FYS-STK4155/doc/LectureNotes/_build/jupyter_execute/week46.ipynb

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    "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
    "doconce format html week46.do.txt --no_mako -->\n",
    "<!-- dom:TITLE: Week 46: Decision Trees, Ensemble methods  and Random Forests -->"
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    "# Week 46: Decision Trees, Ensemble methods  and Random Forests\n",
    "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
    "\n",
    "Date: **Week 46, November 11-15**"
   ]
  },
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    "## Plan for week 46\n",
    "\n",
    "**Lab sessions on Tuesday and Wednesday.**\n",
    "\n",
    "1. Work on and discussions of project 3\n",
    "\n",
    "**Material for the lecture on Monday November 11, 2024.**\n",
    "\n",
    "Basics of decision trees, classification and regression algorithms and ensemble models \n",
    "1. Readings and Videos:\n",
    "\n",
    "a. Lecture notes at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week46/ipynb/week46.ipynb>\n",
    "<!-- * [Video of lecture](https://youtu.be/PMswUwhYa7k) -->\n",
    "<!-- * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov16.pdf) -->\n",
    "\n",
    "b. Video on Decision trees at <https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn>\n",
    "\n",
    "c. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at <https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf>."
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    "## Decision trees, overarching aims\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",
    "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**."
   ]
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    "## 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."
   ]
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    "## A typical Decision Tree with its pertinent Jargon, Classification Problem\n",
    "\n",
    "<!-- dom:FIGURE: [DataFiles/cancer.png, width=600 frac=0.8] -->\n",
    "<!-- begin figure -->\n",
    "\n",
    "<img src=\"DataFiles/cancer.png\" width=\"600\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
    "<!-- end figure -->\n",
    "\n",
    "This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using **Scikit-Learn**'s decision tree classifier. Here we have used the so-called **gini** index (see below) to split the various branches."
   ]
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    "## 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."
   ]
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    "## How do we set it up?\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!"
   ]
  },
  {
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   "id": "b87f6367",
   "metadata": {},
   "source": [
    "## Decision trees and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a74fdf82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd degree coefficients:\n",
      "zero power:  1.391131752817723\n",
      "first power:  0.023929544236763082\n",
      "second power:  -0.00011233101388608391\n"
     ]
    },
    {
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3337j2rVruLq60qxZM9avX0+7du0sHr9q1ars3buXt956i88++4yMjAyaNm3Kb7/9Vq7WOIGgItBIItpLIKj0PPjgg5w4cUJkerUBBoMBf39/HnroIb7++mtbiyMQ/KcRdkaBoJJz48YN/vjjj1JbaQRFk5GRUcAl9sMPP3D79m2TMgkCgcA2CEuNQFBJiYiIYM+ePXzzzTccPHiQixcvEhgYaGux7mq2b9/Oa6+9xqOPPoqfnx+HDx/m22+/pUGDBhw6dMgkSZ1AIKh4REyNQFBJ2bFjB08//TQ1a9bk+++/FwpNBRASEkKNGjWYN28et2/fxtfXl2HDhvHBBx8IhUYgsAOEpUYgEAgEAsFdgYipEQgEAoFAcFcglBqBQCAQCAR3Bf+pmBqDwcD169fx8PAoca0dgUAgEAgEFYskSSQnJxMcHFxogsj/lFJz/fr1AhWEBQKBQCAQVA6uXLlSaLHW/5RS4+HhAcgXxdPT08bSCAQCgUAgsIakpCRq1KihPsct8Z9SahSXk6enp1BqBAKBQCCoZBQVOiIChQUCgUAgENwVCKVGIBAIBALBXYFQagQCgUAgENwV/KdiaqzBYDCQlZVlazEEgjLB0dERnU5nazEEAoGgQhBKjRFZWVlERERgMBhsLYpAUGZ4e3sTGBgocjMJBIK7HqHU3EGSJG7cuIFOp6NGjRqFJvcRCCoDkiSRlpZGdHQ0AEFBQTaWSCAQCMoXodTcIScnh7S0NIKDg3F1dbW1OAJBmaDX6wGIjo4mICBAuKIEAsFdjTBH3CE3NxcAJycnG0siEJQtipKenZ1tY0kEAoGgfBFKTT5E3IHgbkPc0wKB4L+CUGoEAoFAIBDcFQilRlAqNBoNv/76q63FKBO2b9+ORqMhISEBgMWLF+Pt7W1TmQQCgUBgPUKpqeSMGDECjUaDRqPB0dGR0NBQxo8fT0ZGhq1FKzcWLFiAh4cHOTk56rqUlBQcHR3p0qWLSVtFUbl48WKFyNalSxf1+3B2dqZatWo88MADrF69uth9TZkyhebNm5e9kAKBQHCXIpSau4DevXtz48YNLl26xKeffsrChQt59913bS1WudG1a1dSUlL4559/1HW7du0iMDCQ/fv3myh027Zto2bNmtSpU6fC5Hv++ee5ceMGFy9eZNWqVTRs2JAhQ4YwcuTICpNBIBAIKoK07DRbi2CCUGruApydnQkMDKRGjRoMGjSI7t27s2nTJnV7XFwcjz/+ONWqVcPV1ZUmTZrw888/m/TRpUsXXnnlFcaPH4+vry+BgYFMmTLFpM358+fp1KkTLi4uNGzY0OQYCidOnKBbt27o9Xr8/PwYOXIkKSkp6vYRI0YwaNAgZsyYQdWqVfH29ua9994jJyeHN998E19fX6pXr86iRYssnm+9evUICgpi+/bt6rrt27czcOBAQkND2bdvn8n6rl27AvDjjz/SunVrPDw8CAwM5IknnlBzuFhDTEwMrVu35sEHHyQzM9NiO1dXVwIDA6levTrt2rVj1qxZLFy4kK+//prNmzer7d566y3Cw8NxdXWldu3aTJo0SZ2htHjxYqZOncqxY8dUy8/ixYsBmD17Nk2aNMHNzY0aNWrw0ksvmVxjgUAgqAiWnliKx0wPlp5YamtRVIRSYwFJkkjNSrXJIklSieU+efIke/fuNZmanpGRQatWrfjjjz84efIkI0eO5KmnnuLAgQMm+37//fe4ubmxf/9+PvzwQ9577z1VcTEYDDz00EM4OTmxf/9+FixYwFtvvWWyf2pqKr169cLHx4eDBw+ycuVKNm/ezJgxY0zabd26levXr7Nz505mz57Nu+++S//+/fHx8WH//v28+OKLvPDCC1y9etXieXbt2pVt27apn7dt20aXLl3o3Lmzuj49PZ39+/erSk12djbTpk3j2LFj/Prrr0RGRjJixAirruuVK1fo2LEjjRs35pdffsHZ2dmq/RSGDx+Oj4+PiRvKw8ODxYsXc+rUKebOncvXX3/Np59+CsBjjz3GG2+8QaNGjbhx4wY3btzgscceA0Cr1TJv3jz+/fdfvv/+e7Zu3cr48eOLJY9AIBCUlp1ROzFIBnZF7bK1KCoi+Z4F0rLTcJ/pbpNjp0xIwc3Jzer2v//+O+7u7uTk5JCZmYlWq+Xzzz9Xt1erVo1x48apn19++WU2btzIihUruOeee9T1TZs2Vd1WYWFhfP7552zZsoUePXqwefNmzpw5w8aNGwkODgZgxowZ9OnTR91/6dKlZGRk8MMPP+DmJsv/+eef88ADDzBr1iyqVq0KgK+vL/PmzUOr1VKvXj0+/PBD0tLSmDhxIgATJkzggw8+YPfu3QwZMsTsOXft2pWxY8eSk5NDeno6R44coXPnzmRnZ7NgwQIA/v77bzIzM1Wl5plnnlH3r127NvPmzaNNmzakpKTg7m75uz579iw9evTgwQcfZM6cOSWaIq3VagkPDycyMlJd984776j/h4SEMG7cOJYtW8b48ePR6/W4u7vj4OBAYGCgSV9jx4412e/999/nxRdf5Msvvyy2XAKBQFBS4tLjTP7aA0KpuQvo2rUr8+fPJzU1lU8//RQHBwcefvhhdXtubi4zZsxgxYoVXLt2jaysLDIzMwtkTm7atKnJ56CgINU9c/r0aWrUqKEqNAD33nuvSfvTp0/TrFkzVaEB6NChAwaDgbNnz6pKTaNGjUzKUFStWpXGjRurn3U6HX5+foW6hrp06UJqaioHDx4kPj6e8PBw/P396dy5M08//TQZGRls376d2rVrU7NmTQAOHTrElClTOHbsGPHx8WqNr8uXL9OwYUOzx0lPT6djx4488cQTzJkzx6I81iBJkolCtHz5cubNm8fFixdJSUkhJycHT0/PIvvZvHkzM2fO5MyZMyQlJZGTk0NGRgZpaWkiG7ZAIKgw4tKEUlNpcHV0JWWCbeIUXB2L92Byc3Ojbt26AHz33Xc0a9aMb7/9lmeffRaAjz76iLlz5zJnzhw1FmPs2LEFqpE7OjqafNZoNOVS3NPccYp77Lp161K9enW2bdtGfHw8nTt3BiA4OJgaNWqwd+9etm3bRrdu3YA811ivXr1YsmQJ/v7+XL58mV69ehVald3Z2Znu3bvz+++/8+abb1KtWrUSnXNubi7nz5+nTZs2gGxFGjp0KFOnTqVXr154eXmxbNkyPvnkk0L7iYyMpH///owaNYrp06fj6+vL7t27efbZZ8nKyhJKjUAgqDBUS02aUGrsHo1GUywXkL2g1WqZOHEir7/+Ok888QR6vZ49e/YwcOBAnnzySUCOjzl37pxF64Q5GjRowJUrV7hx44ZaGNE4IFdps3jxYlJTU1VrzZ49e1Q3U1nTtWtXtm/fTnx8PG+++aa6vlOnTqxfv54DBw4watQoAM6cOUNcXBwffPABNWrUADCZPWUJrVbLjz/+yBNPPKEez9haZS3ff/898fHxqgVt79691KpVi//9739qm6ioKJN9nJyc1PIdCocOHcJgMPDJJ5+o1q4VK1YUWx6BQCAoLfZoqRGBwnchjz76KDqdji+++AKQ42M2bdrE3r17OX36NC+88AK3bt0qVp/du3cnPDyc4cOHc+zYMXbt2mXyQAYYOnQoLi4uDB8+nJMnT7Jt2zZefvllnnrqKdX1VJZ07dqV3bt3c/ToUdVSA9C5c2cWLlxIVlaWGk9Ts2ZNnJyc+Oyzz7h06RLr1q1j2rRpVh1Hp9OxZMkSmjVrRrdu3bh582ah7dPS0rh58yZXr15l3759vPXWW7z44ouMGjVKlScsLIzLly+zbNkyLl68yLx581izZo1JPyEhIURERHD06FFiY2PJzMykbt26ZGdnq+fx448/qjFEAoFAUFFIkkRsWiyA+tceEErNXYiDgwNjxozhww8/JDU1lXfeeYeWLVvSq1cvunTpQmBgIIMGDSpWn1qtljVr1pCens4999zDc889x/Tp003auLq6snHjRm7fvk2bNm145JFHuP/++02ClsuSrl27kp6eTt26dU2Ups6dO5OcnKxO/Qbw9/dn8eLFrFy5koYNG/LBBx/w8ccfW30sBwcHfv75Zxo1akS3bt0Kjff5+uuvCQoKok6dOjz00EOcOnWK5cuXmwTyDhgwgNdee40xY8bQvHlz9u7dy6RJk0z6efjhh+nduzddu3bF39+fn3/+mWbNmjF79mxmzZpF48aNWbJkCTNnzrT6PAQCgaAsSMtOIzNXTm2RkZNhN/lqNFJp5g9XMpKSkvDy8iIxMbFAQGZGRgYRERGEhobi4uJiIwkFgrJH3NsCgaCsuZx4mVpzauV9HnuZGl41yu14hT2/jRGWGoFAIBAIBMUif3CwvcTVCKVGIBAIBAJBscgfR2MvcTVCqREIBAKBQFAs8ltm7GVat1BqBAKBQCAQFAvhfhIIBAKBQHBXICw1AoFAIBAI7gryx9AIS41AIBAIBIJKiaLEBLnLucBEoLBAIBAIBIJKieJuCvcLlz8LS41AIBAIBILKiKLE1POT6/qJmBqBXbN9+3Y0Gg0JCQm2FsXu0Gg0/Prrr7YWQyAQCGyG4m6qV+WOUiMsNYKyYObMmbRp0wYPDw8CAgIYNGgQZ8+erZBjh4SEoNFo0Gg06PV6QkJCGDx4MFu3bq2Q45cnxuemLNWrVwfgxo0b9OnTB4DIyEg0Gg1Hjx61obQCgUBQseR3P4mYGkGZsGPHDkaPHs2+ffvYtGkT2dnZ9OzZk9TU1Ao5/nvvvceNGzc4e/YsP/zwA97e3nTv3r1AscvyICsrq1z7V85NWY4cOQJAYGAgzs7O5XpsgUAgsFeycrNIzkoG8pSapMwksnOzbSkWUImUmtzcXCZNmkRoaCh6vZ46deowbdo0/kP1OM2yYcMGRowYQaNGjWjWrBmLFy/m8uXLHDp0SG2j0Wj45ptvePDBB3F1dSUsLIx169aZ9PPnn38SHh6OXq+na9euREZGWnV8Dw8PAgMDqVmzJp06deKrr75i0qRJTJ482cRidPLkSfr06YO7uztVq1blqaeeIjY2T7NPTk5m6NChuLm5ERQUxKeffkqXLl0YO3as2iYkJIRp06YxbNgwPD09GTlyJAC7d++mY8eO6PV6atSowSuvvGKi1GVmZjJu3DiqVauGm5sbbdu2Zfv27Vafm7L4+/ur11NxP4WGhgLQokULNBoNXbp0seq6CQQCQWXldvptALQaLaHeoWjQmKy3JZVGqZk1axbz58/n888/5/Tp08yaNYsPP/yQzz77rHwOKEmQmmqbpRSKWmJiIgC+vr4m66dOncrgwYM5fvw4ffv2ZejQody+Ld+AV65c4aGHHuKBBx7g6NGjPPfcc7z99tslluHVV19FkiTWrl0LQEJCAt26daNFixb8888/bNiwgVu3bjF48GB1n9dff509e/awbt06Nm3axK5duzh8+HCBvj/++GOaNWvGkSNHmDRpEhcvXqR37948/PDDHD9+nOXLl7N7927GjBmj7jNmzBj+/vtvli1bxvHjx3n00Ufp3bs358+fL/E5Khw4cACAzZs3c+PGDVavXl3qPgUCgcCeUVxNPi4+OOoc8dH7AHYSVyNVEvr16yc988wzJuseeughaejQoVb3kZiYKAFSYmJigW3p6enSqVOnpPT0dHlFSookyepFxS8pKSW6Rrm5uVK/fv2kDh06mKwHpHfeeUf9nJKSIgHS+vXrJUmSpAkTJkgNGzY02eett96SACk+Pt7i8WrVqiV9+umnZrdVrVpVGjVqlCRJkjRt2jSpZ8+eJtuvXLkiAdLZs2elpKQkydHRUVq5cqW6PSEhQXJ1dZVeffVVk+MNGjTIpJ9nn31WGjlypMm6Xbt2SVqtVkpPT5eioqIknU4nXbt2zaTN/fffL02YMKHQc3NycpLc3NzUZe7cuZIkyddzzZo1kiRJUkREhARIR44csdiXrSlwb1ciUrNSpcj4SOl60nVbi1JsUrNSJYPBYGsxBIIyZ3vEdokpSOGfhUuSJElh88IkpiDtiNxRbscs7PltjIPt1Kni0b59e7766ivOnTtHeHg4x44dY/fu3cyePdviPpmZmWRmZqqfk5KSKkJUmzF69GhOnjzJ7t27C2xr2rSp+r+bmxuenp5ER0cDcPr0adq2bWvS/t577y2VLJIkodHIJsljx46xbds23N3dC7S7ePEi6enpZGdnc88996jrvby8qFevXoH2rVu3Nvl87Ngxjh8/zpIlS0yObTAYiIiI4NKlS+Tm5hIeHm6yX2ZmJn5+foWew5tvvsmIESPUz1WqVCm0vaBsiUmNIfzzcBIyEgCY3m06EztOtK1QVnI29izNFjRjZKuRzOszz9biCARlimKR8dPLY6ifqx/nb5+3i2ndlUapefvtt0lKSqJ+/frodDpyc3OZPn06Q4cOtbjPzJkzmTp1askO6OoKKSkllLaUuLoWe5cxY8bw+++/s3PnTnWWjjGOjo4mnzUaDQaDocQiFkZcXBwxMTFqvElKSgoPPPAAs2bNKtA2KCiICxcuWN23m5ubyeeUlBReeOEFXnnllQJta9asyfHjx9HpdBw6dAidTmey3ZySZUyVKlWoW7eu1bIJypbjt46rCg3AjqgdlUap2X9tP5m5meyI2mFrUQSCMkdRXqq4VjH5aw/up0qj1KxYsYIlS5awdOlSGjVqxNGjRxk7dizBwcEMHz7c7D4TJkzg9ddfVz8nJSVRo0YN6w6o0UC+B6g9IkkSL7/8MmvWrGH79u2qIlEcGjRoUCBweN++fSWWae7cuWi1WgYNGgRAy5YtWbVqFSEhITg4FLzlateujaOjIwcPHqRmzZqAHBt07tw5OnXqVOixWrZsyalTpywqHy1atCA3N5fo6Gg6duxY4nOyhJOTEyAHsgvKFnstmGcNiqyVSWaBwFqUmBo/1zuWmjsWG3u43ytNoPCbb77J22+/zZAhQ2jSpAlPPfUUr732GjNnzrS4j7OzM56enibL3cbo0aP56aefWLp0KR4eHty8eZObN2+Snp5udR8vvvgi58+f58033+Ts2bMsXbqUxYsXW7VvcnIyN2/e5MqVK+zcuZORI0fy/vvvM336dFXRGD16NLdv3+bxxx/n4MGDXLx4kY0bN/L000+Tm5uLh4cHw4cP580332Tbtm38+++/PPvss2i1WtWFZYm33nqLvXv3MmbMGI4ePcr58+dZu3atGigcHh7O0KFDGTZsGKtXryYiIoIDBw4wc+ZM/vjjD6uvkSUCAgLQ6/Vq8LMSqC0oPcoAqdSWsYe3QGtRZK1MMgsE1lLA/aQoNXZwv1capSYtLQ2t1lRcnU5Xbi6UysL8+fNJTEykS5cuBAUFqcvy5cut7qNmzZqsWrWKX3/9lWbNmrFgwQJmzJhh1b6TJ08mKCiIunXr8tRTT5GYmMiWLVt466231DbBwcHs2bOH3NxcevbsSZMmTRg7dize3t7qdzp79mzuvfde+vfvT/fu3enQoQMNGjTAxcWl0OM3bdqUHTt2cO7cOTp27EiLFi2YPHkywcHBaptFixYxbNgw3njjDerVq8egQYNMrEKlwcHBgXnz5rFw4UKCg4MZOHBgqfsUyKhp2KvYVxp2a1BkzcjJIC07zcbSCARli7mYGrCPBHyVxv30wAMPMH36dGrWrEmjRo04cuQIs2fP5plnnrG1aDZFsmL6t7k2+csf9O/fn/79+5use/rppwvt19pcNgBhYWGFTnf28PAwCfZNTU1l6tSpai6awo7Xpk0b/vrrL4t9Ozo6MnXq1GLFVxV2bvmv53PPPcdzzz1ndd8C61AzlvqGsz1yO8lZyWTlZuGkc7KxZEVj/MYalxaHq1fx4+QEAntF+W0qyoyIqSkBn332GZMmTeKll14iOjqa4OBgXnjhBSZPnmxr0QRlwJEjRzhz5gz33HMPiYmJvPfeewDC8vEfJjZdfuur7VMbrUaLQTIQlxZHkEeQjSUrGuM31ti0WGp4WRnLJxBUApT7W1Fm7CmmptIoNR4eHsyZM4c5c+bYWhRBOfHxxx9z9uxZnJycaNWqFbt27RLTqP/DKANkgFsAvnpfYtNiiUuvHEqNiaXGDt5eBYKyxJL7yR7u9Uqj1Ajublq0aGFS2kEgUAdOVz/89H6yUmMHb4LWYCxnZZFZILCW/O4nRbmxh5iaShMoLBAI/luoA6fez67eBItCkiRhqRHcteQaconPiAfylBnFDXU7/TYGybaTd4RSIxAI7BLjXBj29CZYFGnZaWTkZKifK4PMAoG1JGQkqIqLaqm589cgGUjMsG1aC6HUCAQCuyPHkENipjw4+un98mZXVAJXTmVOGigQFIVyf3s4eagzEZ10Trg7uZtstxVCqREIBHbH7XS5grwGDT56H7tK7lUU+ZWYyiCzQGAt+eNpFOzFmiqUGoFAYHcoA6e3izcOWodKFVNTwFJTCWQWCKwl/8wnBXuxpgqlRiAQ2B2WasvY+i3QGvLLWBlkFgisJX+OGgV7efEQSo3AIiNGjFCLUgoKZ8qUKTRv3tzWYtw15H8btJe3QGsoULOqEsgsEFhLUe4nW9/vQqmp5OzcuZMHHniA4OBgNBoNv/76a4E2kiSpNZr0ej3du3fn/Pnz6vbIyEg0Gg1Hjx4ttTyLFy9Go9Gg0WjQ6XT4+PjQtm1b3nvvvUpf7NH43IyXb775hnHjxrFlyxa1rVAIS4cyMKoZS+3kLdAaFBnD/cJNPgsEdwOW3E/2EvcmlJpKTmpqKs2aNeOLL76w2ObDDz9k3rx5LFiwgP379+Pm5kavXr3IyMiwuE9p8PT05MaNG1y9epW9e/cycuRIfvjhB5o3b87169fL5ZgKubm55VrkVDk342Xo0KG4u7vj5+dXdAcCqzBOvAf28xZoDYqM9fzkQpxJmUlk52bbUiSBoMwwzh9ljPICYmt3q1BqKjl9+vTh/fff58EHHzS7XZIk5syZwzvvvMPAgQNp2rQpP/zwA9evX1etOqGhoYCc1Vej0dClSxeTPj7++GOCgoLw8/Nj9OjRZGcXPkBrNBoCAwMJCgqiQYMGPPvss+zdu5eUlBTGjx+vtjMYDMycOZPQ0FD0ej3NmjXjl19+Melr3bp1hIWF4eLiQteuXfn+++/RaDRqQc7Fixfj7e3NunXraNiwIc7Ozly+fJnMzEzGjRtHtWrVcHNzo23btmzfvt2k7927d9OxY0f0ej01atTglVdeITU11apzM170er2J+2nKlCl8//33rF27VrXm5D+2oHDyD5yKcnM7/Ta5hlybyWUNikJW17cuGjRA3mwugaCyo9RkK+B+shNrqiiTYAFJkkhLS7PJsV1dXdFoNGXSV0REBDdv3qR79+7qOi8vL9q2bcvff//NkCFDOHDgAPfccw+bN2+mUaNGODnlVUHetm0bQUFBbNu2jQsXLvDYY4/RvHlznn/++WLJERAQwNChQ/nuu+/Izc1Fp9Mxc+ZMfvrpJxYsWEBYWBg7d+7kySefxN/fn86dOxMREcEjjzzCq6++ynPPPceRI0cYN25cgb7T0tKYNWsW33zzDX5+fgQEBDBmzBhOnTrFsmXLCA4OZs2aNfTu3ZsTJ04QFhbGxYsX6d27N++//z7fffcdMTExjBkzhjFjxrBo0aKSX3Bg3LhxnD59mqSkJLUvX1/fUvX5X0MNFNabWmokJBIyEgoMqPaEIru/mz8+eh9up98mNi2Wqu5VbSyZQFB68ruGFfz0fpADl45dgkdtIZmMUGoskJaWhru7u02OnZKSgpubW5n0dfPmTQCqVjUdUKtWrapu8/f3B8DPz4/AwECTdj4+Pnz++efodDrq169Pv3792LJlS7GVGoD69euTnJxMXFwcXl5ezJgxg82bN3PvvfcCULt2bXbv3s3ChQvp3LkzCxcupF69enz00UcA1KtXj5MnTzJ9+nSTfrOzs/nyyy9p1qwZAJcvX2bRokVcvnyZ4OBgQFY0NmzYwKJFi5gxYwYzZ85k6NChjB07FoCwsDDmzZtH586dmT9/Pi4uLmbPITEx0eS+cHd3V6+j8Tq9Xk9mZmaB6ymwDuVtTxk4HXWOeDp7kpSZRFx6nF0rNcYxB356P26n37b526tAUFaYi6nJyMhg9y+7YS4cyThC9CvRBAQE2EQ+odQICqVRo0bodDr1c1BQECdOnChRX5IkAbIL58KFC6SlpdGjRw+TNllZWbRo0QKAs2fP0qZNG5Pt99xzT4F+nZycaNq0qfr5xIkT5ObmEh4ebtIuMzNTjXs5duwYx48fZ8mSJSbyGQwGIiIiaNCggdlz8PDw4PDhw+pnrVZ4cMuD/DE1IA+iSZlJ8pui/eo0Jm+yVVyrcP72+UoRCyQQWEP+2U8ZGRn07NmTXbt2AaDx0nD+/Hmh1Ngbrq6upKSk2OzYZYViKbh16xZBQUHq+lu3blk1BdnR0dHks0ajKXEg7unTp/H09MTPz49Lly4B8Mcff1CtWjWTds7OzsXqV6/Xm7jrUlJS0Ol0HDp0yEQhA1QrS0pKCi+88AKvvPJKgf5q1qxp8VharZa6desWSz5B8TEXjOjn6kdEQoTNAxGLwqS6uJ3EGQgEZYFxsVY/vR8Gg4Fhw4axa9cuPDw9SO6YjENrB9q3b28zGYVSYwGNRlNmLiBbEhoaSmBgIFu2bFGVmKSkJPbv38+oUaMA1Bia3NzyC8CMjo5m6dKlDBo0CK1WaxLU27lzZ7P71KtXjz///NNk3cGDB4s8VosWLcjNzSU6OpqOHTuabdOyZUtOnTpVbgqKk5NTuV7PuxVJkriadJWYtBigoKUG7FtByMrNIikzCchzPwFcuH2BqIQok7bVPKvhoBVDsKDykJKVQlZuFiBbImfNmsXKlStxcnJi+S/L6bu3L1lkkZadhpuTbZ6f4hdVyUlJSeHChQvq54iICI4ePYqvry81a9ZEo9EwduxY3n//fcLCwggNDWXSpEkEBwereVQCAgLQ6/Vs2LCB6tWr4+LigpeXV4llkiSJmzdvIkkSCQkJ/P3338yYMQMvLy8++OADQHbjjBs3jtdeew2DwcB9991HYmIie/bswdPTk+HDh/PCCy8we/Zs3nrrLZ599lmOHj3K4sWLAQoNpA4PD2fo0KEMGzaMTz75hBYtWhATE8OWLVto2rQp/fr146233qJdu3aMGTOG5557Djc3N06dOsWmTZv4/PPPS3zuCiEhIWzcuJGzZ8/i5+eHl5dXAauXoCDPrHuGxUcXq5+NLTWVIQGfcc0qbxdvVeZZe2Yxa88sk7b3VLuH/c/tr3AZBYKSorxQOOucibsZx7Rp0wCYP38+vbv3xmm/E1m5WcSlx9lMqREBAZWcf/75hxYtWqhxKK+//jotWrRg8uTJapvx48fz8ssvM3LkSNq0aUNKSgobNmxQg2EdHByYN28eCxcuJDg4mIEDB5ZKpqSkJIKCgqhWrRr33nsvCxcuZPjw4Rw5csTEBTZt2jQmTZrEzJkzadCgAb179+aPP/5Qp5iHhobyyy+/sHr1apo2bcr8+fP53//+BxTtolq0aBHDhg3jjTfeoF69egwaNIiDBw+qrqWmTZuyY8cOzp07R8eOHdVrpgQWl5bnn3+eevXq0bp1a/z9/dmzZ0+Z9Hu3sz1yOyBX/e1ZpydBHnn3S2Ww1CgKl6/eF51WxwPhD+Dv6o+Lg4u6OOvke/fAtQNk5JRPriiBoDwwjqeZMGEC6enp3HfffTz99NNoNBr8Xf3xcvZSrZW2QCMp0Zv/AZKSkvDy8iIxMRFPT0+TbRkZGURERBAaGmpx5ovA9kyfPp0FCxZw5coVW4tSaahM97bnTE+Ss5I5M/oM9arUM9n23o73eHf7uzzf8nm+euArG0lYODujdtJ5cWfC/cI5O+as2TYGyYDjNEcMkoGrr12lmmc1s+0EAnvjr4t/0eunXtRJrcPFjy6i0Wg4dOiQ+lKda8hFp9UV0UvJKOz5bYyw1Ajsmi+//JKDBw9y6dIlfvzxRz766COGDx9ua7EE5UBWbhbJWclAwRwYUDksNfnz65hDq9FWinMRCPKj3N9JW2VLzLBhw1SFBig3haY4iJgagV1z/vx53n//fW7fvk3NmjV54403mDBhgq3FEpQD+eNR8lMZYmosFfvLj5+rHzFpMXZ9LgJBfuLS4iAeYg7LgfxvvvmmjSUqiFBqBHbNp59+yqeffmprMQQVgPKA99H7mH3jqwzTo/MnDbSEsNQIKiNx6XGwH5CgZ8+eNGrUyNYiFUC4nwQCgV2gmLYtKQSVoailpWJ/+VEUNHvPuSMQGHMj9gbcyT36+uuv21YYCwilJh//obhpwX+EynJPm0u/boyxImCv56QW+ytCqamit39XmkCQn6Nbj0IW+Nf0p2fPnrYWxyxCqbmDknk2KyvLxpIIBGWLUpjV3vPkFBWPolhwsg3ZpGTZJtt3URQnpgaE+0lQuTi//TwAnQd2LrOiy2WNiKm5g4ODA66ursTExODo6Chq+ggqPUql+ejoaLy9vQuUjLA3irLUuDq64uLgQkZOBnHpcXg4e1SkeFYhYmoEdytRUVHEn44HoO9DfW0sjWWEUnMHjUZDUFAQERERREVFFb2DQFBJ8Pb2rhTVwouKqQFZGbiWfI24tDhCvEMqSDLrKW5MjXA/CSoLavHfEGhQ13zBX3tAKDVGODk5ERYWJlxQgrsGR0dHu7fQKBRlqQFZGbiWfM1uA2zNVRc3h6K42et5CATGSJLEjz/+KH9oVrTSbkuEUpMPrVZr91lXBYK7EWviUdRcNXbotjFIBjXXTpGWGuF+ElQiTp06xZkzZ0AHNCjavWpLROCIQCCwC6yy1NjxtO6EjAQMkgEoRqCwHZ6HQJCfNWvWyP/UBq1ei5dLyQselzdCqREIBHaBtTE1YJ8WDkVB8XDywEnnVGhb5TwSMhLIMeSUu2wCQWlQlZoGcrFWrcZ+VQf7lUwgEPynsMb9ZM9J66yNpwH5wQAgIRGfHl+ucgkEpeHy5cscPnxYnhEcbt/xNCCUGoFAYAcYJAPxGfLDvbBB055jaqwpZqngqHPEy1k24dvjuQgECr/++isA9VrUA3f7jqcBodQIBAI7wNp4FHuOqVFksnbQF3E1gsrA2rVrAWjUSa7zZI0l0pYIpUYgENgca+NR7DkTb3HcT2Df8UECAUBycjK7du0CoGabmoBwPwkEAkGRqK6bomYN6e04psbKxHsKqitNWGoEdsrWrVvJzs6mTp06SL5yvTWh1AgEAkERWDOdG+xbEShOTA3Yd9CzQACwYcMGAHr37m11CRBbU6mUmmvXrvHkk0/i5+eHXq+nSZMm/PPPP7YWSyAQlBJr41EURSA1O5XMnMxyl6s4FHfQF+4ngT0jSZKq1PTp08fqYq22ptIoNfHx8XTo0AFHR0fWr1/PqVOn+OSTT/Dx8bG1aAKBoJRYG4/i5eyFTqMz2cdeKHFMjR1anQSCc+fOERkZiZOTE126dLHammprKk2ZhFmzZlGjRg0WLVqkrgsNDbWhRAKBoDSkZqWqrpdL8ZeAogdMjUaDr96XmLQYTtw6gZ/eD2cH53KXVSEjJwNHrSM6raxYpWWn4eroChQ/psaeg54FgvXr1wPQqVMnEnITuJVyCxCWmjJj3bp1tG7dmkcffZSAgABatGjB119/Xeg+mZmZJCUlmSwCgcD23Eq5RfDsYELmhhAyN4QvDn4BWKcQKO6d3kt60+CLBmTlVkwB2tSsVGrPrU33H7sDsOzkMjxmevDjMbnQX3EtNaKopcCe2bx5MwCZtTKp/ml1IhIiAPu31FQapebSpUvMnz+fsLAwNm7cyKhRo3jllVf4/vvvLe4zc+ZMvLy81KVGjRoVKLFAILDEkZtHSMpMQoMGFwcXXBxcCHIPom9Y3yL3fbLpk7g5ugEQkRDBlcQr5S0uAOfiznEj5QY7o3ZikAzsitol/728C0mSrCrzYIyIqRHYK9nZ2ezYsQOAW1VlC42j1pH2NdoT7hduS9GKpNIoNQaDgZYtWzJjxgxatGjByJEjef7551mwYIHFfSZMmEBiYqK6XLlSMYOfQCAoHMVV0zW0K+n/Syf9f+lcf+M6baq1KXLfiR0nkjIxheqe1eW+KkgpUI5jkAwkZiSqn+PS40jNTlUtRsV2P4mYGoGdcejQIVJSUvDx8SHNNw2A3c/sZs8ze3DUOdpYusKpNEpNUFAQDRs2NFnXoEEDLl++bHEfZ2dnPD09TRaBQGB7ymJ6aEVP7zY+Tlx6XJ5SkxanbnPWOasxNkVhbKmRJKmMpRUISs7WrVsB6Nq1K7czbwP2P5VbodIoNR06dODs2bMm686dO0etWrVsJJFAICgpxQ2qNUdFu2+Mj2OsyBgrOH6ufmg0Gqv6Uyw1OYYckrOSy1hagaDkKEpNx84dScuWLTX2HkujUGmUmtdee419+/YxY8YMLly4wNKlS/nqq68YPXq0rUUTCATFpCymh1a0+8b4OLFpsWoMjfH/xXmbdXV0Re+gV/sQCOyBjIwM9uzZA0Dze5sD4KB1wNO5cng6Ko1S06ZNG9asWcPPP/9M48aNmTZtGnPmzGHo0KG2Fk0gEBQTa8siFEZFl0wwPo4l91NxlTQRVyOwN/bt20dGRgZBQUF4V/cGwFfva7UF0tZUmjw1AP3796d///62FkMgEJSSMo2psYH76VrSNdUsn23IJioxCii+kuan9+Nq0lUxA0pgNyiznrp06cLtDDmeprK4nqASWWoEAsHdQ2WPqTl3+5zJtrNxZ01kshZ7rmUl+G+yc+dOQE66Z235EntCKDUCgaDCKW6iOnPYMqbmbKzppAXlc3EHf1HUUmBPZGdns2/fPgA6duxYJr/TiqZSuZ8EAsHdQXErWpvDZjE1OfDv0X/hOpAAZMERzRHQwL/x//JH+h80bdrUqmSfIgGfwJ44fPgwaWlp+Pn50aBBA9bsXgNULveTUGoEAkGFkpGTkTdNtBRvgBUZU3Pp0iWub7wO/wLXICnXtORKBhkA/LrzV36d+ysAtWrVom/fvgwePJhOnTqh1RY0jIuilgJ7QnE93XfffWi12jJxE1c0wv0kEAgqFGWg1Gl0eDl7lbif8nY/5ebmsmrVKrp160adOnXIXJ8Jl4FcwBWoBTQD2gHtgTbQvmd7mjRpgk6nIyoqivnz59O1a1caNGjAl19+SWZmpskxKjrYWSAojF27dgGy6wnKJqC/ohFKjUAgqFBKkqjOHMrbY3pOOunZ6WUiG4AkSaxcuZKGDRvyyCOPsG3bNlnOUKAv8DLwJvA08CDQG+gJ9IPZ387m+PHjxMfH8+eff/LMM8/g6enJuXPnGD16NPXq1eOnn35SMwiLSt0Ce8FgMLB7925ADhKGsol9q2iEUiMQCCqUsoinAfB09sRBK3vQy0opOHr0KF26dGHw4MGcO3cOb29vJk6cyF///AXDgXsAP8CCLqYM/h4eHvTp04dvv/2Wq1evMnfuXIKDg4mKiuKpp56ie/fuXLx4scLjggQCS5w6dYr4+Hjc3Nxo0aIFUHa/1YpEKDUCgaBCUf30pXz702g0ZaYUJCcn89JLL9GqVSt27tyJi4sLkydP5vLly0yfPh0nPycAgtyDTPbL/9nc4O/h4cErr7zC+fPnmT59Onq9nq1bt9K8eXP+2fgPIGJqBLbn77//BuCee+7BweHOy0IZ/VYrEqHUCASCCqUs/fRlEVdz4MABWrRowfz58zEYDDz22GOcPXuWqVOn4uHhYdJ/Le9aOGrzqhSH+4Wr/2s1WrxcLMcIubq6MnHiRE6cOEGnTp1ISUlh8suT4XeITRGWGoFtUZSae++9V11XFuVMKhqh1AgEggqlLGdUlGZKdG5uLh988AEdOnTg4sWL1KhRg61bt7Js2TJq1qxpKrORImasjNXzq2cii1ZT9JBap04dtm7dyuTJk+VYnX8g/ft0bsbeLPY5CARlRX6lJseQQ0JGAiAChQUCgcAiZfn2V1JLTUJCAv369WPChAnk5OQwePBgjh07RteuXc22N1bEjE3x9aoYKTXFMNHrdDqmTp3K6tWrwRG4CD279yQ2VlhsBBXP7du3OXPmDABt27aV16XfVrf76H1sIldJEEqNQCCoUMqimKVCFX3xp0RfvHiRe++9l40bN+Lq6sp3333HsmXL8PGxPHAbB0waK2PG7qeSKGmDBg3C5yUfcIUTx07QpUsXbt26Vex+BILScODAAQDq1q2Lv78/kKfIe7t4qwH5lQGh1AgEggqlPGJqrA0U3rVrF23btuXMmTNUq1aN3bt38/TTTxc5tdx4aquxMmai1JRQSQsKD4Knwa+qH//++y+9evUiPj6+RH0JBCXhbomnAaHUCASCCsZWMTUrV67k/vvvJy4ujtatW6sBwtZgElOjz1PGQr1D0Wl08jZ9yZQ0P70f+MPk7yZTtWpVjh07Rr9+/UhLSytRfwJBcTGr1FTCYpYglBqBQFDBlGVCL2tjahYtWsSQIUPIzs7m4YcfZseOHQQHB1t9HHMxNd4u3jjqHPHV+5rIUlyU/ZwCnNi0aRM+Pj78/fffDBs2DIPBUKI+BQJrMRgM7N+/H4B27dqp68vSTVyRCKVGIBBUCJIkcSXxCjGpMUD5WWqUulIKn3/+Oc888wwGg4HnnnuO5cuX4+rqWqCvjJwMDJJ5JcJ4gFeOqf51Nf1c0nO4ePsiNcJqsG7dOpycnFi1ahUTJkwoUZ8gX+/LiZeJSogiOze7VP1cSbxCVEIUWblZJe5HYJ+cPn2apKQk3NzcaNKkibpeuJ8EAoGgEIb9Ooyac2qSmJkIlFGg8B3TuKJ0fH3oazxnevLrmV8B+PDDD3n55ZcBGDt2LF999RU6na5AP8mZydSaU4teP/UyexzjAV45piJ//s8lPYeP//6YgI8CcAhx4Ntvv1XlX758eYn6fWL1E9SaU4uQuSE0X9jcosJWFM+ue5aac2oSMjeEhl80LJWCJLA/FNdTmzZt1KR7ULZu4opEKDUCgaBC2B65HQAnnRN9w/ri7+pf6j7zu592Xt5JrpTL7su7+fTTT3nrrbcAmDRpErNnz7YYEHwm9gzRqdHsiNyh1mVSMEgGdXprFdcqdA3tSphvGE80fgKAIY2GUNe3Lt1Cu5XoHB4If4CqblXRarRkG7L5+8rfPPnkk6rszz77LP/++2+x+1WuN8CpmFMlzrps3M/F+ItcT75eon4E9sm+ffsA03gaqJx1n0AoNQKBoIJQFI/To0/zxxN/lKqYpYLyFpmYmUiOIUc9xt5f9/L6668DMHXqVN57771Cj6cM4NmGbFKyUky2JWYkqlYOP1c/anrV5NzL53i13asAjL5nNOdfPk9tn9olOocONTtwc9xNXmz1ooks77//Pvfffz+pqak88sgjpKdbX7RTkqQCcUYlzbqcPwhbFN+8u1AsNcbxNJBn/RSBwgKBQJCP9Ox00nPkh3JZDpLGScFup9+WH7jH4O8F8kA9fvx4Jk2aVGQ/xg/8/A9tZXB3d3LHSedUFmKbRbkuiiwODg78/PPPBAUFcebMGd5++22r+0rJSiHbILuJlPpUJVFGsnOzScpMMu1H1Km6a0hISODUqVNAQaVGxNQIBAKBBZQB0kHrgIeTR5n166B1wMdFVmzi0uKI+jsKfgUkGDNmDB988IFVFiHjB34BC0cFDe6qK81IFn9/f7777jsA5s2bx6ZNm6zqS+nDxcGFGl415HUlUEYUt5sGDXV96xaQT1C5UZLu1alTh4CAAJNtlbGYJQilRiAQVADGQYdl4XYyRhl0t+3cxq0fboEEnu08mTt3rtXHMo43KeBuqaDB3VLOnd69e/PSSy8BMGLECG7fvl1g3/yYTEEvRX0sZR9vF28C3AJM+hZUfiy5nkBYagQCgcAi5Znzwk/vBzEw/pnxkAOEg8MAB7Ra64c34wd1/oDassyAXBj5Z3IZ89FHHxEeHs7169cZNWpUgWDm/JhMQS9m1mWL/ehL3o/APrEUJGwckyViagQCgSAf5akYuGW6wU+QmpQK1YBHICE7oVhTmAtzPxnXfSpPCksk6Orqyk8//YSDgwMrVqxgxYoVhfZlLgNySSwsxg82c+4xQeVFkiTV/aQUsVRIzEwkV8oFhPtJIBAIClBeOS9SUlI48skRSATPYE94AnCSp2EnZCRYL5+xUmPJ/VTeSk0RbqI2bdowceJEAF577TWSkpIs9mUuA3Jp3E/G+XmEUnN3EBERwe3bt3FycqJp06Ym25T7x9XRFRcHF1uIV2KEUiMQCMqd8vDPGwwGnnzySeIj4sEN6rxcB9yMjlkMy4RJTI2lQOHyjqm5039GTkaBrMgKEyZMoG7duty4cYN3333XYl/G17tUMTVG8URqPyKm5q7g4MGDADRr1gwnJ9NZfZU1ngaEUiMQCCqA8oipmTRpEmvXrsXByQGGwGXtZbPHtAaTmJp028TUeDh54Kh1lGWwILuLiwtffPEFIM+GOnr0qNl2ZR5Toy9dPwL7459//gFkC2B+KuvMJxBKjUAgqADKWjFYsmQJM2bMAODJ/z0JNUqXJK7QKd0V5H7SaDRWFejs2bMnjz76KAaDgZdeesls0UuzlpqSxNQYfW+lsfgI7A/FUmNOqamsifdAKDUCgaACKEvFYP/+/Tz77LMAvP322/R7pF+hxyyK9Ox0E3ePpeR7FfHWaq3i8Omnn+Lu7s7ff//NokWLCmw3DvAtTSyM2Zga4X6q9OTm5nLo0CEAWrduXWC7cD8JBAJBIZRVXMrVq1cZNGgQmZmZPPDAA0yfPt3iwGvtQ9xSYHD+7RUxwFtjqQGoVq0aU6dOBeCtt94iMTHRZLvx9Tbus6ip4Pkxiam5009yVrKo1l3JOXv2LCkpKbi5udGgQYMC2ytrMUsQSo1AIKgAymJadFpaGgMHDuTmzZs0btyYJUuWoNVqLSpK1sZ+WFJioOLzdRSWqyY/L7/8Mg0aNCAuLo5Zs2aZbDOJhblzzXOlXLVCurUY9+Pt4o1WIz8yhLWmcqO4nlq2bGm2an1lLWYJQqkRCAQVQGkVA0mSeP755zl8+DBVqlRh3bp1eHjI5RbyK0rFrVGkDODKfilZKWTmZAKQlp1GZq78vz25nwAcHR354IMPAJgzZw7Xrl1TtxlbWPSOevQOepP11mIcU6PVaPNKUoi4mkpNYUHCIGJqBAKBwCI5hhzVQlBSxeDLL79k6dKl6HQ6Vq1aRWhoqLotf5/hfuFAMdxPdx70tX1q51ki7uyrDO5OOifcHN3Md1CGFDeo94EHHuC+++4jPT1dneKdmZNJanYqkPdQKklcjUEyqLWflGtsrXtMYN8olhpz8TQgYmoEAoHAIsZFEZU3/eKwf/9+XnvtNQA+/PBDOnXqZLLdxcHFROGo51cPsP4Brigu/m7+BZQK48G9rGtWmaO4yodGo1FdT4sWLeLUqVPqvjqNDi9nL6BkykhiRqKalVm5LiIBX+UnKytLTQVgyVIjpnQLBAKBBRSlwdvFG522oP++0H1jY3n00UfJzs7m4YcfVpWb/BgPvvWq3FFqiul+Mpd9t6IH95Jk/23fvj0PPvggBoOBCRMmqNfbV++rKmIlmY6t9OPm6Iazg7NJPyJXTeXl5MmTZGZm4uPjQ506dcy2EZYagUAgsEBJ42lyc3MZOnQoV65cISwsjO+++86itcR48FXcT8UNFDaX06WiEu8plFRpmDlzJjqdjnXr1rF9x3a5LyNFrCSJ88ydu3A/VX6UeJrWrVtb/D1VZBqDskYoNQKBoFwp6UyKadOm8ddff6HX61m1ahWenp4W2xo/eMN8w9TjWjOF2dz0Z2VQr6hilgolVRrq1aun5u5Z8NECwPSalKSopTkrlUjAV/kpLOkeyMHxGTkZgAgUrlA++OADNBoNY8eOtbUoAoGgEEqS82LDhg289957ACxcuJAmTZoU2l558Lo5ulHdszoAWblZasBsYRjP9FAf/vndTxWk1JQmZmXSpEk4OTlx+p/TEGkqc0ncWuZcECKmpvJTZJDwnXveQeuAh5NHhclVVlRKpebgwYMsXLiwQGVRgUBgfxTXlB0VFcXQoUORJIkXX3yRp556qsh9lAevn6sfro6uOOvkGBBrLBNmY2ryBwpXVEzNnfNIykwiOze7WPtWr16dZ555Rv6wM59SU4qYGnOWGhFTUzlJS0vj5MmTQCFBwhUcHF/WVDqlJiUlhaFDh/L111/j41P8mRQC64hJjSEqIYrEjLxkXRk5GeQacgvdLzMnk6iEKK4mXTUx/ZurOpycmUxUQhTRqdFlJ7jA7lBjM/RFm7IzMzN59NFHuX37Nq1bt2bOnDlWHUNVau4MxMqD+GT0STXnjEX5zFWiTrdNTI23izca5AfJ8VvHyTHkFGv/t99+G61OC5cgIyJDXa9cj2tJ14hKiDJZUrJSzPZlzkolYmrsixxDDlEJUVxOvKzOVCuMY8eOkZubi39Vf5x9nAtsT89O51TMKaByxtNAJVRqRo8eTb9+/ejevXuRbTMzM0lKSjJZBEXz84mfCfg4gJC5IQR8HMDxW8dJy06j7ry6dP2+q8X9MnIyCPssjJC5IdT4tAYvr38ZgCnbp+D9gTcHrh1Q216Kv0TVj6sSMjeEqh9XZcE/C8r9vAS2oTgziF5//XUOHjyIj48PK1euxNm54MBrDkXpUI6hfO7/c3/CPw9XYwTMymfGUqNYIira/aTT6vDV+wLQ+uvWtP2mbbFKG9SqVYvaXWsD8M+yf9T1yvXYc2UPIXNDTJagT4K4lnStQF9mA4VFTI3dIEkSbb5uQ8jcEGrNqcXDKx4ucp9Vm1cBEOMVQ9WPq7L69Gp1W2JGIrXm1GLo6qFA5Zz5BJVMqVm2bBmHDx9m5syZVrWfOXMmXl5e6lKjRo1ylvDuYGfUTvX/rNws9l/dz4XbF7iWfI3dl3dbtNZExEdwJemK+nlH1A4AtkduJ9uQzd9X/la3Hbx2kPScdLPHFNxdWDs9dMmSJXz55ZdoNBqWLFlCSEiI1cfoWacndXzqMKTREACGNhmq5q65nHiZqIQos/vlGHJIyEgAzFeitsUskGHNhqkZgA/fOGxVXJAxNfrVAA2c239OjZ9oV70dDao0wMXBxWTRoCElK4UjN48U6Mfc9yYsNfZDUmYSR28eVT/viNxR5D47990ZZ4NBQmL35d3qttOxp4lJiwHAw8mDxxs/XqbyVhSVRqm5cuUKr776KkuWLMHFxcWqfSZMmEBiYqK6XLlypeidBGarFCuDmIREfEa8Vfvlj0sw3l6grXjzu2uxRjE4efIkI0eOBOCdd96hT58+xTpGvSr1uPDKBZ5tKc8AGt9hPCkTUwj1ljMPW7q/lMSAAD56nwKVqG2Rr2N2r9mkTkzFSedkIou1pLmnwZ1ww2nTpgGyW+vU6FOk/y/dZOlRp4fFY5j73pTrczv9dpGuaEH5kv+eTshIKPI7iTpzR7kPKtiHcg+0DGpJ0oQkRrUZVXbCViCVRqk5dOgQ0dHRtGzZEgcHBxwcHNixYwfz5s3DwcGB3NyCX6azszOenp4mi6Bo8tfCiUuPM3vzF9gvreB+xgUBjfcr0Fa8+d21FBWXkpSUxMMPP0xaWho9evRQ0/2XBfmVlAKy3Vnv7eKNg9bBYvK9ip7aqtFoSjzTKC49DjqCVqvlt99+49ixYxbbFnYMc643xTUmIakWLoFtUL6fQPdAoPAXToD09HRiImVLTEDdAJM+oOLjx8qLSqPU3H///Zw4cYKjR4+qS+vWrRk6dChHjx41W2lUUDKUG13NzJoeZ/bmL7Bfuul+WblZpGSlFGqpMT6G4O6ksLgUSZJ49tlnOXfuHNWrV2fJkiVl+lsuaipzfkuM8jc+PZ707HSSs5JN+qlIilsHSiEuLQ6qQK+BvQD45JNPSnQMcw85J52TOs1X/GZti3L9A90D8XSWX9gLu1eOHz+OZJDADRrUbmDSh/G+lTWWRqHSKDUeHh40btzYZHFzc8PPz4/GjRvbWry7CuVGD/e9UxgwLc5kCqel6ZzKj6KGZw11Sm1UYhRZuVkm/Vo6huDuw1xRRGPmzp3LL7/8gqOjIytXrsTf379Mj1+UYpC/GrGxJeJi/EUAtBot3i7eZSqXNZQkt4xxjNDY18YC8PPPP3P16lXzx7AwRdvYwpr/exNxNfaB2UzYhdwrhw8flv8JMqqPZuZlVSg1grsOZYAzrnZsjfvJOPuqMvCdiztXYLu5YyRnJavKj+DuITEjkVxJdg3nHyz37NnDm2++CcDs2bNp165dmR+/qLwq+R/cjjpHtQikcu/6uPio1bsrkpLkhIlPj0dCni3VrUM3OnfuTE5ODp999pn5Y1hQnNKy08jMzTSRQ0Ek4LMPjGOerCmDcejQIfmfIPOlRCpzaQRjKrVSs337dqvzWAiswzhFtnFhwMKCfPOvr+JaRR34jJUaczE1dXzrqA8M8eZ396HcE8ZFEQGio6MZPHgwOTk5DBkyhNGjR5fL8Yt6ABc2w0e5d20VY1BUPJA5lPPxcvbCQevAG2+8AchZmZOTky0fw8zkAABHrSPuTu4m20QCPvvAOP+TNfeKaqkJzhvbjYOLRUyN4K5E+VE4ah0J8Q4BTGc/GbcpsK9xDZ07A9/ZuLMFthv/7+/qr5r8xZvf3Ye5QFtFkbl+/ToNGjTg66+/LrfMpUXG1JhLMJfv3rXVm2tJcsLktzz169ePevXqkZiYyHfffWf5GPl+08YPuPzfjXA/2QeFJY3MT2ZmpppJ2NhSYxxcLGJqBHcl5hST+Ix4k6y/RZryjdxPZ2PzlJq07DTSs9NN27oWrIwsuHswV2Zg8uTJbNu2DTc3N1atWoW7u7ul3UtNcWNqjGVV7l1bDfKlqdeknI9Wq+X1118H4NNPPyUnxzRDsSW3RWEJE0UCPvvAJGlkEff5yZMnyc7OBj3gBVXdqhYILq7okiDlhVBqBCaYi4sxSAYuxV9S21gazEx8vPqCMTXKvtm52SRmyuUXqrhWscofLKic5K9y/dtvv6nJM7/99lsaNGhQrscv6t4yN5Dnv3dtbakpzu/CXFXxp556Cn9/f6Kioli9erVJe2MFxThzcWFBo+IlxD4oTkyNcZCwg84BT2dPVfG1VUX68kIoNQITjN/QnHROqj+9ODE1hUXjx6XFqbNhNGjwcfERgYd3Mcb306VLlxg2bBgAL7/8Mo899li5H78oq4I1lahtbqkpTkyNGQuLXq/npZdeAuSAbHPHyF/RvLCgUfVhmC5eQmyJsVWuqPvcOEhYrY+WT6EtTjkTe0YoNQIT8puvzQWNmRtkJUlSlRXjQGFz/SvH8HbxRqfViTe/uxj1u9Z688gjj5CQkEC7du34+OOPK+T4xgGU5moomRvILc32qWhKouxbUsReeuklnJyc2L9/P//8k1cTys3RTU2/YPyWr8ZCmSlCKmJq7ANjd39R94pxkLDy/Rl/j8az3USgsOCuIn+wmLm3VHMmzqTMJLWisLE51Fz/+R8kwkd/9xKXFgcS7PtqH0eOHKFKlSqsWLECJyenCjm+co9lG7LNVqMuLKZG/WwrS00JlH1LwZ4BAQE8+uijAHzxxRfqeuOK5mZzloiYGrvFJP6xEEUzOzub48ePyx+CCo7txi+aTjontWZaZUUoNQIT8vtVzQ1q+f3vxvu5Orri4uBi8UEQmxZb4EEiYmruXmLTY+EwHN1wFK1Wy7Jlyyq0sKxyP4L5BHNqYkAzs5/Uz7aKqblz3OLkcFJcQubetseMGQPIhYHj4vIefuaUlEJjaoSlxuakZ6eTlp0GmLr7zY2hp06dIjMzE727HnwKVrI3HpMV11RlRig1AhPyv6EZD2pKnaYcQ46aPr7AfhaUIXN1pMy9MQjuLiJORsCf8v8zZszg/vvvr3AZLN1fiZlGiQHNFG3Mv39F4+3iXewcToXFRbRt25aWLVuSkZFhMr3bnJJiVUxNWqxZl56g/FHuZQetHPRrPFMu/3eixNMEhgWCJs+laGwJvFviaUAoNYJ8FBZTU92zuvrWWyCvRZrl/cAoO7GZH1BJkowJ7J/Y2FiOf3YccqF9j/aMHz/eJnJYur+Uz8bWHCg4sNsqxkCr0RY7h1NhFhaNRqMmOZw/f75aBDj/LBgwn19IQenbkktPUP4YuxmNi5+ae+FU4ml8Qn3kffLH1Bi9aFb2eBoQSo0gH4XF1BgHABfIa1GIhQeMao0Y/4D0pu4nYam5e8jNzeXxxx8nOz4bfGHGvBk2M2tbur/MxdOA/bifoPhxNUW9cQ8ZMgQfHx8iIiJYv3696TGsdD+5OrqqwcXiN2sb8o+3xop5/ntFUWpca7rK++jzvUwaFSyu7NO5QSg1gnwUUE6MZ4UUkrky/4/Cy8XLpF6OuYrf+RUgEVNz9/Duu++yefNmcAQeg1qBtWwmS1FZc4tSYhRriS0ojsIvSVKRb9yurq4888wzQF7AsLnrU5hyZCm4WFBxmMspY25szsnJ4ejRowBoguWXigITNNIKhgRUZoRSIzChQKBwvh+NpcEs/37GpnOtRkttn9pqOyWYMX/8TXx6vFqHRFB5WbduHdOnT5c/DACq2nawtKQ0W3pwG7/1ejh54KSrmJla5iiOwm8yA7GQ6z1q1Cg0Gg0bNmzgwoULBRSnrNws1YVhqR/xImJbzKYiMDPh4uzZs6Snp+Pu7k6Gl1zTz9wEjbulmCUIpUZgRHZuNkmZSYD52JhCk+qZeUNU/vfV+5rENViy1EhIJGQklOk5CSqWCxcu8NRTTwHw9AtPQxNMkjjaAks5PAp7Oy0sT1NFUpx4M+V89A569I56i+3q1KlD7969Afjqq68KuJSVY2k1WrxdvAuXS7ifbEJ+Fz6Yv1eUIOEWLVpwO8N0pp+5Kd22vt/LAqHUCFSMM/0qg1n+WSHWxtSAqe/WxH+b7wfkqHPMq0MiBslKS0pKCg899BBJSUm0b9+eFye8CNh+mmhxY2qg8JQGFUlxZgYWZwbLCy+8AMDixYvx1Jn+9pS/Pi4+6LQ683IJ95NNKSxppPG9osTTtGzZ0mJoQY4hh8iESJM+KjOlVmoyMjLKQg6BHaAOZvq8wczE/VRI8Umz1Y6NLDHK+oSMBG6l3CrYVpizKzUGg4Hhw4dz4sQJqlatysqVK0nKla1+dqMYWHHPqvtYCHivaIoTU1Oct+1+/foRFBRETEwMp/eelvdXChtaoRyJNAy2Jb8L3/h/4/tcUWqaNW+mWsGVdq6OrugdZIueWrz1v+p+MhgMTJs2jWrVquHu7s6lS3Kxw0mTJvHtt9+WqYCC4iNJkpqYyVoycjI4FXMKMK+YKOvNDbLXk69zI+WG2fbKOh+9j7peKXVvzh9szZufJElqtW+Qq3+XJl9Gdm42UQlRXE68XKAfSZK4kniFqISoIhOg5RhyiEqIIiohCoNkUNdHp0YTlRCluvasJSMnQ+3PeEnMSCxWP6XhRvINszJEJUSpyinAtGnTWL16NY6OjqxevZrg4GC7KZCn3Fs3Um5wPfm6ut6arLm2HuQVOa4kXjG53uYozvV2cHBgxIgRAKxfLs+CikmLISohijOxZ4rsR9kWmRAprDU2oLCYmshE+TsxGAwcOXIEgDqN6qjtjMfi/OO5rX+rZUGJlJr333+fxYsX8+GHH5qkO2/cuDHffPNNmQknKBmvrH8Fvw/9VO27KFKyUgidG8qjK+U06sY/FOPaMOZmP/1vy/+oNruaWYVIDUjT++GgdcDHJe/HZKmtNW9+Q1YNIfCTQG6l3OJa0jWqflyV4b8Ot+pc82OQDLT+ujUhc0OoNaeWeg0URv42kppzahIyN4T6n9cvVLHp8F0HQuaGEDI3hAd+fgCARUcWUfXjqoTMDaHqx1WL/Z0o/RkvAR8HcOzmsRKdb3GYvnM6wbODzcoQMjeEwE8C+Wz/Z6xatYopU6YAsGDBAtq3bw8UnuukIlGOfzL6JNVmV+Odre8A1sXU2HqQV+TYErGFwE8C+fLglxbbFjeB2rPPPgvA7m27IUF+OQiZG8KLf7xYZD+KXN8f+56AjwP4/dzvVh1TUDYUFsP4w7EfCPg4gIUbF5KSkoJer8enujz2+rj44KB1KLCPgq2V+LKgRErNDz/8wFdffcXQoUPR6fJ8rs2aNePMmTNlJpygZGyP2k5GTgYHrx+0qv25uHPcTLkJgLuTO483flzdptFoGN5sOG2rtaWhf8MCMTU7onYA4Kh1pFVQK5pUbaLu+0D4A4R6h/JQg4cAGNZsGHoHPS4OLgxpPARnB2e1bXHycWyP3E5SZhLHbh3j8I3DpGSlqHIUl4SMBI7fOq5+zt/P9qjt6v8RCRFcTbpqtp/07HQOXDuQ10+k3M/OyzvVdRk5GSZtCsP4O3FxcFEXrUZLVm4W+67us6qf0mD83RrL4OLgog6M63auUytvjx07Vp0uDPbz9te0alNaBbXCUesIwM4o+TspLKZmUP1BhHqHMrDewIoT1Az31byPMN8w9XorspujuNe7Tp06dOvWDUmSqHe5nsn36+XsxeCGgy3u26tuL6p7Vken0WGQDOy+vLsYZyUoLeZcpz3r9DT5Tv7cKafybtasGYnZsnU3v9LyROMncHN0w8XBhY41O6qzVCszDkU3Kci1a9eoW7dugfUGg4Hs7OxSCyUoHfl949a2bxzQmBOjThTYvvCBher/+d1EyoNh45Mb6Rra1WS/jrU6cunVS+rnOb3nMKf3HLMyWOujN67XE5cWp1aWLakJPP9+t9NvY5AMFtPTx6XFmf3h55c7NTuVzJxMi7lRrJWrkX8jTr50Ul3/9NqnWXx0cYXEMijHWPPYGvqF9zPZtvTEUob+OJTdn+0mIy2DHj168NFHH5nubyep110cXPhn5D9subSF7j92LzDLx5x8Pev0NLl3bYW/mz/nXj7HD8d+YPivwwv93kuSQO35559n69atpB5IJWVFislLamE09G/IldeuMG3HNCZvnyxcUBWMuSnYynfy/s73mbRtEpFnIgFo1aqVRdfkmx3e5M0Ob1aM0BVEiSw1DRs2ZNeuXQXW//LLL7Ro0aLUQglKjnECruKmVrfGTZBf+SirqYDWFrU0zsVhnMhPUSKKiyJ/oHsgILujlJiVXEOuGlxnXLvKbD9GrhadRpcn35326v7WKpoWrqsyhbMiHiKFuY+8HLxgBWTEZVC3bl2WLVuGg4PpO5K5YEZbkt/FaS+WJGuwZmp3YcUsLTFo0CB8fX25evUqGzduLLlcImC4wsgx5JCYecfyUojr9OY52dLbsmVLu3nBqAhKpNRMnjyZMWPGMGvWLAwGA6tXr+b5559n+vTpTJ48uaxlFBSD1OxUNe6j2KnVrRjclR9FWnYaadlpeVWOS/ljsXZwNEnlbpQJ05p9zfZ359yreVTDzdHNpJ/4jHgk5MDhML8wk/aW5PJ39c+r12OUk8c4o3Jx5Mp/XSuypISlQFpJklg4dSFEgcZZw7p16/D1LZh1115iahSU87idfpvUrFQycjJM1tsz1lgyS/LgcnFxUd2HJYmHFCVOKh7j1BvGQb8Kfno/MEB8hDwho1WrVndVHpqiKJFSM3DgQH777Tc2b96Mm5sbkydP5vTp0/z222/06NGjrGUUFAOTVOdWDjTFmTXh5eylWiIuxV9SZ/mU9m3X2pia/JWEjS07JZkObvzgzu9aU/56OXtR1a2qSXtLchn3YyxfuG94oftblCt/Cv8KmkqbmZOpFivML8O0adP4bflvoAHdYB3169c320dhs4tsgXIeOYYcIhIiADleyMPJw5ZiWYU1swNLanl67rnnADkT9K1bhc+wKiBXMWtTCUqPcq29XbxNgn4V/Fz9IAFy03NxcnKiYcOGd1Vtp6IocZ6ajh07smnTJqKjo0lLS2P37t307NmzLGUTlICSPOSL8/AxrvtyLu4ccGeGlFHQb0mw9o3PpJJwelwBy01xMVbo8gdBG/uti8qjY9yP0jYmLUadvq5UKbf2O7GkaFrrpistynXVarR4uXip67///nveffdd+UNfyKmTYzF9gL0NpHpHPa6OclE/5d71c7VtYkBrUe7N1Ow8C1N+SupiaNSoEW3btiU3N5elS5cWa9+Kuh8FeRQ1Xvvp/eBO5oKmTZvi6OhYqVytpaVESs3BgwfZv39/gfX79+/nn3/+KbVQgpJTEndMcU2Tyg9DmZ5cFiZNa5Pv5T+/klimTPozcpEUFi9UVEyDubbGlixFqSl1TE0x0uaXBmOFRCsBWVls+f139a1+/CuvULWpIz5pEH/9IsTH5y0JCZCQQHZcDF7pUMXgApmZYDBYPmAFkf/erSyDvLGF1NJ3X9hsrqIYPlxOibB48eJi7WfsNi5NriiB9RRlWa/iWgXktGG0bNnSdB87sZqWJyWa/TR69GjGjx9P27ZtTdZfu3aNWbNmmVV4BBWDuUq71u5j7QCv/DDOxpVdFkpj87okSRbfnvOfnzL7Kf82azF+g7HkfrKUdNCcXH56P3VwV66Ph5MHQR6FBxpb7C9/TM2d7yg+NVZWHhITISmp4N+UFEhPh7Q0eVH+z/9X+T87O2/JyaFhVgYZmeBgiIHxOk4ADwE5wBBg5rx5zFKE+rCZ2XO4ovwzq2XeSq0WnJzA0dF0UdY5OYGrq/nFza3gOnd38PKSF2/vvP9dXcHMPeTn6seVpCtleu9WBBqNBl+9LzFpMcSlx1HNs5rJ9vTsdNJz5ISUJVHUhgwZwtixYzl+/DhHjx6lefPmVu1n7NJLzkpWy50Iyo+iYtX8XP1UpaZh04byPv+hmJoSKTWnTp1SNUBjWrRowalTp0otlKDklMRSU1wtXvlhqCb8MnjbVfrMNmSTkpWCh7P5OAfjc4pNizVRakodU5Nm3lJjLulgYf0owcXGqcctxh5kZEBcXN4SGwtxcQzYcIJet+D+Iz9A1gq4fRsSE6mfmEBSHHhkxcOEgkGCZYXuzgJwDegLJAGdgMWUwm9tMMjnXN7lVXS6PAXHSOmZEX+L47ngdnIbmmwIT8uGv/+GKlXkxctLVrzsED9XP1mpMaO8K/efg9ahRIqFj48PAwcOZOXKlSxevJg5c+ZYtZ/eUY/eQU96TjpxaXFCqakAinI/OeucVaWmVoNa8j525gouT0qk1Dg7O3Pr1i1q1zbN13Hjxo0C0zoFFYvxgz0tO4307PRCK/ZC8QMMlXbGcQmlxdXRFRcHFzJyMohLj7Oo1OSPqTGexl0S95NVMTV6vyJjB4zbarKyqZ4IbrGn6B8H7bQGguK+4Yv1EJSSTu5vbdHdioaYGEhNNdvfS+p/20zW6wCTK+PiAp6e8sPY+K+7e541Q68v/K+Li6n1xMGBpadX8tbOSdxXrSunFkVz9d9/qRcWxpqdO3H29weg2/fd2BG1k6UP/sRjjR+T5bljpfo3+l+aL2xBFRdfboy9YmoJysoy/Wy8LjPT1IpkvKSmFvycnCxbp4wXgwFyc2VF8PZtk+vX584Cl++s+Rs+bJ/XQKsFPz9ZwfH3h6Ag80tgIPj6mrUGlReWislC3kPLV+9b4hihESNGsHLlSpYsWVIgW3xh+Ln6cTXpKrFpsYT6hJbo2ALrKUpBiYqKgnRAC1VCTGeVVhbLZGkokQbSs2dPJkyYwNq1a/HykoMIExISmDhxopj9ZGPMJXur7ljdqn2KG1NT1sFnfno/riVfIzYtlhDvELNtjBUXZXaOuW3WYjJrKb+lxmjwUAM1E2Lg7Fm4ciVvuXyZyX/v4cMYCJvzGs4JycjprJQaTZeBGUaKSr6swjqd/CBVHqZ+fnx/9Q9uOGfzTPc3CahZX36A3nGvNF/aiauaFPaMPU694CbFPmdriIzWclUPW7/+l+iz0VStWpX1GzfiGxiotvFxr4JBC7FZCZA/R012Ijk68PSsIitOFYUkycpOfkUnMRESEli3dxEXzu2nShpUSYMmuiBqZLnIVrKkJFkhiomRl9OnCz+Wk5Os3CiKTo0a8lKzZt4SGCh/v2VAYdbC0sTTKPTs2ZPAwEBu3rzJ+vXrGTjQumzKfnpZqRHTuiuGomJqlCKWBEBybrKcu0xYagrn448/plOnTtSqVUtNtnf06FGqVq3Kjz/+WKYCCopH/oElLi2O6p6WlZqs3CySs5IB67X4/O3Kyk/r5yorNYVOWy0s+Vgp3E9VXKsQ6xqLdzr4noqElSvptOZvml+AfltWEpTwMzEXoEr6NXi74BTmdup/8rXM1sJNd7jhDrrgarRq0ZdPIpdywSmV8Q99QmiDeyEgQFZkvLxM3vizcrMY8b48m+z5N9+CfNc7aac/cQkpxOUmF/t8reVWwi1YBtGXovHx8WHTpk2Ehpq+hSuJAM1aDmzlw9doZCuVuztUq1Zg8z/h15i2My/m76MerzOu/Tj5Q1ZWngswJgaio+HGDbh5U/5rvNy+Lbe/fFleLOHgIMuhKDmK0lOrFtSuDSEhsqXMCgqbPl0WLxgODg48+eSTfPzxx3z//fdWKzUVFbwukCnqt6UqNUFy28TMRHKlXEBYaixSrVo1jh8/zpIlSzh27Bh6vZ6nn36axx9/HEdHx7KWUVAMCig1RSWzS8ubuuvt4m3VMQoUQSsj7d+aBHzWJB8rlOxs2bpy6RLSxYuM/fUWteKg0ZrhNImI5IkkgH+AwTyl7iSXjlAmrUvu7miUB9Sd5aUTH3BOn87C59cS7aGlw5oHkO6EZrx8z0O06jOPRV/u5d+Yf3mkSzNCa99rUUTj5FrmvhM/Vz8iEiLK7SGSk5PDrzN+hUvgpHfizz//pEmTghahwoKn7fXNML88Jveyk1Oe1aUoMjPzlJ2bN+H6dbh6NU/JuXxZ/pyTA1FR8mIOjUZWeurUkZfatfP+r1NHttApsltzvUv50Bo+fDgff/wxv//+O7GxsVSpYkWWcZGAr0IpypV06NAh+Z8g0wSgSo2nu50SB8C4ubkxcuTIspRFUAbkf9BZO0Xax8VHrXdUFJZyp5QWaxJ5FXY+6qAqSfKD5uzZgktkpBxvAWiAvKonefWVbnloqdqkHb9l/8txl0Qe7jmWei260+LPAUR6GPh3wmmqeeVZv3IMOcyfJudu8WjZjsy0WFWhMT4va3N6KNt99D7otAVdF9ZOfy8Jubm5jBgxgsv7LoMOxsweQ7t27cy2LcwdYq8+/DJTyJ2dZWtLrVqW2+TmyvehsaJzx11JZCRcvCjPUrt6VV52mCnK6u0tKzphYQx0jeNyEri5n4P7UuXZYHcoK1dw48aNadWqFYcOHWLp0qW88sorRe5TnvejoCCFvTBIkpSn1ATL34m9/hbLixIrNefPn2fbtm1ER0djyJd/QpRKsB3KwBLkHsSNlBtWZ+gtjpvA0jTj0mLN4KjIG+QeRFzCDcLj4N4UbwKuJtAk4TL8eI+svCQlWT6QszPUrk1ajSC+SdjKZT8dH72wiusBesJ/70Wu3pH0/+3m+U+CuJWaSL/nhqMJbM6N01VITI0mNj3ORKlRLCsgB2rmz9ehXC9rswEXZeUor3o7ubm5DB8+nCVLlqDRaZAekejYpaPF9oVluS1OluqKxFLZiXJBp5OtMNWqwb1mLHOSJLu6Ll6Ul0uXTP+/fl2etn/4MBw+TAegA8CqP2Ccu2wlrF8f6tenfu4JuqVAaLiT3G8pApiHDx/OoUOH+Omnn4ql1Aj3U8VQ2GzVq1evEhMTg0arQaoq1wG0199ieVEipebrr79m1KhRVKlShcDAQJNoe41GI5QaG6I86ML9wmWlxspaSsUZ3As14ZcCi2bsnBy4eJHMo4d5c3M6jaPhnvhEqt0EBwkg4U7DLOCg/K9WK8cr1K8P9erlLXXrQnAwaLWcuXGYV79qRbBHVT4eOBDPzGTS/gJyM0nLTivgu/bT+xGdGm02bgny0pYrtZ/yXx9rYw+K8pmXx0PEWKFxcHDAf7g/N2rcKHQgLHQ2jp3mxbBUdsImaDTyDCt/fzBnDUtLg4gIWck5d47I/Ru5cmAzjW874JOSkxeovmkTjwCPAPwwH0b+CI0aQePG0KRJ3t+AAKvEeuyxx3jttdc4ePAg58+fJywsrND2oqhlxSFJUl69PTP3rmKlCQwN5IbjDZMEpfb2WywvSqTUvP/++0yfPp233nqrrOURlIKs3Cx1RlA9v3rsiNpR5IOvJFp8ARN+Gb3tVtH7USMBauw5ASc/hBMn4ORJeRZKZibOwFS1tZyaP94FbtcKYJdLNGerwNvPfIdXs3tk5cW58NIN+S0i7k7uOOmcyMrNIjIhUq0Gnt99ZG6GmXE7R50jXs5eBSrpFttSYykNehnHMOTk5DBixAhVoVm+fDnPX3ge0gv/bgt1P9lpTI2lDM12iaurrJw0agTA1cvt6LRoM3V9Qzj/xD7ZInnmDJw9y9+bF+MbFU1YghZtSgrs3y8vxgQEFFR0GjWSg6pNmgXQo0cPNmzYwJIlS5gyZUqhYoqYmoqjqKBfJUi4bqO63OCGSdFf4X4qhPj4eB599NGylkVQSoyDfuv41gEgNr2ImJoSBBjmrwxbogdXbq48KB85oi6jDu1nbCLAzjuLEa6upIWHsox/iaruTvX2vXk39hdueMA7nUby2YHPSMxMZHjPe/GqYr7AYn7y/9g1Gg1+ej9upNxQM87qHfRqnh9LD3Fz19DP1S9PqXHNpxRZaz2zcF3Lsqhleno6Q4YMYd26dapCM3DQQB6Z9kihMkDh7id7HUiN5bEUiG2vmFjo/PygfXt5AcZ+s50D16JZ++ByBjg0hH//lV8KlBeDixfl2Vxbt8qLMbVrQ4sW8tKyJbRsydChQ1Wl5t133y00941wP1UcRQX9Kpaaxs0bs4tdJpYae3vBKC9KpNQ8+uij/PXXX7z44otlLY+gFCgPEl+9b/FdHXrr31gdtA54u3iTkJGAo9YRdyf3wnfIyJAHVyMFhuPH5SRrRjgjT4WODHQhrNMg+W1SWUJD+TtyG8/+2J2G/jV5qmkrbmz5BUCtjJ2YmVisgdWclcrP9Y5SY5QNWMHSNTXbj96PS/GXTNZbG1BZlPWsrIoIJiUlMWDAAHbs2IGLiwsrVqzggQceIDYtVs2KnN+VZiLHHfmSs5LJys3CSZeXrM1e/fgeTh44aB3IMeRYDMS2V5TvPT4jnhxDjkmFZuV6+3oHQs2G0LAhGL94pqbCqVOygqMoOidOyIHMly7Jy6pVavNBVaviqtNx4cIF/vnoI9oMHiwHRVsoPWEsg6D8KCyexjhIuFWrVnBEbm+vv8XyokRKTd26dZk0aRL79u2jSZMmBaZxWxNcdreRnp5O06ZN0Wg0RS6AVe2KWhwcHHB2dlaX+Ox4iIQc9xzWnV4HF+DE0RPMi5uHp6cnnp6eeHh4qP97eXkRnRQNFP+N2k/vR0JGAlVcq5i+xeXkyG+JBw7kLf/+q844MsHNDZo1U98STwQ70HrvCAL9Aoga+3OB5iZ1mvIpEIoSUZyB1ZyvWc2WfPucxW35j2EufsTc/2UVU1MWeUGio6Pp06cPhw8fxtPTk99++41OnTqZ9Ovl7IWjznKKBm8Xb7QaLQbJQFxanFrfyrgPe3PvKNa4W6m3Kt0gb6xgxqfH4+/mr34u8nq7uUGbNvJiTGwsHDsmv2gcPiz/PXsW91u3GAj8DCx56y3avPUW+PjIv9XWreGee6BtW6heXcTUVCCFWXGvX7/OrVu30Gq13NvqXjiCifvJ3n6L5UWJlJqvvvoKd3d3duzYwY580xA1Gk25KDUzZ85k9erVnDlzBr1eT/v27Zk1axb16tUr82OVBIPBwIULF2wtBgAJJLB251oArnKVV399tfAdXODT7z7l9xq/ExAQgL+/P1WrVqVGjRqEhIQQEhJCzZo1cTaKUaniWoWLty/SNM0Dli/PU2AOHSpggZF3qJJn4laWunVNsq263r5I1gHLD2vjgdtE2XD1K9HAam6AKKyulSX3kTnzrtLWWeeMq6NrofsXkKuomJpSup9Onz5N//79uXTpEv7+/mzYsMGklpu1riOdVoePi49s4k7PU2oMkoH4jHir+rAFVVyrcCv1VqUb5I0tpHHpcapSk2PIKRC/ZTVVqsD998uLQkoKHD/O0B9+4OeFC1nm4MDHgEN8fEH3VXAwNVq14O002F8tjYzb0bj4WheQLCg+hSmvSjxNgwYNqF5Fnp2ZnpPO1aSrgH3+FsuDEik1ERERZS1HkezYsYPRo0fTpk0bcnJymDhxIj179uTUqVO4GeVrsBUuLi7s2rULkM2A5b2AHOCZmZmpLjsu7mD1idWEe4dzX7X7+O7gdzjmOvJg7QdJTk4mKSmJpKQk9f/ExERyc3MhA6IvRxN9ObrQcwwKDKSWtzchDg40jrnE0/HQNusc6QzBpLqUp6f8RnjPPfLSqhVUr17kNFPlR5eanUpmTibODqaBvuYqauf/XBzrhbmHt/JQMOd+shhTU0g/fq5+qiXL2tiDImNqrKxobo7NmzfzyCOPkJiYSGhoKBs2bCA8PNykTXHM1X6ufiZ+e4CEjAQMkpzmoTD3la3IH+NUmVAspMbX2zilQP54txLh7g7t29OzTRv8fvmFW3FxbP3tN3oGB8vWnIMH5ReYEyfg+nUcr19n5p1dpR8DoUGDPEvOPfdA06YFymgISkZhLxzGridjN2tZFh6uDFSaO23Dhg0mnxcvXkxAQACHDh1Szea2RKfTcd9995VL32nZaerbfn7Ss9NxdnBGq9GSuiuV1b6r6dC8Ax90/4DvPv6ObLJZMmmJif9dwWAwEP5ROBevXGRep3kEaYOIiYkhOjqamzduEHXmDFEXLhB56xZpOTncuHmTGzdvsu/O/t/e+asBQjw9aVS7Ns3btaNl9+60aNWKWrVqFeuB6+XshU6jI1fKJS49jmCPYJPtxv7k/FYR5fOl+EtEJRTM3qrRaKjhWUOV51bKLa4lXZP3L8QaY3ab0QMlOjU6701Ib0apMbN/YmYiF29fNPudKLIZt8+P0me2IZvkrGSrKyN/9dVXvPTSS+Tm5tKhQwfWrFmDv79/gXbFcR0pspyJPaPW64pMiATk+BXjOBt7wdx3U1nwc/XjYvxFTseeVsufXLgtW4iVlAJlhaOjI4MHD2b+/PksWbmSnt9/LwcSP/ec3CA1VXZX7d/P2h//R9OoTEITJDl259QpWLxYbufmJk9Z79AB7rtP/t/Do8zk/C9R2AuHsVJj7Ga116D98qLEv4CrV6+ybt06Ll++TFZWlsm22bNnl1qwokhMlM2tvr6W3wQVC4ZCUmEJ2eyUv6/8TafFnZjcaTKTOk8y2RafHk/YZ2G0q96O35/43eSGN35ju51+mwC3giZhrVZLgiYB/KFbh440up4tZzY9eRL27JFTwAMSEAdEAlEBAURUr87G9Bvsu32D7FRnMlMyiUhKIuLoUX4/ehQWLADAx8eH5s2b065dO9q3b8+9996Ln5/lH5ZGo8FX70tMWgyxabEFlBpjRSN/rIvyecGhBSw4tMBs/0MaD+Hnh39m3v55vLohzyVnLhjYuO/825Tr/PWhrxn5e15WbXP9GK/zcfFBgwYJibqf1bV4Hcwd2xiTiuZpcUUqNRkZGbz66qt89dVXADz55JN88803Ju5EY4ozCCrn+eIfBScN2Osgapx3qLKhyP78b88X2FYe5zN06FDmz5/P6tWrmT9/Pq7GxUnd3GQl5b77mKj/jlMxpwhIgf6JAXzl/yy6fw7JFp2EBNiyRV5AziHVtKm8r6LoVC+86K5AprCZTCZBwuS5WRUq4/1eEkqk1GzZsoUBAwZQu3Ztzpw5Q+PGjYmMjESSJBPffHlhMBgYO3YsHTp0oHHjxhbbzZw5k6lTp1rcXhnYe2UvOYYcdkTtYBKmSs2/Mf8Slx7Hjig5rsn4YeSgdcDHxYf4jHji0uIKKjUZGeTu3cPLf8bRKQoafNge0vLFwjg4QMuWaDp0oEqHDlRp357Wd+ritI7czjNrn+HzPp/T2rs1p0+f5sSJExw5coTDhw/z77//Eh8fz7Zt29i2bZvaZf369Wnfvr261K9f38Sa4+fqR0xajPlpwkYWhAC3APrU7YOTzglvF2/6hvXly4NfqrEcxhgkA1m5WeyIlK+Tcr0ctA7U8alD+xrt1ba96vTiU89PiU2LxdvFm37h/fJky+d+Mu6nllctOtXKsxh2r92dur51GdJoiLpOp9UxrNkwlv+7vICM+WkR2IL6FqamK29h15KvEZceR6hPqNl2AJcuXeKRRx7hyJEjaDQa3nvvPf73v/8VakErzhTQwY0GszNqJ5m5mSbrtRotTzZ5ssj9bcGD9R9k86XNDKg3wNaiFJvBDQez5/Ie89e7adlf7/bt2xMSEkJkZCS///47gwcPNttuaJOhzNg1g2j3VL5zj2biy89Sx3eGXPX81Cn5JWn3bvlvRAQcPSovn38ud1Czpqog0bmz7MIqRVbkuxVLQb83btzgxo0baDQamjVrBsjfyfRd08mVcrm3+r3U9KpZ4fLaBKkEtGnTRpo8ebIkSZLk7u4uXbx4UUpOTpYGDBggffnllyXpsli8+OKLUq1ataQrV64U2i4jI0NKTExUlytXrkiAlJiYWO4ylhUTNk+QmILUfEHzAtt+Pf2rxBQkpiBl5mRK/Zf2l5iC9PWhryVJkqS68+pKTEHaGblTktLTJWnbNkl6911J6txZkpydJUlOqJ63eHtLUt++kjR9uiRt3y5JqaklljszM1M6fPiw9NVXX0lPP/20VK9ePQnZ6GOyBAQESEOGDJG+/vpr6dKlS9J9390nMQVp5b8rC/R5z9f3SExBWntmbbFkiYyPlJiC5DTNSTIYDFLnRZ0lpiD9fOLnYvVzK+WWer2zc7Ol3j/1lpiC9N3h74rVT1nQdH5TiSlIG85vsNjm119/lby9vSVAqlKlivTXX39Z1fdza5+TmIL03vb3ykpcQSVmwoQJEiANGDCgyLY1ZteQmIK0/+p+y42uXZOkFSsk6dVXJalVK0nS6QqORQEBkjR4sCTNny9Jp09LksFQdidUien2fTeJKUhLji8xWf/7779LgNSgQQMbSVb+JCYmWvX8LpGl5vTp0/z8szzl1sHBgfT0dNzd3XnvvfcYOHAgo0aNKgN1yzxjxozh999/Z+fOnVQvwmSpTHWuzChvzYUlOFO2m7xhZ2TQ47IjQw9Cgz9GwfELcmVhI3Kq+rPCP4YDdV2Y8/4/8tuR1rqilkXh5OREixYtaNGiBc8/L5vKY2Nj2bdvH3v37mXv3r0cOHCA6Oholi1bxrJlywBwDXCF6rDBcQOd/TubxHyUNN+C8laTlZtFanZqiac45p9Sa8tpy4XlvElMTGTs2LEsvhPTcO+997J8+XJq1KhhVd//tSmggsIZOnQoM2fOZP369dy+fbtQl38V1ypcSbpSeDB8cLCcQ0fJo6NkQN6zB3bulP9GR8OKFfICEBgIXbpA167y37Cw/6Qlx9IYmN/19F+mREqNm5ubGkcTFBTExYsXaXQnlXdsbPkkYJIkiZdffpk1a9awfft2QkMtm9zvJpQHTGGp6AHiUqKpdvoq/zsMXde/B0cf50tViflX/hMUJA8Id5aDLrEMXdSBUO8g5tz5/sqTKlWq0L9/f/r37w9AVlYW+/btY8uWLWzZsoX9+/eTFp0G0fDt4W/57p3vaNOmDX379qVv377EplhOPFUYro6uOOucyczNLKj8FYP8U2ptGYBnaQr71q1befrpp7l8+TIajYZx48bx/vvv4+RkfcDufy2wUFA4jRo1olmzZhw7doyVK1fywgsvWGxbopIJ7u6m08ozM+VYnO3bYds22LtXThK4bJm8gKwYKUpOz56y++o/gKV0D0KpyaNESk27du3YvXs3DRo0oG/fvrzxxhucOHGC1atX085cYbYyYPTo0SxdupS1a9fi4eHBzZs3AfDy8kKv1xexd+VFGRzSstNIz05XU/YDSBcv8MJB6HEJ6s2+j5VJKXe2HAUg3kfP+mrp+PV5iF7PzSzwdhN7tuC05YrEycmJTp060alTJ6ZOnUpycjJPz3uaVX+sosrNKsRGxHLgwAEOHDgg159xBerCzlo7CRgQUOgbozEajQY/Vz+uJ183zbBZgvNWptTaOlNn/unhMTExTJgwgW+/leek1a5dm++//75EM/L+a2nVBUUzdOhQjh07xpIlSwpXasqiZIKzM3TsKC+TJskZyffvlxWc7dvh77/lCuZLl8oLQHi4rNz06CErO57WzQisbFiyogqlJo8S+Rpmz55N27ZtAZg6dSr3338/y5cvJyQkRB1Uy5r58+eTmJhIly5dCAoKUpfly4sOuqzMGLsXbl+7ACtXwsiRULs244d/xYI/4OHT4JiUQrwLrGoASZ9+AGfPMm3pCwx9BLb2qCv/6POZa4vKh1LReHh4cE+Xe6AX9J3dl2vXrvHtt9/y8MMP4+7hLtewPA4vPP0C/v7+dOjQgenTp3PkyBE1d48llHOMSowi25Btsq44KIrQrZRbJGUmmayrSJRjxqTEMH/+fOrVq6f+9l588UWOHTtW4hQDpVH6BHcnjz/+OBqNhl27dnHlyhWL7awtBVIsXFzk4OEpU2SlJiFBTgA4aZJc+0qng3Pn5KDjgQPlulgdO8J778G+fXKW87uAtOw0MnIyANOx69atW1y7dg2NRkPz5s1tJJ39UCJLTe3atdX/3dzcWLDA/BTasqSoh9ZdSU4Otf+9wZDj0PMiBE9tJofR3SFbp2FvdYlNtSHssVE8e20+uTrIevl10Dnid9N0CnJ+SlLMsrwxftMLDg7mmWee4ZlnnuHY9WM0n9gc50hnwuLCOHnypBqb88477xAYGEifPn3o06cPPXr0wNvb26Tf/JmCXRxcLOb+sUa+87fPA3JRRB+XMkh4Vkx8XXzhHPy8+GfmR84HoHnz5nz++ed06NChxP1KkmR3yq7A9lSvXp377ruPXbt2sXLlSl5//XWz7SqkZIJeL7udunaVFZfERNmK89dfsGkTXLggz7TavRvefRe8vGTXVo8e0KsXVNLQBWUcz19vT8kkHB4ejofI/1MyS03t2rWJiyt40yYkJJgoPIISEBMDP/4Ijz+OFBDA2i/i+N8uaHMdNJIkF6p79VX4/Xf6f3YvXZ6G6Z1he2A6uTrwdPZU6/UU5d8uSTHL8sZScbyk7CQIhRoP1+DEiRNERUWxYMECBgwYgJubGzdv3mTRokUMHjyYKlWq0LFjR2bOnMnRo0eRJEntV6m+7af3K1ZiQAVl0Fb6sUVRxK1btzJ/1HxYCgmRCXh5efHZZ59x8ODBUik0IBenzDHIb7b2pOwKbM9jjz0GUKh1vEQxNaXFywsGDYIvv4Tz5+XinAsXwiOPgLe3rPSsXg2jRskVyRs2hHHjZEUoX441e8b4JdR47BKuJ1NKZKmJjIyU0+vnIzMzk2vXrpVaqP8UBoNcL+nPP+Xl4EHVGqMBbrvAxrqwoS488vICHuiW58+++uV49X81rb+ZZHGWBhh7dDMUWYrgzvaaNWvywgsv8MILL5CZmcmuXbtYv349f/75J2fOnGH37t3s3r2biRMnEhQUhGcjT/CBf33loOmSnnOBMgoVZM3Izs5m1apVzJ07l3377uR0doCArgGcXHLSbGbgkqAMnHoHfYksWYK7l4cffphXXnmFAwcOEBkZSUhISIE2ZRJTU1pCQ2UX/ciRciHdQ4dkC87GjXLQ8enT8vLJJ3Jm4549oW9f6NNHnkxhp4h4GusollKzbt069f+NGzfi5eWlfs7NzWXLli1mb3RBPhIS5B/Yn3/C+vWydcaYFi2gb19udGxBjT2PkHvHENDOw2DSzNiaodb3MFeryFKBSDt0M1iqQF3Y9GlnZ2e6d+9O9+7d+eSTT4iMjGT9+vWsX7+eLVu2qImpAA6uOgjBkNg8kc11N9O+fXvTLKlFoFxfc9e7PIiMjOSnn35i/vz5XL9+HZADrAc8MYBf/H7BpbpLmSk0YJ+KrsA+CAwMpHPnzmzbto0VK1Ywfvz4Am0sWVpthk6XV4Puf/+Tx95Nm+CPP+SxNzoaVq2SF5DLQPTtC/36yfXrdBVrhS0MSwH8ivtJKDUyxVJqBg0aBMizSYYPH26yzdHRkZCQED755JMyE+6uIioK1q6Vl507TYPXjN8WeveWpysC168fIndfXjNj64UkSSaF7Mxp8UUNMHYZU3NHloSMBHIMOWotm+JMMw4JCWHUqFGMGjWKjIwMdu3axfvfvs/OLTshFrgKUVej6PF7D5ycnGjXrh2dO3emXbt2tG3bttBSDvktSeWhEN68eZPVq1ezdOlS9uzZo66vWrUqo0aN4oUXXiDFKYVfPvulzN+I7VHRFdgPjz32GNu2bWP58uVmlZoKiakpDd7eeTlyjK3kf/whW8kPH5aX99+XK5j37Su7tnr2lMtC2BBzY2BsbCyXL18GEEHCdyiWUmMwyJaC0NBQDh48SJUq9hOLYXdIklzsTVFkjh0z3d6wofw20LevHMFvJo9IAReM0QMsKTNJjX0wxlxRxdvpt81Wc7bHJGtKgjsJifj0ePzdZCtESadPu7i40KNHD24E3GBng50QD0RCeFI4qedSuXbtGjt37mTnzp3qPmFhYbRt25a2bdvSpEkTGjZsqFpD8l+rsrh28fHxHDhwgE2bNvHXX39x4sQJdZtGo6FLly4888wzDB48WM03k5CRAMgVzTNyMnBxcCm1HFC8YpaC/x4PPfQQo0eP5vDhw1y4cIG6dU1rmNmF+8latFrZGtOmjRxQfOsWbNggKzkbN0JsLPzwg7y4uED37rKC88ADEFCwll55Y24MVFxPYWFhJp6T/zIliqmJiIgosC4hIaHAjJP/HFlZsGOHrMSsWwfGUx+1WrmuycCB8lKnTpHdFXDBGGcQtvAmZK4qdK6US2JmIt4u3iZtbZlnxRIOWge8nL1IzEwkLj1OVWpKmztF3c9HXh7t+CjTuk7jwoULbNu2jb1797Jv3z7Onj3L+fPnOX/+PD/99JO6f5UqVWjUqBFe1b0gBvCUF8dQR5KTk3F3d7cYeCxJEmlpacTGxhIVFUVkZCQRERGcOHGCw4cPm/09tWnThscff5zBgwdTrVq1AttNKpqnxVHNs2CbkiAS7wkKw9/fn27durFp0yZWrFjBxIkTTbYr9016Tjpp2WmVKy6ralUYPlxesrPl+Ju1a+HXX+V6Vb//Li8ajfwiOnCgrOSEhVWIeObGQBFPU5ASKTWzZs0iJCREjYZ/9NFHWbVqFUFBQfz5559qQa3/BDk58Msv8s2/fr0caa/g6ipPIRw4ULbKFNOyld9tZPzZkkvJ+GHk4uCCm6ObXBogLc5EqZEkyS7dTyBbCRIzE00zJpfSqpT/HJXZT2FhYYSFhTFypFxt+/bt2xw4cIB9+/bxzz//cOrUKSIiIoiNjWXHjh0F+v3mu2/4Zvg3aLVavLy8cHV1RaPRqApOamoqiYmJZgPrjQkNDaVr16706NGD+++/v8g4GeOK5nHpZafU2KOiK7AvHnvsMTZt2sTy5csLKDUeTh44aB3IMeQQlxaHq1clUmqMcXSUc+N07iwHFJ88KSs3a9fKLqs9e+Rl/Hi5vIyi4LRpU2alZvJjbgwUSk1BSqTULFiwgCVLlgCwadMmNm/ezIYNG1ixYgVvvvkmf/31V5kKaddotfD663AnEJWqVWXz5MCBcm6EUmQ7Vm7iIPcgbqTcKFDryXibQv6Hvp+rH6mJqcSmxVLHN886lJKVUqokdOWJn6sfF+MvmrVMlXbWkvExzOHr60vv3r3p3bu3ui41NZUzZ85w6tQp9h/ZzxebvoAkIAl0KTpyc3IxGAzEx8cTH1+wSriCo6MjNWvWJCQkhJCQEOrVq0erVq1o0aIFPj7Fz3VTWEXzkiKyCQuK4sEHH+TFF1/k+PHjnDlzhvr186rJazQaqrhW4WbKTeLS46jhZV29MbtGo4EmTeRl0iTZAr9unazgbNuWN5vqgw+genV4+GE5Zufee8tUwTE3Boog4YKUSKm5efOmWhxPKUffs2dPQkJC1EzD/xm0Whg9GpKTZUWmbdsyu5GVB0y4X7is1JixXIT5hZkoNQUe3no/LidetjhFuqRJ6MoTc1lJS2tBKE0sjJubG61ataJVq1Y8MuQRvvD6Qt227JFl9Kvdj4SEBBISEkhPT0eSJDVZpJubG56ennh5eeHm5lai3DiWUM6hLGea2GOclcC+8PX1pUePHqxfv54VK1YwefJkk+1+ej9ZqakMcTUloUYNecwfPVqeTbV+vazg/PEHXL0Kc+fKS3CwrOA88gh06FDqmVT5x8C4uDgiIyMBaNGiRan6vpso0dPXx8dHTZW9YcMGunfvDsgujaLM7Hcl//ufrKWXk2Zez6+eyWcwtdR4OufVOclvgbA0Rdr4B1KWD9qyQE3glVbwfEtqqfFy8UKryftuSqoc6R1N87dUcauCXq8nKCiIBg0a0LJlS1q1akXr1q1p3bo1DRo0oFq1aoXG3JQUSzl9SoOIqRFYQ2GJ+GySgM9WeHvD44/LhTZjYmTl5qmn5NpT16/DZ5/JLqzq1WUlaPt2OXdOCcg/Bh48eBCQg4T/8/GsRpToCfzQQw/xxBNP0KNHD+Li4ujTpw8AR44cKRANLyg5iuJRr4qs1CjTnMF06q25GU/qZwsDjL3G00DBh7Xx9PWSKiNajVadWQWlO+/CrndFUh4zTURMjcAaBg4ciJOTE6dOneLkyZMm28ql/lNlwMUFBgyQZ0tFR8tBxcOHy4rPzZtyxuOuXWULzqhRsGVLsRSc/FZURalp06ZNmZ9KZaZESs2nn37KmDFjaNiwIZs2bcLdXa5DcePGDV566aUyFfC/jHIT1/XNUxSVh7txkjSThHtmAmKN2+fv2x4fXvmtS4mZieRK8o/fHpSRwq53RVIeb8T2rOwK7Advb2969eoFFLTWWLIO/6dwdpYnhyxeLE8V//NPeOYZ8PGRFZ4FC+Qp4tWrw9ixchXyQuobZudm5xXQ1ZtaaoRSY0qJlBpHR0fGjRvH3LlzTXx5r732Gs8991yZCfdfRxkUAtwC1JlLyjpLlpoCgcIW3ubtOR9JfkuNopC5ObqVKh+Lcq46jQ4vl5LndDBJcGgHlhoRUyOwBYoLasWKFSYFh8vDLVqpcXKSSzB8+62s4GzcCM89B76+sgVn7lxo1w7q1oV33oFTpwp0oVxLDRq8XbyRJEkoNRawOlB43bp19OnTB0dHR5NyCeYYMGBAqQUTmD5gqrhWISEjQV1nrJQoDyBzQb+WMnzas5shfybksrIeKPv76n1N4muK3c+da+bu5I6zg3OpZCoNZZ29NSMng7TsNMA+7wuBfTFgwACcnZ05d+4cx44dUzPa/qdiaoqLo6OcnbhnT/jiC7lkw9KlcizOpUswfbq8NGsmx+oMGQK1aqljoFJA9+rVq9y8eROdTieChPNhtVIzaNAgbt68SUBAgFouwRwajea/GSxcxmTmZJKSlQLkWWMucKGgpcY1z1Jj7kFkMabGjgNC87/plZWrTL1OpVWOCrneFYm5gOrSoPTjoHUwCT4XCMzh4eFB3759WbNmDcuXL89Tav6rMTXFxclJdlH16wepqfDbb/Dzz/JsqmPH5OXtt6FDB5x6t6FKKvj6mcbTNGrUqFi16/4LWP26ajAYCLiTGtpgMFhchEJTEOXtF2TfaFRCVIHFUvZgrUaLl4uXSSHFqIQoolOjgTsKj6vlh7UywNxIvsH15OsF+rf1g9kc+X3yZeUqKytlpLDrXZGUtZlfeQj56n3tbkacwD4xngWluKDKWtn+T+DmJltl1q6VXVJffy0HFWs0sGcPYZPmcP0T+OabGPjlFw7+/TcgXE/mKLYN3mAw8N1339G/f38aN25MkyZNGDhwID/88IOJX1UgM3ffXDxnevLXxb8wSAZafdWKkLkhBZaAjwP48/yf6n7KgKC4SpQH+vjN4wmZG6IqKH6ufuo2cw9rZdvp2NNUm12NiVsmmvRvj7ETxtYlSZLKzKqkXqey6sdOLDVl9UYs4mkExaV///7o9XoiIiLU7LZ2X9TS3vH1lWNutm6V897Mnk1Mw1o4GqDjsXh49FH++fRTANr4+RUaYPxfpFhKjSRJDBgwgOeee45r167RpEkTGjVqRGRkJCNGjODBBx8sLzkrLTuidpAr5bL3yl7i0+M5ES0XK3RxcFEXnUaHQTKw53JeReb8lpSHGzyMr97XZL8uIV2o5VWLHrV7UMenDkMaDylw/MYBjWkd3BpHraMqD5jOnrI3lHPOMeSQlJlUZvE/vev2prZPbQY3HFyqfnrW6Ukdnzo81uixUvVTWpSHh/FU/9IgsgkLioubmxv9+/cH8mZBVaqilvZOcDC89hrfLnyR+qPht4ebIFWrxj858u+9zYcfQv36chxOVJSNhbUPipVRePHixezcuZMtW7bQtWtXk21bt25l0KBB/PDDDwwbNqxMhazMGM/gUf53d3IneUKy2mbK9ilM3THV5M1GeZArD64B9QYQN978IBHmF8aFVy6Y3ebs4MzB5w+yPXI7Xb/vanb2lL2hd9Sjd9CTnpNOXHpcmT1smwU24+IrF0stX/0q9S1e74rEOO+OcUXzkmLPcVYC++Wxxx5j5cqVrFixgg8//FC9fxIzE8nOzcZR52hjCSs/cWlxnPWHHQN60mD688TXr4+zVksTZ2c4d06eNfXOO7LLatgwOZOxh4etxbYJxbLU/Pzzz0ycOLGAQgPQrVs33n77bbUmlEDGWImw5PIxZ64t63wh+Y9h7/lIjONqhFvEPEpFcygbU789z4gT2C99+/bF3d2dy5cvs2/fPnxcfNAgx2QpebUEpcP4JfTgnXpPzdu0wTE6Ws6F062bHH+zbRs8/TQEBsrKzY4d/zn3VLGUmuPHj5sU+stPnz59OHbsWKmFupswViIsWUfMmWvL2pKi9HM7/Tbp2emkZqcC9qsoGMfVCAuCZcoyKNOe46wE9oter1fTeKxcuRKdVoePXi7QKuJqygbjcAGT/DTu7nLW4i1bIDIS3n8fwsIgLQ1+/BG6dIHwcLmMz40blg9wF1Espeb27dtUrVrV4vaqVasWWqX4v4YkSXm5VtLjLMaxmJt2XdbxDcoxDJKBi/GyC0an0alv+vaG8bRQYUGwTFkWtbRnl6TAvhk8WI5TW7lyJQaDQcTVlDEmlhpLSfdq1pTrEJ49C3//Dc8/Lys9Fy7AhAlyIc6BA+Wp4zmlj8GzV4ql1OTm5uLgYDkMR6fTkXMXX6zikpyVnFerKc2y+8lcXoeydrk46Zxwd5LLWZyLOwfY99RdYwuEvbvKbElZTusWFjFBSenVqxceHh5cvXqVffv2iQR8ZYwyBno7eXP4jvvJ4nRujUbOUPzVV7J15rvvoH17uc7UunVyfaqaNWHiRFnhucsoVqCwJEmMGDECZ2fzWVQzMzPLRKi7hfzuJEtvwsbxI5IkodFoymV2UhXXKqRkpahKjT0/vKro82KAREyNZcrS/SQsYoKS4uLiwsCBA/npp59YsWIFfveIBHxliTIGJl5NJC0tDQ8PD+rVq1f0ju7ucozN00/D6dNyqYbvv5eVnZkz5aVLF3kK+UMPgV5fvidSARTLUjN8+HACAgLw8vIyuwQEBIiZT0YYv6WkZKXk5ZaxUEk7MzdTTdRXHq4Apa+zcWcB+1YSlGtyJekKGTkZ8jrxsC1AmVpqREyNoBQYu6D8XIT7qawwSAY14DryVCQArVq1QqstZpq5Bg3g44/h2jX45Rfo3Vu26mzfDk8+qU4f5+zZsj2BCqZYlppFixaVlxx3JfnfUixZSNwc3XDSOZGVm0VcehxuTm7l4nJR+jobK9+09qwkqArYHVkdtY6q+0yQR7nE1NixBU9gv/Ts2RNPz/+3d+fxTVV5/8A/N22ahdK90AJd2YRBkIKUiqLsiyPuAoOOMA4OjPzUgZl5YJ5R3OZhdFBHHUdHn2GZERFRWXRGHrQIqAMoKIssFbRQoAs03Zs0bZPz++NyL0mblNYmuUn4vF+vvmjvTW6+udwk35zzPefEoLi4GPZCudWe3U+dV9VQBadwAgCOHTwGoJMzCUdFyUO+b78dKCoCVq6Uu6iKioA//1n+GTsWmD9frsHRh9aQ/B++qh9dUstvKUpS0/KbsCRJrepq/NlSoyZXwZzUmFvEak4M2vofLfmqpabZ2Yyqhiq3YxJ1hMFgUNcFLNpVBIAtNb6gnMPoqGjs/WIvACA3N9c3B09PB5YulRfT/Ne/gB//WG692bYNuPNOef8jjwCnT/vm8QKASY0feV1Esq2FJ60WOJwOVNrkUWS+7Apo+QEYzN/IW8XKD1qPfFVT4zqfiDIcl6ijlC6oIzuOAE6g3Maams5S3gMTIhJw8OBBAMDIkSN9+yAREcDUqfLIqMJCeRRV9+7yOlRPPQVkZsqtNh9+CDidvn1sH2NS40fePmg8JROuk+NVNlRCQJ4wyXXW2M7yNulfMAqlWLXkq5Ya5VqNN8YjUtehXmki1YQJExAbG4vq8mqgiC01vqC03hvPGeFwONCrVy/07NnTfw+YkSEnMkVFwNtvy7MUO53yyKmpU4E+fYCnnwbOnfNfDJ3ApMaPvNU5eGypcZnXQXkjiDHE+HSK8Vbz4wRx64e3uXzIna9qakKh9Y6CX1RU1MU1AA+zpsYXlM8DcUb+ouvzVhpvoqLkLqht2+SRUw8/DMTFyS05ixfL89789KfAhXlzggWTGj/y9oL21OrgWlPjry4Xb6OugpG3WZfJnfJ/WGGrgOjEdOhczJJ8RemCwhGgvI7dT52lfB7YCm0AApjUuLriCuD55+WRUytWAFdfDTQ2yrMWjxgB5OYCb7wBBMG0Lkxq/Ei5GFOjU9VthggDzHpzq9u6TlbVcjFLXwmllpoYQ4xbN0gwx6qlliua/1DBvGo7hZZx48YhLi4OqAcsxyzqyB36YSxWCyCAiuNy3ZsmSY3CbJbnvPniC2DPHuCee+QWnS++kH9XCovLyjQLkUmNHynffvsl9lO3eRvF41pT468ZdEOpTsV1RBgQ3LFqSVnRHOhcUz8nOCRfiYqKws233AwAEN8IVDdUaxxRaLPYLEA1YK20IjIyEjk5OVqHJBsxAvjHP+SRUU89BfTsKdfZPPWU3KKjESY1fqR8+3VNarx9aLjV1LD7CYB7fMEeq5Z8UVfD7ifypZkzZsq/HAXKarX71h4Oyq3lwBn59yFDhsAUbLP+dusmj5YqLATWrwcWLAA0TLyY1PiRkpz0T7w4nbW3Dw23Van99AHTMjHw5cgqf3B9/vyw9c4Xw7o5dJ58aezYsdCZdUA9kP9JvtbhhDSLzaImNZp2PV2KXg/ccQfw0kuahsGkxk8amhvUJQ/6J7kkNV5aHDyuSu3j1gll5mIAiDXEBv3QXbbUtI8vhnWzpoZ8Sa/XIz5Hnu/ogw0faBxNaLNYQySpCRJMavxE+dYcIUUgKy5L3e7tm7Dropb+qm+QJEk9ZijUTiiLWgKhEa9WfNlSw/NMvpJ5TSYA4POtn6O5uVnbYEJYeW05UCL/zqTm0kIuqXn55ZeRmZkJo9GI3NxcfPHFF1qH5JHrN1/XDwqvNTUXPphqG2tRWlcqb/NDV4ByzFD4Ru7WUsNuEa980VLDmhrytT7D+gAmoLayFjt27NA6nJAkhMD5788DDiA+IR69e/fWOqSgF1JJzbp167Bw4UIsXboUX331FYYMGYJJkybhXBDObOhao+Bau+LtQyPOGAedJP93eFv40heUY4bCh5cSowQJccY4bYMJYj4pFObke+RjydHJwAD597ffflvbYEJUfVM9mk/JrVwjckdw/bt2CKmk5rnnnsPcuXMxZ84cDBw4EK+++irMZjNWrFihdWhwCifK6spwrFxeRdV1WLY+Qo8YQ4z6tyc6SYd4o9wH7c+izVBsqYk3xSNCF6FxNMGroy01Sq0XIF+3p6pOsaWGfC7RnAj8SP79nXffYRdUO5TWleJU1SnUNdYBcK+nGZU3SsPIQkfIJDWNjY3Yt28fxo8fr27T6XQYP348du3a5fE+drsdNTU1bj/+UmGrQMqzKRjw8gA0OZpaJSbKt+m2PjRadk35I/FoTxzBIpRi1VJHamo2HtuImGUx+N+v/hcAcOu6W5H5QiYcwuF2LKLOSjInAZkAzECFpQJPrn5S65CC2p93/xmpz6Yi84VMpD6birM1Z91GPuXl5WkbYIgImaSmvLwcDocD3bt3d9vevXt3lJaWerzPsmXLEBsbq/6kpaX5Lb54YzwkyE2DFbaKVrMC3zP4HgxMHoi8NO8X5t2D74ZZb4Yx0ojx2ePRK6aXz+O89YpbkRWXhWn9p/n82L42Km0UBiQNwN2D79Y6lKDWkZaaT099CodwYOepnQCA7Se3A5Bnup7+o+kwRhr9FiddXiZkT0BafBqkgfL74nvvvKdxRMFtx6mLdUd1jXX4quQrHC86DlQBkICrr75as9hCSXCP6e2kJUuWYOHCherfNTU1fktsInQRiDfFo8JW4XGumcdueAyP3fBYm8f4/ejf4/ejf++X+BST+kzC9w9979fH8JVEcyKOPHBE6zCCXkdqapTEx2KzoMnRpC6tcGbhGY58Ip/qn9QfRb8qws+ln+Pve/+O458dR1NTE/R63y3SG05atrRabBYc3H0QAGDuYUZsbKwWYYWckGmpSUpKQkREBMparClRVlaGlJQUj/cxGAyIiYlx+/Enj7MCszmf/Kwj3U9qUmO1oMImryUjQVLruYh8bXjecKALYK+zY9u2bVqHE7RarhVosVpw4IsDAICUAZ4/46i1kElqoqKiMGzYMOTnX5yd0ul0Ij8/P2j6Gt1mBeYMrRQgyjVma7bB1mRr87ZK4uN6jbIQm/ypW0w3joJqB+W1qUzWarFZUPB1AQAgY3CGZnGFmpBJagBg4cKFeP3117F69WocPXoU8+fPR319PebMmaN1aADcuwH8tdI2UUuuK5pfqq5G2e82czUTb/KjRNPFUVAbNmxAY2OjtgEFISGE+trslyCvFVhaWYqSb+VZ9wbkDNAstlATUjU106dPx/nz5/Hoo4+itLQUV111FbZs2dKqeFgrbt1Pflppm6glZUXzsvoylFvL2ywwVxKZqoYqnKuX53fiNUr+lGhOBDIAKVpCZWUl8vPzMWXKFK3DCio19ho0O+Uh730T+wIAThw6AafDCcQA2VnZWoYXUkKqpQYAFixYgFOnTsFut2PPnj3Izc3VOiSV6ygUdj9RILWnrsbhdKDSVqn+faLiBAC2JpJ/JZmTAB0gBggA7ILyRPm8MOvNSIuRB7OcPnRa3pnO12hHhFxSE8yUD5ay+jJUNVS5bSPyp/YM665qqIKAUP8usBS43ZfIH9QZ1dkF5ZXraFnlM6P82IXRjExqOoRJjQ8pHw7HLcfVba5LJBD5S3taalomPAXlTGrI/6IiotA1qiuQDiR3T0Z1dTU++ugjrcMKKq6jZRNNiYADqP++Xt6Zzi/HHcGkxoeUbFpZuynOGKcWcBL5k7KieVtz1bTc5881xohcJZoTAR1w/ZTrAbALqiXXov1EcyJwDhB2ARgAdOMXj45gUuNDrkO6AV6IFDgtrz1PPE3uBfA6Jf9TvvANnzAcALBx40bY7XYtQwoqrgNLEk2JQNGFHWkAdPzi0RFManyo5YcDL0QKlPbU1Hjbx/568jfl+ky+Ihk9evRATU0Ntm7dqnFUwUN5bSaZkmDWm6E7feGjOV1e7DjOGKddcCGGSY0PtUxi+GFBgdKumhov+5h8k78p11hlQyXuvPNOAOyCctVqChClpSZdrsvUSfyobi+eKR9q1VLDZn0KECWBbqulxlu9Da9T8jflGiu3luOuu+4CAGzatAkNDQ1ahhU0ym0Xa2oKCwvhrHHKn849+frsKCY1PmSINCA6Klr9mxcjBYrrh4Y3LdeWUe/LlhryM9eke+TIkejVqxdqa2vxf//3fxpHFhxcW2o+++wzeWNPAHq+PjuKSY2PuSYyvBgpUDoypLtfYj/3+zL5Jj9zrfnS6XTsgmpBrakxJ11MatKhbqP2Y1LjY66JDC9GChTlQ6PaXq1Ot96SumBeYn91W3RUNAyRBv8HSJe1lkm3axeU1WrVLK5g4Tr53s6dO+WN8sTC/NLRQUxqfMytpYYXIwVIvCkeEiQAQIWtwuNtlG+DyirAAK9RCoyW3aO5ubnIyspCfX09Nm/erGVoQUF5bTZXN6OgoACSTgIuLMzN12jHMKnxMdeWGnY/UaBE6iLVYZ/e6mqU7a7dT7xGKRBaFrJLkoSf/OQnAIA1a9ZoFlcwsDXZYG2SW6u+2fMNAKBXv16ASd7P12jHMKnxMWVmV4AZNgVWW3U1Qgh1e9+Evup2dpFSILhem0LI64/NmjULALBlyxaUl3svcA93SqIXqYvErk93AQAGjxys7udrtGOY1PgYa2pIK21NwFfXWIcmZxMAIC02DWa92e0+RP6kXGdNzibUNdYBAAYMGIChQ4eiubkZ69ev1zI8TbnW03zyyScAgNzrctX9fI12DJMaH+PoJ9JKWy01SqJjjDTCrDer1ynfMCkQzHozDBFyQbpr9+jdd98N4PLuglJem12tXXHy5ElERkbiumuvU/fzc6RjmNT4mHIBmvVmGCONGkdDlxOlZdBTTY3rgnnAxeuUb5gUCJIkeVyfbMaMGZAkCZ9//jkKCwu1Ck8z9Y31KCgvAABIhXKh/8iRI9ErqZd6G37x6BgmNT6mfLDwQqRAa6v7qeU07LxOKdDUYmGXlsRSqRTIkn9/8803tQhLMyW1JUh9NhW//PcvAQC24zYAwNixY91KF/jFo2OY1PhYbs9cDOo2CPcMvkfrUOgyoyY1bXQ/KW+WM340A73je2Ni74mBC5Aua56S7l2nd0FcKRcOr1mzRi0ivhx8VfIVahtrIUFCTFQM6grkWqOxY8ci1hCLW664BZP7TEb3Lt01jjS0RGodQLiJNcbi0PxDWodBlyFPzfsK12JEALgv5z7cl3Nf4IKjy55yfbp2j1psFmAAIP1LwtGjR7F//34MHTpUqxADSnmdjssehxeHv4iBvxsIo9GIkSNHQpIkbJi+QeMIQxNbaojCREdqaogCzVNLosVqAYyAfoAewOVVMKychyRzErZt2wYAuPbaa2EwcIbvzmBSQxQm2qypsbnX1BAFmqeV5NWZdH8kL+2xdu1aOByOwAenAfU1aUpUk5qxY8dqGVJYYFJDFCbaM6SbLTWkFU9Jt/K7s48TsXGxKC4uxo4dOzSJL9CU12mCIUGdn4ZJTecxqSEKE8qHRoWtolXBpWtTN5EWPCXdaldpJDB52mQAl08XVLlNfu4NZxtQWVmJrl27YtiwYRpHFfqY1BCFCeVDwyEcqLZXu+1Ta2rY/UQaabmoJeCe4Fz/4+sBAO+88w4aGhoCG5wGlOd+Zv8ZAMDo0aMRGcmxO53FpIYoTBgjjeii7wKgdbEwu59Ia23V1ABA2uA0pKWloaamBu+//37A4ws05bkf2XUEADBp0iQtwwkbTGqIwoi3upqWk+8RBVrLa7PJ0YQae426v7KhUl02YdWqVQGPL9AsVgvQAHyzV16Ze8qUKRpHFB6Y1BCFEU/FmPZmO+qb6gGwpoa0o1yb9U31aGhuaDVKr9xajtmzZwOQV+4uLi4OdIgBI4SQW1O/BxzNDvTr1w99+vTROqywwKSGKIx4mope+fCIkCIQa4jVJC6iWGMsdJL8kWOxWlq3Jtos6NevH6655ho4nU7885//1CLMgLA2WWF32IHj8t9Tp07VNqAwwqSGKIx4mrVV+T3BlABJkjSJi0gn6dxaElu21ChJzpw5cwDIXVDhumyCxWYBBIAT8t9ManyHSQ1RGPE4FwjraShIuNbVeGqpAYC77roLJpMJx44dw549ewIeYyBYrBagFEAtYDabMXr0aK1DChtMaojCiMep6G2co4aCg2vS7W2EXkxMDO644w4AwMqVKwMbYICUW8vVrqdx48ZxaQQfYlJDFEY8Dpu1cjg3BQfX7lHlGk2NTlW3KZQuqLfeegtWqzXAUfqfxWZh15OfMKkhCiNt1dQwqSGtJZkuFrIryXa/xH7qNsX111+PzMxM1NTUYMOG8Fut+nTpaeC0/DuHcvsWkxqiMNLW+jqsqSGtqTU1LoXC/RP7q9sUOp0O9957L4Dw7IL68tMvAQHEp8cjIyND63DCCpMaojDiafI9ziZMwcLT6Kf+SXJS09DcAGvTxa6m2bNnQ5Ik5Ofn47vvvgt8sH506PNDAIB+uf00jiT8MKkhCiOuNTXKcFguZknBwrV7VOkWzYjNgF6nV7crMjMz1aUDXnvttQBH6j9OpxOFXxYCAIZeN1TjaMIPkxqiMKJ8E3b91svFLClYuE4O6TrVgLflPebNmwcAWLFiBex2ewAj9Z/du3fDXmMHDMCwkVyV29dCIqk5efIk7rvvPmRlZcFkMqF3795YunQpGhsbtQ6NKKhER0Wr33qV5n12P1Gw8NT9lGRO8jhqDwBuvPFG9OzZE+Xl5XjvvfcCG6yfqM+jH5ASm6JtMGEoJJKaY8eOwel04m9/+xsOHz6M559/Hq+++ip+97vfaR0aUVCRJKnVt15OvkfBQrkGz9efR4WtQt5mSvQ4vxIAREZGYu7cuQCAV199NYCR+ocQ4uJorgH8ouEPIZHUTJ48GStXrsTEiRORnZ2NadOm4de//nXYZO5EvuT6bdjhdKCqoQoAa2pIe8q1WW2vhlM45W0u3U8tJ+QDgPvuuw86nQ47d+7EkSNH1O0OpwOnqk7hVNUpOJwOj4/nFE40NDf4+mm0qdHRiCZHU6vtpXWl2PLZFnz//fdAJIA+/KLhDyGR1HhSXV2NhISENm9jt9tRU1Pj9kMU7lzrFiobKiEgFwwnmNp+vRD5W8trMDoqGlERUR6nIlD06tULN910EwD3guGJb0xE5guZyHwhE+P+Mc7j401ZMwXpz6ejxh6Y9/5mZzOufOVKDHttmJq0AcDy/yxH6rOpmPrIhYn2egOIYkuNP4RkUnPixAm89NJL+MUvftHm7ZYtW4bY2Fj1Jy0tLUAREmnH0wiTWEMsInWRWoZFBH2EHncPvhvGSCOMkUbcO0Sei8bT6vKulILh1atXw2q1QgiBHSd3qPs/LfrULYkA5K6e7Se347z1PI6VH/PH02mlpLYE31q+xaFzh9QWUgDYfnK7/MtR+R/9ID1u6ncTv2j4gaZJzeLFiyFJUps/x465X4xnz57F5MmTceedd6p9rd4sWbIE1dXV6s/p06f9+XSIgoJbMSbraSjI/PPWf8L23zbY/tuGv0z9CwDPk0a6mjhxIjIzM1FVVYV169ah2l4Nh7jY5eQUTrckAgDqm+rR6JAHk3hLlnzN0/Ik6vZzAM4Ber0eZa+XYfPMzZAkKSBxXU40/eq2aNEizJ49u83bZGdnq78XFxdjzJgxuOaaa9o1b4HBYOBCYXTZcS265GKWFApcZxr2RKfTYd68eVi8eDFeeOEFXDftOgBAF30X6CQdahtrYbFa3Fo+PE1A6W8tH7Mv+l7c/o28ffLkyYiPjw9IPJcjTZOa5ORkJCcnt+u2Z8+exZgxYzBs2DCsXLkSOl1I9pwR+Z3r8FguZkmhQLk+PRUKK+bOnYsnnngCBw4cwNb8rfL9zIkXkxqXJALw3mriT94es9xariY1M2bMCEgsl6uQyAzOnj2LG264Aenp6Vi+fDnOnz+P0tJSlJaWah0aUdDxVFPD7icKZt4m33OVkJCgrt696pVV8v3aGA4eDC01gDxKq/L7SqACMJlMmDZtWkBiuVyFRFLz0Ucf4cSJE8jPz0evXr2Qmpqq/hCRO08TnLGlhoKZt8n3WnrooYcgSRK+3P4lcL7t4eCeVqr3N0+PWdlQqbbSTL1xKqKjowMSy+UqJJKa2bNnQwjh8YeI3Ll+6+W6TxQKlKS7xl7jcY4XRd++fdXh3djT9mzEnlaq9zdP3U9lNWXAQXnb3bPuDkgcl7OQSGqIqP3camrYUkMhIM4YBwnySKBLJSALFy6Uf9kPmBvN7et+0qKm5sLvW7ZuAeoAXRcdpk6dGpA4LmdMaojCjOu33pK6Enkba2ooiEXoIhBvkkcEXSoBGT16NJJ6JwHNQOHHhV6Hg2vSUuOhpmbjWxsBAEm5SYiKigpIHJczJjVEYcb1W+9xy3EAbKmh4NfeuhpJkpA9RZ7qY9+mfYiJjAEQnDU1lZWV2J2/GwDQe0zvgMRwuWNSQxRm3L71Kt1PbKmhIOetG8kT81Az0BWosdTg0JZD8v3aaqnRaEj32rVr0dzYDHQDsgZkBSSGyx2TGqIw1LIwmIXCFOzaWtSypYrGCuBa+ff3//4+0Nx2TY2t2QZbk813wXrRcm4adWXxoUByl/bNyUadw6SGKAy17G5i9xMFu0stleDKYrUAOUBySjLKS8uBr9tuqWnvcTujydGEanu1+nd5QTkOHTqEiKgI4Cq+BgOFSQ1RGHLtbjJFmmDSmzSMhujSLrWopSuLzQLogQd+9YC84VPgfM15t2k+2qqx8YcKW4Xb30175KHpPa/pCZjYBRwoTGqIwpDrt0K+mVIoaG9LjbXJiobmBgDA/F/MR4+ePYAaoGlvE+qb6gEAjY5G1DXWAQBSo+VJWv1dV6PEHW+Mh75BDxyWtyeOlp8XW2oCg0kNURhyraFhPQ2FgkstaqlQWlz0Oj2SY5KxZPESecenQHFlsXyMCwmMTtIhOz67XcftLHWdNXMiDF8bAAcwcMhANHaXVwrn6zAwmNQQhSG3lhp+Q6QQ0J5FLQH35EGSJPz85z+HLlYH1AArVqyQb3MhgUkwJaBbl25u9/MX9TH1CbB9Lhcl3zznZo5ADDAmNURhyPUNlG+mFAras6glgFazZBuNRqRMTgEAvP7866irq3Nbnb4jBcidoSRjtr02OOocQCwwcPRAt1jI/5jUEIUhttRQqGnv5HuuLTWKPuP7APFAxfkKPPPMM26r03dkqHhnWKwWwAmc2nJK3jASOF13Gk3Oplbxkv8wqSEKQ6ypoVCjJN8Vtgo4hdPr7ZTkxPW67hbbDZgg/758+XKcOHlCPWagWmosNgtwGKg5W4OoLlFADvBtxbcA5BGIZr3Zr49PMiY1RGHIrfuJLTUUApRr1imcqGqo8no7T4u0JpoSgQFA+pXpsNlsePP5NwG0WMXbzzU152vPA9vl3/PuygMMwLcWOalhK03gMKkhCkMc0k2hJioiCtFR0QDaTkA81agkmhIBCcj7eR4kScLBjw8C319oqWnnqKrOOvDxAcACdIntgok/mQgAKCgvaBUr+ReTGqIwxJYaCkWXqqs5X38eRTVFADwXwzd0a8A9990jb/wXEBsZq17/ZXVlOFNzxm2CPkBuGXJdQqHWXotTVadwrv6c1zitTVa34zQ0NODI+iMAgFvvuxU9k3q6PQ9+sQgcJjVEYSgqIgpdo7oC4BsqhY62FrVcc3ANui3vho3HNrrdFriYDG0q2IR/JP4D6ALAAux+c7e671T1KaQ9n4YHP3zQ7bjT1k5D2vNpqGqowncV36H78u7IfCET3Zd3x2v7XmsVx8mqk0j+UzLmvj9X3fbss8/CXm4HugIzfzazVR0bv1gEDpMaojB175B7MbzHcAzuPljrUIjapa2RSjtP7QQAREgRyIzLxNisseq+MZljkBWXBWOkEcauRuhv0gMAtqzagorvKnBd+nXQ6+RtO07tcDvu9pPbYbFZcPjcYXxZ/CVszRdbbZTHdLW3eC+sTVb1OGfOnMH//M//yDsnAmlJachLy0P/xP4wRhoRZ4zDHQPv+KGnhDooUusAiMg/Xpr6ktYhEHVIWyOVlG1/nvxnLBixwG1fWmwavn/oe7dtMzETb731Fn42+2fYt28fjlQdwdWvX+12bHuzXV1awWKztEqmPMZxoRXJYrVACIGHH34YVqsVSAcwSE7MEkwJOLbgWAefPfkCW2qIiCgotDVSSUkw2jtFwV/+8hekpKTg2LFjWLBggduxlXoY16TFYrWoj9vWelHKfaoaqrB27Vq8++67iIyMBKYAkNjVpDUmNUREFBTabKnp4My8iYmJePPNN6HT6bBy5Ur8661/AQDsDjusTVa3YyqPqTxu/6T+l4xD1Aos+H9yi9EvF/0SSAW66LvAEGloV3zkH0xqiIgoKLQ1/PqHjCQaM2YMnnzySQDAwocWIuJUhNuxXB+n3Fqu/t0voZ+6zWMczQDWA5UVlcjJycFdv7gLACe6DAZMaoiIKCh4W9RSCHFx6YMOdu8sXrwYt912GxobG+F8ywmcu3h818dx7X7qlygnNTX2GjQ5mtyOV24tB7YAKAK6dO2CN998E9XN1XJsHGmoOSY1REQUFLzV1NQ31aPR0eh2m/bS6XR44403MGrUKAibAFYDX371ZavHce1+6pPQBxIkAPKyDa4OrD8A7JV/X/inhejfv/8PTrjI95jUEBFRUPDW/aQkH4YIww9aQ8lkMmHTpk2ITo8G6oFFMxdhx44d7oXCLqOfunXphnhTvFssQgg88cQTOLPxjHyHCUDWiCy3+NhSoz0mNUREFBRcJ99znbHXtZ5GkqQfduzERNzw2A1AGlBfW4+xY8fiwxUfAg55f7m13C05ce0Kq6mpwfTp07F06VL5xuMAjGpdm5NkYk2N1pjUEBFRUFBaOuyOi/PHAPBZ905qUipwDzB4wmA4nU58tuoz4FUAx4CS6hLUNtaqj5NoTgQcwLp/rsPAgQOxfv166PV6YCqA69zjYktN8GBSQ0REQaGLvguiIqIAtKh38VHSkGhKBKKA0Q+PxooVK6CP1gPnAbwFVD5VCbwDIB/442N/xKnXTwHPAH995K84e/YsMjIysHnrZmDExeMpcZXbWFMTLJjUEBFRUJAkyeOilh2deM8b5f4VDRWYM2cOhvxhCDAKgAmAFcA3AD4Flv9pOUr2lAB2IDohGs899xyOHTuGrEFZbsdTu5/YUhM0uEwCEREFjURTIopriz231HSyJUQtRL5wvGqpGpgAYCyAkwBKgVhbLOYMn4O91Xvxme4z3H/7/fjVlF/J9zvfooC5ZU0N56nRHFtqiIgoaHha1NJXNTUt58FRHyMCQG8Ao4CBswfi+eefx5Q5U4A0oLKxUr1/y6HmrWpq2P2kOSY1REQUNDwtlfBDZhP2eGyXIeMOpwNVDVUALq715HobT91gShLjujaU28SA7H7SHJMaIiIKGp4m4PN1TY3FakFlQyUE5GHjfRP7trqN6/DylnEoMw5bbBZYm6ywO+xu9yHtMKkhIqKg4bGlxlc1NRfuX9tYi9K6UgBArCEWKdEprW7jqRus5TIKzc5mnKw6CQCIiohCdFR0p+KjzmNSQ0REQcPTrMK+6n6KM8apyx98a/lWPaZrsqQmNW10g6XFpMEUaQIAFFgK1Nv/0IkByXeY1BARUdDwtKilrwqFI3QR6vIHBeUXkxG3pKZFTU2FrQJO4QTgnlwpt1OPw3qaoMCkhoiIgkbLmppGRyPqGuvc9vni+N9WuLTUuCQkak3NhW1O4UR1g7wKt2typSRC6nFYTxMUQi6psdvtuOqqqyBJEvbv3691OERE5EMtu5+U5EYn6RBrjO388ZVk5EL3U5I5yS1ZUva71si0HLrteh/X45D2Qi6p+e1vf4sePXpoHQYREflBy1FHSnKTYEqATur8R1arbiMv3U9usbSYZM9j9xNbaoJCSCU1H374IbZu3Yrly5drHQoREfmBkizUNtbiRMUJHDl/RN7uo6ShZaKiLl7ZYr9rLMp8NK6jsFodhzU1QSFklkkoKyvD3LlzsXHjRpjN5nbdx263w263q3/X1NT4KzwiIvKBOGMcdJIOTuFE35cuzh/jq6ShZTdRojnRvfvJQ32NxWZBbWMtmpxNHu8DsKUmWIRES40QArNnz8a8efMwfPjwdt9v2bJliI2NVX/S0tL8GCUREXWWTtLh3iH3whhpVH+io6Ixc9BMnxx/Wv9p6NalG4yRRqTHpmNC9gRkxGbghswbMHPQTHWVcMB9JJbSSmOKNMGsN+PGvjciJToFxkgjenbtiUl9JvkkPuocTVtqFi9ejKeffrrN2xw9ehRbt25FbW0tlixZ0qHjL1myBAsXLlT/rqmpYWJDRBTkVty8AituXuGXY4/OGI2yX5e12v7JvZ+02uZa39Oymym3Vy5KFpX4JUb64TRNahYtWoTZs2e3eZvs7Gxs27YNu3btgsFgcNs3fPhwzJo1C6tXr/Z4X4PB0Oo+RERE7eE6EouLVoYGTZOa5ORkJCcnX/J2L774Ip566in17+LiYkyaNAnr1q1Dbm6uP0MkIqLLlGtNDQuCQ0NIFAqnp6e7/R0dLc8d0Lt3b/Tq1UuLkIiIKMy51tQoc9VwPprgFhKFwkRERIHmOqSb3U+hISRaalrKzMyEEELrMIiIKIy5zkXjOq8NBS+21BAREXngug6Vuu4Ta2qCGpMaIiIiD5QExu6wo6i6CABraoIdkxoiIiIPuui7qJPxKQtXsvspuDGpISIi8kCSJK7xFGKY1BAREXnBNZ5CC5MaIiIiL1q2zLCmJrgxqSEiIvLCtWUmUheJGEOMhtHQpTCpISIi8sI1qUkwJUCSJA2joUthUkNEROSFa3cT62mCH5MaIiIiL1xralhPE/yY1BAREXnh2jrD4dzBj0kNERGRF66JDLufgh+TGiIiIi9YUxNamNQQERF54ZrIsKYm+DGpISIi8sKt+4k1NUGPSQ0REZEXccY46CT5o5LdT8GPSQ0REZEXOkmHBFMCALbUhAImNURERG346eCf4qqUqzA0ZajWodAlRGodABERUTB7dtKzWodA7cSWGiIiIgoLTGqIiIgoLDCpISIiorDApIaIiIjCApMaIiIiCgtMaoiIiCgsMKkhIiKisMCkhoiIiMICkxoiIiIKC0xqiIiIKCwwqSEiIqKwwKSGiIiIwgKTGiIiIgoLTGqIiIgoLERqHUAgCSEAADU1NRpHQkRERO2lfG4rn+PeXFZJTW1tLQAgLS1N40iIiIioo2praxEbG+t1vyQulfaEEafTieLiYnTt2hWSJPnsuDU1NUhLS8Pp06cRExPjs+OSO57nwOG5Dgye58DgeQ4Mf55nIQRqa2vRo0cP6HTeK2cuq5YanU6HXr16+e34MTExfMEEAM9z4PBcBwbPc2DwPAeGv85zWy00ChYKExERUVhgUkNERERhgUmNDxgMBixduhQGg0HrUMIaz3Pg8FwHBs9zYPA8B0YwnOfLqlCYiIiIwhdbaoiIiCgsMKkhIiKisMCkhoiIiMICkxoiIiIKC0xqfODll19GZmYmjEYjcnNz8cUXX2gdUkh77LHHIEmS288VV1yh7m9oaMADDzyAxMREREdH4/bbb0dZWZmGEYeGnTt34qabbkKPHj0gSRI2btzotl8IgUcffRSpqakwmUwYP348jh8/7nabiooKzJo1CzExMYiLi8N9992Hurq6AD6L4Hep8zx79uxW1/fkyZPdbsPzfGnLli3D1Vdfja5du6Jbt2645ZZbUFBQ4Hab9rxXFBUV4cYbb4TZbEa3bt3wm9/8Bs3NzYF8KkGtPef5hhtuaHVNz5s3z+02gTrPTGo6ad26dVi4cCGWLl2Kr776CkOGDMGkSZNw7tw5rUMLaT/60Y9QUlKi/nz22Wfqvl/96ld4//33sX79euzYsQPFxcW47bbbNIw2NNTX12PIkCF4+eWXPe5/5pln8OKLL+LVV1/Fnj170KVLF0yaNAkNDQ3qbWbNmoXDhw/jo48+wgcffICdO3fi/vvvD9RTCAmXOs8AMHnyZLfre+3atW77eZ4vbceOHXjggQewe/dufPTRR2hqasLEiRNRX1+v3uZS7xUOhwM33ngjGhsb8Z///AerV6/GqlWr8Oijj2rxlIJSe84zAMydO9ftmn7mmWfUfQE9z4I6ZcSIEeKBBx5Q/3Y4HKJHjx5i2bJlGkYV2pYuXSqGDBnicV9VVZXQ6/Vi/fr16rajR48KAGLXrl0BijD0ARAbNmxQ/3Y6nSIlJUX86U9/UrdVVVUJg8Eg1q5dK4QQ4siRIwKA+PLLL9XbfPjhh0KSJHH27NmAxR5KWp5nIYS49957xc033+z1PjzPP8y5c+cEALFjxw4hRPveK/79738LnU4nSktL1du88sorIiYmRtjt9sA+gRDR8jwLIcT1118vHnroIa/3CeR5ZktNJzQ2NmLfvn0YP368uk2n02H8+PHYtWuXhpGFvuPHj6NHjx7Izs7GrFmzUFRUBADYt28fmpqa3M75FVdcgfT0dJ7zTigsLERpaanbeY2NjUVubq56Xnft2oW4uDgMHz5cvc348eOh0+mwZ8+egMccyrZv345u3bqhf//+mD9/PiwWi7qP5/mHqa6uBgAkJCQAaN97xa5du3DllVeie/fu6m0mTZqEmpoaHD58OIDRh46W51mxZs0aJCUlYdCgQViyZAmsVqu6L5Dn+bJa0NLXysvL4XA43P6jAKB79+44duyYRlGFvtzcXKxatQr9+/dHSUkJHn/8cVx33XX45ptvUFpaiqioKMTFxbndp3v37igtLdUm4DCgnDtP17Kyr7S0FN26dXPbHxkZiYSEBJ77Dpg8eTJuu+02ZGVl4bvvvsPvfvc7TJkyBbt27UJERATP8w/gdDrx8MMPY9SoURg0aBAAtOu9orS01OM1r+wjd57OMwD85Cc/QUZGBnr06IGDBw/iv/7rv1BQUID33nsPQGDPM5MaCjpTpkxRfx88eDByc3ORkZGBt99+GyaTScPIiDpvxowZ6u9XXnklBg8ejN69e2P79u0YN26chpGFrgceeADffPONW+0d+Z638+xa73XllVciNTUV48aNw3fffYfevXsHNEZ2P3VCUlISIiIiWlXTl5WVISUlRaOowk9cXBz69euHEydOICUlBY2NjaiqqnK7Dc955yjnrq1rOSUlpVUBfHNzMyoqKnjuOyE7OxtJSUk4ceIEAJ7njlqwYAE++OADfPLJJ+jVq5e6vT3vFSkpKR6veWUfXeTtPHuSm5sLAG7XdKDOM5OaToiKisKwYcOQn5+vbnM6ncjPz0deXp6GkYWXuro6fPfdd0hNTcWwYcOg1+vdznlBQQGKiop4zjshKysLKSkpbue1pqYGe/bsUc9rXl4eqqqqsG/fPvU227Ztg9PpVN/EqOPOnDkDi8WC1NRUADzP7SWEwIIFC7BhwwZs27YNWVlZbvvb816Rl5eHQ4cOuSWRH330EWJiYjBw4MDAPJEgd6nz7Mn+/fsBwO2aDth59mnZ8WXorbfeEgaDQaxatUocOXJE3H///SIuLs6typs6ZtGiRWL79u2isLBQfP7552L8+PEiKSlJnDt3TgghxLx580R6errYtm2b2Lt3r8jLyxN5eXkaRx38amtrxddffy2+/vprAUA899xz4uuvvxanTp0SQgjxxz/+UcTFxYlNmzaJgwcPiptvvllkZWUJm82mHmPy5Mli6NChYs+ePeKzzz4Tffv2FTNnztTqKQWlts5zbW2t+PWvfy127dolCgsLxccffyxycnJE3759RUNDg3oMnudLmz9/voiNjRXbt28XJSUl6o/ValVvc6n3iubmZjFo0CAxceJEsX//frFlyxaRnJwslixZosVTCkqXOs8nTpwQTzzxhNi7d68oLCwUmzZtEtnZ2WL06NHqMQJ5npnU+MBLL70k0tPTRVRUlBgxYoTYvXu31iGFtOnTp4vU1FQRFRUlevbsKaZPny5OnDih7rfZbOKXv/yliI+PF2azWdx6662ipKREw4hDwyeffCIAtPq59957hRDysO5HHnlEdO/eXRgMBjFu3DhRUFDgdgyLxSJmzpwpoqOjRUxMjJgzZ46ora3V4NkEr7bOs9VqFRMnThTJyclCr9eLjIwMMXfu3FZfgnieL83TOQYgVq5cqd6mPe8VJ0+eFFOmTBEmk0kkJSWJRYsWiaampgA/m+B1qfNcVFQkRo8eLRISEoTBYBB9+vQRv/nNb0R1dbXbcQJ1nqULQRMRERGFNNbUEBERUVhgUkNERERhgUkNERERhQUmNURERBQWmNQQERFRWGBSQ0RERGGBSQ0RERGFBSY1REREFBaY1BCR5s6fP4/58+cjPT0dBoMBKSkpmDRpEj7//HMAgCRJ2Lhxo7ZBElHQi9Q6ACKi22+/HY2NjVi9ejWys7NRVlaG/Px8WCwWrUMjohDClhoi0lRVVRU+/fRTPP300xgzZgwyMjIwYsQILFmyBNOmTUNmZiYA4NZbb4UkSerfALBp0ybk5OTAaDQiOzsbjz/+OJqbm9X9kiThlVdewZQpU2AymZCdnY133nlH3d/Y2IgFCxYgNTUVRqMRGRkZWLZsWaCeOhH5GJMaItJUdHQ0oqOjsXHjRtjt9lb7v/zySwDAypUrUVJSov796aef4qc//SkeeughHDlyBH/729+watUq/OEPf3C7/yOPPILbb78dBw4cwKxZszBjxgwcPXoUAPDiiy9i8+bNePvtt1FQUIA1a9a4JU1EFFq4oCURae7dd9/F3LlzYbPZkJOTg+uvvx4zZszA4MGDAcgtLhs2bMAtt9yi3mf8+PEYN24clixZom5744038Nvf/hbFxcXq/ebNm4dXXnlFvc3IkSORk5ODv/71r3jwwQdx+PBhfPzxx5AkKTBPloj8hi01RKS522+/HcXFxdi8eTMmT56M7du3IycnB6tWrfJ6nwMHDuCJJ55QW3qio6Mxd+5clJSUwGq1qrfLy8tzu19eXp7aUjN79mzs378f/fv3x4MPPoitW7f65fkRUWAwqSGioGA0GjFhwgQ88sgj+M9//oPZs2dj6dKlXm9fV1eHxx9/HPv371d/Dh06hOPHj8NoNLbrMXNyclBYWIgnn3wSNpsNd911F+644w5fPSUiCjAmNUQUlAYOHIj6+noAgF6vh8PhcNufk5ODgoIC9OnTp9WPTnfxrW337t1u99u9ezcGDBig/h0TE4Pp06fj9ddfx7p16/Duu++ioqLCj8+MiPyFQ7qJSFMWiwV33nknfvazn2Hw4MHo2rUr9u7di2eeeQY333wzACAzMxP5+fkYNWoUDAYD4uPj8eijj+LHP/4x0tPTcccdd0Cn0+HAgQP45ptv8NRTT6nHX79+PYYPH45rr70Wa9aswRdffIG///3vAIDnnnsOqampGDp0KHQ6HdavX4+UlBTExcVpcSqIqLMEEZGGGhoaxOLFi0VOTo6IjY0VZrNZ9O/fX/z+978XVqtVCCHE5s2bRZ8+fURkZKTIyMhQ77tlyxZxzTXXCJPJJGJiYsSIESPEa6+9pu4HIF5++WUxYcIEYTAYRGZmpli3bp26/7XXXhNXXXWV6NKli4iJiRHjxo0TX331VcCeOxH5Fkc/EVHY8jRqiojCF2tqiIiIKCwwqSEiIqKwwEJhIgpb7F0nurywpYaIiIjCApMaIiIiCgtMaoiIiCgsMKkhIiKisMCkhoiIiMICkxoiIiIKC0xqiIiIKCwwqSEiIqKwwKSGiIiIwsL/B5ONj+cl3uUpAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D008/zcqVK5k1axavv/46r7/+Ohs2bOCSSy5h0qRJdOnShczMTBISEvjll19MCsbkyZOZM2cOd9xxB48++ihRUVEsX77c1Cfsidh7zz33sHLlSu6//342bNjAwIEDKS8vZ//+/axcuZKff/6Zvn378uKLL/L7779z3XXX0aZNG9LT05k7dy4tW7Zk0KBBAFx99dVERkYycOBAIiIi2LdvH3PmzOG6666zeZ3DwsKYNm0aM2bMYPjw4dx4440cOHCAuXPn0q9fP9WgYEFwG1w270oQPJSKqa8Vfz4+PkpkZKRy1VVXKbNnzzZNha5KUlKSMmbMGCUyMlLx9vZWWrRooVx//fXKF198ocqXkZGhPPTQQ0qLFi0UHx8fpWXLlsrYsWNN04irTuk+e/asMmXKFKVTp05KkyZNlJCQEOWSSy5RVq5cqaq36pRuRVGUtLQ0Zfz48Urz5s0VHx8fpXv37qZ6K6hoz9qUcUB54YUX7D53tqZ0N2nSxGaZDz/8UOnTp4/i7++vBAUFKd27d1eeeeYZ5fTp06p8GzZsUIYNG6aEhIQofn5+Srt27ZRx48Yp//77b7UyVUzpXrVqldX9Q4YMUYKDg5Xs7GxFUYznbMqUKUqrVq0Ub29vJTIyUomPj1c+/PBDVbkjR44o1113neLv76+EhYUpTz75pPLll18qgLJ582ZTvsGDB9ucbl1SUqK88cYbSteuXRVfX19Fr9crffr0UWbMmKHk5OQoiqIo69evV2666SYlOjpa8fHxUaKjo5U77rhDOXjwoKme+fPnK5dffrkSGhqq+Pr6Ku3atVOefvppUx2KYjmlu4I5c+YonTp1Ury9vZWIiAjlgQceULKyslR5bB3D2LFjlTZt2lg9NkFwBrL2kyAIQgMxa9YsHn/8cU6ePEmLFi1cLY4gNDpEqREEQXAChYWFqgHORUVF9O7dm/Lycg4ePOhCyQSh8SJjagRBEJzArbfeSuvWrenVqxc5OTksW7aM/fv325yiLghC/RGlRhAEwQkMGzaMBQsWsHz5csrLy+nSpQsrVqxg1KhRrhZNEBot4n4SBEEQBKFRIHFqBEEQBEFoFIhSIwiCIAhCo+CCGlNjMBg4ffo0QUFBdgW/EgRBEATB9SiKwrlz54iOjrZYJ86cC0qpOX36tGkROEEQBEEQPIsTJ07QsmVLm/svKKWmIhz4iRMnCA4OdrE0giAIgiDYQ25uLq1atapx+ZYLSqmpcDkFBweLUiMIgiAIHkaNK9Y3kByCIAiCIAhORZQaQRAEQRAaBaLUCIIgCILQKLigxtTYg8FgoKSkxNViCB6Mt7c3Op3O1WIIgiBccIhSY0ZJSQlHjx7FYDC4WhTBw2natCmRkZESD0kQBKEBEaXmPIqikJKSgk6no1WrVtUG9xEEWyiKQkFBAenp6QBERUW5WCJBEIQLB1FqzlNWVkZBQQHR0dEEBAS4WhzBg/H39wcgPT2d8PBwcUUJgiA0EGKOOE95eTkAPj4+LpZEaAxUKMalpaUulkQQBOHCQZSaKsgYCMERSD8SBEFoeESpEQRBEAShUSBKTSNlyJAhPPbYY64WQxAEQRAaDFFqBDZu3IhGoyE7O9vVogiCIAhCnZHZT4IgCIIg1IryonLOfHGGnMM5nMw9Sc9He9K8TXNXiyWWmsZAfn4+Y8aMITAwkKioKN5++23V/qVLl9K3b1+CgoKIjIzkzjvvNMVROXbsGFdccQUAer0ejUbDuHHjAFizZg2DBg2iadOmhIaGcv3115OUlNSgxyYIgiC4HyfePMH+e/aTMiMF3Ts65nSeQ1ZWlqvFEqWmMfD000/z22+/8e2337J27Vo2btxIQkKCaX9paSkvvfQSiYmJfPPNNxw7dsykuLRq1Yovv/wSgAMHDpCSksLs2bMBo7L0xBNP8O+//7J+/Xq0Wi233HKLRFwWBEG4wMnbmafajiyMVL13XIW4n5xAVlYW27Zto0+fPuj1eqe2lZeXx8cff8yyZcuIj48HYPHixbRs2dKUZ8KECabfsbGxvPvuu/Tr14+8vDwCAwNp1qwZAOHh4TRt2tSUd8SIEaq2Fi5cSFhYGHv37qVbt25OPCpBEATBrVHUmz7ePsTFxblGFjPEUuNgsrKy6Nq1K1dddRVdu3Z1ujkuKSmJkpISLrnkElNas2bN6Nixo2l727Zt3HDDDbRu3ZqgoCAGDx4MQHJycrV1Hzp0iDvuuIPY2FiCg4Np27atXeUEQRCERk4VpaZrl65O/4i3B1FqHMy2bdtISUkBICUlxeXmuPz8fIYNG0ZwcDDLly9n69atfP311wA1rkZ+ww03kJmZyUcffcSWLVvYsmWLXeUEQRCExo1iUGs1Oo17LAcjSo2D6dOnj2kRw+joaKeb49q1a4e3t7dJ4QCjtejgwYMA7N+/n4yMDF5//XUuu+wyOnXqZBokXEHF0hAVS0UAZGRkcODAAf773/8SHx9P586d3WIQmCAIguAGVLHUKIpiPV8DI2NqHIxer2fPnj0kJCQQFxfndHNcYGAgEydO5OmnnyY0NJTw8HD+85//mFYZb926NT4+Prz33nvcf//97N69m5deeklVR5s2bdBoNPzwww9ce+21+Pv7o9frCQ0N5cMPPyQqKork5GSmTp3q1GMRBEEQPISqOox76DRiqXEGer2e+Pj4BvMvvvXWW1x22WXccMMNDB06lEGDBtGnTx8AwsLCWLRoEatWraJLly68/vrrzJw5U1W+RYsWzJgxg6lTpxIREcFDDz2EVqtlxYoVbNu2jW7duvH444/z1ltvNcjxCIIgCO5NVfcTbjIpVqO4i82oAcjNzSUkJIScnByCg4NV+4qKijh69CgxMTH4+fm5SEKhsSD9SRCExszO63aS+VOmaTugawAX777Yae1V9/42Ryw1giAIgiDUjqrmEDex1IhSIwiCIAhCrbBwP7mJz0eUGkEQBEEQaoebzn4SpUYQBEEQhNoh7idBEARBEBoD4n4SBEEQBKFxIO4nQRAEQRA8GYNi4MeDP3I8+7gqPbsgm893f05JuWuX0RGlRhAEQRAEu/jp0E9c/9n1HM04qkrPKsxi9Jej+XDbhy6SzIgoNYIgCIIg2MXOtJ0AaNCo0jWKcXtX2q4Gl8kcUWoEp7No0SKaNm3aIG2NGzeOm2++uUHaEgRBuNAwjZ2pMoSmQqlRXDxiWJQawSM5duwYGo2GHTt2OLWdxMRE7rjjDlq1aoW/vz+dO3dm9uzZTm1TEATBXalQWrSKWn2osNy4esCwrNItCNWwbds2wsPDWbZsGa1ateKvv/7ivvvuQ6fT8dBDD7laPEEQhAalQmmx5X4SS42dlJeX8/zzzxMTE4O/vz/t2rXjpZdecrlW6GqGDBnCww8/zGOPPYZeryciIoKPPvqI/Px8xo8fT1BQEO3bt2f16tWA8TxOnDjRdB47duyosjwUFRXRtWtX7rvvPlNaUlISQUFBLFy40C6ZFi1aROvWrQkICOCWW24hIyPDIs+3335LXFwcfn5+xMbGMmPGDMrKykz7NRoN8+bN45prrsHf35/Y2Fi++OIL0/6YmBgAevfujUajYciQIar6Z86cSVRUFKGhoUyZMoXS0lK7ZK/KhAkTmD17NoMHDyY2Npa7776b8ePH89VXX9WpPkEQBE/GpLTYcj+5+J3sMUrNG2+8wbx585gzZw779u3jjTfe4M033+S9995ztWguZ/HixTRv3px//vmHhx9+mAceeIDbbruNAQMGkJCQwNVXX80999xDQUEBBoOBli1bsmrVKvbu3cv06dN57rnnWLlyJQB+fn4sX76cxYsX8+2331JeXs7dd9/NVVddxYQJE2qUZcuWLUycOJGHHnqIHTt2cMUVV/Dyyy+r8vzxxx+MGTOGRx99lL179zJ//nwWLVrEK6+8osr3/PPPM2LECBITE7nrrrsYPXo0+/btA+Cff/4B4JdffiElJUWlZGzYsIGkpCQ2bNjA4sWLWbRoEYsWLTLtv//++wkMDKz2rzpycnJo1qxZjedCEAShsVGhtNh0P7nYUqNRXK1W2cn1119PREQEH3/8sSltxIgR+Pv7s2zZMrvqqG7p8qKiIo4ePUpMTAx+fn7GxL59ITXVYcdgN5GR8O+/dmUdMmQI5eXl/PHHH4DREhMSEsKtt97KkiVLAEhNTSUqKoq///6b/v37W9Tx0EMPkZqaqrKEvPXWW7z55puMHj2aL7/8kl27dhEaGlqjPHfeeSc5OTn8+OOPprTRo0ezZs0asrOzARg6dCjx8fFMmzbNlGfZsmU888wznD59GjBaau6//37mzZtnytO/f3/i4uKYO3cux44dIyYmhu3bt9OrVy9TnnHjxrFx40aSkpLQ6XQA3H777Wi1WlasWAFAeno6ubm51R5H+/btrab/9ddfDB48mB9//JGrr77aZnmr/UmoFdl/ZJO2Po2TJ08SGhpKRn4G3R/uTnjHcFeLVi2KopC5JpOzW85yMv0k3R7oRkT3CFeLJQgO4f9+/T9+/vhnXvv0NYt9L458kbaj27JwhH1W/dpQ3fvbHI8ZUzNgwAA+/PBDDh48yEUXXURiYiJ//vkn//vf/2yWKS4upri42LRd04vMgtRUOHWqriI3GD169DD91ul0hIaG0r17d1NaRITxgZqeng7A+++/z8KFC0lOTqawsJCSkhKVYgDw5JNP8s033zBnzhxWr15tl0IDsG/fPm655RZV2qWXXsqaNWtM24mJiWzatEllmSkvL6eoqIiCggICAgJM5arWY8/A4K5du5oUGoCoqCh27aqcZhgeHk54eO1fjLt37+amm27ihRdeqFahEepP/t58dly+AwAdOrLJRoeOLz76gjtS70Cv17tWwGrIWp/FrmuN/U2Hjl8+/IVr0q8R657QKGi2uZlVhQZg+hfT+TP4TxjRwEKZ4TFKzdSpU8nNzaVTp07odDrKy8t55ZVXuOuuu2yWee2115gxY0bdG42MrHvZ+lDLdr29vVXbGo1GlabRGM2CBoOBFStW8NRTT/H2229z6aWXEhQUxFtvvcWWLVtUdaSnp3Pw4EF0Oh2HDh1i+PDhdTwYS/Ly8pgxYwa33nqrxT5HWDWsnQ+DoXK1tfvvv79G615eXp5qe+/evcTHx3Pffffx3//+t94yCtWTvyvfanrLkpYkJCQQHx/fwBLZT/5Otewtyluwfet24oe5r8yCYC9NjjSpfv/B6vc7G49RalauXMny5cv59NNP6dq1Kzt27OCxxx4jOjqasWPHWi0zbdo0nnjiCdN2bm4urVq1sr9RO11AnsSmTZsYMGAADz74oCktKSnJIt+ECRPo3r07EydOZNKkSQwdOpTOnTvXWH/nzp0tFKTNmzertuPi4jhw4IBNF495uTFjxqi2e/fuDYCPjw9gtPDUlhdffJGnnnrK7vx79uzhyiuvZOzYsRbjfgTnYO4V/4mfuJiLaU5zdBodcXFxLpTMDqw49KtaQgXBYzHr318Hf803/t8wRBnC+PTxAPj6+LpIMCMeo9Q8/fTTTJ06ldGjRwPQvXt3jh8/zmuvvWZTqfH19cXX17Un2N3o0KEDS5Ys4eeffyYmJoalS5eydetW02wiMLqn/v77b3bu3EmrVq348ccfueuuu9i8ebNJmbDFI488wsCBA5k5cyY33XQTP//8s8r1BDB9+nSuv/56WrduzciRI9FqtSQmJrJ7927VoOJVq1bRt29fBg0axPLly/nnn39MY6rCw8Px9/dnzZo1tGzZEj8/P0JCQuw6B7VxP+3evZsrr7ySYcOG8cQTT5B6foyVTqcjLCzMrjqEOmD24Bw+ZTg+a3woSyojKCjIrV1PYH32h76pe8ssCHZj1r27PNiF/3vm/zi4+SCF1xYCoNPqbBRsGDxm9lNBQQFarVpcnU6ncisINTN58mRuvfVWRo0axSWXXEJGRobKarN//36efvpp5s6da7JqzZ07l7Nnz/L888/XWH///v356KOPmD17Nj179mTt2rUW7pphw4bxww8/sHbtWvr160f//v155513aNOmjSrfjBkzWLFiBT169GDJkiV89tlndOnSBQAvLy/effdd5s+fT3R0NDfddFN9T41VvvjiC86cOcOyZcuIiooy/fXr188p7QnnMbutL+p4kUmZrpg26tZYeSQpBo+YjyEINWPWv9u3b49erycoOMjqflfgMbOfxo0bxy+//ML8+fPp2rUr27dv57777mPChAm88cYbdtVR69lPgsvQaDR8/fXXHrvkgfSn+pG6LJX99+wHoMOcDpyae4qCvQXoAnVcdu4yF0tXPclvJHNk6hFV2mX5l6ELcO0XrCA4ggVjF9B+iXHoQPnH5cRPiGffX/tIG5gGwIErDzB5/WSHt9voZj+99957PP/88zz44IOkp6cTHR3N5MmTmT59uqtFEwTB0