89tlnQukRCJ4AhMVHIBAIBAJBsUH4+AgEAoFAICg2CMVHIBAIBAJBsUH4+GTAYDBw7949XF1d81wbSSAQCAQCQeEiSRJxcXEEBARkm2RTKD4ZuHfvXqbK0QKBQCAQCJ4O7ty5k20BXqH4ZMDV1RWQT5ybm5uVpREIBAKBQJATYmNjKVOmjPoczwqh+GRAmd5yc3MTio9AIBAIBE8Zj3NTEc7NAoFAIBAIig1C8REIBAKBQFBsEIqPQCAQCASCYoPw8REIBIIiil6vJy0tzdpiCAQWwdbWFp1Ol+9+hOIjEAgERQxJkggNDSU6OtraoggEFsXDwwN/f/985dkTio9AIBAUMRSlx9fXFycnJ5GMVfDUI0kSiYmJhIWFAVCyZMk89yUUH4FAIChC6PV6Venx9va2tjgCgcVwdHQEICwsDF9f3zxPewnnZoFAIChCKD49Tk5OVpZEILA8ynWdH981ofgIBAJBEURMbwmKIpa4roXiIxAIBAKBoNhQ5BSfkJAQ+vfvj7e3N46OjtSuXZt///3X2mIJBAKB4AlHo9Hw559/WlsMi7B37140Go0a2bd8+XI8PDysKtOTQpFSfKKiomjevDm2trZs2bKFixcvMnv2bDw9Pa0tmkAgEAgew+DBg9FoNGg0GmxtbSlfvjzvvfceycnJ1hatwFi0aBGurq6kp6er6+Lj47G1taV169YmbRVl5saNG4UiW+vWrdXvw97enlKlSvHiiy+ybt26XPc1depU6tWrZ3kh80CRUnw+//xzypQpw7Jly2jSpAnly5enffv2VKxY0dqiCQQFSmJaorVFEAgsQseOHbl//z43b95k7ty5LF68mI8//tjaYhUYbdq0IT4+3mRmYv/+/fj7+3P06FETpW/Pnj0EBgYW6jNt6NCh3L9/nxs3bvDHH39Qo0YN+vbty7BhwwpNBktTpBSfjRs30qhRI3r37o2vry/169dn6dKl2e6TkpJCbGysyUcgeJrYen0rbrPc+Pbot9YWRSDIN/b29vj7+1OmTBl69OhBu3bt2LFjh7o9IiKCV199lVKlSuHk5ETt2rVZs2aNSR+tW7dmzJgxvPfee3h5eeHv78/UqVNN2ly7do2WLVvi4OBAjRo1TMZQOHfuHG3btsXR0RFvb2+GDRtGfHy8un3w4MH06NGDTz/9FD8/Pzw8PPjkk09IT0/n3XffxcvLi9KlS7Ns2bIsj7dq1aqULFmSvXv3quv27t1L9+7dKV++PEeOHDFZ36ZNGwB++uknGjVqhKurK/7+/rz22mtqjpucEB4eTqNGjejZsycpKSlZtnNycsLf35/SpUvTrFkzPv/8cxYvXszSpUvZuXOn2u7999+nSpUqODk5UaFCBSZPnqxGXi1fvpxp06Zx5swZ1YK0fPlyAObMmUPt2rVxdnamTJkyjBw50uQcFwRFSvG5efMmCxcupHLlymzbto0RI0YwZswYVqxYkeU+s2bNwt3dXf2UKVOmECUWCPLP/lv70Ut6/rn1j7VFETyhSJJEQmqCVT6SJOVZ7vPnz3Po0CHs7OzUdcnJyTRs2JC///6b8+fPM2zYMAYMGMCxY8dM9l2xYgXOzs4cPXqUL774gk8++URVbgwGAy+99BJ2dnYcPXqURYsW8f7775vsn5CQQIcOHfD09OT48eP89ttv7Ny5k9GjR5u02717N/fu3WPfvn3MmTOHjz/+mK5du+Lp6cnRo0d58803GT58OHfv3s3yONu0acOePXvU5T179tC6dWtatWqlrk9KSuLo0aOq4pOWlsb06dM5c+YMf/75J8HBwQwePDhH5/XOnTu0aNGCWrVq8fvvv2Nvb5+j/RQGDRqEp6enyZSXq6sry5cv5+LFi3zzzTcsXbqUuXPnAvDKK68wfvx4atasyf3797l//z6vvPIKAFqtlnnz5nHhwgVWrFjB7t27ee+993IlT66RihC2trbSM888Y7Lurbfekpo1a5blPsnJyVJMTIz6uXPnjgRIMTExBS2uQGAR3tjwhsRUpBY/trC2KIIngKSkJOnixYtSUlKSui4+JV5iKlb5xKfE51j2QYMGSTqdTnJ2dpbs7e0lQNJqtdLvv/+e7X5dunSRxo8fry63atVKeu6550zaNG7cWHr//fclSZKkbdu2STY2NlJISIi6fcuWLRIgrV+/XpIkSVqyZInk6ekpxcc/kv/vv/+WtFqtFBoaqspbtmxZSa/Xq22qVq0qtWjx6LeYnp4uOTs7S2vWrMlS/qVLl0rOzs5SWlqaFBsbK9nY2EhhYWHS6tWrpZYtW0qSJEm7du2SAOnWrVtm+zh+/LgESHFxcZIkSdKePXskQIqKipIkSZKWLVsmubu7S5cvX5bKlCkjjRkzRjIYDFnKpJzHt99+2+y2pk2bSp06dcpy3y+//FJq2LChuvzxxx9LdevWzXY8SZKk3377TfL29s5yu7nrWyEmJiZHz+8ilbm5ZMmS1KhRw2Rd9erV+eOPP7Lcx97ePtfarkDwJBGeGG7yv0DwNNOmTRsWLlxIQkICc+fOxcbGhl69eqnb9Xo9n376Kb/++ishISGkpqaSkpKSKWFjnTp1TJZLliypTgVdunSJMmXKEBAQoG5/5plnTNpfunSJunXr4uzsrK5r3rw5BoOBK1eu4OfnB0DNmjXRah9Nnvj5+VGrVi11WafT4e3tne00VOvWrUlISOD48eNERUVRpUoVfHx8aNWqFUOGDCE5OZm9e/dSoUIFAgMDAThx4gRTp07lzJkzREVFYTAYALh9+3am56BCUlISLVq04LXXXuPrr7/OUp6cIEmSSU6dtWvXMm/ePG7cuEF8fDzp6em4ubk9tp+dO3cya9YsLl++TGxsLOnp6SQnJ5OYmFhgSTiLlOLTvHlzrly5YrLu6tWrlC1b1koSCQQFT1hCmMn/AkFGnGydiJ9YsH4T2Y2dG5ydnalUqRIAP/74I3Xr1uWHH37g9ddfB+DLL7/km2++4euvv1Z9Q9555x1SU1NN+rG1tTVZ1mg0qnJgScyNk9uxK1WqROnSpdmzZw9RUVG0atUKgICAAMqUKcOhQ4fYs2cPbdu2BR5Nw3Xo0IFVq1bh4+PD7du36dChQ6bzYIy9vT3t2rVj06ZNvPvuu5QqVSpPx6zX67l27RqNGzcG4PDhw/Tr149p06bRoUMH3N3d+eWXX5g9e3a2/QQHB9O1a1dGjBjBzJkz8fLy4sCBA7z++uukpqYKxScnjB07lmeffZZPP/2UPn36cOzYMZYsWcKSJUusLZpAUGAolp7IpEjSDenYaIvUz1pgATQaDc52zo9v+ISh1WqZNGkS48aN47XXXsPR0ZGDBw/SvXt3+vfvD8j+OlevXs3SymGO6tWrc+fOHe7fv68WuzR2IlbaLF++nISEBNXqc/DgQbRaLVWrVrXQET6iTZs27N27l6ioKN599111fcuWLdmyZQvHjh1jxIgRAFy+fJmIiAg+++wz1S81J/nqtFotP/30E6+99po6nrHVK6esWLGCqKgo1RJ36NAhypYty4cffqi2uXXrlsk+dnZ26PV6k3UnTpzAYDAwe/Zs1Wr266+/5lqe3FKknJsbN27M+vXrWbNmDbVq1WL69Ol8/fXX9OvXz9qiCQQFhrGl52HiQytKIhBYnt69e6PT6fjuu+8AqFy5Mjt27ODQoUNcunSJ4cOH8+DBg1z12a5dO6pUqcKgQYM4c+YM+/fvN3loA/Tr1w8HBwcGDRrE+fPn2bNnD2+99RYDBgxQp7ksSZs2bThw4ACnT59WLT4ArVq1YvHixaSmpqqOzYGBgdjZ2fHtt99y8+ZNNm7cyPTp03M0jk6nY9WqVdStW5e2bdsSGhqabfvExERCQ0O5e/cuR44c4f333+fNN99kxIgRqjyVK1fm9u3b/PLLL9y4cYN58+axfv16k37KlStHUFAQp0+f5uHDh6SkpFCpUiXS0tLU4/jpp59YtGhRbk5bnihSig9A165dOXfuHMnJyVy6dImhQ4daWySBoMBISU8hNuVRCobwBOHnIyha2NjYMHr0aL744gsSEhL46KOPaNCgAR06dKB169b4+/vTo0ePXPWp1WpZv349SUlJNGnShDfeeIOZM2eatHFycmLbtm1ERkbSuHFjXn75ZZ5//nnmz59vwaN7RJs2bUhKSqJSpUomilWrVq2Ii4tTw94BfHx8WL58Ob/99hs1atTgs88+46uvvsrxWDY2NqxZs4aaNWvStm3bbP2Pli5dSsmSJalYsSIvvfQSFy9eZO3atSxYsEBt061bN8aOHcvo0aOpV68ehw4dYvLkySb99OrVi44dO9KmTRt8fHxYs2YNdevWZc6cOXz++efUqlWLVatWMWvWrBwfR17RSFI+Yg2LILGxsbi7uxMTE5MjxyyBwJrcjb1LmbmPUjDsHLCT5ys8b0WJBNYmOTmZoKAgypcvj4ODg7XFEQgsSnbXd06f30XO4iMQFCcyOjQLB2eBQCDIHqH4CARPMRmntkRIu0AgEGSPUHwEgqcYYfERCASC3CEUH4HgKSajhUc4NwsEAkH2CMVHIHiKUSw8Djayk19YorD4CAQCQXYIxUcgeIpRLDw1fGqYLAsEAoHAPELxEQieYhQLT02fmvKy8PERCASCbBGKj0DwFKNYeBTFR0R1CQQCQfYIxUcgeIpRLDw1fWXFJzo5mlR91kUKBQKBoLgjFB+B4ClGUXyqeFdBp9EBws9HIMjI3r170Wg0REdHW1uUJw6NRsOff/5pbTEKFaH4CARPKYlpiSSkJQDg5+xHCacSgJjuEjy9zJo1i8aNG+Pq6oqvry89evTgypUrhTJ2uXLl0Gg0aDQaHB0dKVeuHH369GH37t2FMn5BYnxsyqd06dIA3L9/n06dOgEQHByMRqPh9OnTVpS24BGKj0DwlKJYdux0drjZu+Hr7AsIB2fB08s///zDqFGjOHLkCDt27CAtLY327duTkJBQKON/8skn3L9/nytXrrBy5Uo8PDxo165dpgKmBUFqasFOUSvHpnxOnToFgL+/P/b29gU69pOGUHwEgkImKS2JNze9ydbrW81un/7PdOYfkytA3465zf82/I+zD86atJm1fxb91/cHwMfJB41Goyo+YqpL8LSydetWBg8eTM2aNalbty7Lly/n9u3bnDhxQm2j0Wj4/vvv6dmzJ05OTlSuXJmNGzea9LN582aqVKmCo6Mjbdq0ITg4OEfju7q64u/vT2BgIC1btmTJkiVMnjyZKVOmmFiezp8/T6dOnXBxccHPz48BAwbw8OFDdXtcXBz9+vXD2dmZkiVLMnfuXFq3bs0777yjtilXrhzTp09n4MCBuLm5MWzYMAAOHDhAixYtcHR0pEyZMowZM8ZE8UtJSWHChAmUKlUKZ2dnmjZtyt69e3N8bMrHx8dHPZ/KVFf58uUBqF+/PhqNhtatW+fovD1tCMVHIChktt3YxuITi5myZ0qmbffi7jFl7xTe2foOafo0VpxewbLTy5h3dJ7aJjwhnEm7J3Hg9gEAqpaoCoCPs3wjExYfQSYkCRISrPORpDyLHRMTA4CXl5fJ+mnTptGnTx/Onj1L586d6devH5GRkQDcuXOHl156iRdffJHTp0/zxhtv8MEHH+RZhrfffhtJktiwYQMA0dHRtG3blvr16/Pvv/+ydetWHjx4QJ8+fdR9xo0bx8GDB9m4cSM7duxg//79nDx5MlPfX331FXXr1uXUqVNMnjyZGzdu0LFjR3r16sXZs2dZu3YtBw4cYPTo0eo+o0eP5vDhw/zyyy+cPXuW3r1707FjR65du5bnY1Q4duwYADt37uT+/fusW7cu330+kUgCE2JiYiRAiomJsbYogiLKgmMLJKYilZlTJtO2E/dOSExFYirSvdh70ohNIySmInVZ1UVtczb0rMRUJLdZbtLK0yul+3H3JUmSpDGbx0hMRZq4c2KhHYvgySMpKUm6ePGilJSU9GhlfLwkySpI4X/i4/N0HHq9XurSpYvUvHlzk/WA9NFHHxkdWrwESFu2bJEkSZImTpwo1ahRw2Sf999/XwKkqKioLMcrW7asNHfuXLPb/Pz8pBEjRkiSJEnTp0+X2rdvb7L9zp07EiBduXJFio2NlWxtbaXffvtN3R4dHS05OTlJb7/9tsl4PXr0MOnn9ddfl4YNG2aybv/+/ZJWq5WSkpKkW7duSTqdTgoJCTFp8/zzz0sTJ2b9uy9btqxkZ2cnOTs7q59vvvlGkiT5fK5fv16SJEkKCgqSAOnUqVNZ9mVtzF7f/5HT57eN9VQugaB4ojgfhyeGI0kSGo3m0TajaarwxHCTthn3L+1WmgF1B6jrhcVHUJQYNWoU58+f58CBA5m21alTR/3b2dkZNzc3wsLk6/7SpUs0bdrUpP0zzzyTL1mMf6dnzpxhz549uLi4ZGp348YNkpKSSEtLo0mTJup6d3d3qlatmql9o0aNTJbPnDnD2bNnWbVqlcnYBoOBoKAgbt68iV6vp0qVKib7paSk4O3tne0xvPvuuwwePFhdLlGiRLbtizJC8REIChlFMUlOTyY+NR5Xe9dM25S/leWM60H27TFG9fERUV2CjDg5QXy89cbOJaNHj2bTpk3s27dPjT4yxtbW1mRZo9FgMBjyLGJ2REREEB4ervq/xMfH8+KLL/L5559naluyZEmuX7+e476dnZ1NluPj4xk+fDhjxozJ1DYwMJCzZ8+i0+k4ceIEOp3OZLs5RcyYEiVKUKlSpRzLVpQRio9AUMhkVGKMFR9jpeVxio+i6CiIqC5Blmg0kOEh+yQiSRJvvfUW69evZ+/evaqykRuqV6+eydn5yJEjeZbpm2++QavV0qNHDwAaNGjAH3/8Qbly5bCxyfwIrVChAra2thw/fpzAwEBA9lW6evUqLVu2zHasBg0acPHixSwVlPr166PX6wkLC6NFixZ5PqassLOzA0Cv11u87ycJ4dwsEBQy5qatFIyVlvCEcHXqKzEtkYTUBHU9ZLb4KMtC8RE8rYwaNYqff/6Z1atX4+rqSmhoKKGhoSQlJeW4jzfffJNr167x7rvvcuXKFVavXs3y5ctztG9cXByhoaHcuXOHffv2MWzYMGbMmMHMmTNVZWTUqFFERkby6quvcvz4cW7cuMG2bdsYMmQIer0eV1dXBg0axLvvvsuePXu4cOECr7/+Olqt1mRa2xzvv/8+hw4dYvTo0Zw+fZpr166xYcMG1bm5SpUq9OvXj4EDB7Ju3TqCgoI4duwYs2bN4u+//87xOcoKX19fHB0dVYdtxbm8qCEUH4GgkDFnvTG3fD/+PhFJEeqyoiQ9zuIjwtkFTysLFy4kJiaG1q1bU7JkSfWzdu3aHPcRGBjIH3/8wZ9//kndunVZtGgRn376aY72nTJlCiVLlqRSpUoMGDCAmJgYdu3axfvvv6+2CQgI4ODBg+j1etq3b0/t2rV555138PDwQKuVH6lz5szhmWeeoWvXrrRr147mzZtTvXp1HBwcsh2/Tp06/PPPP1y9epUWLVpQv359pkyZQkBAgNpm2bJlDBw4kPHjx1O1alV69OhhYl3KDzY2NsybN4/FixcTEBBA9+7d893nk4iY6hIIChkTB+YMSoqxBejSw0uZ9ivnUU5tozgzKyjLcalxJKcn42CT/U1WIHjSkHIQ+m6uTcZSFF27dqVr164m64YMGZJtvznN9QNQuXLlbEO9XV1dTRyUExISmDZtmpqrJ7vxGjduzPbt27Ps29bWlmnTpjFt2rQcy5vdsWU8n2+88QZvvPFGjvt+GhGKj0BQiOgNeh4mPkp0lp3F50LYBbPbsrL4uNu7Y6u1Jc2QRnhCOGXcy1hUdoFAkDNOnTrF5cuXadKkCTExMXzyyScARdaC8rQhproEgkIkMikSiUdvWBl9fIwtQDejbppuyxDanlHx0Wg0IqRdIHhCUJITtmvXjoSEBPbv31+sQ8ifJITFRyAoRLKz8GRcNlaQjLdlFc4OsjJ0L+6eCGkXCKxI/fr1TcpsCJ4shMVHIChEslN0jKutmyM8IZxUfSrRydFAZouP8Tph8REIBALzCMVHIChEMk1tJWbt6JyRsMQw1T9Ip9Hh6eiZqY0IaRcIBILsEYqPQFCIKApJOY9yJsvwSAkKcA3ARvtoFtq4rdK+hFMJtJrMP18R0i4QCATZIxQfgaAQURSSmj411WUlnFRRavyc/Sjh9MgJ0ritmrzQObN/DxhZfBKFxUcgEAjMIRQfgaAQUZSbGj41AEgzpBGTImdHVZQaX2dfE/8dRfExtviY8+8xXi8sPgKBQGAeofgIBIWIMp0V6B6Iq51co8tcfh7jiK2avjXVfbOL6AJRoV0gEAgeR5FWfD777DM0Gg3vvPOOtUURCIAMys1/SopinTFWasxZfJLTkwmKDlL3N4eo0C4QmGfw4MFqoVFB9kydOpV69epZW4wCo8gqPsePH2fx4sXUqVPH2qIIBCrGyQczhp6b2wZQ3rM8TrZOAFwIv6C2MYcIZxc8zezbt48XX3yRgIAANBoNf/75Z6Y2kiSpNbUcHR1p164d165dU7cHBwej0Wg4ffp0vuVZvnw5Go0GjUaDTqfD09OTpk2b8sknnzz1BTyNj8348/333zNhwgR27dqlti1qSmORVHzi4+Pp168fS5cuxdMzc8ivQGAtjK06ynRVxuKjPs6Ptuk0OjwcPNTl82Hn1f3Noaw3ruYuEDwtJCQkULduXb777rss23zxxRfMmzePRYsWcfToUZydnenQoQPJyckFIpObmxv379/n7t27HDp0iGHDhrFy5Urq1avHvXv3CmRMBb1ej8FgKLD+lWMz/vTr1w8XFxe8vb0LbFxrUyQVn1GjRtGlSxfatWtnbVEEApU0fRqRSZFAzi0+Ps4+aDXaTG2zsvi42LmoxUnFdJfgaaNTp07MmDGDnj17mt0uSRJff/01H330Ed27d6dOnTqsXLmSe/fuqdah8uXLA3L2ZI1GQ+vWrU36+OqrryhZsiTe3t6MGjWKtLS0bGXSaDT4+/tTsmRJqlevzuuvv86hQ4eIj4/nvffeU9sZDAZmzZpF+fLlcXR0pG7duvz+++8mfW3cuJHKlSvj4OBAmzZtWLFiBRqNRi2yunz5cjw8PNi4cSM1atTA3t6e27dvk5KSwoQJEyhVqhTOzs40bdqUvXv3mvR94MABWrRogaOjI2XKlGHMmDEkJGT/8qMcm/HH0dHRZKpr6tSprFixgg0bNqhWoYxjP20UuZIVv/zyCydPnuT48eM5ap+SkkJKSoq6HBsbW1CiCYo5EUkRAGjQ4OXo9cjiY8bHxyDJb3mKguPn4mfSV8ZlBY1Gg4+TD3di7xCWEKbmABIUbyRJIjEx0SpjOzk5odFoLNJXUFAQoaGhJi+17u7uNG3alMOHD9O3b1+OHTtGkyZN2LlzJzVr1sTOzk5tu2fPHkqWLMmePXu4fv06r7zyCvXq1WPo0KG5ksPX15d+/frx448/otfr0el0zJo1i59//plFixZRuXJl9u3bR//+/fHx8aFVq1YEBQXx8ssv8/bbb/PGG29w6tQpJkyYkKnvxMREPv/8c77//nu8vb3x9fVl9OjRXLx4kV9++YWAgADWr19Px44dOXfuHJUrV+bGjRt07NiRGTNm8OOPPxIeHs7o0aMZPXo0y5Yty/sJByZMmMClS5eIjY1V+/Ly8spXn9amSCk+d+7c4e2332bHjh04ODjkaJ9Zs2Yxbdq0ApZMIMAk+aBOq3tkxUkMQ5Ikk3D2Wr616FurLz2ryW++Y5uNJTYlllR9KlW8q9CkVJMsx/F19uVO7B0R0i5QSUxMxMXFxSpjx8fH4+zsbJG+QkNDAfDzy/Ai4OenbvPxkV8ovL298ff3N2nn6enJ/Pnz0el0VKtWjS5durBr165cKz4A1apVIy4ujoiICNzd3fn000/ZuXMnzzzzDAAVKlTgwIEDLF68mFatWrF48WKqVq3Kl19+CUDVqlU5f/48M2fONOk3LS2NBQsWULduXQBu377NsmXLuH37NgEBAYCsjGzdupVly5bx6aefMmvWLPr166cG8lSuXJl58+bRqlUrFi5cmOXzMCYmxuS6cHFxUc+j8TpHR0dSUlIync+nlSKl+Jw4cYKwsDAaNGigrtPr9ezbt4/58+eTkpKCTqcz2WfixImMGzdOXY6NjaVMmTKFJrOg+JAx+aDx9FVCWgJJ6Unqemc7Z9b0WqPu27Z8W9qWb5ujcYSDs0Bgnpo1a5o8A0qWLMm5c+fy1JeSeFSj0XD9+nUSExN54YUXTNqkpqZSv359AK5cuULjxo1NtjdpkvkFxs7OziQo59y5c+j1eqpUqWLSLiUlRfXDOXPmDGfPnmXVqlUm8hkMBoKCgqhevbrZY3B1deXkyZPqslZbJL1fMlGkFJ/nn38+00U8ZMgQqlWrxvvvv59J6QGwt7fH3t6+sEQUFGMy+ucYh7MrSpGjjSPOdvl7O1b7FT4+gv9wcnIiPj7eamNbCsXi8ODBA0qWLKmuf/DgQY7Cr21tbU2WNRpNnp2HL126hJubG97e3ty8eROAv//+m1KlSpm0y+3zxdHR0WRqMD4+Hp1Ox4kTJzI9wxRrTXx8PMOHD2fMmDGZ+gsMDMxyLK1WS6VKlXIlX1GgSCk+rq6u1KpVy2Sds7Mz3t7emdYLBIWNsfOy8f85ycicG3ydhMVHYIpGo7HYdJM1KV++PP7+/uzatUtVdGJjYzl69CgjRowAUH169Hp9gckRFhbG6tWr6dGjB1qt1sQRuVWrVmb3qVq1Kps3bzZZlxNf1Pr166PX6wkLC6NFixZm2zRo0ICLFy8WmBJjZ2dXoOezsClSio9A8CSTMeuy8v/DxIc8SHggr8uiBlduEBYfwdNKfHw8169fV5eDgoI4ffo0Xl5eBAYGqglpZ8yYQeXKlSlfvjyTJ08mICBAzTPj6+uLo6MjW7dupXTp0jg4OODu7p5nmSRJIjQ0FEmSiI6O5vDhw3z66ae4u7vz2WefAfJL94QJExg7diwGg4HnnnuOmJgYDh48iJubG4MGDWL48OHMmTOH999/n9dff53Tp0+zfPlygGydv6tUqUK/fv0YOHAgs2fPpn79+oSHh7Nr1y7q1KlDly5deP/992nWrBmjR4/mjTfewNnZmYsXL7Jjxw7mz5+f52NXKFeuHNu2bePKlSt4e3vj7u6eyXr2NFHkJ/T27t3L119/bW0xBIIsp7r0kp4rD6+YbMsPwsdH8LTy77//Ur9+fdUvZty4cdSvX58pU6aobd577z3eeusthg0bRuPGjYmPj2fr1q2qA6+NjQ3z5s1j8eLFBAQE0L1793zJFBsbS8mSJSlVqhTPPPMMixcvZtCgQZw6dcpkum369OlMnjyZWbNmUb16dTp27Mjff/+thteXL1+e33//nXXr1lGnTh0WLlzIhx9+CDx+OmzZsmUMHDiQ8ePHU7VqVXr06MHx48fVaaw6derwzz//cPXqVVq0aKGeM8UZOr8MHTqUqlWr0qhRI3x8fDh48KBF+rUWGknx0BIA8kXu7u5OTEwMbm5u1hZHUIToubYnf17+kwWdFzCisWyW9/jMg5iUGAbVHcSKMysYVHcQy3ssz9c4f1/9m65rutKwZEP+HfavBSQXPE0kJycTFBRE+fLlcxzdKrAOM2fOZNGiRdy5c8faojw1ZHd95/T5Laa6BIJCwpwfj6+zLzEpMY8tRZEbhMVHIHgyWbBgAY0bN8bb25uDBw/y5ZdfMnr0aGuLVewQio9AUEhkDGcHWUm5FnmNi+EX1eX8Yqz4SJJkseRxAoEgf1y7do0ZM2YQGRlJYGAg48ePZ+LEidYWq9ghFB+BoJAwZ/FRlKDENDmrblY1uHKD0meKPoX41Hhc7V3z3adAIMg/c+fOZe7cudYWo9hT5J2bBYIngVR9KjEpcjVnk6kuJ1MLjyUsPk62TjjbyqHLYrpLIBAITBGKj0BQCCjTXEq1dYWM4euWCGc37keEtBdfRNyKoChiietaKD4CQSGg5vD5r9q6QkYLjyUsPsb9CItP8UPJr2KtoqQCQUGiXNf5ySMkfHwEgkJAsbxk9OF53HJeyVj5XVB80Ol0eHh4EBYmK72WrI4uEFgLSZJITEwkLCwMDw8PsyWocopQfASCQiCrkhTGyy52LjjaOlpkPGHxKd4oNa0U5UcgKCp4eHjku0q8UHwEgkLAXCg7ZM7pYymE4lO80Wg0lCxZEl9fX9LS0qwtjkBgEWxtbfNl6VEQio9AUAioFp8MUVzGipClprmM+xLOzcUbnU5nkQeFQFCUEM7NAkEhkLEyu0IJpxLq38LiIxAIBAWPUHwEgkLAOKrLGButDV6OXvI2S1p8RDi7QCAQmEUoPgJBIZCVc7PxOmHxEQgEgoJHKD4CQSGQVTg7FKzicy/uHqXnlObLg18CsOHyBsp/U56A2QE0/7E5cSlxFhtTIBAIngaE4iMQFALZWXyaBDQBoFFAI4uN5+/iTxm3MgCExIWw4N8FAKw8u5Lg6GDux9/n0J1DHLxz0GJjCgQCwdOAUHwEggImKS2J+NR4wHxJii9e+IIHEx7QomwLi41po7Xh4qiLbHp1E/AonD7j1JdIcCgQCIobQvERCAoYZZrLVmuLu717pu0ajcai01wKLnYuqjKVkJZAYlqiquiUci1lIptAIBAUF4TiIxAUMMbJCwu7dICrnSt2OjtVDsXiU9O3JiCcnwUCQfFDKD4CQQGTnX9PQWNsTQqJCyEqOQqAmj5C8REIBMUTofgIBAWMNRUfeBRJdin8EgBajZaq3lUBMdUlEAiKH0LxEQgKmOxC2QsDReG6EH4BkLNF+7v8V8RSWHwEAkExQyg+AkEBY3WLz3+RZIri4+Pk8yizs4jqEggExQyh+AgEBYzVLT7/FUa9ECYrPr7OviKzs0AgKLYIxUcgKGCsbfExdm5WlpV1Spi7QCAQFBeE4iMQFDDG4ezWIOO4Pk4+mcLcBQKBoLggFB+BoIB5Uiw+xsvGYe4isksgEBQnhOIjEBQwimJh7XB2dfk/C5CyXvj5CASC4oRQfASCAiQh9ZEPjbXD2TMuCwdngUBQHBGKj0BQgChKhYONAy52LlaRwZyPj/F64eMjEAiKE0LxEQgKEONQ9sKu06XgbOuMo42juqxafJyExUcgEBQ/hOIjEBQg1nZshszV3zNOdQnnZoFAUJwoUorPrFmzaNy4Ma6urvj6+tKjRw+uXLlibbEExRhrh7IrKOPbaG3wcPAwWScsPgKBoDhRpBSff/75h1GjRnHkyBF27NhBWloa7du3JyEhwdqiCYopT4LFx3h84yk3YfERCATFERtrC2BJtm7darK8fPlyfH19OXHiBC1btrSSVILijBrK7mRdxSejQ7PxOmHxEQgExYkipfhkJCYmBgAvLy8rSyIoLugNelaeWUmLsi2o5FVJVSqsPdWV0a/H+O/Q+FCm/zOd7tW6U8evjlXkK44kpiWy6uwqulbpSknXkpwPO8+fl//EIBkA0KAR34lAUAAUWcXHYDDwzjvv0Lx5c2rVqpVlu5SUFFJSUtTl2NjYwhBPUETZcXMH/9v4PzpU7MDW/lufmKmu0m6lTf4H8HPxw0ZrQ6o+lSl7p7Du8jpODT9lLRGLHT+d+Yk3/36TYQ2GsfjFxQzZMIR/7/1r0uaPS39w+s3T1hFQICiiFFnFZ9SoUZw/f54DBw5k227WrFlMmzatkKQSFHWCo4NN/rd2ZXaFgXUHEpMcw6u1X1XXOdk68XPPn9l4dSOrz61WZRYUDuq1EiP/HxQVBEDfWn3RarTiOxEICogi5dysMHr0aDZt2sSePXsoXbp0tm0nTpxITEyM+rlz504hSSkoiigWnoz/W9vi4+HgweRWk6nkVclk/Su1XuHbTt8CEJ0cTao+1RriFUuMr5F0QzoRSREAfNPxG+Z3mg9ATEoMKekpWfYhEAhyT5Gy+EiSxFtvvcX69evZu3cv5cuXf+w+9vb22NvbF4J0guKAEr4elRxFmj7tiQlnzw4PBw90Gh16Sc/DxIcEuAZYW6RigWINDE8IJyJRVno0aPB29Ear0WKjtSHdkM7DxIeUcitlTVEFgiJFkbL4jBo1ip9//pnVq1fj6upKaGgooaGhJCUlWVs0QTEhLPFRhFRQdBApevlt3dpTXdmh1WhFTh8rYGzxUf72dvJGp9Wh0WhE1J1AUEAUKcVn4cKFxMTE0Lp1a0qWLKl+1q5da23RBMUE47pXF8IuAHLJCGc7Z2uJlCPUnD6iblehoVh80gxpXI+8DpiPuhN5lgQCy1LkproEAmti/HZ+Puw88GRPcykI60LhY/ZaMbIMCiucQFAwFCmLj0BgbYwfUhfCZYuPtR2bc4Iio3jIFg5JaUnEp8ary+auFfGdCAQFg1B8BAILoTfo1cgcePQwe5L9exQUGcW0SuGQ8Tybu1bU70RMPwoEFkUoPgKBhYhMilSz7gJceSgXyBUWH0FGMp5nc9eK+E4EgoJBKD4CgYXI+BafZkgDnhKLj7Ow+BQmGa046rVippaa+E4EAssiFB+BwEJk9WYuLD6CjOTkWhHfiUBQMAjFRyCwEFn5YoioLkFGsrLiGCs+wgonEBQMQvERCCxEVuUpniaLj3CkLRyyulaMp0WFxUcgKBiE4iMQWAjlAVXLt5bJ+qdB8VGsC3GpcSSnJ1tZmqJPTq4VRQmKT40nKU1knxcILIVQfAQCC6FMSdT0qWmy/mlwbna3d8dWawsIq09hYO5a0Wl0eDp6qstu9m7Y6exM2gsEgvwjFB+BwEIob/GVvSpjo32UFP1p8PHRaDRiaqUQUc6xseJTwqkEWs2jW7L4TgSCgkEoPgKBhVDeyv1c/CjhVAIAVztXHGwcrClWjhHOtIWHYlWr4VNDXWdOQRZJDAUCy1OkanUJBNbE2GHV19mX0PjQp8K/R8HYuhAdHU1QUBChoaEkJiaSnJxMUlISdnZ2eHh44O7ujqenJxUqVMDFxcXKkj99KNdKKbdSeDh4EJ0cbfZaERYfgcDyCMVHILAQylu5j5OP+qb+pE9zSZLEzZs3OXz4MEE/B8E5eHP2mwyKG5TjPkqVKkXVqlWpXbs2zz33HC1atMDPz68ApX66SUhNIClddlZWrpXo5GizvmDCCicQWB6h+BQi6YZ0IpMiAbDR2uDl6GVliQSWIt2QrtbpUiw+yt9PGunp6ezfv5/169ezfv167t69a7I9if8eyj4+BJQKwMXZBUdHRxwcHEhNTSUmJoaYmBjCw8OJiIggJCSEkJAQdu/ezTfffANAlSpV6NSpE7169eLZZ59Fp9PlSsY0fRo2Whs0Gk2m9bY623wcff6wxPiK9cbBxgEXOxd8nX25FnnNvMXHybzFxyAZeJj4EA0aSjiVyHSeLIkkSeglvYnfWlZk9b0JBE8SQvEpRK5GXKXmgkfOjFNaTmFam2lWlEhgKR4mPgRAgwYvR69Hio/Tk6P4XLp0iUWLFrFq1SoiIh4VU7W1taVhw4boAnUclA7S/bnu/Py/nxm/ZzxrL6zlwsgLlHIrZbbPyMhIrl69ypUrVzh+/Dj79+/n3LlzXL16latXr/LNN9/g5+dHz549ef3112nUqNFj5YxPjafa/GrU86/Hptc2qesn7pzIvGPz+Hfov1T3qZ7/E5JLbsfcpvbC2gyoM4D5nefnuR/jKVFjB2Zzio9i8TFWfCRJotn3zTh+7zgAfWv1ZU2vNXmW53G88NML3Ii6wcWRF3G0dcyyXWJaItW/q05V76psH7C9wOQRCPKLcG62IjuDdlpbBIGFUKa5SjiVQKfV0bVKV0q6lOTFqi9aVS69Xs9vv/1G69atqVGjBvPmzSMiIgJvb2+GDBnCxo0biYmJ4fDhwwx5fwjUhPQS6bi4uLD95nZiUmLUB6w5vLy8aNasGYMGDWL+/PmcOXOGiIgI1q1bx8CBA/Hw8ODBgwcsWrSIxo0b07BhQ5YsWUJ8fHyWfV4Kv0RIXAg7bu5AkiR1/Y6bO0hMS+Tw3cMWPUc55ejdo8SmxLLj5o589aNMWylTWz2r9STANYD2FdtnaqsmljSa6sr4ney4kT95siPdkM6uoF0ERwdzI+pGtm2vRlzldsxtdgXtQm/QF5hMAkF+EYpPIVLDpwbSxxL7Bu8DhMNiUSJjJt52Fdpxb/w9ulXtZhV59Ho9q1atombNmvTp04d//vkHrVZL9+7d2bJlC6Ghofz444+8+OKLODrKb/EZrQsZ/88pnp6e9OzZkxUrVvDgwQO2bt3Ka6+9hp2dHSdPnmT48OEEBgYydepUE8uTgjJeqj6V2JTYTOut9bux1PgZr5UBdQcQMi6EJqWaZGprrpRIxvEjkiJIN6TnS6asUCyZ5sbNiLLdIBnUKX2B4ElEKD5WQERqFD3Ut3grOzNLksTatWupUaMG/fv358qVK3h6evLRRx8RHBzMn3/+SceOHbGxyTzLbWxdSEhNIDEtUV7ORyi1nZ0dHTp0YNWqVYSEhPDVV19RqVIloqKimDZtGmXLlmX8+PE8ePBA3cf4d6GcV0mS1L+t9btRxo9OjiZVn5r3fhJyfq2YKyWi/F3OoxwaZF+aiMTMCqQlMPkuHnMdGG8X9zbBk4xQfKyAcsOLTYklJT3FytIILEFWtZcKk1OnTtGyZUv69u3L1atX8fLyYubMmQQHBzN9+nTKlCmT7f7G1gXjqRVLPcRKlCjB+PHjuXz5Mr/++iv169cnISGBOXPmULFiRaZOnUpcXJzZseNT49VSGtaKcDI+D8aWkLz2kxP/L3M+Psrf/i7+eDt5Z9puSXKjzJhTWAWCJxGh+FgBDwcPNUJC3CCKBspN3xrlKSIiIhg2bBgNGzbkwIEDODk5MW3aNIKDg5k0aRJubm456kdR2hLTEgmODlbXhyVa9qGq0+no3bs3J06cYMuWLTRu3JiEhASmTZtGpUqV2P7LdvjPRcTc9JK1p7ryK4Pym8+Jkqy0SUpPIiE1wWRs4+jBgjonuTnmJ+E7EghyglB8rIBWo1Uz+4qMrEUD5XssbIvPunXrqFmzJkuXLkWSJF599VWuXLnClClTcHV1zVVfLnYuapbpC2EX1PUFdY1qNBo6duzI0aNH+fXXX6lUqRJhYWHsmr8LlgC3H41t/IJgrd+MpWRQleQcTHU52zrjaONosp+xc7Sa2bmAXqBMjvkxYzwJ35FAkBOE4mMlhJ9P0UKxihSWxScsLIw+ffrQq1cvHjx4QI0aNdi/fz+rV6+mdOnSeepTo9Go8l8If6T4FPQ1qtFo6N27NxcvXmT+/PnYOtvCA+BHWPDhAsLCwp4Ia4KlZMjNtKhGo8nS6VxYfASCvCEUHythLkxV8PRSmBafLVu2UKtWLX777Td0Oh2TJk3i5MmTPPfcc/nuW5HfWPEprGvU1taWUaNGUX1adagvrzu97TQ1atRg0++bQHokj3GYe2Fh4mCcj3OSMZz9cWS8VxhPlZlzfrYkuTnm3FiHBAJrIhQfK2EuTFXw9FIYzs2pqamMHz+ezp07Ex4eTu3atTl27BgzZ87E3t7eImMo1oXzYefVdeEJ4Rgkg0X6zwlR2ijoDrwBHuU8iIiIYOlHS2ENEJs5zL0wMM7MDXn/3UqSlOtrJeO9wtifrKDvI8b+XcLiIygqCMXHSoiprqJFQYez37hxg+bNmzNnzhwA3nrrLY4dO0aDBg0sOo5yXRrnYdFLeqKToy06TlYYKwaUhgZTGjB9+nS0Nlq4CnwHnIIH8Q+y68biZAwXz+vvNi41Tg2Fz+m1ktGqY2xdLGjLsQhnFxRFhOJjJVSnROEE+NSTqk9VFYOCsPhs3bqVRo0a8e+//+Ll5cWff/7JvHnzcHBwsPhYWU2/FNaDLD41nhT9oxQPD1Me8tFHH9Hpy04QAKQAG2BQ70Hcv3+/UGSCzMefV0VD6cfZ1hknW6cc7ZOlxcfZx2y4uyUxvj9FJUdlmb8oKS2JuNS4R/uJqS7BE4yo1WUlVIuPhUOFBYWP8nDQaXR4OHhYrF9Jkvjss8/48MMP5fpMzZrx66+/PjYfT37ISnELSwijWolqWe+YmgpRURAXB/Hx8kf5OyEB0tPlj14vf5S/bWzAzg5sbcHOjsTUKHpdgCRbiLEH27gQuH0bG8cE+B9wBNgDR/YeoXbt2vzwww907969QM5FxuPPbjmn5MUXzNiqoxQnVdYrU36F4dwMcv6iANeATO0yKjrC4iN4khGKj5VQ3tSExefpx3iaS6uxjBE1ISGBQYMG8ccffwAwbNgw5s2bZzFfnqzIaPFxN9jhG5mKZsdO0FyDW7cgNBTCwiA8XP6EhUFMjEXG9wN+N1kTAbPL8idgAGIc4JALvJ9qz4WICHr06MHwJk2Y87//4VSlClSoAKVLQy6rwT8O5Tu219mTok/J8+82N6HsCsZWnaikKPSSnOSohFMJYpJjTOSzJKn6VGJS5P6Nj9us4pNgen4ikyJJN6TnqKK7QFDYiKvSSggfn6KDpZMXhoaG0rVrV06cOIGtrS3z589n2LBhFunbLMnJcP06XL7Mswd2s3IPVH0I5aLBN1GZ2pies75cXMDVVf5f+dvJSbbo6HSyhUene/TR62VrUVoapKYSHh3C5ZCzeOGIXXwS7sngk26HJjUVLeCZDF2SoR0pfAR8BSw+doy9x46xGmgA8hiBgVC+vPypUgVq1JA/ZcuCNvfKqfIdV/epzunQ03n+3ebFCd74XqHs7+HggZ3OTt2mlNGw09nlSS5zGFsyK3lV4kL4hSyPW1lfxbsK58POIyHxMPEh/i7+FpNHILAUQvGxEiKcvehgyVD2ixcv0rlzZ27dukWJEiX4888/ad68eb77BUCS4O5dOHVK/pw+DWfPQnAwGOSorar/fYyJt4XEUj74VmsoKw4BAeDrCz4+pv97eORJqTBm48kfeOOvN+hSuS37b+8nNiWWS6PO0HhebZyS0hlUpivHTm5iiH9HvvTvQIcjRxi0cSNXkpJoBnym0zE2PR3NzZtw82bmAZycoHp1WQmqVQsaNICGDcHTM1u5lO+4pk9NToeeJi41juT0ZDXhY07JbSg7mN4rMu7v6eiJTqNDL+mznIbKK8pYJZxK4Ofix4XwC1ner5T1/i7+3I+/z8PEh4QnhAvFR/BEIhQfK6HcuOJT40lMS8yxo6PgySMv0xfm2LNnDz179iQmJobKlSuzefNmKlWqlPcOHz6Ew4fh0CH4919Z2TFTDR0ANzeoVo34CqWZGb6OK94Q6uNAl3Yj+ej0HEY16cP8zvPzLksOyZicLzYlluuR14nXpRPvAh71n+GfqE0E1PJkUK93aAecjYhg6NChrF+/nvF6Pf+88ALL3n4br4gIWfm5cgUuXJD/T0yEEyfkjzEVKsgKUKNG8qdJE9lilUGuSl6VsNXakmZIIzwhnDLuufO3yovFx9i5OeP+Shb4BwkPCEsIs6jik5tEiRnbPkx8KKzZgicWofhYCTd7N+x0dqTqUwlPCKesR1lriyTII2pCuRwUncyKlStX8sYbb5CWlkbz5s3ZsGED3t7eOe9AkuDSJTh4UFZ0Dh2Cq1czt9PpZGtH/fpQr578qV4d/PxAo0GTmsBns9YBUM7DH9eSZeFM4VkmMz5Ar0deV8tnuNq5EugeaNIOwNvbmz/++IOFCxcyduxYNu7YQf0rV1i7di3NBg581Hl6uqwIXbggf86elRUgxTp08yb89pvcVqeTz02LFvDcc6Q+vAOAn7MfPs4+3Iu7R1hCWK4Vn7xYfBSFOlWfyo3IG+r5UfB19uVBwgOL+wuafBdO2SdKzBhifzH8olB8BE8sRVLx+e677/jyyy8JDQ2lbt26fPvttzRp0sTaYpmglAcIiQshPFEoPk8z+UleKEkSs2bN4sMPPwSgT58+rFixImeh6sHBsGuX/Nm9Gx6YyW1TowY8+6xswahfX57eyaZvZzs5zDoxLbFQEuRlxFwdKiWLtI+zT5ZTxBqNhpEjR9KsWTP69OnDjRs3aNGiBZ999hnjxo1Do9HIvj9Vqsifnj0f7RwZCSdPPrIEHT0Kt28/Wv76a5YBE73A5sI6bnrY8b1X3pTBvFwrTrZOONs6k5CW8OhcGClOBRXSrigzOQmbNy7ZUtD1wwSC/FLkFJ+1a9cybtw4Fi1aRNOmTfn666/p0KEDV65cwde3cAtIPg5fZ19C4kLEm9FTTl6nuiRJ4r333uOrr74C4L333mPWrFlos/KTiYuDHTtg2zZZ2blxw3S7oyM0ayYrOs8+K//t5ZXr4/F19iU4OrhQakFlxNz0ivKwz4k8DRo04OTJkwwdOpRff/2VCRMmsHfvXpYvX561Bc3LC9q1kz8Kd+7AgQPyZ/9+DOfPUSUS+G0nXwKfaSBy82jo8Rq0bw9Nm8oO3I8hr/5gvs6+BEUHmZwL421geUVD/S6cHp8o0VxSRXFfEzypFLkEhnPmzGHo0KEMGTKEGjVqsGjRIpycnPjxxx+tLVomREh70cC4dlJO0ev1DBs2TFV65s6dy+eff55Z6QkOhvnzoUMHKFECevWCJUtkpUeng2eegcmTYe9eOY/O7t0wYwZ07pwnpQceWROM3/QL6xo1Tg2gyKGUzzC2JjxMfJhlGQ03Nzd++eUXFi5ciL29PZs2baJ+/focPnw454KUKQOvvgrffQdnz1JhshtdX4XIYQMIKe2GTgKfczdg+nR5OqxECejbF9asgejoLLvNq5KcsZSI8f4FZZUz9108zsfHuK24rwmeVIqUxSc1NZUTJ04wceJEdZ1Wq6Vdu3ZZ3vRSUlJISXmUKTY2tvBqAIk3o6JBbsPZU1NTGTBgAL/++itarZbvv/+eIUOGyBslSY62+v132LgRzp833bliRejSBV54AVq2lJ2SLYxyXRq/6T9MfIjeoEentWx+nIyYs/go2YJ9nX3VB366IZ3o5Gi8HM0rdxqNhjfffJOmTZvSp08frl+/TsuWLfn00095ts+z/HLhF2a0nYG7gzufH/gcH2cf/lf/f+r+x0KOMfvwbNL0aQDc0sZyqypI785l9ksl+HXbXOZqO9E7xJ2UrX9jHx0La9fKHxsbaN0auneHbt0gMJCNVzay4swKHiQ8UI8lN5g7Fxm3/XL+Fy4/vAxAabfSfNX+q3yFt5v7Ls6FnaPPb30Y03QMzwU+x/5b+/n22LdcDL+YqW12yVmT0pKYuGsi3at2p035NnmWUSDIC0VK8Xn48CF6vR4/Pz+T9X5+fly+fNnsPrNmzWLatGmFIV4mVIdBMRf+VJOb6YvExERefvlltmzZgq2tLWvWrKHXSy/BmTPw66/y5/r1RztotdC8Obz4ovypWhU0moI6FAAqe1UG5Agmb0d5ekhCIjIpssBqkYE89af6lTj5UNm7cia57HR2uNu7E5MSQ1hCWJaKj0L9+vU5ceIEw4cP55dffuG9997DZ7UP4e3DaRjQkBcqvMAHuz7ATmfH4HqD1QSUM/bN4K+rf5n05eXohaejp+yb5w5b6vnTu/uPVJtTFr+LcaxzGEjA7uOyk/nOnfLnrbegSRPOlbrJkfIPMbjJTtq5VXyU78TcsvJ3UHQQQdFB6vpOlTrRqXKnXI1jjLG/VXnP8mg1WuJT4/nt4m9EJkWyc+BOpuydwt7gvYCc76ecRznuxt6V98/G4rP1+la+OfoNx+8d52D5g3mWUSDIC0VK8ckLEydOZNy4cepybGxsgZYEMKag6+wICp7k9GS1RtHjlIK4uDi6du3Kvn37cHR0ZP0339Dh9GmYNMk0AsvBQZ6qeukl6NQpz1NWeWVq66m0Kd+GjpU6YquzxcvRi8ikSMISwgpU8YlJiSHNIFtYfJx96FipI+tfWc+D+Ae42LnQs7rskOzr7EtMSgzhCeHZl9H4Dzc3N1avXk2bNm0YM2YM4afD4SYcDjxMLd9agGxJiUyKpIRTCQDux8t1wEY3Hq22ebbMs2g1WhNLrSRJ3EsMJbgMHHulJz3mr4Br12DDBvlz8CAcO8aHwETgTr3yaPq+il1kjJz/KId83Opj6vrVJTk9mUD3QBoGNFS39arRi9+1v6ulLBb8u4CzD86qx5BXjC0+Aa4B7B20lz8v/8mcI3PUvu/Hyf9PeGYC3at1x9/FP0eW7Iz7CwSFSZFSfEqUKIFOp+NBhuiWBw8e4O9vPpGWvb19gZcByAox1fX0o7zV2mptcbd3z7JdfHw8Xbp0Yf/+/bg7OvJ32bI0N87GbG8vKzt9+shTWa6uBS16lrg7uNOtajd12cfJh8ikyAK3TCq/A1c7VzUxYI9qPTK183H24VrktVz9bjQaDcOGDaNp06Y0fKEh+nA9S8csJel6ErgBWnl8RfFR+u5fpz9NSzfNNL7SJjYlVp1+UuWpXBkmTJA/oaGkrl3Nsdnjee4OlD0dBKc/hQ8/l52ihwyRp8Mecw9yd3BnUL1BZrfZaG3oVaOXunz47mHOPjib7/tKxgi0FmVb4OHgwZwjczIVTB1Sfwg1fGqYtM9u/Iz7CwSFSZFybrazs6Nhw4bs2rVLXWcwGNi1axfPPPOMFSUzjwj7fPoxfjhospiCSoiOpuszz8hKD7AjKYnmly/Lzsldu8KqVXLNq3XrZAdZKyo95igsBT2nU4b5iWKqU6cODAPqgGSQ+Onrn+AnIPbR+MZTbuZkMZdJ2Vh+E/z9uT+oFy1ehyrj7ZA+/1zOFq3Xw5YtsqIbEABvvy37dlkAVb58OBcnpSURnxoPmFoylb4jEiNITk8mKjnKZD08uq/FpMRkWc1dkS0hLYHEtMQ8yykQ5IUipfgAjBs3jqVLl7JixQouXbrEiBEjSEhIeOQ8+gQhLD5PP9lG6dy6ReJ77/Giry//nD+PG7ANaFyvHsydC/fuwV9/wWuvPXHKjjGFdZ3mNMeN4huXF3mik6PR2+rhJag5tCa2DrYQBCyCbVu3AfLDOCk9CTD/vZrLpJydPMr6pFK+aN57T84NdPmyPMUZECDnEZo3T86z1KCBHMWXTWTY41Dly8a5+HEoCl1GS6a30yOfr6sR8vSsVqM18bVSymhA1sqXsWwi+ktQ2BQ5xeeVV17hq6++YsqUKdSrV4/Tp0+zdevWTA7PTwLGocKSJFlZGkFeyBTKrtfD339D164klStH9y+/ZE9aGi4aDVtfeYWmp0/LpSPeeUeub/UUUFjhycbh09nKk48Qe2MLjW1DWwZ+N1AuCZ8Is0bM4t133yUkKgQARxtHnG2dM/WhfNeJaYkERweb7dvcmCYKXdWqMHOmnChx82bo3Rvs7ORr4623oFQpGDZMdnrPJZaw+BgnLzS2ZNpobVSHdyWjdgmnEqpTODwqowGPz/uTXRuBoKAocooPwOjRo7l16xYpKSkcPXqUpk2bPn4nK6DcoJLSk0hIS7CyNIK8oLzNV0hzhU8/lcPNu3Yl+e+/6QHsBJwdHNi6Zw/P/PIL1K1rTXHzRKFbfB5T+iMn4dKPG0P5O90rHd4A/kvs/tVXX/FSh5cgIvNDX8HFzgV7neyTozz8M/Ztbkyz6Q50OtmB/ddfZQvgvHlydu3ERFi6VC6b8dxzsHq1XMU+B1giaCI761umnEJmjiuntb3yK6dAkBeKpOLztOBs64yjjSMgzL1PK5qLl1i8EeYN/xM+/BBu3SLZw4Oe5cqxHXB2dmbL9u00b9XK2qLmGdXCUkjOzY+1+OQjYZ/xPuEJ4XJeHVugM3T8sCOenp5cPHMRFoHmX41ZS6xGo8mUVTo7eXJcpsLbW7b2nD0L//wj+//Y2MiRYf36yUkVp0wxX5rECEsoqtkl5TSXUTurNllOdQnFR2BFhOJjRTQajQhpfxqRJDlHS+fOjB/6I8NOgm2aHho1ImXpUno1bcrW4GCcnJz4+++/adGihbUlzheF5tycwwzY+ZnKMd4nzZDG9chHOZNc6rpw+vRpqjaqCmlwa9UtunTpwv37mUOuzSk+j5vWyXFhUo1GTk65dq08FTZtmuwLFBYmZ4ouW1aeBssiN5lx0ERep9Czs1JlrKGWnVXI3DWTbkgnMilSXRYvfYLCRig+VkY4OD9FpKbCihXydNULL8CWLRg0sK4abPr+fVIPHKD3xo1s3rYNR0dHNm3aRKun2NKjUFjRhznNgJ2fl4WM+yjVzpVtgYGBDJw9EDqA1lbLli1bqFWrFr/++qtZGYz3D08IN1tGQ5mSy0sRW0qWlK08wcHydFjTppCSIk+DVa8uZ4c+cEBWxjPIlqpPJTYlb5nos7NSKeuUYzc71ZWNA3pEYgQSj+QV9z5BYSMUHysjQtqfApKTYcECOT/L4MFw7hw4O8Nbb9Frem169YWUJo3p88or/PXXXzg4OPDXX3/Rpk3RSMX/pIazRyRFoDfoczdGht+Z8QNYGT8iKQKegUELBtGgQQMiIyN55ZVX6Nu3r5ojTJHBeH+9pCc6OTrPx5UttrayA/Thw7B/v5z7B+SyJi1ayDXbNmwAScLJ1gkXOxezx5tTjLM2Z0RZpxx7dhYfc+NnXCfufYLCRig+VkZYfJ5gEhJgzhwoXx5GjZKnHfz8YNYsuXr3vHmcco4FPXz73rds2LABe3t7Nm7cyPPPP29t6S2Gco1GJkWSbkgvsHFy6gujRBUZJIPJlEluxshum2KhqVGjBocPH2by5MnodDrWrl1L9erV+f777ynhUCLH/ee1MKlZNBrZ2XnDBnmqa9gwOfnh0aPQo4ccEr9uHb4OpokYc0tOLD5ZLRuvy+58ZLUsEBQ0QvGxMqKS8RNITIwcaly2LIwfD6GhsmPp/PkQFAQffACengCExYXBH/DP1n+wt7dnw4YNvPDCC1Y+AMvi5eiFBjm6SSmLYGkMkkHt+3EKglJGA3JvLcjuIasodsY+OXZ2dnzyySccOXKEevXqERUVxdChQ1n/wXowM7S533GOnZtzS9WqsHgx3LolX5MuLnL4e69ebJv9gD7nISw2byUhjMPZM5LxOLLLdWTu+8n4HQiLj6Cwybfik5ycbAk5ii35Cc0VWJiEBNmaU748fPQRRERApUrwww9y4dBRo8DRUW0ekxhD0tokuChnDV+/fj0dOnSw4gEUDDqtLlMpB0sTlRSFXpKnrZSxsiOvllLlIRvoHqiuK+VaCg0aJCQiEiPMKiqNGjXi+PHjfPXVVzg5ORF0JggWAnuAtEf9ZZRHkqRsp40sgmKFvHULJk8GNzcqhSSx9ndo1XW0HAqvz92UYE7C2RVya/FRlKqszplAUNDkSfExGAxMnz6dUqVK4eLiws2bNwGYPHkyP/zwg0UFLOpYItmYIJ+kpMj5UypWlLPpRkVBjRpyKYlLl+B//5OTyxmRnp7OwIED4QKgg99//51OnfJeCftJJz9JA3OC8vDzcPDATmf3mNZ5D2lX2iuFRwECXAPUjMTGZSgyPuBtbGwYP348Fy5coFGrRmAA/gHmg+d1T5AyWy/iU+NJTpdfDi1u8cmIlxd88gncusWfr9YnygE8g0PlUPj69WHTJhMn6OzISTi7QnZ5fLKzgCnfgVLsVSAoLPKk+MyYMYPly5fzxRdfYGf0QKhVqxbff/+9xYQrDohwdiuSng7ffy87Lb/9tpwfpUIF+OknOZfKa6/JeVQyoNfrGTx4MBv/2AhaKDGoBC+++KIVDqDwKGhftJyGsmeUJzeKmPF0Wk2fmup6H2cf9eH9IP7BY6emypUrx/yf58PLyAVOY+DMd2fgBzhx7ITZ43K0ccTZLnMW6ALBw4Mj/2tP2Xfg74HPgIeH7JD/4ouyf9D+/dnunpD6qH5Wds7NCtlZheJS40hKSzLZppwT5TtITk8WCVwFhUqeFJ+VK1eyZMkS+vXrh06nU9fXrVuXy1nklhCYRzg3WwFJgt9+k606Q4fKjsqlSsn+EpcvQ//+ckZdM+j1ev73v/+xatUqdDY66A1lm5Yt5AMofAo6+jCnoewZ5cnN7yYyKVINN69eorq63tfZ91GIdtQNtbBmdrL4ufhBLWA0eHX2kmt+3YXvR31Ply5dOHHihIl8BW7tyYCvsy9xDrCqWzm4eVP2AXJ0hEOH5BxBXbpkWRRVkdnBxkGNDjPGy9FLLVFho7XBw8EjUxt3e3dstbZA5mtG6b+cRzk1gau4/wkKkzwpPiEhIVSqVCnTeoPBQFpaWr6FKk5YItmYIBccOSK/9fbpA9euQYkScuTWtWtyhIytbZa7GgwG3njjDVauXIlOp2P4rOFQ3ULROk84BW7xyWXId14qtCtjeDh4UMqtlLrex8lH/Q6VEhQudi442jpm7sRoHwDsoOpLVfno14+gPmi0GjZv3kyjRo3o0aMHh44cytVxWQqT8+PpKfsAXb8Ob74pWzE3b5anv/r3lxV/I4x9ksyV7NBpdWpkXVZtjJOzZrTKGVv3CnoKVSAwR54Unxo1arDfjLn0999/p379+vkWqjhhiWRjghwQFAR9+8r5Tg4dkt9+p0yR34bHjjVxWjaHwWBg2LBhLF++HJ1Ox5o1awhsJjtnFvZDzRoUtC9abi0jeZkiNh7DeBxfZ1814V522YiNcbZzxsnWSW1buVxl6A6NP23MgAED0Gq1bNiwgfG9x8OPoL+kR59LB+P8YNYiFhAACxfKfmuvviqvW7UKqlSRfwvx8Sb7ZHcOlG0Z26SmphIaGsrFixdxjXKFe3DgyAFOnjxJUFAQsbGxPIh/lAtJWLwF1iCzA0MOmDJlCoMGDSIkJASDwcC6deu4cuUKK1euZNOmTZaWsUjjZOuEs60zCWkJhCeG4+7gbm2RihbR0XLx0G++kTMvazQwaBDMmCFPb+UAg8HAiBEj+OGHH9BqtaxatYrevXszYfsEoACjdZ4g1AdpAUUf5naqKy8PTOMHuvE4Pk4+xKfKD31F8cmJHL7OvgRHB5tYjBJcE1i5ciWTJk1i1qxZrFq9Cv1tPSfnnqTKhir873//Y+DAgZQpUybHcueFbM9PpUpypNeECbLSv2+fXArj++/h008Jry0raOYsmampqVy9ehXNFQ1chghDBJ03dubmzZvcu3ePuLi4TPu8s+Qd0xVawA0mH5hMhH0E2MCF+hd4sWrR9pMTPDnkSfHp3r07f/31F5988gnOzs5MmTKFBg0a8NdffxW5HCaFga+zL0HRQWy+tplqJaoB0LBkQ7ydvNEb9FwIv0BNn5rotDoikyKJTYmlnEc56wr9pGMwyGHokybBw/9yz7RtC7NnyxWvMyBJEmcfnOVBwgNc7FxoVroZWo0WSZIYPXo0S5YsQavV8tNPP9G7T28O3TnE2QdngeJl8XninJv/288gGTgfdl79nSjcjrmNk60TJZxKmEzhGD/UfZ19Vefa0PjQHMvh4+RDcHSwieXiXtw9dt7cSbMKzVixYgX2HexZumgpDqcduHnzJh999BGTJ0/mhRdeoF+/fnTt2hUvL68cHXNuUI7vYeJDDJIBDRpOh54mPDEcN3s3mpRqgrZBA8L//g3Wr8dn6hey9XPIENpWLMFzz4FLGRe2bt3K2bNnOXfuHGfPnuXSpUsm7gx3//tnjEajwd3dnWSSSdYn42zvjKPOkbjoOFJSUuRouGjYt3Ofus/EDRNZUn4JL7zwAp06daJz587Y2dlxIewCIXEhONo48kyZZ7DR5umRJRCYkOerqEWLFuzYscOSshRb/Fz8CIoO4u2tb6vravrU5PzI83x3/Dve3vo2X3f4mrebvU3r5a25GnGVkHEhagiuIAPHj8s5d44fl5erVYOvvoLOnWWLjxn23dpH6xWt1eX5neYzsvFI3nrrLRYuXIhGo2HFihW89tprfHv0W8ZsHaO2LQ6KT2GFs+fUXyrjVM6C4wt4a8tbzGk/h7HPjAUgJjmGGt/VIMA1gKtvXTWx+Njp7PB08CQqOQpfZ181ikkhJ9+pn4uf2tbPWf47KjmKF356gU6VOrG532YSHRLheZj88WRKh5Rm2bJl7N27l+3bt7N9+3Z0Oh0tW7ake/futG3blpo1a6LV5j+vrHJ+0g3pRCdHczzkOB1XdVS3L31xKW80eIOmPzQjLDKMPxf/xtE5n3F3+z6u3HjIpRtwYMXv/M7vmfp2c3PDwc+BMLswGtVsxLB2w6hQoQJlypTB29sbDw8PdDod47eNZ86ROST892/PoD2UcSpDpc8qoY3W8m3jb/lh+w+cPHkSbYiWoKAglixZwpIlS/Dx8aFjr478pPsJ/rskZrSZwYctP8z3uREI8qT4HD9+HIPBQNOmTU3WHz16FJ1OR6NGjSwiXHFh/DPj+fzg56Qb0tEb9JwLO8fF8Ivy3w/OAXA+7Dx6g57zYeeRkLgeeV0oPhl5+FC28Hz/vRy55eoqV7YePTpbp2VAtd6oy6FnGT16NAsWLECj0bBs2TL69+8PwLkw+Tsp6VKSOn516Fqla8EczxPEk2rxUbItG/9OFIKig0hIS+Ba5DVS0lMyVUmf3HIyJ+6foJ5/PSp7V6ZL5S6ExIXgZOvE0AZDHyvDW03ewiAZeKn6S/i5+DGmyRi23djGlYgrqhzKcZXyLsXA5wcycOBAbty4wU8//cT69es5e/Yse/bsYc+ePQB4enrSokULmjVrRu3atalduzaBgYFmHYizw97GHjd7N2JTYglPCOfM/TMQA0QDUbDo/CJ+j/+doMNBEAMv0DlTH1qgikZDnTp1qPPSS9SpX586deoQGBjI6dDTfLj7Q2Y9P4u6/nXNyjCo3iCO3zvOubBzRCdHcz7sPJ6BnuAOPgE+jBwxkoQ6CZzceZLelXszwH0A27dv59dffyU0NJSfFv0kd1QBaAvnw8+bHUcgyC15UnxGjRrFe++9l0nxCQkJ4fPPP+fo0aMWEa648HKNl3m5xsuA/IZmO91WziKbFKH6VIQlhhGR9KiqsXAGNEKvl6tVf/ghRP5Xu6l/f/jiC7m6dQ5QzqcGDZJBYse3OwjaGYRGo+GHH35g0KBBmdpOaTWFNxu9adljeUJRlIWYlBhS9ak5SjKYG3Lr46OU0ZCQeJj40OR3krFPkBWQjFXSFcsQgJu9G5tey51/YvuK7Wlfsb26/E2nbwiODqb8N+XVpHzmHIUrVqzI1KlTmTp1Kjdv3mTDhg1s2bKFQ4cOERUVxcaNG9m4caPa3tXVlbJly1KqVClKlSqFv78/zs7OODs74+TkhFarJS0tjfT0dFJSUoiOjiYyMhLDUQNEQJsf2vDg3gMw8q0+gWm+IW8/b9xKuxFkG8TzzZ7n80ZDqDFvHo7HjsllMPR6aNNGLuMC1C9Zn839Nmd7fur41WHfkH2M/HskC/9dSFhCWCbLnnJeogxRdOnShS5dujB79mw2b97MpC8nceHgBbgJ3ISd/+7kdMXT1DMzVS0Q5IY8KT4XL16kQYMGmdbXr1+fixcv5luo4oyN1gZvR29Z6TG6URj/rSwLgFOn5DD0f/+Vl+vUkWtqtWiRq26U81mjRA0uLLtA0Ikg1dJjrPQYty0OU1wKno6e6DQ69JKe8IRwk3Dw/KI36IlIjAByfk6VMhrhieGEJ4Sb/E4UMv6dXf0pS6Eobin6FOJS4x4bpl+hQgXGjh3L2LFjSUtL49SpU+zbt4+TJ09y/vx5Ll++TFxcHOfPn+f8+bxZPO7zX70uLdh62pLmmkbpiqV57fnX+OLaF+AD8wfMZ9fNXXx/6ntat2lNw5b95MivZcvg/ffh/Hk5/8/AgfILhZ9fjsc3thZmtOyZm0K1sbGhW7duHHQ8yIW/L+BxzIPoI9E8PP2Q+vXrM3LkSD777DNcXV3zdD4EgjwpPvb29jx48IAKFSqYrL9//z42ZjLdCnKHj7MPEUkRhCeEqzcE479BFPYjMRE+/hjmzpXfRt3c5EitESPMZlt+HOGJ4WCApPVJcALQwIoVKxgwYID5thSPaC4FrUaLj7MPofGhhCdaVvFRLJkaNLmavvVx9pEtOUZKjclvJMPfhaGwKmHuiWmJZi0c2WFra0uTJk1o0qSJui4tLY3r169z584dQkJCCAkJISwsjISEBPWj7GtjY4OdnR0eHh54enry560/OZ9wngmdJ3Ai8QR7Hu6hZ+2e/HrhV7z8vGjRtgVfrPkC+O/+kvG61mrh9dflqu+TJslW1ZUrYeNG+PJLeVsOpuCMc5Vl/A6yremVGA6e0GFcB9b+sxb7A/aknE5hwYIFbNq0iSVLlhTJ2niCgidPWkr79u2ZOHEiGzZswN1dDr+Ojo5m0qRJIqrLAvg6+3L54WVh8cmK7dvlRGxBQfJynz5yuLq/f567fBD3AP6Cm6duggac+jiZVXqgeFp8QH6AhcaHWvzaU/rzcvTKVdSOr7MvF8MvZvqdZOxX+buwvjclzP165HXSDHIEVF6VZFtbW6pXr0716tUf3zgDD/56wPmT53Gt5Erc1TjQPioTYe5+kuX58faWs5q//rr8YnHypJzxfPVqWLJEDo/PBmPlJuOUpnF0niRJJr5MStuaPjWhBKT3TGf7l9sZPmw4QUFBdOzYkddff51vv/0Wx8fk4RIIjMlT+MBXX33FnTt3KFu2LG3atKFNmzaUL1+e0NBQZs+ebWkZix3KTeFO7B3iUuW8GHGpcdyJfZRhtVhafMLDYcAA6NBBVnrKlIG//oK1a/Ol9Oj1es5/fx5OIUfU9ITE6olq6QJjUtJT1ESTxSFjszEF5eCc26zNCsrvJCQuhJiUGAAS0h7VmTL+jdyPv09kUqTJfgWF0n9Os0AXtBzG1mJF8XmY+FBNJAjZF2dVadIEjh2TU0I4OsKePVC7tjz1lZ6etRzOmeVQp7qcsk7gqshT3UdW+vSSngbPNuDcuXO8/fbbqv/dc889x61bt3J4VgSCPCo+pUqV4uzZs3zxxRfUqFGDhg0b8s0333Du3LkCT8xVHFBuChfDTf2ljJeLlcVHkuDnn6F6dfl/jQbGjIELF6Br/iKq9Ho9r7/+OrFHY0EDny34DG1d+WehFLQ0RrkZZ1WjqChTUCHtuQ1lV8jqd6LIZ/wbufzwcp6m0/KCIldOs0AXtBxhiY8sLTV8agByEMW1yGtq2xxbxHQ6GDdO9vlp1w6Sk2UfoCZNZH+77ORICFMdzBWFx9HWUa0HlvGepiyXci2Fp4Onus7Z2Zmvv/6anTt3UqJECU6ePEnDhg3ZvXt3Ds+MoLiT54QRzs7ODBs2jO+++46vvvqKgQMHYvuYkGFBzsh441QwXi42tW1CQ6FnT9nSExEhv2EeOSJPbeXTuTE1NZVXX32VFStWgAboBf8b8D+TN+WMGIdEK4UaiwtKWQeLW3xyGcquypPF70Tpz9jio7TJ7XRaXsgol7V8wRRFMjg6mKR0uUJ6GfcyuNu7m8gHcDf2rmpxydH3UKGCPOW8bJlcC+zUKWjcGCZOhJQUk6Zq5FZyFPfi7mUaI6sCuMbWIVXpNmrTtm1bTpw4QcOGDYmIiOCFF15g4cKFj5ddUOzJ8x3g2rVr7Nmzh7CwMAwGg8m2KVOm5Fuw4kxGU7mC8XKxsPisXQsjR8oh6ra2sjPze+89NidPTkhMTKRXr15s3boVW1tb0nqmoaupw9PREx9nHx4kPDB7jvNqnSgKmHv4WILchrKr8mTxOzHn76O0KQzrS0a5rG3xUeRwsHHA2dYZH2cfYlJiTM6bYjWz1dqqitFj0Whg8GDo1Aneegt++w0++ww2bZKdoP+r26hUczdIBi6FXwLIlDk7KDrI5PtKTEtUs2n7OPvg6+zL1YirmX6TgYGB7N+/nxEjRrBixQpGjhxJdHQ0EydOzMWZEhQ38qT4LF26lBEjRlCiRAn8/f1NHNI0Go1QfPKJcsNSfvgKxsvmnAGLDOHhcubl336Tl+vVgxUr5FB1CxATE8OLL77I/v37cXR0ZPYPsxl5dSQlnEqg1WizrfydV+tEUaCgfHzy6nSc1e/EXISX8UO0oMkol7UVH2M5NBoNvs6+XI+8bnLejM9Pru8pfn7w66/w558wfLg8DdakiVz4dOJEtDY2lHAqQVhCmNlzYm4KVfnbXmePq51rtteeo6Mjy5YtIzAwkOnTpzNp0iRiYmKYNWtW0bw/CvJNnmz1M2bMYObMmYSGhnL69GlOnTqlfk6ePGlpGYsdObk5F9lq7uvXQ82astJjYyNbeY4ds5jS8/DhQ9q2bcv+/ftxc3Njx44dVGoiR6Uo591sZev/yKt1oiiQnUKYH/KqTGb1OwlLCCMpLUkNDDCmUCw+GeSy2lSXk3k5spMnX7L26CErPb16yc7OU6bAs8/CpUtZygLmp1CNLasajSbb6WeQX7g/+eQTvvzySwA+//xzRo4cmWk2QiCAPCo+UVFR9O7d29KyCP4jpzfnIjXdFRMjJ0d76SXZ4lOrFhw9ClOnWmRqC+TM4i1btuTkyZOUKFGCvXv30rx581zlFimuoeyQvUKYH/KqTGb1HRgnysu0j1PBf28Z5bLWtVLCqYRZObKTJ9+y+vjILy0//wweHnK9vAYNGHkgBc1/OkjGwABzCnVufpPGTJgwgcWLF6PRaFi0aBETJkxAkqT8HZOgyJEnxad3795s377d0rII/iPjA8DVztXscpEJaT90SJ7O+uknOWnaxIlyJmYz2cHzyvnz52nWrBmXLl2iVKlS7N+/n/r/+SBkTNyWU+fm4ob6gLKwY31+w9kVjH8XSp8ZfzuFMdWVybphJX8wW52tGg1lLEd29xeLyKrRQL9+svWnQwdITmbkmuvsWgmlYuTxjaeglDEzlhgxljUrB2hzDBs2jOXLlwMwd+5cPvvss/wfk6BIkScfn0qVKjF58mSOHDlC7dq1M0VzjRkzJos9BTnB2BkQoKZvTY7cPQLIGXSrlqjKv/f+ffotPunpcrbl6dPBYIDy5eU3xWeftegwe/bsoWfPnsTExFC1alW2bt1KuXLl1O1Zvl0mmrH4JBZji89/D6i41DiS0pIslpsmrw7jxmU04NHvxDg0u4JnBS4/vEyKXo40Kozv7Umx+ChjRyVHyX87Zbb4ONs6U9ajrFpU1aIWsVKlYMsWWLKElHfeok1wGmcXwvRB9plkBPNTXbm1+CgMHDiQiIgIxo0bx6RJk/Dx8eGNN97I9yEJigZ5UnyWLFmCi4sL//zzD//884/JNo1GIxSffKLT6vB29Fbfbmr6PFJ8vB298XeRk/U91SHtQUFyIdFDh+Tl/v3hu+/k0hMWZNWqVQwZMoS0tDSee+45NmzYgJeXl0mbjFac7PLVFEa9pycVd3t3bLW2pBnSCE8MJ9A9MN99punTHj2Yc6kgaDVaSjiV4EGCnIhP+Z0YJ+PzdfYlIimCu7F3gcKx1D0pPj4gy3Il4or6t/H/yt/G8ln8utZoYPhwlrpcpOl782h8D+YuDgbehDlzwMnJrDUny99kLqzcY8eOJTw8nFmzZjF8+HC8vb3p2bOnZY5L8FSTp6muoKCgLD83b960tIzFEuOHgJJtVVlfUPlUCo1Vq+SprUOHZEVn1Sp5msuCSo8kScyaNYv+/fuTlpZG79692bFjRyalBzJbcYSPj3k0Go3FkxgqSSK1Gi1ejpm/m8dh7neSMRmfcZvC+N4cbBxMpo+sbfHJ+HfGdYVxfmyqVqf5/+Dz5v+tWLwYGjWCM2fMW3xy8ZvMjpkzZzJ06FAMBgP9+vXj9OnT+TsQQZGgeGVge4owvgHV8q1lsr6gomsKnPh4ORFh//4QGwvNm8OZM/DaaxYdJi0tjREjRjBp0iQAxo0bxy+//IKDg4PZ9hl9TEQ4e9ZYOqRd6UdJJZBXeeDR7yRjQdLCVnwyjmNN66Dx1JU1FR9fZ1/SbOCDF2Dhpy9ByZJw6RI0aUL5n/4CSVaClen9rEpbRCRGoDfoczyuRqNh4cKFdOzYkaSkJLp37054+FN23xRYnDwnMLx79y4bN27k9u3bpKaa1jSaM2dOvgXLLcHBwUyfPp3du3cTGhpKQEAA/fv358MPP8TOzq7Q5ckvxjfLmr41TdabcwZ84jl/Hnr3hsuXZQfmKVPgww/zVEk9O8LCwujduzf79u1Do9Hw9ddfP3bqNaOPiXKTjU2JJTk9GQcbWWFKTEskPjXepE1xIzdOpjkhv5Xuzf1OktKTCIoOUvt96PTQbPuCxMfZhxtRN3C3d8dOZ737j8m0lplwdh8nn0zLBSKHUb9RzzWENxbJRU//+guP9z9mYxUY1COd6ORovBy9Mv0mvZ280aBBQiIiKSJXCppOp2P16tU0adKE69evq9ZfUWmg+JKnp86uXbvo1q0bFSpU4PLly9SqVYvg4GAkSaKBBSNxcsPly5cxGAwsXryYSpUqcf78eYYOHUpCQgJfffWVVWTKD8qbmr3OnpIuJXGwcSA5PRlfJ1+Lv3UXOMuWyQkJk5IgIAB++QVatLD4MCdPnqRHjx7cuXMHV1dXVq1axYsvvvjY/TJacTwcPLDR2pBuSCc8IZwy7nL9OeUt1E5nh5u9ZX2RnhYKyuKTV0uD8jux09lR0qUkjjaOJKUnmWRNjkiKAPI+nZYnuXIQOl6Ychj/bRzmXpgWH5O/fXxgwwZYsADGj+fFqymcWgxxz+/Cq33vTNeFjdYGL0cvIpIiCEsIy7Wcnp6ebNiwgaZNm/LPP/8wbtw4vv32W8sdoOCpIk9TXRMnTmTChAmcO3cOBwcH/vjjD+7cuUOrVq2slt+nY8eOLFu2jPbt21OhQgW6devGhAkTWLdunVXkyS/GjojGCbyMnRGf+KmuhAQ5pf3//icrPR06wOnTBaL0rFmzhueee447d+5QuXJljh49miOlJzk9+VG19f/Oq0nCNGOHSyPrRHHNCGvpkPa8hrIrGFvpjH2QLj18VBpB+S7zOp2WJ7kyOOVaC3OOy8Zh7j5OPpmcnQtEDjOWJzQa+YXoyBFulbClbAyU6foa0rx5hJvJ7ZTfa69GjRqsWrUKgPnz57Ny5co89SN4+smTxefSpUusWbNG7sDGhqSkJFxcXPjkk0/o3r07I0aMsKiQeSUmJsasM6sxKSkppBgV1YuNfTKyIWd8Y/R19uVO7B2TN7QLYReo8E0Fs/vX8q3FulfWFXhBxiy5dAlefhkuXpSntj75RM7Po83/g+ftLW8TmRzJyh4r0ev1TJo0Sc3Y2rlzZ1atWoWHh0eO+lJuouaSqt2Pv0+X1V1wtJHDtpVCj9Z+i7cmahLDDKH+72x9h4ikCFb2WJmtUpimT+OlX1+iSUATJreanO9M2OZ+J7djbptsj0iMMGlTGDxpFh9nW2ecbJ1M1kclR5ncT5TyEAWBsRU10zmpV4+hH9dn2IJjvHwpHd5+mxU14I1umS1Flx5eype1sVu3bkydOpWpU6cycuRImjZtyldXvmJX0C61jb2NPbPbz6Zz5c7quhGbRpCqT+X7bt9neX2/tfkt/r72d6b17g7urOixgjp+lsk+L8g/eXoKOTs7q349JUuW5MaNG+q2hw8fZrVboXL9+nW+/fZbhg8fnm27WbNm4e7urn7KlClTSBJmT4OS8pRhA3/5/4YlGwJQ378+Fb0q4mbvhl7SExQdZPbz19W/OBN6xjrCr1olR2xcvCg7Me7eLfvzWEDpSUpLYt6xefx89mf+vfwvzz//vKr0TJw4kY0bN+ZY6YGsrTjK+Q6ND1XPaWh8KPDouymOmJvqSk5P5puj3/Dz2Z+5E3sn2/1Ph55m09VNzD48G8i/s7j6Oylp+jsB+WFfxbsK9UvWN2lTGChyKL9fa1HDpwb2OvtMx94wQJavnn89qpWohqONIw1KNigwS6ZWo6VByQY42TpRtUTVTNurVWxK7z7wdkdI00Kfi3DmB1ucLz96tliqSO5HH31E69atSUhIoE/fPnx//HuTe+flh5f54dQPavuY5BgWnVjEj6d/zFLpSkhNYP7x+WbvxadDT/PrhV/zJbPAsuTJHNCsWTMOHDhA9erV6dy5M+PHj+fcuXOsW7eOZs2aWVTADz74gM8//zzbNpcuXaJatWrqckhICB07dqR3794MHTo0230nTpzIuHHj1OXY2NgnQvlpUqoJd8beUXP2fNflOya3mkxpt9IAXH/rOjeibpjdd/Cfg7kScaXwp8LS0mDCBJg3T15u105OSOjnZ7Eh1GO6Ci80f4GYqBhcXFz44Ycf6NOnT677y8rHZPGLixnReATphnST9TZaG+r518uT7EWB7ApKKn9nl99HOd8xKTGk6lPzXe2+UUAj099J5+8Y2mAoaYY0KnhWwMvRCy9HL5M2hUGvGr249c4tyrhZ917i5+LHrXduZfJJW959OV+0+4JSbqUACH4nuMCsPQp7Bu0hPjXerJ/V3A5zGVR3EGlvpHHl5Hkqj/iI8vceQNOmsh/QkCEWS+Oh0+n4+eefqVOnDmdPnwVHcO/mztb+W9kTtIdJuyeZXNMZM0r7uWS+nyn3JXudPXsH71XXLzu1jCUnlzzdOdeKIHlSfObMmUN8vBzdMm3aNOLj41m7di2VK1e2eETX+PHjGTx4cLZtKlR4NN1z79492rRpw7PPPsuSJUse27+9vT329vaPbWcNFCUH5Aeu8bJxdFdGynqU5UrElcJ1fn7wAPr0gX375OXJk+UCozqdRYe5E3EHtgGHIYYY6tevr157eSErHxMbrQ2NAhrlV9wiR3ZZdjP+bQ6Th0iGsPO8Yvy70Gl1qjUjqzaFhSUSPFoCcw9qW52tqvRA4UzJOdk6mUy3GWPyvZVuBs17yqkvtmyRfQT//Rf/HrLCZIn7WqlSpfjxxx/p0aMHHAbXeq40K92MlPSUTGPk5Po2vo6blX708n869LS83UwWeIH1yJPiY6xoODs7s2jRIosJlBEfHx98fHL2NhgSEkKbNm1o2LAhy5YtQ2uBqZWnkcdVMrY4R47IFZnv3ZOTEP70E3TrZvFhTp48Sb++/eCavPzCqy/w17K/8qW45tfiUNzIzuk749/myNg2v+HsgiKKtzds2iSXtPn4Y1iwgNcPVmT+C5YL6ujevTsdXu3AtjXbeLDqAQ8/eWh2Os1cRumMZJXRvdDvxYIckSfNoEKFCkRERGRaHx0dbaIUFSYhISG0bt2awMBAvvrqK8LDwwkNDSU0NNQq8liTQg13X7oUWrWSlZ7q1eHYMYsrPWlpaXzyySc0bdqUW9dugTPwKnQY3SHf1jrVx6QQqnYXBZRrKzEtkYTUBCDvFp+MGZYFAhOUfF9//QVubgScucGJxeB9zvwUf17oOKIj+EBabBpjxoxRr8PIpEjS9GlA7i0+xjx1qUeKCXlSfIKDg9HrM2fPTElJISQkJN9C5YUdO3Zw/fp1du3aRenSpSlZsqT6KW5kV2TTYqSkwLBh8ic1FV56CY4ehaqZHRfzw5EjR2jUqBEff/wx6enp1G5dG0YCVS3z5icsPrnDxc5FTeionP+M01fZYdz2buxdNZWAUHwEWdK1Kxw7RkLFQErHwfzPzsKPP1qk62h9NPQAjVbDmjVr+GfrP2rKA6WciokPWxb3nKyc9IXi82SSq6mujRs3qn9v27YNd3d3dVmv17Nr1y6TqteFyeDBgx/rC1RcKHDzamgo9OwpT3FpNDBzJnzwgfy3hYiJiWHSpEksXLgQSZLw8vLi22+/5VSJU5w7fA6wzM1EWBxyh5Lj6E7sHcISwijnUc7UEfQxyrbxg0NJMpgxlYBAkImqVbmz/Tcud25KjyuSnPX5xAmYOxfykZk/LCEMSsGzfZ/l4OqDjBo5Cq8RXjzkIeGJ4ZR0LZkri0/GKVvlhUpx5rdmFm/BI3Kl+PTo0QOQb36DBg0y2WZra0u5cuWYPXu2xYQT5I0Cfcs4dUqeyrp7Fzw9YfVq6NjRYt3r9XpWrlzJpEmT1GnKQYMG8eWXX+Lj48P2P7erbS1xfMLHJPcoOaWU82+s7ORmqutCuKz4FOeEkIKcU8K/Ai+9Ah/uh0/2atAsWADnzsG6dVCixOM7MIPy++/1Zi8iT0Vy6dIl3De7Q2fMXt9ZWXyyeoHKmAXe2KFcYD1yNdVlMBgwGAwEBgYSFhamLhsMBlJSUrhy5Qpdu3YtKFkFOaTAanmtWwfPPScrPdWqyf48FlR6du/eTaNGjfjf//5HaGgoVapUYffu3Sxfvlx1cM8YWppfhMUn92QMac/NVJfxdlXxEdOMghzg5eiFRqdlRiuI+mW5HEixf78c8n7xYp76VK7dAM8ANSAm5lgMXHm0LavQdmOyeoHSarRqiRAx3fXkkCcfn6CgIEpk0LCjo6MtIY/AAli8erskydNZvXpBYiK0bw+HD0OlShbp/t9//6VLly48//zznD59Gnd3d7766ivOnj1LmzZtTNoaH5NFLD5ZRGMIsiajRTEnDwYASZIy+fgY9ycQZIexEnGnZV35HlShAty8Cc88A9u25bpP4xefpk2bMmHCBHnD33A3/K5Jm4x/Z9VPRix+PxbkmzwpPp9//jlr165Vl3v37o2XlxelSpXizBkrZQsWqChvHcaRN3kmOVnOp/HRR/LyW2/B339DLrIjZ8XJkyd58cUXady4MZs3b0an0zF69GiuX7/O+PHjzUZs5ca68DgS0xJJSJPPj3j45pyMIe05tcLFp8aTok/JtF5MMwpyiokSUaOGHFDRogXExkKXLjB/fq76y5jHa+rUqbj5uUEsrFu07tFYGdpn1Y+5Fyi1zIuw+Dwx5EnxWbRokZrdeMeOHezcuZOtW7fSqVMn3n33XYsKKMg95iJv8kRoKLRuLZeg0Olg4UI5K7NN3ut/GQwGtmzZQvv27WnYsCGbNm1Cq9UyYMAALl26xLfffpvJmqggSZLJjSchLYHEtMQ8y6L0VZA1iooimSw+RtdYdsp2Vjd+oXQKckomJaJECdixQy6GrNfLL2ajRkF6etad/Ee6IZ2IJDkti6KwODo60m2snI7j2B/HOHP2jMk9Jyo5Sg1zVzC2ZGZr8RG5fJ4Y8qT4hIaGqorPpk2b6NOnD+3bt+e9997j+PHjFhVQkHs0Gk3+HZzPnYMmTeQ3Kk9P2L4d3nwzzzLFxMSwaNEiatWqRefOndmxYwdarZZ+/fpx8eJFVq5c+djsywlpCWqhUCXkND83E+NQduFcm3OM37oTUh8pn+p38hgH0IwV0oXiI8gpZpUIe3s5vP3zz+XI0gULZOvPY9wvlOK1GjR4O3qr6597/jmoDpJBYvibw9XULRnD3BWMLZnmrJcipP3JI0+Kj6enJ3fuyMUIt27dSrt27QBZ8zWX30dQ+OTLvLpzp+zEfOcOVKkiKz9t2+a6G0mS2LdvH4MGDaJkyZKMGDGCS5cu4erqytixY7lx4wY///wzVXOY+0c5FkcbRwJcA0zW5YX8FsgsrhhfW8r5d7BxoJRrKXW9OZTzXdnLVMEVU12CnJLlfU2jgffekwMwnJzkF7Vnn4Xg4Cz7UvrwdvJGp31UWsfH2Qc6gtZey9HDR+E0uNu7Z+mkrCw72TrhbOecc5kFViNPis9LL73Ea6+9xgsvvEBERASdOnUC4NSpU1SykMOrIH/k2by6YgV06iTPmbdsKTsQ5qIOliRJHDlyhHHjxlG2bFlatWrFypUrSUpKonr16syZM4e7d+8yZ86cXOd8Mp6Pt8RbVFa5NwTZY3xtGSuPj/tOlPUVvSqa5DMRiqcgpzzWUbhHDzhwAEqVgkuXZKfnkyfNNs026aA7eHbylFfsAG/JO8uxHxcZKpybnzzy5Kwxd+5cypUrx507d/jiiy9wcXEB4P79+4wcOdKiAgryRq5D2iUJpk+X6+IA9O0Ly5fLZuTHEB0dzc6dO9m2bRtbtmwxyd7t4uLCK6+8wuuvv06zZs3yNaVkPDWlmKbzczMRoex5w/jaMlYezVVuN8ZYcfVx8iEkLsSkP4HgceTovla/vpxctUsXOHtWfoH7/fdMqTeyTDr433Jqo1QCLwZy++ptEnckUnZQWbNjPy4XWIGlFxHkmTwpPra2to/C/owYO3ZsvgUSWAal9lSOFIO0NNl/R0kD//778Omncq0cM4SGhnLw4EH1c+LECZMpThcXF7p168bLL79Mx44dcXR0zPfxgOkbmqL45GuqKyH7G5bAPMr5StGncCNSrpvk6+z72Bu8qmg6ydYhRfERiqcgp+TY0lu6NOzbJ6fg2LVLLnuxZIlc6f0/MkZ0ZRwjLj2OgW8P5LtR3xG6N5S6Xeqa7KcgLD5PHzlWfDZu3EinTp2wtbU1KV1hjm4FUJlbkDty/JYRGwu9e8tz4lqtHA46YgQgW3KuX7/O9evXOXfuHKdPn+b06dPcu3cvUzfVqlWjY8eOdOzYkVatWuHg4GDxYzJ+Q1MtPvlxbk4UFp+84GznjJOtE4lpiSZJCM1VbjdGfTN29jGx8gjFU5BTHneNmeDuDps3wxtvwE8/yWUubt+WrdoaTY6yLaeWSYXqwCW48NMF6GHG4vOYXGDCx+fJI8eKT48ePQgNDcXX11ctXWEOjUYjHJyfALJ6M5IkicTERGJiYoi8fJnQoUMJvXmTB7a2hHbtyr0DB7ixYgXXr18nIiLCbN8ajYZatWrx3HPP0bx5c1q0aEFgYGCBH5PxjUq1+OSjEKtIXph3fJ19CY4OVhUfX6dcWHyM/IHsdHa42bsVgsSCokCuffvs7GS/xcBAOQnrtGmy8rN4cZZTXUo9uvvx9+Xruz3oruu4e+YuVIPwhllYfJyyt/jEp8aTlJaEo61lLOCCvJNjxcdgMJj9W5BzHjx4wJw5c5AkqUA+KSkppKSkkJycTEhUCITAfu1+6nxRh7i4OGJjY4mJiTGvmKalwfr1mVb7+/tTqVIlqlWrRv369alXrx516tRR/boKE3NTXZYIZxcWn9yjKj7/FRo1nurKST0j5SHh6+wrUgkIcozyW41NiSUlPQV7m8f7IKLRwIwZsvIzYgQsWwb37hH3qp1JnxnHuR9/X76+PeH5fs+zffl22A73u9w3afu46FA3ezfsdHak6lMJTwwn0L3gXxIF2ZNrHx+DwcDy5ctZt24dwcHBaDQaKlSoQK9evRgwYIC4iWXDw4cP+eKLLwp1zEQSOce5TOu1gCfga2uDa90aVKpSC39/f/z9/alQoQKVKlWiYsWKFlFwrjy8wsYrG5GQeKb0M7Qo2yLLtofuHOLA7QNmt/1771/gv6kuJ1nxOR92nq+PfM0rNV+hpGvJTPv8deUvLj28pC672bvRv05/XOxcRDh7PlDekmNSYuRlo6muC2EX+OKgfJ272bsxoM4AnO2cTZxAFSVJTHMJcoPxNNSMfTPoUqULzUo3y9nOw4ZBQAC88gps28ZHV13Y1juLbMvOptd33zf7cvzv40SFR3F2w1kYIOfzWX1u9aP7UhaWY8WCFBIXwpzDc3ixyos8X+H5TO2CooI4GnKUV2q+gkajYdPVTVwMv4it1pbeNXtT2q10zo5T8FhypfhIkkS3bt3YvHkzdevWpXbt2kiSxKVLlxg8eDDr1q3jzz//LCBRn35KlCjBuHHj0Gg0BfKxt7dXP0lSEqO3jwYd8rdsB9jD517dGfn1Lpzj49HXq0vZ9he473Wbw++dypRYzlIMWD+A4/fkxJaONo5EvBdh1tybpk+jw88diE+Nz7a/Um6lVIvPndg7jN02lhP3T/BTz59M2t2Mukm3XzL7m8WlxDHh2QkinD0fKDl7jJeVG/+d2Du8v/N9dVtSWhLvNHvHxJlU2V9UqxbkBo1GQ4BrALdjbjNj/wzmHZtH5HuRJnl4sqVrV9izBzp1okZQJPt/hNiemZtlvL4r+ldk5KSRzBw7kzub7hAeHs6Mf+Xxs9rHpD+3UoTEhfDN0W9YcHwBDyY8wNPR06TN8E3D2XFzB37OfpT3LM+La15Utx2+e5hfe/+as2MUPJZcKT7Lly9n37597Nq1K1PxyN27d9OjRw9WrlzJwIEDLSpkUcHPz4/Zs2cX3oC1URWOKxFXKLn9CGOX/IVtugHatOHKkpncW/UsJEcTnRyNl6NXgYhxM+qm+ndSehIPEh5QzqNcpnbhieHEp8aj1WgZUGeA2b4C3QNpXa41Oo2OWc/PYsfNHewO2m0yhkJQVBAAng6edKvajbMPznIq9BQ3o26SkJZAcnoyICw+eeHd5u+i1WhJSk+ijFsZ2pRvo34nlx9eBuDMgzOcDj3NzaibxKTEkGaQU/37OPvwUvWXuBJxhZdrvGzNwxA8hSzuupi1F9ay/PRyYlNiiUyKzJ2fXpMmcOAAIc/UpMZDibTeY2BnbTBKpDrxuYk42DiQnJ5MOY9yPBf4HD6v+TDzi5lI9yVmzJjBzWbyPad5mea0Ld+W1uVaZznknPZz+PHUj6w5v4ak9CRC4kIyKT7KPexm1M1MMyfm7m+CvJMrxWfNmjVMmjQpk9ID0LZtWz744ANWrVolFJ8nhFFNRql/H/t4KA1/O4JOMsBLL8GqVYTeO6huD0sIKxDFx7gejqONI0npSYQlhJlVfIwtMMt7LH9s3x889wHNyzRnd9Bus86Oyrq6/nVZ3mM53x37jtFbRhOW+Cj/jKONo9lsq4LsqeRViYVdF2Za/8FzH6h/f3PkG94JfcfkfLvauap15Ga0nVE4wgqKFB0rdaRjpY5svLKRyKRIwhLCch2gkFqlIs8Mkdj+E1S7e1/OVL91KzRsCEDVElVZ1HWRyT7+rv7wArASFi5cSHW/6gC8++y7dK/WPdvxmgc2p3lgcw7fPcylh5eyvV+FJYThai/XDlSULxERZllyNbdx9uxZOmZIAmVMp06dRHX2Jw1Jgk8/pckn36OT4LfmnvDrr+DgYOKEWlA/LKWujVajpWoJ+Y0qK4fkvDgbZ5ehOqMPj3FEiHBsLniMz7eIoBNYmvzkx3mY+JA7HtD6dS1Sw4bw8KFckHnPniz38XDwwKaSDVSEtLQ0rv9+3USO3Mic8X6bnJ5MXGqcuk3ZXtOnJiAfoyRJOR5HkD25UnwiIyPx8/PLcrufnx9RUVH5FkpgISQJxo+HDz8EYEYLGNVNJ1dax/THV1CVg5V+vR298XfxzzSuuba5eTgaOyGmpKeYbMvow2OcWVg8iAse4/MtFE2BpclPfhz1fufjg2bPHmjTBuLj5ezOZqJb4ZGTMi/IfyeeSoSQvN2vMt5vjZfDEx/dn2r6yopPcnryY30fBTknV4qPXq/Hxibr2TGdTkd6enq+hRJYAL0ehg+HuXMBiP38EyY/Dw+TItAb5HB24xtGQVl8zOVuyUmel5yiRHnA42voCItP4SLOt6AgyU+9PpPr0dVVTnTYsyekpsLLL8vlerIa0x9adWslr9iRu+AIJY1DVoVOlb+V5XLu5XCydTK7jyDv5Dqqa/DgwdhnUb8pJSXF7HpBIZOeDoMHw6pVcjbmH37AcUA/mDEFCUl1Bsz4llEQmGTrzWlm31zcSLQaLSWcShAaH0p4QrhJyGfG/pT/o5KjuBcnZ58WD+KCQznfEUkRhMaHmqwTCPKLej/Jg7Xa+L4EgIOD7ALw5pvwww8wZAgkJalZ7NUx/2vfqF8j9m7aC8FwcPdBOnfunDOZs8h1ZbwcnhiuJvVU7pu3Ym4RnhhORa+KuT1UgRlyZfEZNGgQvr6+uLu7m/34+voKx2Zrk5oqFxhdtQpsbGD1ahg8GFudreq8rDrRJT79Fh/j9lm9RSnbvRy91JB9JbePeBAXHCWcSgBgkAxcibgCCEVTYDksZvFRsLGBpUthzBh5eeRI1WKeccwQbQg0lde9//77Oa5W8Lh7lfJ3bu6bgtyTK4vPsmXLCkoOgSVISpLNtJs3y6naf/sNjOqm+Tr7EpkUqb5dFIrFx6gQ6OOcEfOaUDCrfjMWIdRpdZRwKkFYQtijUgviQVxgKMp2ZFLko5peQtEUWIj8ODdnmcNLo4GvvwYnJ/jsMxg3DhITVT9JNUln+AV4DnSndZw/f56ffvqJwYMH51lmk3txQjiudq5q++wCOAR5o2Ay1gkKn/h4OTnX5s3g6Ah//WWi9EBmZ8DC9vF5nDNiXhMKZtWv2p+ZgphKrhnxIC5YMp5voWgKLEWOCzGbIavK7ICs/Hz6KXzyibz80UfyR5LU9pcfXgYnqNhNnnqaPHkySUlJj5f5MfcqgDRDGkHRQWr7/BynwDxC8SkKxMRAhw6weze4uMj5KNq3z9Qs45tDYYSzG1dAf9ybS36nuoz7TdWnqunmjftT/k7Vp+ZpLEHuEOdbUFDky+KT+Jh7jUYDkyeDUmJo5kyYMAHf/xQX5Xpu1KMRgYGB3L17lwULFuRY5kwV3jMcg/HvRXGILiirfHFEKD5PO5GR0K4dHDoEHh6wcye0bGm2qfHbRqo+lejkaHVbQYezZ3xzMZeTIq8h5ubeopS+dBodHg4ej9pm6FuEsxcs4nwLCgpLhLP/v737Do+qzP8+/p5JmTSSkJCQIM3QFGkCCgi2BSmCglKVFdFd/NEWXFkEFARdEdujrq4F1wIqFhABdS10KQIqCtJkQWlKQklMQgqp5/ljnGEmmUwmbSbl87quuZw55z7n3Od2cvjOXUut8Z0+HV54wfr+mWfo/fRHmBzW6I6LjGPu3LkAPP7445w7d859nv/4/qeeT7UHN+D6HkyYiAqOUo1PFVDgU5OlpMANN8B330GDBtbJt7p1KzG5qwnlbM5mnbUPc69Mrpq6cgpy7JN12ThO4FXuzs0uOmvHhMY4rUFm+/VU9FipGipvqSq271JKdgp5BXllOrZMtcuTJ8Nrr4HJxMXvf8FrH2MPfmJDrQN6WrduzdmzZ3nuuefcnspxgIVtcldwXZvTIKQBfmY/dW6uAgp8aipb0PP99xATYw16OnVye4jjUErbH5ptsU/bMPfK5jhsNDQw1D4nRUkTeAWYA4iwRJTpGq4mBStpaHyxGgj18alSRcvbNtJLpKKigqMwYV3TyrYsjqeKDWcvzV/+Au+8g+Hnx9274D+fWIOfmNAY/P39efjhhwF4+umnSUkp+Tlqm34DnJ9Xrvo32vJW2jQgUnYKfGoiV0FPu3alHuZqQrlG9RoVG+ZeWRyb01xNIujIsYam6AJ9pXF1zpJ+0Tl+DgsMc7lKvFQex/KODIok0C/Qh7mR2sQ2ShPK9uzKyc8hPScdKGMN5O23k73oPxSY4C8/wKufQGyw9fojRoygQ4cOpKen89RTT7k9javnVdGZmh3Tqcan8inwqWlcBT2XXVb6cTh3BnTsT1NVvyhsVbmO/WxKHM5ZzqHsJZ2zpFEbrjo6S9VxLGPVrkllK89Qb9tzojy1y8Gjx3LnMD8KTPDXH+DKR96AwkLMZjP//Oc/AXj++edJSkoqPc9/5CMzN5PMvEzgwtpcjukc71HrdVUOBT41SQWCHnDuDOiNCbJc9bMpdeh5Of5xtB2TkZtBdl622/M5VSXrH+Iq51jGCjSlspWn429FapdNJhNf9YjnjlugwASx762yzvZcWMhNN93ElVdeSVZWFgsWLCg5z0WegbYAyOJnIaF+QrF0tnvMK8yzj1SVilHgU1NUMOgB586AiRmJ1m0hXgh8XPzjV/QXWkXWcgq3hNubUOyTM5ZQg6QaH+9SeUtVKs+Qdrdz+Hh4zfc6wJhbwDCbrbM9T5yIyTCYP38+AK+88grHjx93m2d74OOQn4ahDYulC/IPsk9oqEkMK0etDHxycnLo1KkTJpOJXbt2+To7FVcJQQ84dwbcf2Y/UKSpq5L/qFw9YEqq8fF4eKkL9lWTHc7ravLCop9V41P1VN5SlcozpL0itcuOx63oHIxp8WLreogLF8KkSfS+/nquu+46cnNz7U1fJR1ve+Y5PqtK+nvRkPbKVSsDn/vvv59GjRr5OhuVIy3NOhlhBYMecO4M6LhkgzeaumxcDT13/FyRX2GO1yypBslxNXfVQFS96OBoe7Ct8pbKVp5nV0Vqlx2Piw2NhT//2bqSu8kEr7yC6W9/Y/6jjwLWJZ4OHTpUcp6znJu6HJ/FRfOnDs6Vq9YFPp9//jmrV6/m6aef9nVWKi4jA268EXbuvDBPTzmDHhtbEHI09aj1s8PEgpXdudn+B+0wl4vtWonnEsnKy7qQtpyTFxY97/G046TnpJf4q85xOKkm06t6fmY/okOsUyaovKWy2f6+EzMSSc9JJz0nnZz8nBLT5+Tn8Nu535yOLe817d/nO+64EPy8/DJXffghN954IwUFBcybN6/48aEl1PiExLis5XG85on0E2Wes0iKq1WBz6lTpxg3bhxvv/02ISEhHh2Tk5NDenq606tayM6GwYOdZ2SuYNADrvu8VHWNj6tfLhuObqDegnq89O1LJaYtC9txE/47gYjHI+xr3bg6X9HRElK1VN5SVWzfqU//9ykRj0cQ8XgE0U9G881v3xRLu+nYJuo/UZ9ntz/rdGx5r+l0/Jgx8Prr1vfPPcejf7Q4vPfee+zdu9fl8a76+LiqHXd8P/WLqcQ8FcPhlMPlyrtY1ZrAxzAMxo4dy/jx4+natavHxy1YsICIiAj7q0mTJlWYSw/l5lpXWXdce6tjx0o59eA2g+0jrC6OvJgODTtUaM0bd1xNEnZFoyu4qN5FABQahXx++HOg4oHPwFYDi80R0ymuE80jmxdLO6TNEGJDY+nVtFe5riVlc8sltxAbGkvPJj19nRWpZa5qcpVTh2CAzLxMNh7dWCzt2l/Wkp1vHfVZL7AevRN6l+uavRN6ExUcxU2tb3Lecddd8O9/A3D5a68xrF07DMNgzpw5TsmKNcs7NPMH+gUyqPUgOjTs4DTC68ZWN2LxswCQlpPG1uNby5V3+YNRzc2YMcMA3L4OHDhg/Otf/zJ69uxp5OfnG4ZhGEeOHDEA44cffnB7/vPnzxtpaWn214kTJwzASEtL88LduZCXZxhDhxoGGEZwsGF89VWlXyInP8fIzss2CgoLDMMwjL2n9hrMw4h6IqpSr9P9te4G8zBWHFjhtL2gsMBYunepwTyMbv/pZhiGYYTMDzGYh3E4+XC5r5ebn2tk52XbX4WFhSWmdbdPKp/KW6pKfkG+/W9+6udTDeZh/OPLfxRLN/6T8QbzMB5Y+4CRV5BXoWu6/T4/8YRhgLEfDLPJZADGN998Y9+dkpViMA+DeRjZednGgHcGGMzDeOP7N+zndnX+3Pxc4/bltxvMw3hq61MVyn9tlZaW5tG/3/6+Cbc8N23aNMaOHes2TUJCAuvXr2fbtm1YLBanfV27dmX06NEsXrzY5bEWi6XYMT5TUABjx8Ly5RAYCKtWlbjgaEUUrRkpuuZNgF9ApVzHXT+bi8IvsqfJzM209/epSHNIgF8AAXiW97LO3yEVo/KWquJn9sPP7AdAfFg8UHzwhOO2RvUa2Qc4lJfb7/P990NmJpc+8gh/NgzeAmbPns2XX34JXBhgkV+Yz5nMM8Vqu0s6d4BfAHGhcdZ7USfnCqn2gU9MTAwxMaV3Qnv++ed59I/e9AAnT56kX79+fPDBB3Rzs3BntWEYMGECLFkC/v6wbJl1CLsX2Ia5GxgkZycTFxZXKed1N1+G0yzSDhN4hQWGVcq1RaTucTeTc0Xn7ymTefMgI4O5zzzDu8Dq1avZtGkT11xzjX36jcSMRKfnnyed/6uqW0JdU+0DH081bdrU6XNYmPUf0BYtWtC4cWNfZMlzhgF//7t1IiyzGd55B26+2WuXtw1zP5Nl/fVRGYGP42rrrv6gHWdbPpZ6DLD+UatmQETKy918NyXN7VUlTCZ4+mkSMjP568KFvAI8OH48m/btw2QyERsaS2JGYrFZ9Euj+XwqR63p3FyjPfww/Otf1vdvvAEjR3o9C65WOK+I0lZbd5xt2XEyRRGR8nJXI1KR9QDLxWSCl15i9tChBAFbDhxg9R9LWdiedb/8/gvn889bt3kwvF7z+VSOWhv4NG/eHMMw6NSpk6+z4t4LL1gDH7COCLjzTp9ko7L/oEpbD8dxtuW9p/c65UFEpDwcZ3I2HBb0zCvIIyU7xSmNV5jNXPT++0xs1QqA2XPmYGzbZn/W7TttnUg2JCCE0MDQUk9XVbPs1zW1NvCpEZYsgSlTrO8feQQmTfJZViq77diTX1f2P36HWaRFRMrL9gw5n3+ejNwM+/azWWcB68CKqOAo72bK35+ZGzYQ5ufHd4WFrOzbl/ZnrJ2xy/rsc/yBamil9nJT4OMrn39uHcEF1uBn9myfZqc8a96448l6OEUDH63lJCIVERoYSrB/MOD8I872Pjo42j4CzJtiLrqIe6dNA2BORgb/N3slTVPL/uyzNZFl52eTmZdZJXmtCxT4+MLXX8PQoZCfD6NHw7PPWtuDfaiqmrrc/ZKx/RHbfo2pxkdEKsrVs6yiE6RWhmmzZhEZEcE+4LOz51j9NnCmbM++0ACHwE7NXeWmwMfb9uyBgQOtS1IMGABvvmkdyeVj9rbjymrq8mDoqOMaXo55EBEpL1cDNSq6FmBliIyMZPr99wPwkJ+ZhGT4bAmE5XieL5PJpJFdlcD3/+LWJb/8Av36QWoq9OwJH34IAZUzWWBFVXqNT5bnTV0lfRYRKavqWuMDMGXKFGJjY/mloJB/B8AVJ2Hl+xDv73m/I83lU3EKfLwlKQn69oXERGjfHj75BDxcSNUbqmo4uydNXSV9FhEpK1eBgX2wRYhvA5+wsDBmzZoFwIxAOOsPvY/A2KfXWGfu94CGtFecAh9vSE2F/v3h55/h4ovhyy+hfn1f58pJVQ5nL+2aJX0WESkrVwM1vDp5YSnGjx/PRRddRF4mXNUJcvyg9cY9MHGidTLbUlT2QJS6SIGPN2RlWVdcb9gQ1qyB+Hhf56gYW9CRlpNGbkFuhc/nyXD2os1g6uMjIhVVnZu6AIKCguwrth86ACNvBsNkgldfvTCnmxvuluUQzyjw8YZGjWDzZli3Dlq08HVuXIoMisTPZB3mWRl/UGUZzg6eT+AlIuKOq4Ea9vWwqsmPq7vvvhu/aD/IhFXpcPyxGdYdDz9snb3fDXuNj4uFWMUzCny8JToaLrvM17kokdlkrrTRAp6utu64rzr8EhORmq+61/gABAQE0PjmP9aQ3AJpI2+DBx6wfr7nHvjiixKPVY1PxSnwEbvKGtLu6WrrjpONVZdfYiJSs1XX4exFtbmuDTQEcuC1f70Gjz4Kd9xh7eQ8bBh8/73L4zScveIU+IhdZXVwdvx1Vdpq67ZrVpdfYiJSsxVd1iEnP4e0nDSnfdVBw3oN4Qbr+4UvL+TI0aPw2mvQuzdkZsKNN8LRo8WO06iuilPgI3aVNaTdk6HsRa9ZnX6JiUjNZas9zivMIz0n3T4zvJ/Jj8igSB/mzFlMSAy0gODWweTm5jJ79mwIDITly6FDBzh1yjoaOCWl+HFYa9a1Xlf5+Ps6A1J92Oa4eGPXGxxJPcIj1z9S4oPix1M/8tbut3jw6gepH2wdml9QWMCcDXPYeHQj4FkwY6/x8fH8GiJSOwQHBBMWGEZGbgZjVo4hvzAfsD6PzKbq81s/NjQWTNByZEv2/HMP7777LtOmTaNz587w2WfQvTscPAg33wxr10JQEHDhuZpbkMuo5aPsg1IcxYXF8eifHiUkoPrMFVedKPARuxZR1hFne0/vZe/pvbSPbc+4LuNcpp2/eT5L9y0loX4CE6+YCMDm45tZsGXBhfPVL30Emy2N7doiIhXVon4Ldp/azccHP3baVp3YnnkdOnWg/e3teffdd7n//vtZs2YNposusi5k3asXbN0Kf/4zLF0KZjMhASHEh8WTmJHI0n1LSzx/98bdGXHZCG/dTo2iwEfsxnUeR73Aeryx6w22HN/CyXMnS0xr2+eYxva+TXQb7u1+L8PbDi/1mvOum0fPJj0ZfMngCuZeRMRq6fClfH7ocwysTUFmk5lBrQf5OFfOhlwyhGXDl9GraS+yL8/mww8/ZN26daxevZp+/fpBu3awcqV1maPly2HaNOuC1sDnoz9nw9ENLs/7zo/vsDNxp9vnd12nwEfsggOCuevyu/jl91/YcnyL285ztn2uhox2iuvE+K7jPbpmVHAUI9uNrECuRUSctY5uTevo1r7Ohlv+Zn+GtR1m/RAGkyZN4tlnn2XGjBn06dMHPz8/uO46WLQIbr8dnnsOWraESZPoGNeRjnEdXZ73l99/YWfiTnV+dqP6NHhKtWEfNeBmgix3gU91GjkhIlITPPjgg0RERLB7926WLFlyYcdtt8Fjj1nfT5nido4f0KgvTyjwkWJKG92VW5BL6vlUa5osF3NlaE4eEZEyiY6Oti9gOnv2bLKzsy/snDkTxo6FwkIYMQL27i3xPJU1H1ttpsBHiintF4NteGjRNLYaItX4iIiU3ZQpU2jcuDEnTpzg6aefvrDDZIKFC+Haa+HcORg0yDrc3QXV+JROgY8UU9ovBlczojq+15w8IiJlFxwczJNPPgnAggULOHHixIWdtjl+WrWCY8dg8GBwrBX6g5a0KJ0CHynG9oeTnJVMQWFBsf2OvyTSctLIyc9x2q4aHxGR8hk1ahS9evUiOzub+++/33lndDT8978QFQU7dlxo/nKgJS1Kp8BHiokOicaECQOD5OzkYvuL1gTZmr5s2xX4iIiUj8lk4vnnn8dkMvH++++zefNm5wStWsFHH0FAgHVun7lznXbbnr/ncs9xPv+8t7JdoyjwkWL8zf5EBUcBrn81FN12OvM0Ofk5pOekA+rcLCJSEZdffjl//etfAZg6dSoFBUVq3q+9Fl591fr+0UfhrbfsuyIsEQSYAwA1d5VEgY+45K6DXNFtZ7LO2Gt7/M3+1Wo9HBGRmmj+/PlERETwww8/8PrrrxdPMHYsPPCA9f1f/wqbNgHWGiM1d7mnwEdccjekvei205mn7X9gMSExpa7ILiIi7sXExDBv3jwAZs2axZkzLmpv/vlPGD4c8vJg6FD7au4a0u6eAh9xyW2NT1bxpi51bBYRqVyTJk2iY8eOpKSk8I9//KN4ArMZFi+Grl3h7FnrSK+MDA1pL4UCH3HJtlq6q18MthqfJuFN7J81lF1EpHIFBASwcOFCTCYTb731FuvXry+eKDgYVqyAuDj48UcYM4aGwe4noa3rFPiIS+7aiG3bLou9zP5ZNT4iIpWvW7duTJw4EYDx48dz/ryLkVqNG1uDn8BAWLGC0St/BlTjUxIFPuKSfRIsVzU+f2y7LOYy+2fbNo3oEhGpXPPnzyc+Pp5Dhw6xYMEC14m6d7eP9Oq/ZAdD96mPT0kU+IhLtgCm6C+G8/nn7cPWbYGPanxERKpOREQEzz//PGCd0Xn//v2uE955J9x3HwCLV0LovkNeymHNosBHXCqpc5ytzdjf7E/r6NbWbQ41Pgp8REQq39ChQxk0aBB5eXmMGTOGvLw81wmfeILTvS4nNA8e+H/fwGk1dxVV6wKf//73v3Tr1o3g4GDq16/PkCFDfJ2lGqmk4eyOTVqOwZHjcHYREalcJpOJhQsXUr9+fXbu3Mn8+fNdJ/T35+hLC/hfFMSn5MKwYZCb693MVnO1KvBZvnw5d9xxB3fddRe7d+9m69at3H777b7OVo1kC2p+P/87eQUXflk4NmnZ0mTkZnAs9ZjTcSIiUrkaNWrESy+9BMCjjz7Kd9995zJd9EUtufk2SLMAmzfD5MlgGF7MafVWawKf/Px8pk6dylNPPcX48eNp3bo1bdu2ZcSIEb7OWo0UFRyF2WT9etjW4gLnFdjDLeH2qdFPZZ6ybxcRkaoxatQoRowYQUFBAWPGjCG7hBXaD8bAqGFgmEzwn//Ayy/7ILfVU60JfL7//nt+++03zGYzl19+OfHx8QwYMIC9e/e6PS4nJ4f09HSnl4DZZKZBSAPAuZ+PY42PyWQqVsOjGh8Rkar10ksvERcXx4EDB3jwwQeL7Q8LDMPiZ+GLVvD7vBnWjVOnwtatXs5p9VRrAp9ffvkFgHnz5jF79mw+/fRT6tevz3XXXUdKSkqJxy1YsICIiAj7q0mTJt7KcrXnaki7vRPzHxMcTr9qOpc0uIQ20W2YcuUUwi3h3s+oiEgdEh0dzWuvvQbAs88+yyeffOK03/FH6c933wIjR0J+vrW/T2Ki1/Nb3VT7wGfmzJmYTCa3r59++onCwkIAHnzwQYYOHUqXLl148803MZlMLFu2rMTzz5o1i7S0NPvrxIkT3rq1as/VkHZ7J+Y/mrSmdp/KgUkH+GnyT/xrwL+8n0kRkTpo4MCBTJkyBYAxY8Zw5MgRp/32SWizzsDrr0O7dpCUZF3bq453dvb3dQZKM23aNMaOHes2TUJCAol/RLFt27a1b7dYLCQkJHD8+PESj7VYLFgslkrJa23jaki75usREakennrqKXbs2MGOHTsYPnw4W7ZsISgoCChSYx8aap3ZuWtXa3PXfffBv//ty6z7VLUPfGJiYoiJKb3DbJcuXbBYLBw8eJBevXoBkJeXx9GjR2nWrFlVZ7NWsq/wm1m8qUvD1kVEfCswMJClS5fSuXNndu7cyX333Wcf9VWsxr5lS1iyBAYNghdfhCuusE54WAdV+6YuT4WHhzN+/Hjmzp3L6tWrOXjwIBMmTABg+PDhPs5dzaQaHxGR6q1p06a88847mEwmXn75ZRYvXgyUMAntwIEwb571/fjx8P33Xs5t9VBrAh+wVvuNGjWKO+64gyuuuIJjx46xfv166tev7+us1UguOzdrFXYRkWqlf//+zJ49G4Bx48axcePGktdbnDPHWutz/jzceiucPVv0dLVerQp8AgICePrppzl16hTp6emsWbOGyy67zNfZqrGKrtCelZdFZl4moBofEZHqZN68eQwfPpy8vDxuueUW8pKsE88WW6HdbIa337Y2fR07BrfdBgUFPsix79SqwEcqV9FfDLbaHoufhXqB9XyWLxERcWY2m1m8eDE9evQgNTWVF6a+ABnFlx0CIDLS2tk5JATWrgUXcwHVZgp8pERFO8c5DmU3mUw+y5eIiBQXHBzMqlWraNGiBad+OwXvQlJykuvE7drBm29a3z/xBHz0kfcy6mMKfKREthqf9Jx0cvJz1LFZRKSai4mJ4bPPPiOyfiSchJMLT3Lu3DnXiUeMgGnTrO/vugsOHfJaPn1JgY+UKDIoEn+zdcaDM1lnNJRdRKQGaN26Nas+XQUWMI4ZDLhxAJmZma4TL1gAvXpBerp1ZmcXa3/VNgp8pEQmk8mpuUs1PiIiNcM1V12D5S4LWGDrlq3cdNNNZGVlFU8YEAAffACxsfDjj/C3v3k/s16mwEfcsndwzjxzYSi7anxERKq9+Evi4c8QEhbChg0bGDBgAKmpqcUTNmoE774LJpN1eYtFi7ydVa9S4CNuOQ5pP52lGh8RkZoiJiQGmsBDrz5EeHg4mzZtolevXvz666/FE/fuDQ8/bH0/cSLs2ePdzHqRAh9xy3FIu63GR4GPiEj1Z3tWN2jTgM2bNxMfH8++ffvo0aMHe/fuLX7Agw9Cv37Wfj5Dh1r7/dRCCnzELVd9fDRrs4hI9edYY9+hQwe2bdvGJZdcwq+//srVV1/NunXrnA8wm+Gdd6BxY+sIr7/+FQzDBzmvWgp8xC3H9V7UuVlEpOaIDXGehLZZs2Zs3bqVnj17kpqaSr9+/eyLmto1aADLloG/v/W/tXAVdwU+4pZjjY+Gs4uI1ByuFiqNiopi7dq1jB49moKCAiZNmsTkyZPJz8+/cGD37vD009b306bBjh3ezHaVU+Ajbtn+cI6kHuF8/nmnbSIiUn3ZmrqKLlQaFBTE22+/zWOPPQbAiy++yIABA/j9998vJJoyxdrPJy8Phg+H5GSv5buqKfARt2xBzv4z+wEICQghNDDUl1kSEREPuKrxsTGZTMyaNYsVK1YQGhrK2rVr6datGwcPHrQlgDfegFat4MQJGDu21vT3UeAjbhXtyKxmLhGRmqHoeouuDBkyhK1bt9KkSRMOHTpE9+7dWbNmDat+WkXfVcM4vehFDIsFPv2U/068odRrZuRmMPDdgbz+/euVdh+VTYGPuNU4vDFRwVH2zx3jOvowNyIi4inHCWgNN7U1HTt25Ntvv7Wv7D5gwABmPDaDNT+vYUXgL6Q8al29/YZX15G/Y7vba244soHPDn3GM9ufqbwbqWQKfMStIP8g9k/cz4Y7N/DV2K94f+j7vs6SiIh4wFZjn1eYR1pOmtu0DRs2ZMOGDYwZM4aCggIOvn0Q/guJaYn8PKovH14KgYXAqFGQVvK5bLVL7mqZfE2Bj5SqYVhDrmt+Hdc0u4bggGBfZ0dERDwQ5B9EvcB6APYJaN2xWCwsWrSIJ554AkzAd/DW7Lf4LfUk426GoxHgf/QY/N//ldjfxxbwJGclk1+Y7zKNrynwERERqaXcdXB2xWQy8Y/p/8A8ygx+cGT7EeaMm0OqP9w2DAr9zNZFTV97zeXxtusYGCRnVc+RYAp8REREaqmShrS7k3o+lcI2hTAa/Cx+7Nu+D5bB9kaw+28jrImmTIF9+4od63idslzTmxT4iIiI1FJlrfFxSpsATSY0wS/QD/4HrISNw7pa1/M6fx5GjICsLNfHlvGa3qTAR0REpJbyZEh7UY79gbIuyuLa6ddao4U9sPK1j+GttyAuDvbvh6lTnY91rPHxoF+RLyjwERERqaUch7R7yjFIOpt1Fr82fjDQ+nnTok2s2LrVupipyWTt6/P++y6PVY2PiIiIeJW9qSurDDU+DrU2hUYhB5MPQhfgSuu2u+66i2MtW8IDD1g33HMPHD6MYRhOAZb6+IiIiIhX2Zq6ylvjA3A87bj1TT8ISwgjLS3NOt/PnDnQqxecOwe3305axlnyCvNKPE91ocBHRESklqpQ5+ai/CDq9ihCQ0PZtGkTz77wArz7LkRGwrffUjD3Ic/O42MKfERERGqp8gxnd5c2NTSV5557DoB58+bxq8kEr74KQNRzC7n6qGfn8SUFPiIiIrWUY+fmQqPQo2NsNTVm04UQwfY+PSed0WNG07NnTzIzM5k2bRoMHw5jx2IyDN75CKLOm5zOU90o8BEREamlGoQ0AKDAKOD37N89OsbWH6hF/Rb2bU3Cm+Bv9gcg+XwyL774ImazmaVLl7J+/Xp4/nnSGsfQNB3e/jIUDA1nFxERES8L9AskMigS8LzpyVZTc1nsZfZtDcMaOs0J1LFjRyZMmADA7NmzMcLCWD77VvJNcOMPGYzZDb+f/528grziF/AxBT4iIiK1WFk6OBcUFpCcbV1j67KYC4FPTEjMhf5Cf9TkPPjggwQFBbFt2za+/PJLdjUNZO711vT//gwSUqzzAFU3CnxERERqsbIMaU/JTrH3Bbq0waX27bGhscUCqPj4eCZOnAjAQw89xOnM0zzeC37tlEC9XFiyHE6n/lap91IZalXg87///Y/BgwfToEEDwsPD6dWrFxs2bPB1tkRERHymLDU+tjRRwVFcFH6R0znsHaUdmszuv/9+QkJC+Pbbbzn4zUEKzfDt41NIDzbT/Teo98RzlXgnlaNWBT6DBg0iPz+f9evXs3PnTjp27MigQYNISkryddZERER8wl7j40EfH1uamJAY+3FFPzsGUA0bNuTuu+8G4OcvfgYgtOWl/Osua21R85ffhc2bK+EuKk+tCXzOnj3LoUOHmDlzJh06dKBVq1Y8/vjjZGVlsXfvXl9nT0RExCfKU+PjWMNT9HPR80yePBmAc3vPQYo17f4/tWdRRzAXGvDnP0NqamXcSqWoNYFPdHQ0bdq04a233iIzM5P8/HwWLlxIbGwsXbp08XX2REREfKIskxja+gHFhMYQFRyFCZP9s63G57uT3/HCjhd4YccLvLfnPRJaJtC3X18wgG8v1A797UZIjA2G48cpnDAewzB4fMvjvPb9a2TnZVfNzXrA32dXrmQmk4m1a9cyZMgQ6tWrh9lsJjY2li+++IL69euXeFxOTg45OTn2z+np6d7IroiIiFeUq8YnJBY/sx9xYXEkZiTSqF4jCgoLANhzeg9TvphiP8bP7Mdd99zF6i9Xww9Qz68ejeo1IsMCQ27OZuvr4P/+B2T1v4FZR2cBcHv72yv7Nj1W7Wt8Zs6ciclkcvv66aefMAyDSZMmERsby+bNm/nmm28YMmQIN910E4mJiSWef8GCBURERNhfTZo08eLdiYiIVK2yBD62WiHbMS/e+CIPX/cw7WPbc0OLG5jWYxojLxvJyMtG2ic4PJxymPY920MEcB5Wf76asZ3G8n9d/o+UDi159BrruS1T76NxGoQGhBISEFL5N+ohk2EYhs+u7oEzZ86QnJzsNk1CQgKbN2+mb9++/P7774SHh9v3tWrVir/85S/MnDnT5bGuanyaNGlCWlqa03lERERqoj2n9tDhlQ7EhMRwerr74GfY0mEsP7CcFwa8wOQrJ7tNO2PNDJ78+knu7XYvt1x6C9f++VrYAoMHD2blypUAzF4/myc2zufQB7E0/99p1l0M9/ytOT///Uhl3Z5deno6ERERpf77Xe2bumJiYoiJiSk1XVZWFgBms3MlltlsprCw5PVJLBYLFoulYpkUERGppmy1N2ezzlJQWICf2a/EtI6dmz097+ms09bjOgBb4LPPPiM5OZno6GhiQ2PJ94NnJ3bm/83aQO8jOUze4dmaYVWl2jd1eapHjx7Ur1+fO++8k927d/O///2P6dOnc+TIEQYOHOjr7ImIiPhEdEg0AAYGKdkpbtM6DmcvjeMCqGcyz0AsRDSPIC8vj2XLljml+THyPNunDgNg0ke/woED5buZSlBrAp8GDRrwxRdfkJGRwZ/+9Ce6du3Kli1bWLVqFR07dvR19kRERHzC3+xPdLA1+Cmtn09Zanxso8VOZ562H3fp9db5e5YsWWJN4zD3z6YBl/Jxa1g7qC0kJJTjTipHtW/qKouuXbvy5Zdf+jobIiIi1UpMaAzJ2cluh7TnF+bba4RsQY07jp2mbYFP1xu6sv3N7WzdupUzZ844p8k6wy2jYPrVA7nRh11Mak2Nj4iIiLjmycgu24KiJkz2GiJ3HGeEPp1lPW+rhFZ06tQJwzD47LPP7AFUclYySRlJFJo9q02qSgp8REREajlPAh/b5IUNQhq47QBtYwtq8gvzOZR8yH6dm266CYCPP/6YBiENAGv/op/O/mQ9zoP+Q1VJgY+IiEgt58kK7bagyJNmLoAg/yDqBdYDcApqbr75ZgBWr15NQV4BUcFRTmlU4yMiIiJVypMan7J0bC563pyCHPvnzp07Ex8fT0ZGBhs3biyWxtPAqqoo8BEREanlPFmhvSxD2W2KBkkxoTGYzWZ7c9cnn3xSLI1qfERERKRKVVWNT9HaG1ufnv79+wOwfv36YoGU+viIiIhIlbJPNuiuxifTeZ0uj84bciFtdHA0/mbrLDnXXnstJpOJAwcOEJoTak8TbgnH4u/b1RIU+IiIiNRyjpMNlsQ2JL0sNTKONT6O76OioujUqRMAGQcz7Nt93cwFCnxERERqPVvAkZKdQl5Bnss05arxcUhb9Ljrr78egKS9SfZtvm7mAgU+IiIitV5UcBRmk/Wf/OTsZJdpyjqcHZwDmaJBzZ/+9CcAfv7+Z/s21fiIiIhIlTObzPaOxyU1d1VkOLur466++mr8/Pw4deIUpLpO4wsKfEREROoAd5MY5hbkkpaT5pTOo3OGllzjEx4eTpcuXawfjrpO4wsKfEREROoAd0PabcGQn8mP+sH1y3zOou9trr76auubX0tO4221anV2ERERcc0WdCRlJBXr4JyYkQj8MQGhyfM6EVvzmeP5HXXv3t36RoGPiIiIeJOtmem+1fdx3+r73KbxVKBfIJFBkaSeT3XZKbpbt27WN6eBXN8vVwFq6hIREakTbmhxAwHmALdpBrQcUObzDmw1kPiweDo27FhsX+PGjWnUqBEUQv3f63N53OVlPn9lU42PiIhIHXBzm5tJnZlKbkGuy/1+Jj/qWeqV+bzv3PoOBYUF+Jn9iu0zmUx069aNFStWMKP5DKJDost8/sqmwEdERKSOCAkIISQgpNLP6yrosenevTsrVqzg22++rfTrloeaukRERKTK2Pr5bN++3cc5sVLgIyIiIlWma9eumM1mfvvtN3777TdfZ0eBj4iIiFSd0NBQ2rVrB8DOnTt9nBsFPiIiIlLFOna0jvjavXu3j3OiwEdERESqmAIfERERqTMU+IiIiEidYQt8Dh8+zLlz53yaFwU+IiIiUqViYmKsMzgDe/bs8WleFPiIiIhIlasuzV0KfERERKTKKfARERGROqNTp06AAh8RERGpA2w1Pj/++CMFBQU+y4cCHxEREalyrVq1Ijg4mKioKE6dOuWzfGh1dhEREalyfn5+JCUlER4e7tN81Jgan/nz53PVVVcREhJCZGSkyzTHjx9n4MCBhISEEBsby/Tp08nPz/duRkVERMQlXwc9UINqfHJzcxk+fDg9evTg9ddfL7a/oKCAgQMHEhcXx9dff01iYiJjxowhICCAxx57zAc5FhERkerGZBiG4etMlMWiRYu49957SU1Nddr++eefM2jQIE6ePEnDhg0BeOWVV5gxYwZnzpwhMDDQo/Onp6cTERFBWlpatYhMRUREpHSe/vtdY5q6SrNt2zbat29vD3oA+vXrR3p6Ovv27SvxuJycHNLT051eIiIiUjvVmsAnKSnJKegB7J+TkpJKPG7BggVERETYX02aNKnSfIqIiIjv+DTwmTlzJiaTye3rp59+qtI8zJo1i7S0NPvrxIkTVXo9ERER8R2fdm6eNm0aY8eOdZsmISHBo3PFxcXxzTffOG2zzRMQFxdX4nEWiwWLxeLRNURERKRm82ngExMTQ0xMTKWcq0ePHsyfP5/Tp08TGxsLwJo1awgPD6dt27aVcg0RERGp2WrMcPbjx4+TkpLC8ePHKSgoYNeuXQC0bNmSsLAw+vbtS9u2bbnjjjt48sknSUpKYvbs2UyaNEk1OiIiIgLUoOHsY8eOZfHixcW2b9iwgeuuuw6AY8eOMWHCBDZu3EhoaCh33nknjz/+OP7+nsd3Gs4uIiJS83j673eNCXy8RYGPiIhIzVPn5vERERERKY0CHxEREakzFPiIiIhInVFjRnV5i63Lk5auEBERqTls/26X1nVZgU8R586dA9DSFSIiIjXQuXPniIiIKHG/RnUVUVhYyMmTJ6lXrx4mk6nSzpuenk6TJk04ceKERotVIZWzd6icvUdl7R0qZ++oynI2DINz587RqFEjzOaSe/KoxqcIs9lM48aNq+z84eHh+qPyApWzd6icvUdl7R0qZ++oqnJ2V9Njo87NIiIiUmco8BEREZE6Q4GPl1gsFubOnat1w6qYytk7VM7eo7L2DpWzd1SHclbnZhEREakzVOMjIiIidYYCHxEREakzFPiIiIhInaHAR0REROoMBT5e8uKLL9K8eXOCgoLo1q0b33zzja+zVKPNmzcPk8nk9Lrkkkvs+8+fP8+kSZOIjo4mLCyMoUOHcurUKR/muGbYtGkTN910E40aNcJkMrFy5Uqn/YZh8NBDDxEfH09wcDB9+vTh0KFDTmlSUlIYPXo04eHhREZG8pe//IWMjAwv3kX1V1o5jx07ttj3u3///k5pVM6lW7BgAVdccQX16tUjNjaWIUOGcPDgQac0njwrjh8/zsCBAwkJCSE2Npbp06eTn5/vzVup1jwp5+uuu67Yd3r8+PFOabxVzgp8vOCDDz7gvvvuY+7cuXz//fd07NiRfv36cfr0aV9nrUa77LLLSExMtL+2bNli3/f3v/+dTz75hGXLlvHVV19x8uRJbr31Vh/mtmbIzMykY8eOvPjiiy73P/nkkzz//PO88sor7Nixg9DQUPr168f58+ftaUaPHs2+fftYs2YNn376KZs2beKee+7x1i3UCKWVM0D//v2dvt/vvfee036Vc+m++uorJk2axPbt21mzZg15eXn07duXzMxMe5rSnhUFBQUMHDiQ3Nxcvv76axYvXsyiRYt46KGHfHFL1ZIn5Qwwbtw4p+/0k08+ad/n1XI2pMpdeeWVxqRJk+yfCwoKjEaNGhkLFizwYa5qtrlz5xodO3Z0uS81NdUICAgwli1bZt924MABAzC2bdvmpRzWfICxYsUK++fCwkIjLi7OeOqpp+zbUlNTDYvFYrz33nuGYRjG/v37DcD49ttv7Wk+//xzw2QyGb/99pvX8l6TFC1nwzCMO++80xg8eHCJx6icy+f06dMGYHz11VeGYXj2rPjss88Ms9lsJCUl2dO8/PLLRnh4uJGTk+PdG6ghipazYRjGtddea0ydOrXEY7xZzqrxqWK5ubns3LmTPn362LeZzWb69OnDtm3bfJizmu/QoUM0atSIhIQERo8ezfHjxwHYuXMneXl5TmV+ySWX0LRpU5V5BRw5coSkpCSnco2IiKBbt272ct22bRuRkZF07drVnqZPnz6YzWZ27Njh9TzXZBs3biQ2NpY2bdowYcIEkpOT7ftUzuWTlpYGQFRUFODZs2Lbtm20b9+ehg0b2tP069eP9PR09u3b58Xc1xxFy9lmyZIlNGjQgHbt2jFr1iyysrLs+7xZzlqktIqdPXuWgoICp/+ZAA0bNuSnn37yUa5qvm7durFo0SLatGlDYmIiDz/8MFdffTV79+4lKSmJwMBAIiMjnY5p2LAhSUlJvslwLWArO1ffZdu+pKQkYmNjnfb7+/sTFRWlsi+D/v37c+utt3LxxRfz888/88ADDzBgwAC2bduGn5+fyrkcCgsLuffee+nZsyft2rUD8OhZkZSU5PI7b9snzlyVM8Dtt99Os2bNaNSoET/++CMzZszg4MGDfPTRR4B3y1mBj9RIAwYMsL/v0KED3bp1o1mzZixdupTg4GAf5kyk4kaNGmV/3759ezp06ECLFi3YuHEjvXv39mHOaq5Jkyaxd+9ep76AUvlKKmfH/mft27cnPj6e3r178/PPP9OiRQuv5lFNXVWsQYMG+Pn5FRslcOrUKeLi4nyUq9onMjKS1q1bc/jwYeLi4sjNzSU1NdUpjcq8Ymxl5+67HBcXV6zTfn5+PikpKSr7CkhISKBBgwYcPnwYUDmX1eTJk/n000/ZsGEDjRs3tm/35FkRFxfn8jtv2ycXlFTOrnTr1g3A6TvtrXJW4FPFAgMD6dKlC+vWrbNvKywsZN26dfTo0cOHOatdMjIy+Pnnn4mPj6dLly4EBAQ4lfnBgwc5fvy4yrwCLr74YuLi4pzKNT09nR07dtjLtUePHqSmprJz5057mvXr11NYWGh/0EnZ/frrryQnJxMfHw+onD1lGAaTJ09mxYoVrF+/nosvvthpvyfPih49erBnzx6nQHPNmjWEh4fTtm1b79xINVdaObuya9cuAKfvtNfKuVK7SotL77//vmGxWIxFixYZ+/fvN+655x4jMjLSqfe6lM20adOMjRs3GkeOHDG2bt1q9OnTx2jQoIFx+vRpwzAMY/z48UbTpk2N9evXG999953Ro0cPo0ePHj7OdfV37tw544cffjB++OEHAzCeeeYZ44cffjCOHTtmGIZhPP7440ZkZKSxatUq48cffzQGDx5sXHzxxUZ2drb9HP379zcuv/xyY8eOHcaWLVuMVq1aGbfddpuvbqlaclfO586dM/7xj38Y27ZtM44cOWKsXbvW6Ny5s9GqVSvj/Pnz9nOonEs3YcIEIyIiwti4caORmJhof2VlZdnTlPasyM/PN9q1a2f07dvX2LVrl/HFF18YMTExxqxZs3xxS9VSaeV8+PBh45FHHjG+++4748iRI8aqVauMhIQE45prrrGfw5vlrMDHS1544QWjadOmRmBgoHHllVca27dv93WWarSRI0ca8fHxRmBgoHHRRRcZI0eONA4fPmzfn52dbUycONGoX7++ERISYtxyyy1GYmKiD3NcM2zYsMEAir3uvPNOwzCsQ9rnzJljNGzY0LBYLEbv3r2NgwcPOp0jOTnZuO2224ywsDAjPDzcuOuuu4xz58754G6qL3flnJWVZfTt29eIiYkxAgICjGbNmhnjxo0r9kNJ5Vw6V2UMGG+++aY9jSfPiqNHjxoDBgwwgoODjQYNGhjTpk0z8vLyvHw31Vdp5Xz8+HHjmmuuMaKiogyLxWK0bNnSmD59upGWluZ0Hm+Vs+mPTIuIiIjUeurjIyIiInWGAh8RERGpMxT4iIiISJ2hwEdERETqDAU+IiIiUmco8BEREZE6Q4GPiIiI1BkKfERERKTOUOAjIjXCmTNnmDBhAk2bNsVisRAXF0e/fv3YunUrACaTiZUrV/o2kyJS7fn7OgMiIp4YOnQoubm5LF68mISEBE6dOsW6detITk72ddZEpAZRjY+IVHupqals3ryZJ554guuvv55mzZpx5ZVXMmvWLG6++WaaN28OwC233ILJZLJ/Bli1ahWdO3cmKCiIhIQEHn74YfLz8+37TSYTL7/8MgMGDCA4OJiEhAQ+/PBD+/7c3FwmT55MfHw8QUFBNGvWjAULFnjr1kWkkinwEZFqLywsjLCwMFauXElOTk6x/d9++y0Ab775JomJifbPmzdvZsyYMUydOpX9+/ezcOFCFi1axPz5852OnzNnDkOHDmX37t2MHj2aUaNGceDAAQCef/55Pv74Y5YuXcrBgwdZsmSJU2AlIjWLFikVkRph+fLljBs3juzsbDp37sy1117LqFGj6NChA2CtuVmxYgVDhgyxH9OnTx969+7NrFmz7Nveeecd7r//fk6ePGk/bvz48bz88sv2NN27d6dz58689NJLTJkyhX379rF27VpMJpN3blZEqoxqfESkRhg6dCgnT57k448/pn///mzcuJHOnTuzaNGiEo/ZvXs3jzzyiL3GKCwsjHHjxpGYmEhWVpY9XY8ePZyO69Gjh73GZ+zYsezatYs2bdowZcoUVq9eXSX3JyLeocBHRGqMoKAgbrjhBubMmcPXX3/N2LFjmTt3bonpMzIyePjhh9m1a5f9tWfPHg4dOkRQUJBH1+zcuTNHjhzhn//8J9nZ2YwYMYJhw4ZV1i2JiJcp8BGRGqtt27ZkZmYCEBAQQEFBgdP+zp07c/DgQVq2bFnsZTZfePxt377d6bjt27dz6aWX2j+Hh4czcuRI/vOf//DBBx+wfPlyUlJSqvDORKSqaDi7iFR7ycnJDB8+nLvvvpsOHTpQr149vvvuO5588kkGDx4MQPPmzVm3bh09e/bEYrFQv359HnroIQYNGkTTpk0ZNmwYZrOZ3bt3s3fvXh599FH7+ZctW0bXrl3p1asXS5Ys4ZtvvuH1118H4JlnniE+Pp7LL78cs9nMsmXLiIuLIzIy0hdFISIVZYiIVHPnz583Zs6caXTu3NmIiIgwQkJCjDZt2hizZ882srKyDMMwjI8//tho2bKl4e/vbzRr1sx+7BdffGFcddVVRnBwsBEeHm5ceeWVxquvvmrfDxgvvviiccMNNxgWi8Vo3ry58cEHH9j3v/rqq0anTp2M0NBQIzw83Ojdu7fx/fffe+3eRaRyaVSXiNRprkaDiUjtpT4+IiIiUmco8BEREZE6Q52bRaROU2u/SN2iGh8RERGpMxT4iIiISJ2hwEdERETqDAU+IiIiUmco8BEREZE6Q4GPiIiI1BkKfERERKTOUOAjIiIidYYCHxEREakz/j+lg1A3L4LufQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "steps=250\n",
    "\n",
    "distance=0\n",
    "x=0\n",
    "distance_list=[]\n",
    "steps_list=[]\n",
    "while x<steps:\n",
    "    distance+=np.random.randint(-1,2)\n",
    "    distance_list.append(distance)\n",
    "    x+=1\n",
    "    steps_list.append(x)\n",
    "plt.plot(steps_list,distance_list, color='green', label=\"Random Walk Data\")\n",
    "\n",
    "steps_list=np.asarray(steps_list)\n",
    "distance_list=np.asarray(distance_list)\n",
    "\n",
    "X=steps_list[:,np.newaxis]\n",
    "\n",
    "#Polynomial fits\n",
    "\n",
    "#Degree 2\n",
    "poly_features=PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_poly=poly_features.fit_transform(X)\n",
    "\n",
    "lin_reg=LinearRegression()\n",
    "poly_fit=lin_reg.fit(X_poly,distance_list)\n",
    "b=lin_reg.coef_\n",
    "c=lin_reg.intercept_\n",
    "print (\"2nd degree coefficients:\")\n",
    "print (\"zero power: \",c)\n",
    "print (\"first power: \", b[0])\n",
    "print (\"second power: \",b[1])\n",
    "\n",
    "z = np.arange(0, steps, .01)\n",
    "z_mod=b[1]*z**2+b[0]*z+c\n",
    "\n",
    "fit_mod=b[1]*X**2+b[0]*X+c\n",
    "plt.plot(z, z_mod, color='r', label=\"2nd Degree Fit\")\n",
    "plt.title(\"Polynomial Regression\")\n",
    "\n",
    "plt.xlabel(\"Steps\")\n",
    "plt.ylabel(\"Distance\")\n",
    "\n",
    "#Degree 10\n",
    "poly_features10=PolynomialFeatures(degree=10, include_bias=False)\n",
    "X_poly10=poly_features10.fit_transform(X)\n",
    "\n",
    "poly_fit10=lin_reg.fit(X_poly10,distance_list)\n",
    "\n",
    "y_plot=poly_fit10.predict(X_poly10)\n",
    "plt.plot(X, y_plot, color='black', label=\"10th Degree Fit\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#Decision Tree Regression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regr_1=DecisionTreeRegressor(max_depth=2)\n",
    "regr_2=DecisionTreeRegressor(max_depth=5)\n",
    "regr_3=DecisionTreeRegressor(max_depth=7)\n",
    "regr_1.fit(X, distance_list)\n",
    "regr_2.fit(X, distance_list)\n",
    "regr_3.fit(X, distance_list)\n",
    "\n",
    "X_test = np.arange(0.0, steps, 0.01)[:, np.newaxis]\n",
    "y_1 = regr_1.predict(X_test)\n",
    "y_2 = regr_2.predict(X_test)\n",
    "y_3=regr_3.predict(X_test)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure()\n",
    "plt.scatter(X, distance_list, s=2.5, c=\"black\", label=\"data\")\n",
    "plt.plot(X_test, y_1, color=\"red\",\n",
    "         label=\"max_depth=2\", linewidth=2)\n",
    "plt.plot(X_test, y_2, color=\"green\", label=\"max_depth=5\", linewidth=2)\n",
    "plt.plot(X_test, y_3, color=\"m\", label=\"max_depth=7\", linewidth=2)\n",
    "\n",
    "plt.xlabel(\"Data\")\n",
    "plt.ylabel(\"Darget\")\n",
    "plt.title(\"Decision Tree Regression\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a tree, regression\n",
    "\n",
    "There are mainly two steps\n",
    "1. We split the predictor space (the set of possible values $x_1,x_2,\\dots, x_p$) into $J$ distinct and non-non-overlapping regions, $R_1,R_2,\\dots,R_J$.  \n",
    "\n",
    "2. For every observation that falls into the region $R_j$ , we make the same prediction, which is simply the mean of the response values for the training observations in $R_j$.\n",
    "\n",
    "How do we construct the regions $R_1,\\dots,R_J$?  In theory, the\n",
    "regions could have any shape. However, we choose to divide the\n",
    "predictor space into high-dimensional rectangles, or boxes, for\n",
    "simplicity and for ease of interpretation of the resulting predictive\n",
    "model. The goal is to find boxes $R_1,\\dots,R_J$ that minimize the\n",
    "MSE, given by"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where $\\overline{y}_{R_j}$  is the mean response for the training observations \n",
    "within box $j$. \n",
    "\n",
    "\n",
    "Unfortunately, it is computationally infeasible to consider every\n",
    "possible partition of the feature space into $J$ boxes.  The common\n",
    "strategy is to take a top-down approach\n",
    "\n",
    "The approach is top-down because it begins at the top of the tree (all\n",
    "observations belong to a single region) and then successively splits\n",
    "the predictor space; each split is indicated via two new branches\n",
    "further down on the tree. It is greedy because at each step of the\n",
    "tree-building process, the best split is made at that particular step,\n",
    "rather than looking ahead and picking a split that will lead to a\n",
    "better tree in some future step.\n",
    "\n",
    "\n",
    "### Making a tree\n",
    "\n",
    "In order to implement the recursive binary splitting we start by selecting\n",
    "the predictor $x_j$ and a cutpoint $s$ that splits the predictor space into two regions $R_1$ and $R_2$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\left\\{X\\vert x_j < s\\right\\},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\left\\{X\\vert x_j \\geq s\\right\\},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so that we obtain the lowest MSE, that is"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\sum_{i:x_i\\in R_j}(y_i-\\overline{y}_{R_1})^2+\\sum_{i:x_i\\in R_2}(y_i-\\overline{y}_{R_2})^2,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which we want to minimize by considering all predictors\n",
    "$x_1,x_2,\\dots,x_p$.  We consider also all possible values of $s$ for\n",
    "each predictor. These values could be determined by randomly assigned\n",
    "numbers or by starting at the midpoint and then proceed till we find\n",
    "an optimal value.\n",
    "\n",
    "For any $j$ and $s$, we define the pair of half-planes where\n",
    "$\\overline{y}_{R_1}$ is the mean response for the training\n",
    "observations in $R_1(j,s)$, and $\\overline{y}_{R_2}$ is the mean\n",
    "response for the training observations in $R_2(j,s)$.\n",
    "\n",
    "Finding the values of $j$ and $s$ that minimize the above equation can be\n",
    "done quite quickly, especially when the number of features $p$ is not\n",
    "too large.\n",
    "\n",
    "Next, we repeat the process, looking\n",
    "for the best predictor and best cutpoint in order to split the data\n",
    "further so as to minimize the MSE within each of the resulting\n",
    "regions. However, this time, instead of splitting the entire predictor\n",
    "space, we split one of the two previously identified regions. We now\n",
    "have three regions. Again, we look to split one of these three regions\n",
    "further, so as to minimize the MSE. The process continues until a\n",
    "stopping criterion is reached; for instance, we may continue until no\n",
    "region contains more than five observations.\n",
    "\n",
    "\n",
    "The above procedure is rather straightforward, but leads often to\n",
    "overfitting and unnecessarily large and complicated trees. The basic\n",
    "idea is to grow a large tree $T_0$ and then prune it back in order to\n",
    "obtain a subtree. A smaller tree with fewer splits (fewer regions) can\n",
    "lead to smaller variance and better interpretation at the cost of a\n",
    "little more bias.\n",
    "\n",
    "The so-called Cost complexity pruning algorithm gives us a\n",
    "way to do just this. Rather than considering every possible subtree,\n",
    "we consider a sequence of trees indexed by a nonnegative tuning\n",
    "parameter $\\alpha$.\n",
    "\n",
    "Read more at the following [Scikit-Learn link on pruning](https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py).\n",
    "\n",
    "\n",
    "For each value of $\\alpha$  there corresponds a subtree $T \\in T_0$ such that"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is as small as possible. Here $\\overline{T}$ is \n",
    "the number of terminal nodes of the tree $T$ , $R_m$ is the\n",
    "rectangle (i.e. the subset of predictor space)  corresponding to the $m$-th terminal node.\n",
    "\n",
    "The tuning parameter $\\alpha$ controls a trade-off between the subtree’s\n",
    "complexity and its fit to the training data. When $\\alpha = 0$, then the\n",
    "subtree $T$ will simply equal $T_0$, \n",
    "because then the above equation just measures the\n",
    "training error. \n",
    "However, as $\\alpha$ increases, there is a price to pay for\n",
    "having a tree with many terminal nodes. The above equation will\n",
    "tend to be minimized for a smaller subtree. \n",
    "\n",
    "\n",
    "It turns out that as we increase $\\alpha$ from zero\n",
    "branches get pruned from the tree in a nested and predictable fashion,\n",
    "so obtaining the whole sequence of subtrees as a function of $\\alpha$ is\n",
    "easy. We can select a value of $\\alpha$ using a validation set or using\n",
    "cross-validation. We then return to the full data set and obtain the\n",
    "subtree corresponding to $\\alpha$. \n",
    "\n",
    "\n",
    "### Schematic Regression Procedure\n",
    "\n",
    "Building a Regression Tree\n",
    "\n",
    "1. Use recursive binary splitting to grow a large tree on the training data, stopping only when each terminal node has fewer than some minimum number of observations.\n",
    "\n",
    "2. Apply cost complexity pruning to the large tree in order to obtain a sequence of best subtrees, as a function of $\\alpha$.\n",
    "\n",
    "3. Use for example $K$-fold cross-validation to choose $\\alpha$. Divide the training observations into $K$ folds. For each $k=1,2,\\dots,K$ we: \n",
    "\n",
    "  * repeat steps 1 and 2 on all but the $k$-th fold of the training data. \n",
    "\n",
    "  * Then we valuate the mean squared prediction error on the data in the left-out $k$-th fold, as a function of $\\alpha$.\n",
    "\n",
    "  * Finally  we average the results for each value of $\\alpha$, and pick $\\alpha$ to minimize the average error.\n",
    "\n",
    "\n",
    "4. Return the subtree from Step 2 that corresponds to the chosen value of $\\alpha$. \n",
    "\n",
    "!eblock\n",
    "\n",
    "\n",
    "\n",
    "## A Classification Tree\n",
    "\n",
    "A classification tree is very similar to a regression tree, except\n",
    "that it is used to predict a qualitative response rather than a\n",
    "quantitative one. Recall that for a regression tree, the predicted\n",
    "response for an observation is given by the mean response of the\n",
    "training observations that belong to the same terminal node. In\n",
    "contrast, for a classification tree, we predict that each observation\n",
    "belongs to the most commonly occurring class of training observations\n",
    "in the region to which it belongs. In interpreting the results of a\n",
    "classification tree, we are often interested not only in the class\n",
    "prediction corresponding to a particular terminal node region, but\n",
    "also in the class proportions among the training observations that\n",
    "fall into that region.  \n",
    "\n",
    "\n",
    "\n",
    "The task of growing a\n",
    "classification tree is quite similar to the task of growing a\n",
    "regression tree. Just as in the regression setting, we use recursive\n",
    "binary splitting to grow a classification tree. However, in the\n",
    "classification setting, the MSE cannot be used as a criterion for making\n",
    "the binary splits.  A natural alternative to MSE is the **classification\n",
    "error rate**. Since we plan to assign an observation in a given region\n",
    "to the most commonly occurring error rate class of training\n",
    "observations in that region, the classification error rate is simply\n",
    "the fraction of the training observations in that region that do not\n",
    "belong to the most common class. \n",
    "\n",
    "When building a classification tree, either the Gini index or the\n",
    "entropy are typically used to evaluate the quality of a particular\n",
    "split, since these two approaches are more sensitive to node purity\n",
    "than is the classification error rate. \n",
    "\n",
    "\n",
    "\n",
    "If our targets are the outcome of a classification process that takes\n",
    "for example $k=1,2,\\dots,K$ values, the only thing we need to think of\n",
    "is to set up the splitting criteria for each node.\n",
    "\n",
    "We define a PDF $p_{mk}$ that represents the number of observations of\n",
    "a class $k$ in a region $R_m$ with $N_m$ observations. We represent\n",
    "this likelihood function in terms of the proportion $I(y_i=k)$ of\n",
    "observations of this class in the region $R_m$ as"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We let $p_{mk}$ represent the majority class of observations in region\n",
    "$m$. The three most common ways of splitting a node are given by\n",
    "\n",
    "* Misclassification error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Gini index $g$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Information entropy or just entropy $s$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Tree, Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
      "0          17.99         10.38          122.80     1001.0          0.11840   \n",
      "1          20.57         17.77          132.90     1326.0          0.08474   \n",
      "2          19.69         21.25          130.00     1203.0          0.10960   \n",
      "3          11.42         20.38           77.58      386.1          0.14250   \n",
      "4          20.29         14.34          135.10     1297.0          0.10030   \n",
      "..           ...           ...             ...        ...              ...   \n",
      "564        21.56         22.39          142.00     1479.0          0.11100   \n",
      "565        20.13         28.25          131.20     1261.0          0.09780   \n",
      "566        16.60         28.08          108.30      858.1          0.08455   \n",
      "567        20.60         29.33          140.10     1265.0          0.11780   \n",
      "568         7.76         24.54           47.92      181.0          0.05263   \n",
      "\n",
      "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
      "0             0.27760         0.30010              0.14710         0.2419   \n",
      "1             0.07864         0.08690              0.07017         0.1812   \n",
      "2             0.15990         0.19740              0.12790         0.2069   \n",
      "3             0.28390         0.24140              0.10520         0.2597   \n",
      "4             0.13280         0.19800              0.10430         0.1809   \n",
      "..                ...             ...                  ...            ...   \n",
      "564           0.11590         0.24390              0.13890         0.1726   \n",
      "565           0.10340         0.14400              0.09791         0.1752   \n",
      "566           0.10230         0.09251              0.05302         0.1590   \n",
      "567           0.27700         0.35140              0.15200         0.2397   \n",
      "568           0.04362         0.00000              0.00000         0.1587   \n",
      "\n",
      "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
      "0                   0.07871  ...        25.380          17.33   \n",
      "1                   0.05667  ...        24.990          23.41   \n",
      "2                   0.05999  ...        23.570          25.53   \n",
      "3                   0.09744  ...        14.910          26.50   \n",
      "4                   0.05883  ...        22.540          16.67   \n",
      "..                      ...  ...           ...            ...   \n",
      "564                 0.05623  ...        25.450          26.40   \n",
      "565                 0.05533  ...        23.690          38.25   \n",
      "566                 0.05648  ...        18.980          34.12   \n",
      "567                 0.07016  ...        25.740          39.42   \n",
      "568                 0.05884  ...         9.456          30.37   \n",
      "\n",
      "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
      "0             184.60      2019.0           0.16220            0.66560   \n",
      "1             158.80      1956.0           0.12380            0.18660   \n",
      "2             152.50      1709.0           0.14440            0.42450   \n",
      "3              98.87       567.7           0.20980            0.86630   \n",
      "4             152.20      1575.0           0.13740            0.20500   \n",
      "..               ...         ...               ...                ...   \n",
      "564           166.10      2027.0           0.14100            0.21130   \n",
      "565           155.00      1731.0           0.11660            0.19220   \n",
      "566           126.70      1124.0           0.11390            0.30940   \n",
      "567           184.60      1821.0           0.16500            0.86810   \n",
      "568            59.16       268.6           0.08996            0.06444   \n",
      "\n",
      "     worst concavity  worst concave points  worst symmetry  \\\n",
      "0             0.7119                0.2654          0.4601   \n",
      "1             0.2416                0.1860          0.2750   \n",
      "2             0.4504                0.2430          0.3613   \n",
      "3             0.6869                0.2575          0.6638   \n",
      "4             0.4000                0.1625          0.2364   \n",
      "..               ...                   ...             ...   \n",
      "564           0.4107                0.2216          0.2060   \n",
      "565           0.3215                0.1628          0.2572   \n",
      "566           0.3403                0.1418          0.2218   \n",
      "567           0.9387                0.2650          0.4087   \n",
      "568           0.0000                0.0000          0.2871   \n",
      "\n",
      "     worst fractal dimension  \n",
      "0                    0.11890  \n",
      "1                    0.08902  \n",
      "2                    0.08758  \n",
      "3                    0.17300  \n",
      "4                    0.07678  \n",
      "..                       ...  \n",
      "564                  0.07115  \n",
      "565                  0.06637  \n",
      "566                  0.07820  \n",
      "567                  0.12400  \n",
      "568                  0.07039  \n",
      "\n",
      "[569 rows x 30 columns]\n",
      "     malignant  benign\n",
      "0         True   False\n",
      "1         True   False\n",
      "2         True   False\n",
      "3         True   False\n",
      "4         True   False\n",
      "..         ...     ...\n",
      "564       True   False\n",
      "565       True   False\n",
      "566       True   False\n",
      "567       True   False\n",
      "568      False    True\n",
      "\n",
      "[569 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "from IPython.display import Image \n",
    "from pydot import graph_from_dot_data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
    "print(X)\n",
    "y = pd.Categorical.from_codes(cancer.target, cancer.target_names)\n",
    "y = pd.get_dummies(y)\n",
    "print(y)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "tree_clf = DecisionTreeClassifier(max_depth=5)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "\n",
    "export_graphviz(\n",
    "    tree_clf,\n",
    "    out_file=\"DataFiles/cancer.dot\",\n",
    "    feature_names=cancer.feature_names,\n",
    "    class_names=cancer.target_names,\n",
    "    rounded=True,\n",
    "    filled=True\n",
    ")\n",
    "cmd = 'dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png'\n",
    "os.system(cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Common imports\n",
    "import numpy as np\n",
    "from sklearn.model_selection import  train_test_split \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.tree import export_graphviz\n",
    "from pydot import graph_from_dot_data\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "np.random.seed(42)\n",
    "X, y = make_moons(n_samples=100, noise=0.25, random_state=53)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)\n",
    "tree_clf = DecisionTreeClassifier(max_depth=5)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "\n",
    "export_graphviz(\n",
    "    tree_clf,\n",
    "    out_file=\"DataFiles/moons.dot\",\n",
    "    rounded=True,\n",
    "    filled=True\n",
    ")\n",
    "cmd = 'dot -Tpng DataFiles/moons.dot -o DataFiles/moons.png'\n",
    "os.system(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other ways of visualizing the trees\n",
    "\n",
    "**Scikit-Learn** has also another way to visualize the trees which is very useful, here with the Iris data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.9166666666666666, 'x[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n",
       " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n",
       " Text(0.5769230769230769, 0.75, 'x[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n",
       " Text(0.3076923076923077, 0.5833333333333334, 'x[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n",
       " Text(0.15384615384615385, 0.4166666666666667, 'x[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n",
       " Text(0.07692307692307693, 0.25, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n",
       " Text(0.23076923076923078, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.46153846153846156, 0.4166666666666667, 'x[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]'),\n",
       " Text(0.38461538461538464, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
       " Text(0.5384615384615384, 0.25, 'x[2] <= 5.45\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
       " Text(0.46153846153846156, 0.08333333333333333, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
       " Text(0.6153846153846154, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.8461538461538461, 0.5833333333333334, 'x[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]'),\n",
       " Text(0.7692307692307693, 0.4166666666666667, 'x[1] <= 3.1\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
       " Text(0.6923076923076923, 0.25, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
       " Text(0.8461538461538461, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(0.9230769230769231, 0.4166666666666667, 'gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_24_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn import tree\n",
    "X, y = load_iris(return_X_y=True)\n",
    "tree_clf = tree.DecisionTreeClassifier()\n",
    "tree_clf = tree_clf.fit(X, y)\n",
    "# and then plot the tree\n",
    "tree.plot_tree(tree_clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, the tree can also be exported in textual format with the function exporttext.\n",
    "This method doesn’t require the installation of external libraries and is more compact:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- petal width (cm) <= 0.80\n",
      "|   |--- class: 0\n",
      "|--- petal width (cm) >  0.80\n",
      "|   |--- petal width (cm) <= 1.75\n",
      "|   |   |--- class: 1\n",
      "|   |--- petal width (cm) >  1.75\n",
      "|   |   |--- class: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import export_text\n",
    "iris = load_iris()\n",
    "decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)\n",
    "decision_tree = decision_tree.fit(iris.data, iris.target)\n",
    "r = export_text(decision_tree, feature_names=iris['feature_names'])\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algorithms for Setting up Decision Trees\n",
    "\n",
    "Two algorithms stand out in the set up of decision trees:\n",
    "1. The CART (Classification And Regression Tree) algorithm for both classification and regression\n",
    "\n",
    "2. The ID3 algorithm based on the computation of the information gain for classification\n",
    "\n",
    "We discuss both algorithms with applications here. The popular library\n",
    "**Scikit-Learn** uses the CART algorithm. For classification problems\n",
    "you can use either the **gini** index or the **entropy** to split a tree\n",
    "in two branches.\n",
    "\n",
    "### The CART algorithm for Classification\n",
    "\n",
    "For classification, the CART algorithm splits the data set in two subsets using a single feature $k$ and a threshold $t_k$.\n",
    "This could be for example a threshold set by a number below a certain circumference of a malign tumor.\n",
    "\n",
    "How do we find these two quantities?\n",
    "We search for the pair $(k,t_k)$ that produces the purest subset using for example the **gini** factor $G$.\n",
    "The cost function it tries to minimize is then"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}G_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}G_{\\mathrm{right}},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where $G_{\\mathrm{left/right}}$ measures the impurity of the left/right subset  and $m_{\\mathrm{left/right}}$\n",
    " is the number of instances in the left/right subset\n",
    "\n",
    "Once it has successfully split the training set in two, it splits the subsets using the same logic, then the subsubsets\n",
    "and so on, recursively. It stops recursing once it reaches the maximum depth (defined by the\n",
    "$max\\_depth$ hyperparameter), or if it cannot find a split that will reduce impurity. A few other\n",
    "hyperparameters control additional stopping conditions such as the $min\\_samples\\_split$,\n",
    "$min\\_samples\\_leaf$, $min\\_weight\\_fraction\\_leaf$, and $max\\_leaf\\_nodes$.\n",
    "\n",
    "\n",
    "### The CART algorithm for Regression\n",
    "\n",
    "The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the\n",
    "training set in a way that minimizes say the **gini** or **entropy** impurity, it now tries to split the training set in a way that minimizes our well-known mean-squared error (MSE). The cost function is now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the MSE for a specific node is defined as"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the mean value of all observations in a specific node.\n",
    "\n",
    "Without any regularization, the regression task for decision trees, \n",
    "just like for classification tasks, is  prone to overfitting.\n",
    "\n",
    "\n",
    "\n",
    "### Computing the Gini index\n",
    "\n",
    "The example we will look at is a classical one in many Machine\n",
    "Learning applications. Based on various meteorological features, we\n",
    "have several so-called attributes which decide whether we at the end\n",
    "will do some outdoor activity like skiing, going for a bike ride etc\n",
    "etc.  The table here contains the feautures **outlook**, **temperature**,\n",
    "**humidity** and **wind**.  The target or output is whether we ride\n",
    "(True=1) or whether we do something else that day (False=0). The\n",
    "attributes for each feature are then sunny, overcast and rain for the\n",
    "outlook, hot, cold and mild for temperature, high and normal for\n",
    "humidity and weak and strong for wind.\n",
    "\n",
    "The table here summarizes the various attributes and\n",
    "<table border=\"1\">\n",
    "<thead>\n",
    "<tr><th align=\"center\">Day</th> <th align=\"center\">Outlook </th> <th align=\"center\">Temperature</th> <th align=\"center\">Humidity</th> <th align=\"center\"> Wind </th> <th align=\"center\">Ride</th> </tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr><td align=\"center\">   1      </td> <td align=\"center\">   Sunny       </td> <td align=\"center\">   Hot            </td> <td align=\"center\">   High        </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   0       </td> </tr>\n",
    "<tr><td align=\"center\">   2      </td> <td align=\"center\">   Sunny       </td> <td align=\"center\">   Hot            </td> <td align=\"center\">   High        </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   3      </td> <td align=\"center\">   Overcast    </td> <td align=\"center\">   Hot            </td> <td align=\"center\">   High        </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   4      </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   High        </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   5      </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Cool           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   6      </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Cool           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   0       </td> </tr>\n",
    "<tr><td align=\"center\">   7      </td> <td align=\"center\">   Overcast    </td> <td align=\"center\">   Cool           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   8      </td> <td align=\"center\">   Sunny       </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   High        </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   0       </td> </tr>\n",
    "<tr><td align=\"center\">   9      </td> <td align=\"center\">   Sunny       </td> <td align=\"center\">   Cool           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   10     </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   11     </td> <td align=\"center\">   Sunny       </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   12     </td> <td align=\"center\">   Overcast    </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   High        </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   13     </td> <td align=\"center\">   Overcast    </td> <td align=\"center\">   Hot            </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
    "<tr><td align=\"center\">   14     </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   High        </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   0       </td> </tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "### Simple Python Code to read in Data and perform Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 0)\t1.0\n",
      "  (0, 7)\t1.0\n",
      "  (0, 9)\t1.0\n",
      "  (0, 13)\t1.0\n",
      "  (1, 3)\t1.0\n",
      "  (1, 5)\t1.0\n",
      "  (1, 8)\t1.0\n",
      "  (1, 12)\t1.0\n",
      "  (2, 3)\t1.0\n",
      "  (2, 5)\t1.0\n",
      "  (2, 8)\t1.0\n",
      "  (2, 11)\t1.0\n",
      "  (3, 1)\t1.0\n",
      "  (3, 5)\t1.0\n",
      "  (3, 8)\t1.0\n",
      "  (3, 12)\t1.0\n",
      "  (4, 2)\t1.0\n",
      "  (4, 6)\t1.0\n",
      "  (4, 8)\t1.0\n",
      "  (4, 12)\t1.0\n",
      "  (5, 2)\t1.0\n",
      "  (5, 4)\t1.0\n",
      "  (5, 10)\t1.0\n",
      "  (5, 12)\t1.0\n",
      "  (6, 2)\t1.0\n",
      "  :\t:\n",
      "  (8, 12)\t1.0\n",
      "  (9, 3)\t1.0\n",
      "  (9, 4)\t1.0\n",
      "  (9, 10)\t1.0\n",
      "  (9, 12)\t1.0\n",
      "  (10, 2)\t1.0\n",
      "  (10, 6)\t1.0\n",
      "  (10, 10)\t1.0\n",
      "  (10, 12)\t1.0\n",
      "  (11, 3)\t1.0\n",
      "  (11, 6)\t1.0\n",
      "  (11, 10)\t1.0\n",
      "  (11, 11)\t1.0\n",
      "  (12, 1)\t1.0\n",
      "  (12, 6)\t1.0\n",
      "  (12, 8)\t1.0\n",
      "  (12, 11)\t1.0\n",
      "  (13, 1)\t1.0\n",
      "  (13, 5)\t1.0\n",
      "  (13, 10)\t1.0\n",
      "  (13, 12)\t1.0\n",
      "  (14, 2)\t1.0\n",
      "  (14, 6)\t1.0\n",
      "  (14, 8)\t1.0\n",
      "  (14, 11)\t1.0\n",
      "Train set accuracy with Decision Tree: 0.73\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Common imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from IPython.display import Image \n",
    "from pydot import graph_from_dot_data\n",
    "import os\n",
    "\n",
    "# Where to save the figures and data files\n",
    "PROJECT_ROOT_DIR = \"Results\"\n",
    "FIGURE_ID = \"Results/FigureFiles\"\n",
    "DATA_ID = \"DataFiles/\"\n",
    "\n",
    "if not os.path.exists(PROJECT_ROOT_DIR):\n",
    "    os.mkdir(PROJECT_ROOT_DIR)\n",
    "\n",
    "if not os.path.exists(FIGURE_ID):\n",
    "    os.makedirs(FIGURE_ID)\n",
    "\n",
    "if not os.path.exists(DATA_ID):\n",
    "    os.makedirs(DATA_ID)\n",
    "\n",
    "def image_path(fig_id):\n",
    "    return os.path.join(FIGURE_ID, fig_id)\n",
    "\n",
    "def data_path(dat_id):\n",
    "    return os.path.join(DATA_ID, dat_id)\n",
    "\n",
    "def save_fig(fig_id):\n",
    "    plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
    "\n",
    "infile = open(data_path(\"rideclass.csv\"),'r')\n",
    "\n",
    "# Read the experimental data with Pandas\n",
    "from IPython.display import display\n",
    "ridedata = pd.read_csv(infile,names = ('Outlook','Temperature','Humidity','Wind','Ride'))\n",
    "ridedata = pd.DataFrame(ridedata)\n",
    "\n",
    "# Features and targets\n",
    "X = ridedata.loc[:, ridedata.columns != 'Ride'].values\n",
    "y = ridedata.loc[:, ridedata.columns == 'Ride'].values\n",
    "\n",
    "# Create the encoder.\n",
    "encoder = OneHotEncoder(handle_unknown=\"ignore\")\n",
    "# Assume for simplicity all features are categorical.\n",
    "encoder.fit(X)    \n",
    "# Apply the encoder.\n",
    "X = encoder.transform(X)\n",
    "print(X)\n",
    "# Then do a Classification tree\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2)\n",
    "tree_clf.fit(X, y)\n",
    "print(\"Train set accuracy with Decision Tree: {:.2f}\".format(tree_clf.score(X,y)))\n",
    "#transfer to a decision tree graph\n",
    "export_graphviz(\n",
    "    tree_clf,\n",
    "    out_file=\"DataFiles/ride.dot\",\n",
    "    rounded=True,\n",
    "    filled=True\n",
    ")\n",
    "cmd = 'dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png'\n",
    "os.system(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above functions (gini, entropy and misclassification error) are\n",
    "important components of the so-called CART algorithm. We will discuss\n",
    "this algorithm below after we have discussed the information gain\n",
    "algorithm ID3.\n",
    "\n",
    "In the example here we have converted all our attributes into numerical values $0,1,2$ etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1 < 0.000 Gini=0.408\n",
      "X1 < 0.000 Gini=0.408\n",
      "X1 < 1.000 Gini=0.394\n",
      "X1 < 2.000 Gini=0.394\n",
      "X1 < 2.000 Gini=0.394\n",
      "X1 < 2.000 Gini=0.394\n",
      "X1 < 1.000 Gini=0.394\n",
      "X1 < 0.000 Gini=0.408\n",
      "X1 < 0.000 Gini=0.408\n",
      "X1 < 2.000 Gini=0.394\n",
      "X1 < 0.000 Gini=0.408\n",
      "X1 < 1.000 Gini=0.394\n",
      "X1 < 1.000 Gini=0.394\n",
      "X1 < 2.000 Gini=0.394\n",
      "X2 < 0.000 Gini=0.408\n",
      "X2 < 0.000 Gini=0.408\n",
      "X2 < 0.000 Gini=0.408\n",
      "X2 < 1.000 Gini=0.407\n",
      "X2 < 2.000 Gini=0.407\n",
      "X2 < 2.000 Gini=0.407\n",
      "X2 < 2.000 Gini=0.407\n",
      "X2 < 1.000 Gini=0.407\n",
      "X2 < 2.000 Gini=0.407\n",
      "X2 < 1.000 Gini=0.407\n",
      "X2 < 1.000 Gini=0.407\n",
      "X2 < 1.000 Gini=0.407\n",
      "X2 < 0.000 Gini=0.408\n",
      "X2 < 1.000 Gini=0.407\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 0.000 Gini=0.408\n",
      "X3 < 1.000 Gini=0.367\n",
      "X3 < 0.000 Gini=0.408\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 1.000 Gini=0.405\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 1.000 Gini=0.405\n",
      "X4 < 1.000 Gini=0.405\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 1.000 Gini=0.405\n",
      "X4 < 1.000 Gini=0.405\n",
      "X4 < 0.000 Gini=0.408\n",
      "X4 < 1.000 Gini=0.405\n",
      "Split: [X3 < 1.000]\n"
     ]
    }
   ],
   "source": [
    "# Split a dataset based on an attribute and an attribute value\n",
    "def test_split(index, value, dataset):\n",
    "\tleft, right = list(), list()\n",
    "\tfor row in dataset:\n",
    "\t\tif row[index] < value:\n",
    "\t\t\tleft.append(row)\n",
    "\t\telse:\n",
    "\t\t\tright.append(row)\n",
    "\treturn left, right\n",
    " \n",
    "# Calculate the Gini index for a split dataset\n",
    "def gini_index(groups, classes):\n",
    "\t# count all samples at split point\n",
    "\tn_instances = float(sum([len(group) for group in groups]))\n",
    "\t# sum weighted Gini index for each group\n",
    "\tgini = 0.0\n",
    "\tfor group in groups:\n",
    "\t\tsize = float(len(group))\n",
    "\t\t# avoid divide by zero\n",
    "\t\tif size == 0:\n",
    "\t\t\tcontinue\n",
    "\t\tscore = 0.0\n",
    "\t\t# score the group based on the score for each class\n",
    "\t\tfor class_val in classes:\n",
    "\t\t\tp = [row[-1] for row in group].count(class_val) / size\n",
    "\t\t\tscore += p * p\n",
    "\t\t# weight the group score by its relative size\n",
    "\t\tgini += (1.0 - score) * (size / n_instances)\n",
    "\treturn gini\n",
    "\n",
    "# Select the best split point for a dataset\n",
    "def get_split(dataset):\n",
    "\tclass_values = list(set(row[-1] for row in dataset))\n",
    "\tb_index, b_value, b_score, b_groups = 999, 999, 999, None\n",
    "\tfor index in range(len(dataset[0])-1):\n",
    "\t\tfor row in dataset:\n",
    "\t\t\tgroups = test_split(index, row[index], dataset)\n",
    "\t\t\tgini = gini_index(groups, class_values)\n",
    "\t\t\tprint('X%d < %.3f Gini=%.3f' % ((index+1), row[index], gini))\n",
    "\t\t\tif gini < b_score:\n",
    "\t\t\t\tb_index, b_value, b_score, b_groups = index, row[index], gini, groups\n",
    "\treturn {'index':b_index, 'value':b_value, 'groups':b_groups}\n",
    " \n",
    "dataset = [[0,0,0,0,0],\n",
    "            [0,0,0,1,1],\n",
    "            [1,0,0,0,1],\n",
    "            [2,1,0,0,1],\n",
    "            [2,2,1,0,1],\n",
    "            [2,2,1,1,0],\n",
    "            [1,2,1,1,1],\n",
    "            [0,1,0,0,0],\n",
    "            [0,2,1,0,1],\n",
    "            [2,1,1,0,1],\n",
    "            [0,1,1,1,1],\n",
    "            [1,1,0,1,1],\n",
    "            [1,0,1,0,1],\n",
    "            [2,1,0,1,0]]\n",
    "\n",
    "split = get_split(dataset)\n",
    "print('Split: [X%d < %.3f]' % ((split['index']+1), split['value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entropy and the ID3 algorithm\n",
    "\n",
    "The ID3 algorithm  learns decision trees by constructing\n",
    "them in a top down way, beginning with the question **which attribute should be tested at the root of the tree**?\n",
    "\n",
    "1. Each instance attribute is evaluated using a statistical test to determine how well it alone classifies the training examples.\n",
    "\n",
    "2. The best attribute is selected and used as the test at the root node of the tree.\n",
    "\n",
    "3. A descendant of the root node is then created for each possible value of this attribute.\n",
    "\n",
    "4. Training examples are sorted to the appropriate descendant node.\n",
    "\n",
    "5. The entire process is then repeated using the training examples associated with each descendant node to select the best attribute to test at that point in the tree.\n",
    "\n",
    "6. This forms a greedy search for an acceptable decision tree, in which the algorithm never backtracks to reconsider earlier choices. \n",
    "\n",
    "The ID3 algorithm selects which attribute to test at each node in the\n",
    "tree.\n",
    "\n",
    "We would like to select the attribute that is most useful for classifying\n",
    "examples.\n",
    "\n",
    "What is a good quantitative measure of the worth of an attribute?\n",
    "\n",
    "Information gain measures how well a given attribute separates the\n",
    "training examples according to their target classification.\n",
    "\n",
    "The ID3 algorithm uses this information gain measure to select among the candidate\n",
    "attributes at each step while growing the tree.\n",
    "\n",
    "\n",
    "### Cancer Data again now with Decision Trees and other Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(426, 30)\n",
      "(143, 30)\n",
      "Test set accuracy with Logistic Regression: 0.94\n",
      "Test set accuracy with SVM: 0.63\n",
      "Test set accuracy with Decision Trees: 0.90\n",
      "Test set accuracy Logistic Regression with scaled data: 0.96\n",
      "Test set accuracy SVM with scaled data: 0.96\n",
      "Test set accuracy with Decision Trees and scaled data: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import  train_test_split \n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Load the data\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "# Logistic Regression\n",
    "logreg = LogisticRegression(solver='lbfgs')\n",
    "logreg.fit(X_train, y_train)\n",
    "print(\"Test set accuracy with Logistic Regression: {:.2f}\".format(logreg.score(X_test,y_test)))\n",
    "# Support vector machine\n",
    "svm = SVC(gamma='auto', C=100)\n",
    "svm.fit(X_train, y_train)\n",
    "print(\"Test set accuracy with SVM: {:.2f}\".format(svm.score(X_test,y_test)))\n",
    "# Decision Trees\n",
    "deep_tree_clf = DecisionTreeClassifier(max_depth=None)\n",
    "deep_tree_clf.fit(X_train, y_train)\n",
    "print(\"Test set accuracy with Decision Trees: {:.2f}\".format(deep_tree_clf.score(X_test,y_test)))\n",
    "#now scale the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "# Logistic Regression\n",
    "logreg.fit(X_train_scaled, y_train)\n",
    "print(\"Test set accuracy Logistic Regression with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
    "# Support Vector Machine\n",
    "svm.fit(X_train_scaled, y_train)\n",
    "print(\"Test set accuracy SVM with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
    "# Decision Trees\n",
    "deep_tree_clf.fit(X_train_scaled, y_train)\n",
    "print(\"Test set accuracy with Decision Trees and scaled data: {:.2f}\".format(deep_tree_clf.score(X_test_scaled,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another example, the moons again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_42_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)\n",
    "\n",
    "deep_tree_clf1 = DecisionTreeClassifier(random_state=42)\n",
    "deep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\n",
    "deep_tree_clf1.fit(Xm, ym)\n",
    "deep_tree_clf2.fit(Xm, ym)\n",
    "\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    if not iris:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    if plot_training:\n",
    "        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"Iris-Virginica\")\n",
    "        plt.axis(axes)\n",
    "    if iris:\n",
    "        plt.xlabel(\"Petal length\", fontsize=14)\n",
    "        plt.ylabel(\"Petal width\", fontsize=14)\n",
    "    else:\n",
    "        plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "        plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n",
    "    if legend:\n",
    "        plt.legend(loc=\"lower right\", fontsize=14)\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
    "plt.title(\"No restrictions\", fontsize=16)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
    "plt.title(\"min_samples_leaf = {}\".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_43_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(6)\n",
    "Xs = np.random.rand(100, 2) - 0.5\n",
    "ys = (Xs[:, 0] > 0).astype(np.float32) * 2\n",
    "\n",
    "angle = np.pi/4\n",
    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n",
    "Xsr = Xs.dot(rotation_matrix)\n",
    "\n",
    "tree_clf_s = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_s.fit(Xs, ys)\n",
    "tree_clf_sr = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_sr.fit(Xsr, ys)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Quadratic training set + noise\n",
    "np.random.seed(42)\n",
    "m = 200\n",
    "X = np.random.rand(m, 1)\n",
    "y = 4 * (X - 0.5) ** 2\n",
    "y = y + np.random.randn(m, 1) / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_46_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel=\"$y$\"):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
    "    y_pred = tree_reg.predict(x1)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "    if ylabel:\n",
    "        plt.ylabel(ylabel, fontsize=18, rotation=0)\n",
    "    plt.plot(X, y, \"b.\")\n",
    "    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_regression_predictions(tree_reg1, X, y)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "plt.text(0.21, 0.65, \"Depth=0\", fontsize=15)\n",
    "plt.text(0.01, 0.2, \"Depth=1\", fontsize=13)\n",
    "plt.text(0.65, 0.8, \"Depth=1\", fontsize=13)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"max_depth=2\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_regression_predictions(tree_reg2, X, y, ylabel=None)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "for split in (0.0458, 0.1298, 0.2873, 0.9040):\n",
    "    plt.plot([split, split], [-0.2, 1], \"k:\", linewidth=1)\n",
    "plt.text(0.3, 0.5, \"Depth=2\", fontsize=13)\n",
    "plt.title(\"max_depth=3\", fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_47_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "x1 = np.linspace(0, 1, 500).reshape(-1, 1)\n",
    "y_pred1 = tree_reg1.predict(x1)\n",
    "y_pred2 = tree_reg2.predict(x1)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", fontsize=18, rotation=0)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"No restrictions\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.title(\"min_samples_leaf={}\".format(tree_reg2.min_samples_leaf), fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pros and cons of trees, pros\n",
    "\n",
    "* White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)\n",
    "\n",
    "* Trees are very easy to explain to people. In fact, they are even easier to explain than linear regression!\n",
    "\n",
    "* No feature normalization needed\n",
    "\n",
    "* Tree models can handle both continuous and categorical data (Classification and Regression Trees)\n",
    "\n",
    "* Can model nonlinear relationships\n",
    "\n",
    "* Can model interactions between the different descriptive features\n",
    "\n",
    "* Trees can be displayed graphically, and are easily interpreted even by a non-expert (especially if they are small)\n",
    "\n",
    "### Disadvantages\n",
    "\n",
    "* Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches\n",
    "\n",
    "* If continuous features are used the tree may become quite large and hence less interpretable\n",
    "\n",
    "* Decision trees are prone to overfit the training data and hence do not well generalize the data if no stopping criteria or improvements like pruning, boosting or bagging are implemented\n",
    "\n",
    "* Small changes in the data may lead to a completely different tree. This issue can be addressed by using ensemble methods like bagging, boosting or random forests\n",
    "\n",
    "* Unbalanced datasets where some target feature values occur much more frequently than others may lead to biased trees since the frequently occurring feature values are preferred over the less frequently occurring ones. \n",
    "\n",
    "* If the number of features is relatively large (high dimensional) and the number of instances is relatively low, the tree might overfit the data\n",
    "\n",
    "* Features with many levels may be preferred over features with less levels since for them it is *more easy* to split the dataset such that the sub datasets only contain pure target feature values. This issue can be addressed by preferring for instance the information gain ratio as splitting criteria over information gain\n",
    "\n",
    "However, by aggregating many decision trees, using methods like\n",
    "bagging, random forests, and boosting, the predictive performance of\n",
    "trees can be substantially improved."
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}