Zh/amlAo3WPwF72YFVG9xdbEOzDrC9rdMb7suL+rLrfFXiM+ykoKIhZs2Zx/PhxCgsLSUpK4uWXX65xjIfgWK655hqbAeteffVVV4snNBbMTdia839V090VcT8JjRmz/l0xs1al1Lj4HvUYS43gHixYsIDCwkKr+xwZh8MTvsgF52F+/TVaTaVS4wndwpqMniC3INiDuaWmQqnRuI+lRpQaoVa0aNHC1SIIFwJVLDUe5X6yYpURS43QaDDvyud9PRVuKIv9LsBj3E+CIFxAVBlT41HuJ7HUCI0Zc/eT1tJS4+oZiqLUCILgdoj7SRDcn4pFLGWgsCAIQnWI+0kQ3BLVB4fO/cbUiFIjCIL7UdVvL+4nQXALNAYzBeb8T3NLjbifBEEQqqD6GtSI+0kQ3AWLexOx1AgXGIsWLaJp06YN0ta4ceM8NgqxYIYN9xO4vwtK3E9Co8Z8SnfFQGGZ/SQI9ePYsWNoNBp27Njh9LY0Go3F34oVK5ze7gWNLfdT1X3uiFhqhEaMufvJ2uwnV/d1iVMjCHbwySefMHz4cNN2Q1meLlTMLRsq9xO4/KFZE1atMp4wFkgQ7EBRmWrO/5PZT4KjGDJkCA8//DCPPfYYer2eiIgIPvroI/Lz8xk/fjxBQUG0b9+e1atXA1BeXs7EiROJiYnB39+fjh07Mnv2bFN9RUVFdO3alfvuu8+UlpSURFBQEAsXLrRLpkWLFtG6dWsCAgK45ZZbyMjIsMjz7bffEhcXh5+fH7GxscyYMYOysjLTfo1Gw7x587jmmmvw9/cnNjaWL774wrQ/JiYGgN69e6PRaBgyZIiq/pkzZxIVFUVoaChTpkyhtLTULtlt0bRpUyIjI01/skilk7Gx9hO4v/vJ2kPd7WUWBHux4n7SaitVCRkoLNSbxYsX07x5c/755x8efvhhHnjgAW677TYGDBhAQkICV199Nffccw8FBQUYDAZatmzJqlWr2Lt3L9OnT+e5555j5cqVAPj5+bF8+XIWL17Mt99+S3l5OXfffTdXXXUVEyZMqFGWLVu2MHHiRB566CF27NjBFVdcwcsvv6zK88cffzBmzBgeffRR9u7dy/z581m0aBGvvPKKKt/zzz/PiBEjSExM5K677mL06NHs27cPgH/++QeAX375hZSUFL766itTuQ0bNpCUlMSGDRtYvHgxixYtYtGiRab9999/v831qyr+qjJlyhSaN2/OxRdfzMKFC+Ul5Wyqcz+5u9VD3E9CY8bs/qtQZrQaM1VC3E/uS98P+5Kal9rg7UYGRvLvff/anb9nz57897//BWDatGm8/vrrNG/enEmTJgEwffp05s2bx86dO+nfvz8zZswwlY2JieHvv/9m5cqV3H777QD06tWLl19+mXvvvZfRo0dz/PhxfvjhB7tkmT17NsOHD+eZZ54B4KKLLuKvv/5izZo1pjwzZsxg6tSpjB07FoDY2FheeuklnnnmGV544QVTvttuu417770XgJdeeol169bx3nvvMXfuXMLCwgAIDQ0lMjJSJYNer2fOnDnodDo6derEddddx/r1603n48UXX+Spp56y8+wa81955ZUEBASwdu1aHnzwQfLy8njkkUfsrkOoHeJ+EgQ3xdraT27kfhKlphpS81I5de6Uq8WokR49eph+63Q6QkND6d69uyktIiICgPT0dADef/99Fi5cSHJyMoWFhZSUlNCrVy9VnU8++STffPMNc+bMYfXq1YSGhtoly759+7jllltUaZdeeqlKqUlMTGTTpk0qy0x5eTlFRUUUFBQQEBBgKle1HnsGBnft2hWdTmfajoqKYteuXabt8PBwwsPD7ToeMFqMKujduzf5+fm89dZbotQ4E3E/CYJ7Yq7U6ESp8SgiAyNrzuQG7Xp7e6u2NRqNKq1CmzYYDKxYsYKnnnqKt99+m0svvZSgoCDeeusttmzZoqojPT2dgwcPotPpOHTokGqQbH3Jy8tjxowZ3HrrrRb7HDFWxdr5MBgqP5Xvv/9+li1bVqOMtrjkkkt46aWXKC4uxtfXt37CCtYR95MguCWq2U9WlklQBedzAaLUVENtXECewqZNmxgwYAAPPvigKS0pKcki34QJE+jevTsTJ05k0qRJDB06lM6dO9dYf+fOnS0UpM2bN6u24+LiOHDgAO3bt6+2rs2bNzNmzBjVdu/evQHw8fEBjBae2lJb91NVduzYgV6vF4XGwRSnFnN88XFOHDpBk8NNTOlV3U9H3j9C7IOx6JrorNTiGo5lH+PrfV/jtdeL7u90t9j/0eSPKA4sJjg4mPz8fJo0aUJBQQG97+jNkFuGNLi8glAX/v7hb9qvNntuV6zSbTalu11iO9Z+upar77y6gaUzIkrNBUaHDh1YsmQJP//8MzExMSxdupStW7eaZhOB0T31999/s3PnTlq1asWPP/7IXXfdxebNm03KhC0eeeQRBg4cyMyZM7npppv4+eefVa4nMI7xuf7662ndujUjR45Eq9WSmJjI7t27VYOKV61aRd++fRk0aBDLly/nn3/+4eOPPwaMLiR/f3/WrFlDy5Yt8fPzIyQkxK5zUBv30/fff09aWhr9+/fHz8+PdevW8eqrr9ZLKRKsk3hTIgX/FKBDRxFFpvS8/DzKyitnxp165hTFR4vpNrebK8S0wKAYuHLxlWSfyGblOyut5umxrofV9JKvSti3bR+de9b8wSAIruTI7iMU3Gi8PysoKCgAqgTfAz6f8Tn9rumHXq9vUBlBZj9dcEyePJlbb72VUaNGcckll5CRkaGy2uzfv5+nn36auXPn0qpVKwDmzp3L2bNnVWNLbNG/f38++ugjZs+eTc+ePVm7dq1pEHMFw4YN44cffmDt2rX069eP/v37884779CmTRtVvhkzZrBixQp69OjBkiVL+Oyzz+jSpQsAXl5evPvuu8yfP5/o6Ghuuumm+p4aq3h7e/P+++9z6aWX0qtXL+bPn8///vc/1YBmwTHk78q3SCuhhCO+R8iJyVGln9l8pqHEqpH8knyOZh+l9dnWeBlq953oU+7Dph83OUkyQXAch/85jE6pVGgyAzLJ9zLes83Cm5EclWzaV1xcTEJCQoPLCKBRLqARbLm5uYSEhJCTk0NwcLBqX1FREUePHiUmJkZikLgBGo2Gr7/+2mOXPJD+VHt+D/wdQ76BNNJ4X/M+BsVAVkQWf+77E0VRGNVhFP/J/A8AgZcH0ve3vi6W2Mi54nMEvx5Mr6O9eGfxOwAc8znG9B7TefG+F/nsrc/ILzA+/DUaDYqiMIxhXHbqMgCKFxczbMwwl8kvCPbw84Kf8Z1kdLkfbH6QN/zf4N/Ef03WmBPHTjBh2AQK8wvJ881jw78bHGqpqe79bY64nwRBcA/Of16Fx4SzeP1ijhw5QlxcnOnBuGLXCna1MM5i89K5z6OrIsKqedCx1iNbkzgnEb1ez00jb+K3334DjOEXjhw5woF3D8D5iZX+fv4NLrMg1BZz+0f+oHz+XfivSmlp1bYVKzevJCEhQXXfNjTu82QQPIJrrrmGP/74w+q+5557jueee66BJRIaDednNQUGBRITE6Ma5wXQtFlT0293XCDSXKlpG9PW9FDX6/Uqi2NMTAzHPjpm2nbHYxGEqpj3U30zvVWlRa/XEx8f35BiWSBKjVArFixYQGFhodV9zZo1c1g7F5BXVDiP6ZrbmBHqTovmmVMht8ZMcJWsdpYXBHfGIiCmmyJKjVArWrRo4WoRhMZKhU6jtfHANJ/W4IZ6gGrNmxqmYChaswNww2MRhGpx4ylGbiyaIAgXFBVB9Wx9BJobatzIZWNtTI3NY6jYbf6l6+7BBAUBUMotV+d2R0SpEQTBLfBU91MFtXI/uamCJgi2MHeT2rSmugGi1AiC4B54qPvJNKamFu4nVIsau9HBCIINVGO/3FenEaVGEAQ3oQb3k7n1wx2tG7VxP5mjMusLgpti3k/deaCwKDWCILgX9jwv3UgPMI2pqaP7yZ2ORRBsobLUuLHm4MaiCY2FRYsW0bRp0wZpa9y4cR4bhfhCxm5/fcUTyw0Vgbq6n9zxWATBAnPvk1hqBMGxHDt2DI1Gw44dO5zazqJFi9BoNFb/0tPTndr2BYX5DKDqnpfn97mT+8nqmJqanvkyUFjwMFT91H11GolTIwjVMWrUKIYPH65KGzduHEVFRXav9C3UjL2DEDUajdHd44Z6QJ1nP0nwPcEDUAXfk9lPgrMYMmQIDz/8MI899hh6vZ6IiAg++ugj8vPzGT9+PEFBQbRv357Vq1cDUF5ezsSJE4mJicHf35+OHTsye/ZsU31FRUV07dqV++67z5SWlJREUFAQCxcutEumRYsW0bp1awICArjlllvIyMiwyPPtt98SFxeHn58fsbGxzJgxg7KyMtN+jUbDvHnzuOaaa/D39yc2NpYvvvjCtL8ihH7v3r3RaDQMGTJEVf/MmTOJiooiNDSUKVOmUFpaapfsVfH39ycyMtL0p9Pp+PXXX5k4cWKd6hNsYK7TeJj7yWqcGnE/CY0N837qvjqNKDWNgcWLF9O8eXP++ecfHn74YR544AFuu+02BgwYQEJCAldffTX33HMPBQUFGAwGWrZsyapVq9i7dy/Tp0/nueeeY+XKlQD4+fmxfPlyFi9ezLfffkt5eTl33303V111FRMmTKhRli1btjBx4kQeeughduzYwRVXXMHLL7+syvPHH38wZswYHn30Ufbu3cv8+fNZtGgRr7zyiirf888/z4gRI0hMTOSuu+5i9OjR7Nu3D4B//vkHgF9++YWUlBS++uorU7kNGzaQlJTEhg0bWLx4MYsWLWLRokWm/ffffz+BgYHV/tliyZIlBAQEMHLkyBrPhVALPNj9VEFdZz9J8D3BE1BZanTuq9WI+6ka/u37LyWpJQ3erk+kD33/7Wt3/p49e/Lf//4XgGnTpvH666/TvHlzJk2aBMD06dOZN28eO3fupH///syYMcNUNiYmhr///puVK1dy++23A9CrVy9efvll7r33XkaPHs3x48f54Ycf7JJl9uzZDB8+nGeeeQaAiy66iL/++os1a9aY8syYMYOpU6cyduxYAGJjY3nppZd45plneOGFF0z5brvtNu69914AXnrpJdatW8d7773H3LlzCQsLAyA0NJTIyEiVDHq9njlz5qDT6ejUqRPXXXcd69evN52PF198kaeeesrOs6vm448/5s4778TfX1ZWthd73CtV/fW2ypi7n0xjWVw8aLFOaz+Zx6kR95PgAagG87uxqUaUmmooSS2h5FTDKzW1pUePHqbfOp2O0NBQunfvbkqLiIgAMA1sff/991m4cCHJyckUFhZSUlJCr169VHU++eSTfPPNN8yZM4fVq1cTGhpqlyz79u3jlltuUaVdeumlKqUmMTGRTZs2qSwz5eXlFBUVUVBQQEBAgKlc1XrsGRjctWtXdDqdaTsqKopdu3aZtsPDw+s0Hubvv/9m3759LF26tNZlL1S+3vc1935/L5mFmdXm8yn14Wd+BmDD8Q3EvRhnNd9P5T/hjz87U3dy8YsXA9C/ZX/Wj1lPgHeAY4WvJbVyP8lAYcHTMO+mbuzjEaWmGnwifTyiXW9vb9W2RqNRpVV8NRoMBlasWMFTTz3F22+/zaWXXkpQUBBvvfUWW7ZsUdWRnp7OwYMH0el0HDp0yGKwbH3Iy8tjxowZ3HrrrRb7/Pz86l2/tfNhMFTa+O+//36WLVtWo4xVWbBgAb169aJPnz71lvFC4f2t79eo0EDtXTfmX4qbT25mXdI6bup0U11ErDf1XvtJdBrBA/CUgcKi1FRDbVxAnsKmTZsYMGAADz74oCktKSnJIt+ECRPo3r07EydOZNKkSQwdOpTOnTvXWH/nzp0tFKTNmzertuPi4jhw4ADt27evtq7NmzczZswY1Xbv3r0B8PExKn7l5eU1ylSVurif8vLyWLlyJa+99lqt27uQKSorMv3u37K/TbO1T1GlIh/sF8ylLS+1mk+rNX4iNvFqQpuQNhzPOW7RjquQtZ+ExoxM6XYCp06d4tlnn2X16tUUFBTQvn17PvnkE/r2bXzKh7Po0KEDS5Ys4eeffyYmJoalS5eydetW02wiMLqn/v77b3bu3EmrVq348ccfueuuu9i8ebNJmbDFI488wsCBA5k5cyY33XQTP//8s8r1BMYxPtdffz2tW7dm5MiRaLVaEhMT2b17t2pQ8apVq+jbty+DBg1i+fLl/PPPP3z88ceA0YXk7+/PmjVraNmyJX5+foSEhNh1Durifvr8888pKyvj7rvvrlU5oZJNEzah1Vi3W5edK+PPh/4EoF/Lftw78V6r+f544g/Ki8uJCYnhsf6P8fjPjztN3tois5+ERo0E33MsWVlZDBw4EG9vb1avXs3evXt5++230ev1rhbNo5g8eTK33noro0aN4pJLLiEjI0Nltdm/fz9PP/00c+fOpVWrVgDMnTuXs2fP8vzzz9dYf//+/fnoo4+YPXs2PXv2ZO3ataZBzBUMGzaMH374gbVr19KvXz/69+/PO++8Q5s2bVT5ZsyYwYoVK+jRowdLlizhs88+o0uXLgB4eXnx7rvvMn/+fKKjo7npJue6Hj7++GNuvfXWBouM3Fiwe7FGe6eLVuyrUq0rF4Wsb/A9UWoET8BTVunWKB4y9H7q1Kls2rSJP/74o8515ObmEhISQk5ODsHBwap9RUVFHD16lJiYGIeM6xDqh0aj4euvv/bYJQ+kPxkZuHAgf534C4Dy6eUmS03279mk/ZrGyZMnCQ0NJeVYCv4rjTPK9MP09FzT02p9f+r/pCy7DCVEIemWJNafXM+eVnuY9vg07uh+h1OPJXdrLik/pnDy1Ek6je6EV6oX2YeyOZhxkMfLHueZb57hotSLAOgwpwMtprSwWdfCRxYS+16ssd72uVzy5SVE9IhwqvyCUFtSk1P5+Z2fyTiZQZOjTei4rSMA6c+nc/uLtzeoLNW9v83xGPfTd999x7Bhw7jtttv47bffaNGiBQ8++KBpmq41iouLKS4uNm3n5uY2hKiCIFihYsxJ3s48dgzeAYAOHdlk40/lFHnzIIxVUTTnrSI5Gtovak97jOOyjl91HLrbLFZvik8Vk3BpApQbZT604JBpXxBBLGCBKn9BYUH1FZrZyIMPB7MhbgNXp11Ns9BmjhRbEOrFDzf8QPud7WmD2opeUuq+s4I9xv105MgR5s2bR4cOHfj555954IEHeOSRR1i8eLHNMq+99hohISGmvwp3ilB3rrnmGpsB61599VVXiye4GdYMwXk7LWeWmZMTk2NzX2lX65Ghs//OrpVctSV/bz7YOSbdgIHkpsnV5jnX4xwGs6h7keWRbN+8vT4iCoLDaXq8qUVauaYcTYz7up88xlJjMBjo27ev6cXZu3dvdu/ezQcffGAK4laVadOm8cQTT5i2c3NzRbGpJwsWLKCwsNDqvmbNHPeV6SFeUaEumF3aH/iBLWypnBbdTMO3b3xrs2ivL3sxuvNoCjILGBA2gGvPXAtAeHMnr8NVi+74ePjjbByxsdo8hRcVctejd/HZ7M9MaT17Wne5CYKrqLCu5vjlMLP5TBRFIdM7k023bXKxZLbxGKUmKirKNEi0gs6dO/Pll1/aLOPr64uvr6+zRbugaNHC9jgBQaiK+QBe04wJMwXh2keuZcpjU0hMTARg8ODB1Q7+bxbejM8Pf05CQgJHVx2F+cZ0p9/ndio1BzsfZOOmjXZNYEjVp/Jv7L/0PWKcvSmD0AV3o2Lwe6lvKUt+X8KRI0eIi4tz6wk6HqPUDBw4kAMHDqjSDh48aDFjRhAE98Y83kXHzh1pEdNCFVKgJvR6PfHx8Xy6+lOrdToDe+v38fWx64Ff8QWsmrUla0AJboZpRp/WuKRObe5TV+ExY2oef/xxNm/ezKuvvsrhw4f59NNP+fDDD5kyZYpD2xG3h+AIzCMYC1VwULwL87Kq6dTOwN7Hgp1imGSXqd2CG+P0+8oJeIylpl+/fnz99ddMmzaNF198kZiYGGbNmsVdd93lkPq9vb3RaDScOXOGsLAwtw4uJLgviqJQUlLCmTNn0Gq1NQYrbOxY/Uhw1BoyDbkopL3hdjT2Zayw1Bg0lcqvfFAJ7oqi9Zy+6TFKDcD111/P9ddf75S6dTodLVu25OTJkxw7dswpbQgXDgEBAbRu3doU1l+oxCnh1p1sGLPbvVVLS424nwR3RqsYn1+uDG5ZWzxKqXE2gYGBdOjQgdJS69NGBcEedDodXl5eYu2zhaPCrTek68bBlhoT4n4S3JmKPulBjzJRaqqg0+nQ6XSuFkMQGgWmqdqqFRzNMoj7qbKsuJ8EN8M0oL22yroLEdu4IAgNisPcT+Zlxf0kCA5HYxClRhAEoXqcMPvJXdxP9mKyXIn7SXBjNJ7kdzqPKDWCIDgN0wrWthQQB7mf3EWpsXeWSMX5EPeT4Nac75KeNPtJlBpBEBoUZ7if3CX4Xq3rFfeT4MZ4YpwaUWoEQWhYnDH7ydk4OvieuJ8ED8ATBwrL7CdBEJyGU2c/NeBA4YZwP83dMpeS8BJahbRiROcR+HrJunWCa6mw1IhSIwiCYANHuZ802oYbKOxo95O1AZiv//E66U3TAUiOT2bqoKkObVMQaovJ/eRBXihxPwmC4DRqWiahsQXfs/fhf1mby9CgUX0Bmys6u9J31UI4QXAOVt2kbo4oNYIgOB2nzH5qwIHCjnY/9YrsRdIjSQxsM9CU9soVr1TWIzOhBHegYvaTB7mfRKkRBKFhMX8+1ucLsAGndDtDaYrRx9AypKVp+7LWl1W2J6OGBTfAtPaTKDWCIAjWX87mCkKjC75X28Mxy28+fVYsNYI7IMH3BEEQrOD02U9uotTUNkiZ+WBnlVIjlhrBHZDge4IgCDXgDPeTh639ZC2/ueInlhrBHdAp5xd39iCDjUzpFgTBaVS8nEPPhXLw9YOcOHyCJoeamPbXy/1k9qQNXhTMqZtPUbS3iGMJx2jTsw1tx7fFK9C+R1zullxSVqdw8vRJOo/tTPTAaNO+wiOFHH/reJ3lrBazw985fSeji0dzJvgMmg71e4sUnyrm+JLjnEg6QWhoKBkZGXQc1ZGWV7WsubB5PanFHF98nBOHKuu5aMRFtLqmVb3kE9yb/Nx8vp35LdFE15zZzRClRhAEp/PCpy9w+tRpdOgoosiUnpefR3Oa16lOc5O4X44fh644BIAOHSc5SdHuIrrN71ZjPUXJRSQMSACDsWzigkR8jvrQvE1zlHKFhMsTKD1VapdM5Up5rY7B3P2k/1HPZCYD8If3H3BnrapSseO6HRQmFqJDRzbZ6NBx8OODeCd6E9Ejwu56Em9OpGBLgaqepI+T8NriRdTFUXUXUHBrlk9ezkUrLjJte5I7VNxPgiA4nZi0GIu0YopJ8kmqc52lPUop0ZXY3H9m8xm76snfk69yX/kr/iSuTQSgLKfMboUG4GDrg3bnBWg6pKnV9OCk4FrVU5WC3QUWaVq07P5hd63qyd+VbzV974976ySX4BmU7S5TbWd2ynSRJLVHLDWCIDiNqsskpJDCPM08DIqBzIhMNsVvqnPdhnADdzx2B0veW0KTkiYW+wMDA+0V0oLOnTpb3fdo+KOsWrGKp0Y8RWpWKmkRacR4x1A2sIzUpqn0GdKnVscQOTaS8rblTL5pMtoiLU8UPwGAj7dPreqpSsX5PsUp9rOfeOIBaN+ufa3q0Wl1GDCQTjrb2c4whgEQGxNbL/kE98bby9v0+42Ob7Bs6TIXSlM7RKkRBMH5nFcOIttHsnjtYo4cOUJcXBx6vb5e1WYGZfJH5z8YnjjcYp+Xzs7HmxWlJigoyLjLbMCu0l9h408b0ev1LE5aTEJCAnFxcSRlJ9FvST8ALvW9tNbH0GJwC5YeXcr3n34PDxnTtJp6GtHPix3dMZqu13cl++1sAJo0sVT+7KknrG0Yo+8cTdarWXWrR/AozPvfrC9mERoa6kJpaocoNYIgOJ2KIF5BwUHExMQQE2PpjqotFYOMbQUGs3fGktV8hir/geZhzU1KmF6vJz7eaP0IMYRUylTHaSJ6vZ4rrriCJIzuOI2hntNNzssdHBKMvpmebLJV6bWtJzAokGbNm5FFVt3qETwLs1uiaUhTl4lRF2RMjSAITqPC0lHxslctQllPalQg7B3baC2fYmWfjeYcNYjSKcEENdQvnk9Ffo362smU80aO+fpsDrxnGwJRagRBcD5mL0dHY9DYMBvUQ6mpeGmbv7ztebjXa4q6zjFKTVWZ66OMmJRSraZhgx0KLsU8EKRW51lqgmdJKwiCR+KMIF4N7X6yaalxkNXCYZaaqjJrbOyrTV31rUfwLMz6X73HdzUwMqZGEASnoaCoTdmGMsjIcEjdmry86jOU2tlW7jnLtOwcyCiFTLPp3GUl1uvLzqqUqdhGHjvQ5ORUbpQrda5HKTM74eVlUGA2vTv3HGTU4iVVobCVl6EprKxHyT0HGdW8Ppo1g3pYrQQX48HuJ1FqBEFwHllZKlM2/26F5v0dU3c3YGQ17qeE7Xa1pXA5MEOdNmwYsA+FMGAlAJrvv4PmMyzKEwo8fP73ihUweoWdB6BG46cHvjL+Tj8DzesWlND4WF9nrGfL32i2bAIeBEAZPx743e6aFH4BdGh27oCda4DHjDsenAIPrrVdsFMn2LIFgusXb0dwEeaWGnE/CYIgGFEyM5220m9FrTbdT3a3ay2ftpp9VdpRrbRtZ5M1SlGfc1Zd2do+8k1nmVot2rV/P/zySy3bEtwF8w+R+owTcwViqREEwXkoVdxP+qYw4DqHVK1pdhrYbjtDcAhcZkdbpztaVnPpAGjWFgqCYcP5tKhIiLNSn18e8Jvxd8uWcF3Pmtu0gqbMG34+v+HjC9fV7Twp5TpYc36jWTOI6Az7zm/3joPoWsSY+fG8EtS0KZqWXeF8QGKlR09oZeX1ceQI7DvfWKn9kZgFO1i9GiZMgPR057cV/hZgDLugiYiActuRu63y8sswbZrj5bIDUWoEQXAqFTFqAOjWFX74wTEV7/4cvhxt2/0U296utpQVaXDHPnXam2/CoKYoRwshdgsAmsGXw2f3W5Y/sw/mdjHmueoquGlhLQ/EiPbkWWhl1Bo0wU3rfp4KyqHJH8Z6enZHc9MV8Nhho6zPPAOj7Vv7SVEU0BqVNU2njjB+MEw+vwzEo4/BBCtrP82aBY8/Xje5heqZMwdSUxuosUrrjM5QDoZajgx34ZR/UWoEQXAaClXiyTjQ4V3j7Cd7H6zWshms7HOyFd5hs5+qylzXWUtV69Ha2Cc0DIWFlb/j4kCnc1pTmlOVyyRo4npT6wse5brFTkWpEQTBiVRxPznQP9/gwfdsKGTmwffqM35INcvEQXFq6hV8r4pSY37tJPiei/nzT/D3d179HReZfmp++w38/ZzXloORgcKCIDgVlfupIYPv2WmVsBanxhR8z2yfswdMmis1qhljtcXsuOsTfM8i8GBtLT6i+HguZpdO50SLkDMQpUYQBKfRqNxPtiw1imMUH0dZahzmfqoaxE/cTxcMnjz7SZQaQRCcirMekDW6eux9gVvLd/6lrbLiOPnZbh65tT6WGguZneF+shWt2cNegB5FQ1q+JPieIAiCdVQv6AZ0P6Xnp/Of9f+psXzUnii60U2VtnDbQjLJpMnxJgxgAAAJqQksW7/MovyZgjOm3/UaU+OgtZ+qvpCc4n4SS02jxpMtNaLUCILgNJzpfqqwbNhyP2XmZ/Lqn6/WWM/wg8MtlJoVu1bwb+G/tElvY1JqEtMTeevPt+optW0aZPZTbeoV95P74mxFwzyisNazHDqeJa0gCB6Hs776BrYeSBNv24Hk7HXhqAYyV5Q9rwmY16HY8SYf1n6YXW1aw1EDhat1P9VjSrdd7idVedF8PJbzl87ggSuXeqyl5vXXX2fatGk8+uijzJo1y9XiCIJgA2e5n8KbhHPyiZNsT94Of1nubxnUkvVj1tdckS/wnTrp9fjXYTCwH5hnTLv2omu5Zsw1NqtpE9KGds3a2Su+Baov4nroA6dzT5t+nys9R3FesWn7RM4JzqSdsVbMAiW/Uoi8sjySc5NN2ym5KUQr0R63grNgHxX3rKL1PMXUI5WarVu3Mn/+fHr06OFqUQRBqAYFxWnuJ4Cmfk1po29DMskW+/y9/OkfU/OClqdDT3OQg6q0nuE9CY0JJe9cHv/yLwDRIdF0jOnoGMGt4AhLzTt/v8NLX73EV+cXxtx4fCObNm7iGZ4B4MWNL/LjuR/tqiugKIAfMeb9++TfrF6/mulMB+CNP9/gxMcn2Dxxs8eNufBYXDBQ2B7rpLvhcUpNXl4ed911Fx999BEvv/yyq8VpdJQXlXPmizPkHM4hKSkJgE5DOhHaJZTUn1M5eeokncd0JvqyaIuy5xLOcfr705w8cZLQ0FAycjPofFdnCrYWcHzPcWIuiaHtmLZofDRk/ZLFmU1nOHnyfN6iDHo82oOw2LCGPmTBifiW+HPd3uGmbae8AG1UWZheyI5nd5CRkcFFN19Eq+tbWeZJKuT4m8ct0hOnJ9IpuBNnF5ytsR1HYX5umqQ3Yf64+eTn59N7dG+uGHGFXXV8t+07rttWuWaUgqIac3TxoYsJKgyyKJfcPJnNF23GoDW6G/yK/bhmu5lVqooba9SmUTzZ/klO5J6gdUhr84OwS06h/uTvz+fkypOcTD7/DM3OoMvELkRdbD2ar1KukPFjBhkJGaZn+0X9LqLt+LZ4BXnx13d/se+7feTk5NA2u62xTH1WaHURHqfUTJkyheuuu46hQ4fWqNQUFxdTXFxpes3NzXW2eB7PibdOcGz6MQACCAAgeVmy6UtYh47dC3bjneStUkBK0ktI6J+AUqqgQ0c22ejQcfCDg6ZyyR8nU3q6lIgrI9h59U5TekXeTz78hEmnJ6HX6xvwiAVncsvvE7g88XrTdmmZExY5tGH90ZzTkP2msW8lfZyE7nedShk3lBnYdtk2ylLKLKvcpuXgZWrrTUlJLRf1qyVaXeWBhGWHEbbYeH+VfVXG7n92061PN1tFTQxbMYz+myqtU61CWjG47WDT9uX7L+fy/ZdbLbvhqQ2kXm5cW6jnvJ50+bmLaV90UDRXxl5ZKd+5MN77+D0yHspQKzVCg1BeZCBhQALlWeWqZ+i/H/3LgFMDCI0KtShz5qsz7L19L1D5bD+57CSFuwrxe9iPopuKaIfafaqgkJWV5VHPZI9yiK5YsYKEhARee+01u/K/9tprhISEmP5atbL8UhPU5O/MrzGPDz7sXL1TlVZwoACltGat/tSfp2y2EVUURUJCgn2CCh5BdHqMaju7bbbD22g6uKld+fb/tF+1XZZRZlWhscWZaPvGotQVbx9vktomWaR7GbzY9MMmu+qISFYvVtmqbyseffhRu8ZGDE4bzPwb5jP/hvl0PtVZtS+qTxRPPfwUildlPSGFIez7c1/VaiqRgcJOo+R0KeVZ5RbpQUoQO37ZYbWMrefu2S1nOfz3YbRW1IHE8ESPeyZ7jKXmxIkTPProo6xbtw4/P/vWoZg2bRpPPPGEaTs3N1cUmxowj02xhCWMYYzVfB0vqjK2wOz59Qd/0IIWxBJrUa6ZvpmqjVWs4jquI4AAvHXexMXF1e8ABLfCfDzNC81f4OtXvnZ4G82uakbHzR2ZfM1kUrJSSItIo2NJRwqzCulPf67HaClq06aNqlzVmC0PRzzMl8u/5PTQ01RlWtg0fnryJ4fLXpXhvw9nzNAxFOcXc7Xmai4/abSqtIxuaVd5nbYypP3zzZ/n25e+xb+ZP112dGHS4ElkZmWiQWMaK9GBDoxlLABREZVui8DAQPIxvgRfCn2JVW+uwq+ZH10Tu5LQIwG/cuMz+KL2F9X/oIVaYz7WJZFEAHrSE4CuXbtaL2Pj2R7YJJBCQ+Vimb+0+oXflN8oLi8mRZvCzridFnW5Mx6j1Gzbto309HTVS6+8vJzff/+dOXPmUFxcbLFGha+vL76+vg0tqmdj9py/9YVbYYb1bEFBVfzyZuUGjhpIQHIARX8XWZTz8fZR5R0xbQRe870wZBpo3bq1R5k5hZoxV2q+PvA1zZo1c0o7UZdEsThpMQkJCaZnREJCAmGbwsh8IROAJgFVpn+b9UNlkMLv3/2OXq8nOy6bgoSCyn2hCj8d+KlB+maLVi34YvMXJCQkcGDuAThpTPe3c/HCivNdrinnu4PfmWSO6B5hOj+xsbEkJhpfhBedu4j0MekAqo9FL13lq2HV3lWm6xbeJZzk/slctMmozAQGBtbjaIVaYa6Em/3sPrQ7QYFB5H9jVEKDg4NtlK/8af5s1+l0KoUn9OJQlry1hCNHjhAXF+dxz2SPUWri4+PZtWuXKm38+PF06tSJZ5991uMW3XJbzMISDLx8oOkroLp8oI5b0TamLblncynCUqnBoC7btXtXDusOY8Bg1fwpeDbms3icpdBUoNfriY+PN23Hx8dz6uApMjEqNRaxVcz6YVh4mOnhrfOu8nHk59ugD/aK4zi+sHIAs11xYTCbiqtRLGQ2Pz8xMUa3YNb6LNJJt2jD/HfTZk3VbZhFPraQSwYKNwxmfTcqOgoUTJY1m6FlbD3bqzyTQ5uHEhMTY+ojnobHKDVBQUF066YeKNekSRNCQ0Mt0oW6ozLJV6djVH3GVln4z9YsF0VR1G2YRyoVF3yjo8Jy4KogXtUtEmnRDyt+Vum7LpuyXJcIvhVTce2dtWIr2rD5qal6/Oan1E5lS3AsKs9p1e5p45LYfLYrYFAq709Pn6LvMUqN0ECYP8x0tju3xRoyVW8yWwqRUqUNraZytWUZWNj4MLMcuITqlgiwtWhf1b7rKgNiPZQHu8+3LcWputXJZbkE11O175p7pmw9R230d0VR1P3Lww3mHq3UbNy40dUiND7MPqirU2qqcz9pNBrLrwfzclXXlNGY7RMaFRWWGndQaqpzP6n6a9W+66IPV9WyBHYq/BrD+fNtp7Zha+kDi/tZVajyp8FQzU0rHynOozpLjR3uJ9NzVzGmq663h63KXRUP18kERyPuJ8GRmMZ4uOjierL7STUN225vUi2VSDvcT9Ui92zDYdZfLfquHYuW2iyjVFFqXKXFOwhRagQ1dlpqqn45WiyiZ6tnGbDtfhL/fKNDI+6nulOXhShrO6amJveTlWM3V7aUchko7BLMn9NajYU7ySpVn7vayueuuJ+ExoudY2osHrJVBxbaKqpg2/0kOk0jxH2UGo92P9V29pOj3E9Wjr0ubjHBwTjK/XS+LvPr6OnuJ1FqBBXifhIcSaUuI+6nWmMuur1jaiqm0Nsrcg3uJ2vHrlKY5J51CfWe/VSdUuPh1jZRagQ14n4SHIhGMXYEgxtYajzO/VSPKd0GjZ3+qjq4n8zTqr1nxYrjPBww+0mjNUaWVgwKmK+4IO4noVHhCPeTtpbuJ7HUNFo0rvYtVvcCdnP3U12mdJsHO7Qrfx3cT3WxIAkOwEZEYYe7n8RSIzQmHOJ+0tTS/SRjahovtR246mBU/fBCcD85ePaT1WOvTtny8Beip2Cr7xp32lnG1uwnD7+GotQIasT9JDgQzfmO4A4DhT3O/WTHNF0LGmD2k0QUdgOqzH6qq/sJzs9+MrfUVGeh9wBEqRHUiPtJcCAV71ZXxanxaPeTuUJh5xCZhpj9JO4nN8DczegI91PVj1IPRpQaQYXd8QqqPsuq3DA23U8G2+4n+eprfFQMFEbcT7WnHrOfnOl+Mq9bBgq7hmqVkFq6nxSDorb8iPtJaFTYaamxcD/ZmqZtrX4b7iex1DReZPZT7VG9XNwo+J6s/eQGOGj2U0W6xKkRGi8N4X6qOnJf3E+NlsrZOK53P9VkXbT629p2Q2HuOnOSpcZmIL2KnzUcuwwUbkDOX5+0JvDDvi9pRwcAtp7eCgpEEQXAsh3LyCvLsygeczqGcMIBWLh9IV3Ku+CFF5kFmexO300kkYBYaoRGRoO4n6qaTsX91GipcD+5w+ynqv3Lo9xP9k7prq0GZp7d7B6uaK/Ws58EpzPydsjZ8h7v8i4Avyf/jgYNt3M7AB9s/YDdabstyj116imu4zoA3tnyDrPKZhFCCJn5mSSeTmQoQ40ZPTxOjYeLLzgccT8JDsRkpxH3U72wO/5MA7ufZKBww7MzQt0fFI2iDrZo45KYK7yKRlH1EfN9HZp3cJywLkAsNYIacT8JjsTFq3SL+6mG/HVxP9k71VwUHqegaNRKyM2dbzZeh7+M269e+Srlfcstyvkn+sN24+93r32XwBWBUACRTSK5v8/98INxX1hgmHMPwMmIUtPIKDlTwvFFx0k+kEzsoFja3N0GrVf1n5q5/+SS8lMKJ0+exOe4T+WOah7mu1/YTcfwjuT/k0/y3mSC0oJU5WyZ7LP2Z1FYWKhu43xWQ6GBIx8eoc2YNuj8dDbbzsrKYtu2bfTp0we9Xm+xXRvy9+VzctVJTiafJDQ0lIzsDLpM7ELUxVGqttq1a0dSUlK1bVjLC9hd3pzcrbmk/Gi8JqGhoWRkZBAaGsrp06cB6DSkE23vaYvWxzlmhLKcMo4tPMbxPcdV7Vv8L8qgxyM9CGunfhAaSg2c/fYszc9FAK5Tasz74eEvDpOZmUlGXgad7+jMmXlnzDJaL2Ntu8Ewa7bg6wJ2ZO4wnu9HexAWa+V8f30WfY6xb9l9vs3aSPk9haJni8jIyMD7rLfteszKHH/vOBlfZdDxko60GddGXigNQEi+nlu33Gra7tC8AyhwghMA+Czxofzn8sprEuhFzl85bP96u6lMfLt4dnjtoJRStNlaQv8KpRDjc1nG1Ahuxc5RO8nbkIcOHcc/Pk55bjntH2lvM3/RiSISLk0AA+jQUW62CEhObo7NcprdGg5edhAwliugwLSvsLDQpslek6qhOLXYtJ2Xl4dBqfxkTp6cTMmJEjq91Mlq+aysLLp27UpKSgpRUVFs2rSJgQMHmrb37Nljt2JTXlBOwqUJlOeUo0NHNtno0LF1wVYGnhqI1ldrakur1WIwGGy2YS5XRd6IiAg0Gg2pqak1ljen6OT5a1KOSq5ssgkgwHieliVTdraMi569yK5jrS177t9D1oosi/at/V8yfwkTUiaojil1USoH7ztYWaFGISsrq9ZKZ70x64faf7Vk/2uU+eDcg6psxSWVfbK0rFS1r8xQ5lQRbWHuEgvfEk72FqPsn8z/hEkpk1TnMmVBCocePGTaNigG+8632fnRHNKQ/aaxDcN5M1ZmVqZlPWbvvPANxoGnJ5adoHBPId16ePYL0a05b/ma+tX/0fVkD1NycXExfn5+pm3fdb7A+Wuyu5DYp2LZPnC7qqrc3FyTNU+Tp6FwXeWHZn5BvtMOoSFwA2+x4EjOJZ5TbSdvTK42f8HeAqvTRbezncRDiSjtaveFbcBActNkmg5pWmPePPI4aDhIfnv1TXT6z9M2y2zbto2UlBQAUlJSWLVqlWo7ISHBblmLTxZTnmNppg02BLPj1x2qtgwGQ7VtWMublpZGamqqXeXNKdhboF5gzgYnfztZc6Y6krk10+68LYpbWBxT/k71Nd3bKrFW18ZRBF8SjOJTcx9Oj0o3/c5soz72qv2zocjrmWdSLsyx53zvaLXDrvPt384fJcz2+dlu2G5Rz9nuZ63mPbPljNV0wbG0PROr2k6JSCFkcIjVvGf+OUP+LnXfSCONvVl7KelcYpG/nHKOBR5zmKyuQCw1jQxvL2/KqPyyDA8Lrza/uR99AxvYoNlAkVJEWlQaO/rswPCbgXu630NuVi7nws+x6vNVnL5CrXQUUshrmtcwKAbywvPYcNsG9Ho9hhgDD9z6AMlZyWRFZNG5pDN5WXloNVoMioEzEWfYfNlmlIEK96+/nwdzHgRA39T212WfPn2IiooiJSWF6OhobrvtNmbNmmXajouLs/9kmT3Ld7ADLVp6YPwC6tqlK75tfE1tVVhabLVhLldF3shI4xRJc0uNXTKaybWRjfyq+RWDYkCDhnDCeZiHAWge2tz+Y60l/v7+FFFEGWW8qHkRg2IwXbeK/8/yLEEE4aXzsjgm8341f+h8NvZYyZS4J50mry18wn3omtiVewfcS2ZWJlOYYpr6WsHUsKmsfmy1abv3i725ccWNkAE+oT58MfeLhhYbgOJ2xdz56J20T20Pf8HUE1MJJLDG8z336rl83fNrnol7psY2tN5aum/tzrje48jOylZd43wln/SodB6Ne1RVJr1fOuMeHEfLjJb4rPVhetZ0AIICg6w1ITgY80HCT4c9zdrJa9Hr9XT8uyOThk+iIKeA6ZhdE7PnyU528m7ku2y9eCvKSoXRnUZTkFlguu45ETn8fv3vDX1IDkWUmkZG1Smdvj6+1Rcw6/CDxwxm3P+N48iRI8TFxRlNznpYnrSchIQEU1rOpTnk/12p/fsF+7F4x2J1OaDFkBYsTlpsKguQkJBAbGysRd73/niPfT32AeDjbTaupwp6vZ49e/ao5Km6bS/mL4IeV/UgKCCI/G+NxxUSFIK/3t9UtzWZbcllnre6Y7ZHrsvvuZyxM8Zy5MgRYmNj2fXDLnjEuM/Hx/Z5qi86rXFMk85Hx+L9i03tm//3ucuH8rRyoiKjLI/JrF9ti92GolMa3vV0nvBO4aZ+6P+oPyV7Kr9QlQiF1ftWq2Rr1qwZ3x/6vk59ypFo0JCmTyNNn8Z/bv4PwQ8FY8gw0KplqxrPt7653m65m7dpztKkpap+Wl1/1Wg0HA8/zvHw47wx7A2YYEz30lV5nchAYSdhfMafijjF2n1rTdcnqn8US48u5bd1v8EoY04vnZeqb3S7rxtbX99qKvP54c9r/Xxyd+qk1CxZsoRRo0bh66t+YZaUlLBixQrGjBnjEOGEOlDFWl1jHAmz/LHtYmkb05aYmBhVFr1eT3x8vGnby1vdbXx8fIiJibEoZ61sxe+qeUP0lebTmmSuWmfVbbsxayYqOgoMkI9RqVHKFYu6rR2fLbnM89pb3kQ11yR8aDhb2WqRz+Gcr1un06murfn/v7z+opxy67FRzGeYahSXLydTcW3+9f2XEiqVGn9/f5tKap36lJPo3q07Oi/jWBd7zrdWW7uRBdb6rq3+at7+oEGDTOdTYtY0DNrzlhqNTmPRd/V6PTfedCO/Y7S2VI0L1uGiDqoytXm+eQp1GlMzfvx4cnIsB5GeO3eO8ePH11sooe5YTP2s4Tmjym9vb3DClNfq1uhxGubT0DWa6hc/bEjMm656TcxPkxO/hE11V9MnqosvZC6by6ZzW8NdpmvbgWq6NUqtzrczZ7Co5NJamQYuOIeKa1zhfrJ1iauGMagaQqORUydLjaJYv2lOnjxJSIj1AUtCA1FNgLGa8tv7ILRYG8QRw81dsZ5MlZu9TmvtOAFbkW6hyrl35nk6X3e1faK6+ELm7zpXBd6zglP6rpNQBUtTFLc53yq5qKLUePh0YE+g4vzbvM5V4whV95HUCKmVUtO7d280GmME2Pj4eLy8KouXl5dz9OhRhg8f7nAhhVpQD/eT3Vp81RetIx5k5u/qamQuPlXMmS/OYChSH6jGS0Pzm5vj387f7iarKg/mLzyXWmqqWzFXYz2f02So7tJWGA6snauq7id30Wuc0XedRFVLTa3Od0N9ktt53wqOQ1ODpabq0iDm18Wd+7ujqJVSc/PNNwOwY8cOhg0bRmBgoGmfj48Pbdu2ZcSIEQ4VUKgdDeF+svjabUD3067rd5G3w3KxNoAT/ztB/+P9aww2aLUdDerjt2NKtdMQ95Pz8CT3UxVLjce5n2SgsFPQ1BSlW9xP9vPCCy8A0LZtW0aNGqUK+CO4CQ3gfrK4MRrQ/ZS307pCA1ByuoSyzDJ8wu2cFVTl2N3FUiPuJ+fhUe6nqpaaClmtWeiqnG9nWmqqdT8JTsd0/m0FOK26iKu4n2pm7NixZGdns2zZMpKSknj66adp1qwZCQkJRERE0KJFC0fLKdhLLd1PFbN8AGb+PZO1c9bW2MTDJx6mK11N26n5qXSaYz0CsL00yWvC27wNwIYjG7htzm1W831g+ACAtLA0vrrpKwCG/TKM2GPGgFSDFgwiNzjXrjZbnGzB8zwPwMq9KynXlTOEIQCM+GwEyZuqD1zoLHrt6MX93A/A23+/rbomoWdDeYVXAPhx/4+88tUrfHTDRwR4BzhWCEe7nxwlV33xJPdT1TE1GrPfVal6vhvIUiPupwbk/HWv0VKjKoO4n+xh586dDB06lJCQEI4dO8akSZNo1qwZX331FcnJySxZssTRcgp2Ulv30+GMw6bfaQVpHMg4UGMbeWVqa0mpodSuctURXBBs+l1QUmC9PrNjOeN7hpUtVwLQ1b8rsRiVmiMZR8gozbCrzbLsyiCF2cXZlOoqw+MnZydzoEn9jqmuRJxfLwkgvTBddS4isiv3nSs+x6e7PuWGi25gdLfRDpVB3E/uR23Ot9aJn+TmytbyncsZrRmNRtGQci6FN/LXwkAYcAIuc5oEFy4KNY+pAYz3rQFxP9nL448/zrhx43jzzTcJCqqMInnttddy5513Okw4oQ7U0v2UX5KPN8bF63Q6HU39mtbYRNUgWxqtxq5y1dHE0MT021vrbbU+raHyQW0uq/mA9aa+TSn3s29ATLBPpSLl4+WjWpU8xDuk3sdUV5p4V54LP28/lRxB/pX3W8XLJbso2/FCiPvJ5dTH/dRQcn2Y8CG3czs6dJzMOcnUvK/hKuO+3SWnzOy5giNQNKBVjB1BNZ6pKuZWVHE/1cy///7Lhx9+aJHeokUL01o3gouorfvJbP+VsVfy6bOf1tjEzt93knmgcn2c1k1bk/VsVu3krEJpdimbXtwEwNC2Q3nyWcuQ+oZSA78/Zwwq1b91f1ObexL2cGaXcd2Z3ffvxq+1fWO9ziWcY9t72wAY33s8Wn8tJ/8yrqe09q61hAxwTXiC9JXp7F2+F4CX4l9i0ROLTPuKThSx+fXNgDpcen1RFIVfjvxCQopxnZ+ehT3xw4+C8gLe+PMNq2V6FPXAH3+KSoss8rRLbUdzjMs4NOhsnJrwUPfTK3+8wqt5rxJKKNlF2QxeNFiVd2TSSLrTHXC++2lwm8G8sanyeisa44uz4mVbwe7Sk6LUOBh7PXwarcY4tEDcT/bh6+tLbq7luIWDBw8SFhZWb6GEulPr2U/md4m944RdNfvJXGEzX124rgN8q5n9lPR0Et7NvO2vy0782vnRdnrb6uuuzlxsPpyhwrfugFkmm05s4uplV5u2Pyv4jEgiOVdyjqnrp1ots6hgEW1oQ1FpkUWe51Ke46rzn+wK7jumxn0Es8TPq1I5P5hxkMIy40rKZeVl/H5cvT7P4LxKJUdBUZV1NNd0uIY9D+7hYIZxpXPdKzowQGzTWCb4D2Bh4V9GOWT2k8NRNJrK54Md493E/WQnN954Iy+++CIrVxrHNGg0GpKTk3n22WdlSrerqaX7qbqZNjZx0ewn1ReHuWJlXrYWsVuqHrvWt7Ki3L/sG2xcF3wifGgzrY3dcpljftyOtH7sSttlXZZqXBkV+6xZjFSDXDUKQ047XkGsC57kfrqh4w10DevKnjN7ADBojJ27qkUE1Ofb19uXRy951KmydQnrQpewLgD8pvkNBYWmvk3p4SWTRJyGov48qNbNKO6n2vH2228zcuRIwsPDKSwsZPDgwaSmpnLppZfyyiuvOFpGoTbUI/iexQPfFlVujAYLvudkS0347eGkLEihNL3UZhFHUHyquPoM1V0Ta5YaBw/EfeTiRwj3D4cc0Afo+XrU11bzBS0LgjMQ4BVgkSfgnwA4ryct/UphcImbrODsQe6n5gHN2fXALkrKjWsrbf9sO0UZRTT1bUrRf4pUeQ/sOkDmHqNLeO9De2nSpolFfc5Co9WgYAzyptFU3phuNUC8kWCw01JTcU3E/WQnISEhrFu3jj///JOdO3eSl5dHXFwcQ4cOdbR8Qi2pV/A9e91P1UW5rSP2uJ8cbampGqcmsGcgl564lLKcMttl6kj+rnwS4xONGzWNY66l+8kRmL+A+kT3wU/nRwkl+Hn7cWWnK62W2eq3lXzy0aHj5k43q/btCdrDGYzjnAacVPAKd5NPRA9yP4GxX/p6GRcO1lYoDAZMaRWYz3bS6XQNJh+gdnWYISqN46mtpcY0A6pqeiOmTkpNBYMGDWLQoEGOkkVwBLV1P9VFi3dV8D1nj6kBtD5afMLsDN5XC0qaV64MbR4byKpYLnA/WQpxvg0HzH7SuNHrzZPcTxbYe77ttbg6CjOl5gJ4Z7oUg40YQRaYxzQS91PNvPvuu1bTNRoNfn5+tG/fnssvv7zhvxiE2s9+UmxYP6rDRe4nZ4+pcSYaLzPFq6wmTdOsnD3uJwcMyFT1AzSV57G6h6CNL3TLNAU0bvI09SD3kwXVBd9z4dd4RR+tKpcMFHY8ip1Kjem5oVS5tz2pv9eROik177zzDmfOnKGgoAC9Xg9AVlYWAQEBBAYGkp6eTmxsLBs2bKBVq1YOFVioJTU9V+qwoKWr3E/2WGpqZRRowBeBeQycmiw11V4TJ7mfqmJ6EFb34Dx/zawpoeo0N3q5eZj7yRzTPWJFca/LLEaHYebqcJup+40RRcFQF/dTXRYt9mDq9Pn06quv0q9fPw4dOkRGRgYZGRkcPHiQSy65hNmzZ5OcnExkZCSPP/64wwR97bXX6NevH0FBQYSHh3PzzTdz4IBrIr66KzV+wdVUphHMfqqr+8nZXzC1sdTU1v3klAGZjnY/uckXorifnIC5+6lqwEDBodhrqbmQ3U91OsT//ve/vPPOO7Rr186U1r59e2bOnMm0adNo2bIlb775Jps2bXKYoL/99htTpkxh8+bNrFu3jtLSUq6++mry8/Md1obHU9MXnBVUioLOzoehG89+qutA4QZ1P9Vkqaml+8kRmL+ANBonuJ/cBXE/ORyb7id3uu6NBHsHCov7qZakpKRQVmY5Q6SsrMwUUTg6Oppz587VTzoz1qxZo9petGgR4eHhbNu2jcsvv9xh7bgTSrnC2e/Pkrkjk5NnT9L9oe6Edwo37c/5O4fUn1M5efokne7oROHuQos6zn51lhPfn6DVDZZuwMKkQnwX+lqk10TVG6PcYN+yBPbWmbspl+Mrj9N6ZGvTzVmWW8aR2Ucq82s1ZGVlsW3bNiJKK9dD+vO5PzE0MxAdHU1GRgahoaGV/7Mz6DqpK77tfNm2bRstMs1iapgdUlZWFhs3bgRgyJAhJhdrxb5t27bRp08fALZt20a7du1ISkqiT58+NvP66/xN6elb0il9ttQk1+nTpwFMMgedMpv+XI275JLDl9AnqY9DFxL0KvPCf40/pWeN09oNim0t0eR+KlPY8ewO1fn2220e/M19LDWNwf1kKDVYnG/fvWb3sYvcTwUpBQT/1JnRymj2tNoDzRpYjkZODjp+jL6WFsfPJ9hhqSlILeDUN6cs0hszdVJqrrjiCiZPnsyCBQvo3bs3ANu3b+eBBx7gyiuN0z937dpFTEyM4yStQk5ODgDNmtm+c4qLiykurowJYi0KsjuTvjKdfXfuA0CHjq8+/IpRaaPQ6/UUHC5g+4Dtpn2HPjpks56kG5PQbtDSYkjlS9xQZiDh8gR8TlfO9CktszM+S5Wv9wMHD9Axq6PqhV5rqtR5dNRRWAltbjMGqdv74F4yl1cuzVBQUEDXrl1JSUlhasBUhjEMAL+fjC/TbLLRobP4v3nBZh4Ke4hTaafoqenJLGYBmPpJVlYWnTt3Ji0tDYDIyEj27t2LXq8nKyvL1GZERAQajYbU1FS0Wi0Gg4GoqCj27NljkTcqKoodv+4wya45piH7zUq5AghQyVxAgeo4zalquZm5dCZ7+u2B/nU56ZVUfM3d+O+NhK4JNaUnJyfTOauz1WtrrsyaH48OHaVU9iV3eo6WlZdVu+3OlCvG860xaCzOdxmVx5GTk0NY84aL7G4KwpirodW6y5h8finL4/ovGkyGC4FVuYNof/xa03Z1A7HNr0nh+sqP3fyCxu/ZqJP76eOPP6ZZs2b06dMHX19ffH196du3L82aNePjjz8GIDAwkLffftuhwlZgMBh47LHHGDhwIN26dbOZ77XXXiMkJMT052mDlvN3qjtgy9KWJCQY1+bJ3127znlgtXr8UVl2GaWnK1885ZpyDoYftKuupoObqrb/LfvXJFdd0Wg0KN3VN2nSuiTT74yt6pW39/nuIyUlBYC/Cv6yu52mhqYUpxkVGPOHQlq6UYnZtm2bSaEBSE1NNR3btm3bTG2mpaWZrJIGg9GakZKSYjVvSkoKu07sQmldO4tKOeUcDzmuStMF6VDaq+sp3lZDML9aEJsWq9r+t9z2tc1rn2c13RxvjuFNlttYarLaZKm27TkGdyG/fc33fBJJ7Dy6swGkqaS0q/WPoaJDjg+NcCGjzVVHa87tZPsjvbSL5TUpo4yjAUcdLpe7UWtLjaIolJSU8N1335GcnGwarNuxY0c6duxoynfFFVc4TsoqTJkyhd27d/Pnn39Wm2/atGk88cQTpu3c3FyPUmyqauI6jY64uLjzO22Xe7vZ28z6bRbf9v2WuGJj/jat21SpXL1556N38li/x+ySK2pSFEoHhQdGPEByVjJno87yYtyLdpWtjh5rezCr3SziC+IBaBFdeRMHBARQiPGL4/nmz7P4ncVEbY0iJSWFw9GHiVoQxZN3PEl2TjZgDFRmUAym/6MYZVrwL7J5JGfPnjUGMzt/HiIijS6sPn36EBERobLUVJzzPn36EBVlbDMyMhJAZamJjo62mjc6Opq4vnGUbypnbI+x5GTlmOTSoDGNPagqc254Lr/d9JvqHGk0Gnps7MHcXnO57Kzxi1jftB4WsvNUyGA+e2U2s9katZX/xP3Haplec3sxYv0ISjNKLWTXarSUKqWs0uzEjRbppvervbnpi5tQMhS8Q7358r0vXS2S3fSa34uRG0ZSklli83ynRqaS0Kd+Hxi1luuLXozuPJqCzAJubn4pfc9eB0BwYGCDytHY0ZjZIF646AW+mv+Vzbzm16Sij2RHZPPHNX80hKguRaPUMpiAwWDAz8+PPXv20KFDB2fJZZOHHnqIb7/9lt9//73W7q3c3FxCQkLIyckhODjYMQJlZ8OvvzqmriokLfXnxDeVYzG0fgYuX54NwJnN3ux5y3ro+V5zT9A0oglHv4fji4zuuY5T8oi6sjIAXEmOhr8mGF+Gmzpu4r93/JfZURN4pPl1dsuXl5fHkSNHiI2NJdBBD7ATv5eTNNtoOo+5q4A2txrDwf/7dDB5R7xAa6DvJycJDAy0aD8vL489e4zr5LRt25a0tDSTglL2U29yt4YA0O3N05zOP0hgTgzJs4zKXqubC2l3T6HpuCrq6dq1q+rYzNsEOHLkiKmNqufB2vmpSKsoExERwbFjx6zKXN15XfndQcIXG31Op0Zv4a7b6ncvvnv2Rx5NWcgz3zzDNTuuASDkwX/ocElktdfW2vGY/+/27rtos7KgRQs4ebJeMjqKrKwsEhISiIuLq5/L1AVUyB4bG2vqW+b/XXVMFXKdfOcP2vw4BIAz9/zBbUueb3BZGiuLWsyh7WmjZ6LTqU5ERkdWm79qX/HE/m6Ove/vWltqtFotHTp0ICMjo0GVGkVRePjhh/n666/ZuHGjU8fr1IqjR8Fpi3hOBkZXbhYVm9pSuByYYbVUkwcfAPLx5XrgSWPi+/Pg/Z9MeRT0wFfnf5//Sv94Ifyz0G7pAoEedue2D38GAOfXD1v+KSxffn7Ph0AHNIYyAseOtdp+IHCJ2XaE2f99PEsuw41tPDOVHpwgi14k8w4Amm++gm8WWK3HnKptVvyOsCNv1bSIKv9tpVnDu+VQTANp/t4CK6yvpG03lwDXqGdUXTR3Dk3mnqi2WHXHU9MxuAq9Xk98fLyrxagT5rJXPAOr/nelXJ/P21KZ6EYWusZB5b2pb1azcmKtr1wI1GlMzeuvv87TTz/N7t27HS2PTaZMmcKyZcv49NNPCQoKIjU1ldTUVAoLLWf8NBaUaqdq2B6jYB4pwvKXlVzn/QNat3gI2YgmXM/hphqzBZcUdKZUz8Wxc3grajN3Pzn07PRwtPoruD1u8TxpPJh/cGi1F0DAmTpSp9lPY8aMoaCggJ49e+Lj44O/v79qf2Zmpo2SdWfevHmAcYqtOZ988gnjxo1zeHt2Ex0Nb77pnLq/bwXmLlAvH3j1fFuJelhutRS8OAP8DGj+aQ4VExBGjIRLhlTmyfE2GURMI+VvvhluG+A4+euAZl8IfGL8rQwbDvE9jRvvREMKaLx18Ertz7f2qzaw+fzGY09CdCEcCoKPzqddeQUMv6je8jcY23Lh8/O/u3WHKfWc/lT6J5R+p4598/RTEOaAQchBQXDbbfWvR3B/zONNiVLjNLQ6UWpsUSelZtasWQ4Wo2bcdh2RiAh4+mnn1H36MPxROQ5B0epMbSkr0mD5PuvlHn8UAr1gYQp8cX7W01XDYHJ0ZV0ni+AV41ve5H666mro94ATDqQW/JQBn+wy/h4wCJ5ua/y9ZCuk5IO3d53Otyb5EGw2xmtIKRuGr8GXQqUQMM5Q4tL+8HSs7QrcDM3MbyqVmnYd4Om76lWfstkbfq6i1Nx3L7QPqFe9woWFxnxUuBOX8bgQ0SiVisyFEESvrtRJqRl7fkyD4FwsFDk7l9MxxTIxXz6gmrpMlhp3uFHMRTCX0Y61iKqt1iyi76k5pyz3u8OxuwEq95OcE6GWKLZuYMGhiPvJNvU+M0VFReTm5qr+BAdR9Zlg7xqB558r1S4SaRYo1jSmxg1WUjYPLqdSxBTL/bWh6ZCm1e4PuTykTvW6jDquTG6LinOtstS4vjsIHoZGK5Yap3H+fBocccM3YupkqcnPz+fZZ59l5cqVZGRkWOwvL69/2HwBi5eVeTj8akPja6r8t1aXmcJgLUaJy7Als8HK/lrQ/Kbm9N3Vl8LDlgPLAzoF0KRTk7pV7Cqc9EGsUmrcoDsIHoYYapyGaQFbdwr85IbUSal55pln2LBhA/PmzeOee+7h/fff59SpU8yfP5/XX3/d0TJesNTV/fT232+jeCuEHgqlPe0BWHd4HWl/VkbK9T3tSy96Gau6ANxPAIHdAgns1kgCgjlLqRH3k+Ag3HUYpMcili+7qJNS8/3337NkyRKGDBnC+PHjueyyy2jfvj1t2rRh+fLl3HVX/QYtCuepxv2Udi4NWzy34TnKdeUM3TuU/2CMBvvdge/4ev3XpjzRmdEsPz99yqTUuMGnubPcT40Oc9eQA14eJmuduJ+E+qBStuVedSzn3U8acT9VR50eW5mZmaaIqsHBwaYp3IMGDeL33393nHQXOlb6bsWL/mS27eisFS8oxextp1W0VTOp8ms1Wi5rc1ndZXUUTnI/NTrE/SS4IxrLDxHBMVTcm+J+qp46WWpiY2M5evQorVu3plOnTqxcuZKLL76Y77//nqZNmzpYxAsXq9PYFUCjXiG5Kl+O/hK04B3oXRE0mIm9JnL3qLtNebTHtPCe8ffgmMFMfnQyrUNaO1D6OuJE91NjQuUacuRAYXE/CfVAuozzkFNrH3VSasaPH09iYiKDBw9m6tSp3HDDDcyZM4fS0lL+97//OVrGCxdrCvn5tOqUmps634RGoyE9MZ297AWgW3g3WnWqXMyzQFPAP/wDQKumrdxDoUHcT3bjpNMg7iehXshAYeehiPvJHuqk1Dz++OOm30OHDmX//v1s27aN9u3b00PCoTsOa+4ng4JGp8FQZrtjm76wq5n2a64wuJWiIO4n+6hmZlu9qhX3k1AfzFwjirx7HYrp3hT3U7XUWqkxGAwsWrSIr776imPHjqHRaIiJiWHkyJF0797dGTJesNh0P1GNpcbG8lAWdRms53M54n6yjwYYKCzuJ6G2qNcOk/7jWM6PqXGxFO5OrQzMiqJw4403cu+993Lq1Cm6d+9O165dOX78OOPGjeOWW25xlpwXHBkFGZzOPW2R/mvSr/xy5BdO5VpGxQVUL/1qg+85dj1EhyHuJztxlvsJcT8JdUdRRTF3nRyNkco4NWICq45aWWoWLVrE77//zvr167niiitU+3799VduvvlmlixZwpgxYxwq5IVGal4q7d5tx0OHHuIarlHtu3b5tZR6l3LD0RvoTW+LsqqXfjVf8+J+8mycNlBY3E9CPdAgs5+chsx+sotafYt99tlnPPfccxYKDcCVV17J1KlTWb7c1tLRgr38mfwnBaUF6hfMeSqmZlvbZ9xh/bdFBGJxP3k2DTClW9xPQq3R2HgACfXG5jNfUFErS83OnTt58803be6/5pprePfdd+st1IVOxXgZaz7pZwc8i8HfQIvMFtYLi/vpwsDsc8QRDzurS2WI+0moLaq1n8Si4Fhk9pM91EqpyczMJCIiwub+iIgIsrKy6i3UhY7h/LQBay+r5wc/j1egF6f2neIQhyz2i/vpAkFmPwluiKrLGKQDOZLKe1OUxeqolVJTXl6Ol5ftIjqdjrKysnoL1VgoO1fGsUXHOJ54nLZxbQm/JJzTP57mZPJJQkNDycjIIDQ0lNOnjQOCO/bvSNtxbSlXyul2vBtX77zaos5d/91FZl4mgcetr2Gk8reaPVOSvkwiKyeLjHMZdL6zM2c/PGs1n8sxk+XUe6fwv84fnxwfSlJLgEqFr65kZWWxbds2+vTpg16vd3k9dcbsPEV+Gsnpe08TfXl03etToE9SHy5OuriyCXE/CbXFrMvof+1OyuYUovpHuU6eRkBpSSlrPlhDRLbROi9eqOqplVKjKArjxo3D19fX6v7i4mKHCNVY2PfEPjIWZKBDx4mPT3CCEwDo0JFNtul/AAEAnFh2gqJ9RWiHannvk/es1pkzOwcdOgqxXG0aoLCokKysLPR6vcoCo03Qkp1gbPPgvIOqMsUl7nPdqlqNDg8/rNo+dfqU6fhqS1ZWFl27diUlJYWoqCj27Nnj0nrqg+ralmvZPXg33knehMWG1am+JjubMHPpTFVadnY2zUOa10tO4QLD7Pb1LQgkYUAC/U/0J7RFqOtk8nC+e/M7Qp83P39KnZ+BFwK18pqPHTuW8PBwQkJCrP6Fh4fLzCczzm45W3OmKqRvTsfrUJ1iIgKwQ9lBQkICAEH9glB8azZVpken17k9RxPQJQAlxLbM28q3mY6vtmzbto2UlBQAUlJSXF5PfSjrXEa+T75p2wcfdq7eWef6ApICVNsHOUjiocQ61ydcmJTGnKVEV2LabqI0YccvO1wnUCMge0e2ajstdIdLnjmeQq3enp988omz5GiUBDYJJJ98i/QEEvhG8w0GxYAGDT748DzPAxAUGERaeeUK3NsDt/NewHu0KGuBkmlceNKgGEz/syOy+WrZVzx1+1OkZKWQFpVGYpzxZeQT5kPXxK7ce+m9ZGZl8iAPEo3aRTE1bCqrH1/txLNQO3T+Orpt78bqi1bTuky9dMMc5rA5ajNT46bWqe4+ffoQFRVFSkoK0dHRxMXFubSeehEEdz56J/M/nE9kTiQAHS/qWPf6zPTI9UHrWRq4lO19ttdTSOFCwxBSxB2P3cHiOYsJLDa6yLt07uJiqTwcs3vzWJtPGJW8nPK4M66Tx82pu0lAqBGdTmc1vdewXtwy7xaOHDlCbGwsiVsS4Q7jPi8vL9UgXp+bfNj23jYAEhISiI2NNZU7cuQIcXFx6PV6FictJiEhwbRdQXjHcNM+/8f9KdlV+RWlhCmsPrDa7cyYYTFh9HusH2kz01TpD3z4AB+N/KjO8ur1evbs2WP1PLminvqg0WjIbZLLH53/4LbNtwEQFBRU9wrNHpwtxrZg+4vb3a5fCO6PBg2ZQZn80fkPrtlhjLEVHBzsYqk8HLN7s33hvwR569DIvWkTUWqciY0xrdEtoomJiSEmJgaA1hGt+YM/TGXMY8pERUWZXi7x8fEApnIV/8H4oq3YX5WKfdv8tlFCpVLj5+fnti8uvwA/i7RLLr2EQL31AdL2Ut15ckU9DqUeY6jNZz21atPKbfuF4N5YXRpBZiDXD7Pzp1EUt5rX4Y5IJApnYmtoSNVeWSWQmsFQ2YsdOgOlunbdDSuyyWycSipDpjsmgqu5Ii3nWagvjuqXghoNVAlwKFRFlBonYnVBSixn+FQNOKcq58ArVF277oZV2aS3WmAeiMtWf7MLNw3GKHgWFQqxw/qloLo3NWL2qhF5TTgTW/2vOotJFfeTVutIraaGbXfCmmzuLK87UJ/nnfl7R54KgiOR93D9UJ0/URBrQsbUOJGK5Q6qsiNtB9/8+Y1pW1Om4WKMQc9O5JxgT9oeWmOc+eNQa4oHKTXWjtudLUsNjbUv4vo87ySSsOAIHO0WFVBbasTqVSOi1DiRU9mn0GM54HLzqc3MWj/LtK01aFnPegCOZx0nMTXRtDq3I8c3eJL7SSw19mH+8rBYtLQ29SgypkaoP+J+cgLmSo2s0F0jYmh2IoWl1qP+Vl06XjHrtRpFo/pqjm0W6ziBPMhSI0pN9VidZVIfS41BLDWCkxD3U/0wO39aRZGBwjUglhonYvXFA1zT4RqGjBpSmaAALxp/dgrtRGy/WPjWuK1v4sCptR6k1Ij7yT4c5X5SIadZqCPifnIu4n6qGVFqnImNL5TY0Fg6dOqgStvIRgD0fnqiI6M5wAHjDkcOqRH3U6PDUe4nGSgsOBJxPzkQ8zg1oiHWiDy+nIgtS021L+wqs58c+iL3IEuNKDXVY3XcS32ed+YKuJxnoY5Y7TrifqofqteBKDU1IUqNM7E3+J55mqIuJ7Ofqk+70HGY+0k1GFHOs1A3xP3kBESpqRXifnIiqsGX5uk2XtiK4XzgPScFQhP3U+PB2stD3E+Cyzl/j5or20t3LCWvNI+wJmHc1uU2gnzrsUbZhYi5pUsGCteIKDWuoBbupwvVUmPtxerWSpiLcNgXsUQUFhyANZf7vH/msSd1DwAJKQnMuXZOQ4vVaBBLTc3IN5kTsWWpqY376UIdU2PVBeLG8roK83AADrPUyHkW6kh/3/b4lKmVbXNFZ3f6bleI5dlUWdBSqB5RalxAdeNFxP10HnE/VUuF0ucoS40qorA8FYQ6EuXVlGOz4MYDlW/iF4e8aPqtiKWh9siYmlohjy8nonpRqHZUk6ZUie4q7icTbq2EuQiHrdItEYUFBxGVBx0yK7cva3WZ6bdM764fotTUjCg1TqROSo0B502v9SClRtxP1WMaKOwg95Os/SQ4BI3Z11kFMqW7XpgPY9CKUlgjHqfUvP/++7Rt2xY/Pz8uueQS/vnnH1eLZBsb/U/cT3Yg7ie7cMZAYbfuF4JHoMF6qAFxP9WBqu4nsaRWi0cpNZ9//jlPPPEEL7zwAgkJCfTs2ZNhw4aRnp7uatGsU8c4NeJ+QtxPdiKznwRPQtxPdUBW6a4VHjWl+3//+x+TJk1i/PjxAHzwwQf8+OOPLFy4kKlTp7pEplNHTrH+LeMK26WlpeTk5ND9xu70CuhF84zm1gtVo9Tkn87n9Henq89bVzxIqRH3U/WYBgqbPfF2T99Nlw5diOgeYbWModjAma/OkH0gm5PZJ+k8sjN5v+dx4sgJAhIDzCp3qujCBUFlv/z39X8ZzWhyAnIoDLO+yO+FTtm5Mo59cozjO48TGhpKRkYG7a5sR0h0CLGbKhc1ljE1NeMxSk1JSQnbtm1j2rRppjStVsvQoUP5+++/rZYpLi6muLjYtJ2bm+twuVIOp9D6g9bqxC9gD3tslikqKrJMPP8i0WRrKNxQeePn5+c7QkwASstLVdvl5eUOq9vhWLHUZOdkE9YsrOFlcWNUU2f3aFgXt45r066lWbNmFnlPzTlF0lNJAOjQcXD2QdPvMCrPa2GRvHiE+mHufvL7xY/JTAZgU+4meNBVUrkv+x7fR8bHGejQkU02OnQc+/iYRT5RamrGY9xPZ8+epby8nIgI9VdoREQEqampVsu89tprhISEmP5atWrVEKLWSEp4ikVaabdSyzRKORJwxGHtZrXOUm3ntc9zWN2OJuSyEBRt5Q18mMPsTNrpQonci4qBwoltElWKTcuylmz/d7vVMnk7a77e+T75HPQ+6BghhQuP8xbEEBKt7g5PDm9IaTyGs1vO1pjnZLOTeGlqzneh4zGWmrowbdo0nnjiCdN2bm6uwxWbtj3asuW9LRQWFbLz/Z1ceexK1f4FVy7giz5fcPmCyynIKqAorIh1k9ZZ1NPrq16M7jyagswCtBotBsVAVkQWf179p8Nk7f1yb25ceSNkgHeoN1/O/dJhdTuaJp2a0GV7FyYNmcSZrDOkRqaS0CfB1WK5HQdbHOS3Bb8xZOIQU1rvXr2tZ7bxkXeMYyzovgC6wq5Wu1jYc6HjBRUuKJqRQPcndzJ2wVrO5ZxjBjMA0Gl1LpbMPWnSpAkFFACwlKXcwz2q/VvbbWX67dPZO08GCteExyg1zZs3R6fTkZaWpkpPS0sjMjLSahlfX198fX2dK1dkc6576DoAcvflQpX3wb/t/sVH78PnSZ+TkJBAXFwcer3eop5m4c34/LAxT2xsLEeOHLGZt640a9aM7w99X60c7kREjwgWJy32GHldhRKj0GRAE/L/MroqmzZtaiOj9eQ2PdoQ939xvLfzPQCCgmRtHqH+hLbVsvToUn779TcYaUyztoyCAF66ylfxk4ufJH2sevLLXx3/osi3SM6eHXiM+8nHx4c+ffqwfv16U5rBYGD9+vVceumlLpSsEh8fH4s0BQWtRoteryc+Pr7aF3NFnpiYmBrz1hV75HAnPE3ehsJ8ILWCgpd35UPRVrwaW+n6ZnpCQkIq65ZHp+AIFAW9Xs9NN99kSrIZu+tCx2wGfFTLKIvdFRMCNDKkpkY8xlID8MQTTzB27Fj69u3LxRdfzKxZs8jPzzfNhnI1Wq21ecgSoVVwPqo+ZuvBZyvdYz5tBI9EFddRnoXWKDdUTtpYsXsFnems2l8xbk7OXs14lFIzatQozpw5w/Tp00lNTaVXr16sWbPGYvCwq7AWR8WgMciNLDgc8z6lKIpaMamtUiPdU3AUNYViEEuDVZKzkwklFAMG5m+fzyxmqfablBo5fzXiUUoNwEMPPcRDDz3kajGsYs0iU+F+EgSnYm6oqaX7SaPRqOLdiGVRcCTm/ck85L9QSWGJMYyColGsRl1WUGh/zocW50qgiZzD6pC3rQOxGvFW3E9CAyDuJ8GdMWhkAajqqBhrpGgUXrryJYv9Uy6ZwvZ1sWjFUlMj8jhzIOJ+EhqKqgOFVXeyrfdHNe4n1dIc0l8FR2DWp0zuE7HUWEep+KdweczlFrt7RvUksFymw9uDKDUOxNpAYQVFLDWC8zE31NhYH6Y695MgOBNZyLJ6zC01tiz+yLpPdiFKjQOxtV6RfPkKjqbqQGFxPwlugQ0FuXKgqzwLrVJhqdEo1S94LNSIPM4ciEZnw/0kX8KCs6mv+0kGCgtORJSaGjBTaqxZalRpcn9Wiyg1DqRds3YWaQoKQ9oOaXhhhAuL+rifrJm7BcGBKJWDRgQrmLufxFJTPzxuSrc707xJc7JQLxq58OaFDLlyiGsEEhotVQcK18v9JAOFBWdgbaCw9K1qURClpr6IpcaRWDmbA9oMwEsruqPgZOrpfhIEZyKzn6rHdF401i2nGq1GBgrbiSg1jkQ0bKGBsIgoXE/3k4ypERyCrYHC4neqnvOnx6AxyHuknohS40CsvQzkBSE0BKp+JpYawd2oMESIpcYqqgHUNSk18k6pFlFqHIm1sylnWGgIZO0nwY2piCgsY2psUJvZT0K1yGAPRyJmQ6GBMLfMfJTwEeFHwulNbwCuXHQl2fpsizJTjk+hC10s0tceWcuChAWVdUunFRyBNTeoeKGsUnHPyeyn+iNKjQMR95PQUAR4B5h+ZxRmkFOSY9o+fPYw6WXpFmXyi/Ot1pVTkkNGYYbVugXBEUicmuqpcMtVq9TIQGG7EKXGkYj7SWgg+kb35faut7P60GoUFHS6ynVhmng3IdAn0KKMrVl4Oq2OQJ9ANGi4tsO19Inu4zS5hQsTUWrsR9xP9UOUGkciZkOhgdBqtHw+8nPT9p7EPZzZdcb4+8E9+Lf1tyiz45cdZB/Otkgf2W0k06dNd5qswgWEzH6qG7VZJkGs/9UiSo0DEfeT4CrM+9nfU/+mx+s9aN62OWCc4p21NovsX7NtFG4AAYULmgpLTdjZMI4uPkqbu9qg0WrI+DGDjG0ZnDxzku5TuhPeJdzFkjYcG1dsJOmXJHJycrio4CJAxtQ4AlFqHIkVV1NOTg7hLS6cG1VwEWZ9T/u5lg++/YApp6eg1+vJ3pjNzuE7bRYtKS1pAAGFCw4rEYUBjo87juGcgeAWwey5dQ8AOnR8/9H33Jp2K3q9vsFFbWj+Xfsv3AHtUC+tU24oJ/dcrkX+/Px8whpKOA9HRnw4kJCBISjayps3mWR2ndrlQomEC4WmQ5qqtqOKokhISAAgf6f1AcIVZLbKdJZYggDArrbq5+DxDcct+mWr0lamPtvYObb5mNX0xGaJ7M3aixJW+R4poYQk3yQZKGwnYqlxIP6x/nRJ7MKkyydxJusMaZFpbOu7zdViCRcA0ZOiUTooHLriEAA+Xj7ExcUB6gjDP/Mzn0Z8SrvSdpRklqA0V/jhvz+4RGbhwmHW6Fkc/PUg9/56LwDhYeEWka81aEx99kLiu9bfsdWwlYKyAtLK03ji4ico31rOuN7jyM7KJjMik01DN8HzrpbUMxClxsFEdItgcdJiEhISiIuLuyBMqYJ70GJIC5ICkzDkGejQvkNl3zN7d1z136t45olnAKSPCo7H1kBhL4VtsdtMSo2vr69FzBofb58Lpi+aL1nSKr4VDz//MEeOHKm8H/WwNGmp9XtUxmlWiyg1TkCv1xMfH+9qMYQLkIoBw1qNmWfZbNmEbj26mR6Q0keFhkKDRjWuBgMWy3nYWpusUWJ2qFFRUcTExBATE6PKIu+RuiFjagShMVHxEWf20FSZ+eUjT3ABGo160VRFUSwXXr2QdBpzBU7ewg5FTqcgNCIqgnSpXhiK5X5BcDpVlBaVpUbBUom5gJQa1T1prztJBgrbhSg1gtCYqHg+mpv2DVb2C0IDYo/76UJSasRS4zzkdApCY0LcT4IrsWF1sMv9BFbTGiWqW7KWN6UMFK4WUWoEoREh7ifBHbGw1ChYWmoq0i8EzI9TZzOXUAdEqRGExoS4nwQ3xcL9ZEWBuVBmQJkfZ60tNUK1yJRuQWhMiPtJcBfM+l1V91NiSiLleeVEEVWlTEMJ52LMj9Ne08KF4pqrJ2KpEYRGhLifBHekqvvp39P/8uexPy0zXiDvbdX9KW9hhyKnUxAaE+J+ElyJjUGsQ9oOUSk1GsVG5OELxP1kfk/aPaW7soBjZWlkiPtJEBoT4n4S3JBlty5jY8hGeN+4Hd82HiVIgc1VMl4gOo3qOOWedChiqRGERoS4nwR3xEvrxYA2A0zbrYJbEds01jLjBaLUmN+fck86FlFqBKExIe4nwV2oOrDVvO9d4LOf6nRPykBhuxClRhAaE+J+EtwVs76nKIp1BeZCeW/XZZkEwS5EqRGERoS4nwSXUs0LWtX3rK39hI20Roi4n5yHKDWC0JgQ95Pgrpj3PRtKzQXjfqpLnJoKxLJTLaLUCEJjQtxPgrtibqgxXODuJ/Mp3XJTOhSPUGqOHTvGxIkTiYmJwd/fn3bt2vHCCy9QUlLiatEEwa0Q95PgNlQZ2CruJzPqsvaTDBS2C4+IU7N//34MBgPz58+nffv27N69m0mTJpGfn8/MmTNdLZ4guA/ifhLcFXE/mVCqLCEhOA6PUGqGDx/O8OHDTduxsbEcOHCAefPmiVIjCOacfz6WZZVxZMERQruFcmLmCYv9gtDgmPW99K3p+Ab5WmQ58t4R2j/VHq+g6l9NhUcKOfHpCU4cO0FoaCgZmRl0HtuZ6EHRlXmOFXJi+QlOHD1Bh2s60OrWVk5XIPJ25XHqq1OcPHGSTqM70WJoC8pyyzj2yTGO7zpulDUjA78Ev8pCck86FI9QaqyRk5NDs2bNqs1TXFxMcXGxaTs3N9fZYgmCSzEolWaZ5EnJJJOs2p93Lo+mNG1gqYQLBjtnP2mOaSjBcvhA6ouplCaX0v2T7jbrUcoVEgYnUHqyFB06sslGh45dC3bhfcSbsJgwFIPC9iu2U3KsBB06jnx8BM2PGlpd26p+x1cNZTllJFyagCHfgA4dBz4+gNdOL9JmppG5JFMla3Oam8qZv6PsQiw71eIRY2qqcvjwYd577z0mT55cbb7XXnuNkJAQ01+rVs7r0ILgDhR2LLS57xznOFB+oAGlEYRKfCJ9UFrW7F46+8/ZaveX5ZRRerLUIt0XX3au3glAeX45JcfUStOhNYdqIW3tKTxSiCG/8qNCi5bdP+0m458Mm2WKvIo4FOhcuS40XGqpmTp1Km+88Ua1efbt20enTp1M26dOnWL48OHcdtttTJo0qdqy06ZN44knnjBt5+bmimIjNGp6fdKLR9o9wr2596rST3CClyNfZvPAqovtCIKTsDJQuPtf3Rnbcyw5WTloNVrKlXL2jN9DtC6auQvmAtAkoEkN9Vb+PMhBssjiEi4BoFPHThZ5KmjV0snPfitttm/XnrSANAooAGC6ZjoGxYCmswalh8LeVnuZ3Xu2nfVfGOON6otLlZonn3yScePGVZsnNrZyfZDTp09zxRVXMGDAAD788MMa6/f19cXX19JvKwiNlWbNmzFz/Uz299uvSm8xqgWb521Gr9e7SDJBgOatmrMsaRkJCQnExsZy5MgRph2extbUraY8Ol3104HMB9l2uKQDwZ2CObf4HABBQUHnM1mW8/f3r/8BVCuYZVJgYCBndUbLk6JVWHx4MUeOHGFN/hpmbjeOBw0ODHauXBcYLlVqwsLCCAsLsyvvqVOnuOKKK+jTpw+ffPIJWq1Hes4EwekEN7V8SLZp20YUGsEt0Ov1xMfHAxATE4P3cW8ADBjQolXP1rOG2f7mYc3xC/HjHEalpmL2lNVZVDXVW09stVmRrtFoiImJISYmhi1/bDFlkdlPjsUjBgqfOnWKIUOG0KZNG2bOnMmZM2dM+yIjI10omSC4H9YekvLgFBqEevQzRaPYjl+jymj2W2sl/k3VPBVJznbf2Iq7cz7d/B5UTemu7fQnuZerxSOUmnXr1nH48GEOHz5My5YtVfuc3lEFwdOwZsQUw6bgplS81CuUmpqe6RYRsqvGvzH/j5V9zsKWIlWRrjXPKnFqnIVHPOrGjRtnXNXVyp8gCFWw9oyU56bgplS81E0v+lq4nzQajcXyC+b/bZVzBtba/HL3l6SdSwOgnHLe+PMN3vjzDX4//rspjyyT4Fg8wlIjCIL9WHU/yfIIQkPz3HPwwgs1ZtPcVQytzltqAHYkgv9ltgsoocAy4+/vvkGjSQVGGrfjh4J2DyghwAp1sanT4L9f1vow7CVP1wl4R5W2NHEp48+Npx3tKDWUMnX9VItymltugSN2rJVQVOQgSRs3otQIQmPDmv1VdBqhITAPiFpebvyrifMWlAqlRlGUal/gCmbB6gzlQFnldkkpUARYmelUVgZlzlMMTjcrg3x1mkbRmNxPBo2lqci/FPodK4Uiy7g7NpEB/9UiSo0gNDbE/SS4issug0cegd9+s7uIpslhIL/S/eQXAB172i5Q0hT2nS8bEgI+ZXB+7ojSrgMEalBKg2FvlXJRLSC8mnrriUI0ZKrTph6IwK/AGFbEz6Dh681tVfsvzQwgrIO3/Y0EBsK0afWUtHEjSo0gNDLE/SS4DK0WZtsZTO48mkVD4Phvle6n9h1gxw7bBY4XQdvzQSSHXYUm1h9eP78cyEcfwRV6OF0MLf5WFVMeeQSmvl0r2WqD8tUWGKGO6H3xf+dxdPpRCvYW4OXfhJtXH3Va+4IRjxgoLAhCLRD3k+BBmAYKm7ufqsFdZz8p5ZYNKAbrs58E5yGnWRAaG+J+EjyQOs1+0mrUU6VdOfvJmjKmmMki92CDIEqNIDQyxP0keBKqODVQu+B7mir93YXB9wyKFa3JRvA9wXmIUiMIjQ1xPwkeRGNxP2Flope4nxoeOc2C0NiwosCIpUZwV0yWGg93P9my1Ij7qWERpUYQGhlWzdzyQBXcHE93P1lTpMwtNeJ+ahhEqRGExoa4nwQPorG4n6xah8wX6JS3bYMgp1kQGhvifhI8iFq7n8x1mirup4p9tmYiORObbVYkyy3YIIhSIwiNDHE/CZ5EVUtNjcqHudJTxf1kspZYG95izZLiQGzFqaloV9xPDYMoNYLQ2BD3k+CBeLz7qSZLjbxtGwQ5zYLQ2BD3k+BBiPtJcCSy9pMgNDLE/SR4ElXdT8Unijn6yVHa3N0GrbdRYykvKuf44uMc3XqUpuVNzQqr+/vO93bS7K9mpO5LxQ8/VTvHfzxObnkuGbkZdHuwGxHdIyhKLiJ5aTInjp6g/bD2BIUEceavM5w8eZLQ0FAyijLo8WgPwmLDjDIqClnrszjz5xlOpp6k6/1diewVSdHJIgwfGdChU7W575N96LJ1prKC8xGlRhAaG1bsrwWFBQ0vhyDYgUVEYeD4hOOUZ5fT/vH2ABz8v4OkvZGGDh3nOGfKV1JSgr/W37StXa8le322hUIDoNmpIXtnNjp0rP9oPcPThnMg/gDFh4vRoePox5WLTerQkY0x78IPF3Lf6fvQ6/Xk/p3Lzqt2mvL8seAP4lPjOTj8IN57LFfb1v6pNVmgUlJTyMrKQq/X1+d0CTUg7idBaGRo/bQonSpfEAYMnGh6woUSCULN7Gi7Q7Wd/Fuy6XfKphSrZc60OEPTy5vWuq3o8mi2/7Wd4sPFNeaNKooiISEBgLydeap9YeVhbN+0naI9RTXWk2BIMNUjOA+x1AhCI0Oj0dBjfQ/u6XYPuVm55IXnseH2Da4WSxCsUuE+euOmNzhz4gx3Zd0FQFjzMFMefVM9OeQA8JbmLXKUHMrDyvnpyZ8I1gfT6d9O3HfVfWRmZaJBY7KOlIWWseznZTx+1eNkZWUxkYnEEANAzx492c1uqzKtYhXXcz3++OOl8yIuLs64w4oHqWdPdT13P3w393S/hz3P7iEzKxOtRkuekseZqDM8Hfd0/U6WUCOi1AhCIyQ0OpTlSctJSEggLi5OTN6C21LhfjLoDDz+2eOkD08HwNfH15TH26vStTN782yOnzuu6teRfSJZnLSYhIQEYmNjSUxMBGDw4MHo9XqWJC0hISGBgP8GULzZaJ0JCQqxKdOI/4zA630vDNkG2rZtW3n/WFFqQoIr69ndcjenQk/hf5G/Sp4jR47IfdhAiFIjCI0UvV5PfHy8q8UQhGoxH+gbFBREOkalxjyujPnv1m1a0z6ivUU95v09JibG6r4dr+ygGKNSYy2uTAXdunfjoO4gBgxozUZpWF0KobzKFHNAq9FWK4/gPGRMjSAIguAWmA8WVllFqqz3VGfMy1Y3dVxjFgbBlhxW6qlwe2lkuqHLEEuNIAiC4DLMFYC3/n6LEYwA4Pdjv/PWd28BMCR1CC1pCcDjax+nOLjmAb7WiM+IJ4ooAJ786UlTW1X5YNsH9Cvphz/+nMk/w73f3QtAx50d6Uc/VV7zegwao4Yj0YNdhyg1giAIgsvw0la+hn48/KNJQTh45iAfb/8YgPbZ7U1KzbJdy8gNyK1TWx3PdTQpNZ/v+tymUrP+2Hq6lXXDH3/yi/JNcow4McJCqVHVo7E8JqFhEfeTIAiC4DJGdhmJTnM+QJ2Z+0nlwlF5peoRxM6sSq3B9utPQTFZXdTFLS0w5vUoKAT6BHJdh+vqLqNQLzTKBRTmMDc3l5CQEHJycggODna1OIIgCAKQnp/OmfwzlB0oI+vyLAD8RvsRNDsIgOw7sin9tRSA0IOhaEPq9j1uXk+zLc3IvCTTar7gT4LJm5aHIdWANlpL6PZQAAo+KCD/hXxVXvN6vAd602N9D4J8g+okn2Abe9/fYiMTBEEQXEp4k3DCm4STn5HPVrYCEOIbQufwzgAkeiWShVHZ6RzeGa+Qur26dvruJBOjAtJe355/+Mdqvtb61hzUHaSEErw0XnQN7wpAckAyRziiymteTxPfJqLQuBhxPwmCIAhugWrhVSfPfqpuSndtZj9Zm9ItuA5RagRBEAT3wFwpsKVM1OOtpVKayqvJqDWTxXxojTU9yKweVf2CSxClRhAEQXAPzC0pNoLv1Wu6tI36LbJpNCYFxXzYqdXgewax1LgTotQIgiAIboE7uZ9MecX95FGIUiMIgiC4B+J+EuqJKDWCIAiCe2D2RnK6+6kaS02t3E9iqXErRKkRBEEQ3AKVwuIu7qcaLDUypsa9EKVG+P/27j0oyrL9A/h3IdhUWA5yWFAQUNMxDoYHBpu0N0hw1LFsJlPeEms0FGfUPJTOpJZNqJXT4FjOO78pHMfX0jyQTjgeEBwLSTnkmUShTQVU5Iwc9/r98ebG5iKrwO4+6/czwwT73M/N9Vyty9f7fmSJiGyDJbefHvaGlh23nx7hDS25/WR9DDVERGQbOv5E6vjWCMLtJzIPQw0REdmEjoHFKEB0XFWxte0nhhqborhQ09zcjJEjR0KlUqGwsNDa5RARUU8xZ/upOws13H6ye4oLNStWrIC/v7+1yyAiop7G7SfqJkW9oWVGRgYOHz6MPXv2ICMjw9rlEBFRD+oYWG7n30bh+4WorKyEs875rwHd/QZ/f3p6w2mooe583F9j9Q16XPvPNXiN9MKfG/98YOjp9R3mYaixOsWEmoqKCsydOxf79+9H3759zTqnubkZzc3Nhq9ra2t7qzwiIuquDis1Kp0K1Rur4QhHtP/1G+70okdVVRU8PDwea/qO20PqzE4CDYC6+jq0y9+/VU/3rg466EyO7ThPa2vrY9VFPUcR208igsTERCQlJWH06NFmn5eSkgI3NzfDR0BAQC9WSURE3eHk5QQJ6nxbqBCFyM/Pf+z53V9073JMPerxe/vvaBzS+MjzVw2qeoyqqCeppOOGoYV98MEH2LBhw0PHXLp0CYcPH8auXbuQnZ0NR0dHlJaWIjg4GAUFBRg5cmSn55paqQkICEBNTQ00Gk1PXQYREfWQO9fv4K3wt1BbVQsHlQP0ooeDygFN0oQKvwoUXih87JUaALiRdQPvvvIuqmuqAQDt/dux/fB2LIldgqqqKtzW3sapi6cgesH8wfMxv2a+0fnlKMda37VI/yYdS2ctNcwj/QUHfj8AT0/Px66NOldbWws3N7cuf35bNdTcvn0blZWVDx0TEhKC119/HQcOHDDab21vb4ejoyMSEhKwbds2s76fuU0hIiLrqaqqQn5+PkJCQnDt2jXDfyMjI7sVaDrOn52dDQCYMGECPDw8DN+z4/eoOFuBSxGXjM7VT9fjuf97znDOP+eh3qGIUGMunU5ndD/MzZs3ERcXhx9++AFRUVEYOHCgWfMw1BARkbmadE04NeiU0WMDlwzEkE1DrFTRk8vcn9+KuFE4MDDQ6GsXFxcAwODBg80ONERERI/E1L9m4r9wsmmKuFGYiIjI0kz9Mj3+gj3bpoiVmn8KCgqCAnbNiIhIybhSozhcqSEiIjKFoUZxGGqIiIhM4PaT8jDUEBERmcKVGsVhqCEiIjKFoUZxGGqIiIhM4PaT8jDUEBERmcKVGsVhqCEiIjKFoUZxGGqIiIhM4PaT8jDUEBERmcKVGsVhqCEiIjKFoUZxGGqIiIhM4PaT8jDUEBERmcKVGsVhqCEiIjKFoUZxGGqIiIhM4PaT8jDUEBERmcKVGsVhqCEiIjKFoUZxGGqIiIhMMLXVdO/ePStUQuZiqCEiIjJB5aiChIrRY9f7X7dSNWSOp6xdABERka0KPxyOfz/7b9RV1aHBpwGZszKtXRI9BEMNERFRJ/r79cd/r/4X+fn5iIyMhIeHh7VLoodgqCEiInoIDw8PxMTEWLsMMgPvqSEiIiK7wFBDREREdoGhhoiIiOwCQw0RERHZBYYaIiIisgsMNURERGQXGGqIiIjILjDUEBERkV1gqCEiIiK7wFBDREREdoGhhoiIiOwCQw0RERHZhSfqDS1FBABQW1tr5UqIiIjIXPd/bt//Od6ZJyrU1NXVAQACAgKsXAkRERE9qrq6Ori5uXV6XCVdxR47otfrcfPmTbi6ukKlUvXYvLW1tQgICMCff/4JjUbTY/OSMfbZcthry2CfLYN9toze7LOIoK6uDv7+/nBw6PzOmSdqpcbBwQEDBw7stfk1Gg3/wFgA+2w57LVlsM+WwT5bRm/1+WErNPfxRmEiIiKyCww1REREZBcYanqAWq3GmjVroFarrV2KXWOfLYe9tgz22TLYZ8uwhT4/UTcKExERkf3iSg0RERHZBYYaIiIisgsMNURERGQXGGqIiIjILjDU9IAtW7YgKCgITz/9NKKiovDrr79auyRFW7t2LVQqldHH8OHDDcebmpqQnJyM/v37w8XFBa+99hoqKiqsWLEynDhxAlOnToW/vz9UKhX2799vdFxEsHr1avj5+aFPnz6IjY3FlStXjMbcvXsXCQkJ0Gg0cHd3xzvvvIP6+noLXoXt66rPiYmJDzy/4+Pjjcawz11LSUnBmDFj4OrqCh8fH7zyyisoKioyGmPOa4VOp8PkyZPRt29f+Pj4YPny5Whra7Pkpdg0c/r84osvPvCcTkpKMhpjqT4z1HTT999/j/feew9r1qxBfn4+IiIiEBcXh1u3blm7NEV79tlnUVZWZvg4efKk4diSJUtw4MAB7N69G9nZ2bh58yamT59uxWqVoaGhAREREdiyZYvJ4xs3bkRqaiq2bt2K3Nxc9OvXD3FxcWhqajKMSUhIwIULF3DkyBEcPHgQJ06cwLx58yx1CYrQVZ8BID4+3uj5vXPnTqPj7HPXsrOzkZycjFOnTuHIkSNobW3FxIkT0dDQYBjT1WtFe3s7Jk+ejJaWFvzyyy/Ytm0b0tLSsHr1amtckk0yp88AMHfuXKPn9MaNGw3HLNpnoW4ZO3asJCcnG75ub28Xf39/SUlJsWJVyrZmzRqJiIgweay6ulqcnJxk9+7dhscuXbokACQnJ8dCFSofANm3b5/ha71eL1qtVj777DPDY9XV1aJWq2Xnzp0iInLx4kUBIKdPnzaMycjIEJVKJTdu3LBY7Uryzz6LiMyePVumTZvW6Tns8+O5deuWAJDs7GwRMe+14qeffhIHBwcpLy83jPn6669Fo9FIc3OzZS9AIf7ZZxGRCRMmyKJFizo9x5J95kpNN7S0tCAvLw+xsbGGxxwcHBAbG4ucnBwrVqZ8V65cgb+/P0JCQpCQkACdTgcAyMvLQ2trq1HPhw8fjsDAQPa8G0pKSlBeXm7UVzc3N0RFRRn6mpOTA3d3d4wePdowJjY2Fg4ODsjNzbV4zUqWlZUFHx8fDBs2DPPnz0dlZaXhGPv8eGpqagAAnp6eAMx7rcjJyUFYWBh8fX0NY+Li4lBbW4sLFy5YsHrl+Gef79uxYwe8vLwQGhqKlStXorGx0XDMkn1+ot7QsqfduXMH7e3tRv+jAMDX1xeXL1+2UlXKFxUVhbS0NAwbNgxlZWX46KOP8MILL+D8+fMoLy+Hs7Mz3N3djc7x9fVFeXm5dQq2A/d7Z+q5fP9YeXk5fHx8jI4/9dRT8PT0ZO8fQXx8PKZPn47g4GBcvXoVq1atwqRJk5CTkwNHR0f2+THo9XosXrwYzz//PEJDQwHArNeK8vJyk8/5+8fImKk+A8CsWbMwaNAg+Pv74+zZs3j//fdRVFSEvXv3ArBsnxlqyOZMmjTJ8Hl4eDiioqIwaNAg7Nq1C3369LFiZUTd98Ybbxg+DwsLQ3h4OAYPHoysrCzExMRYsTLlSk5Oxvnz543uvaOe11mfO97vFRYWBj8/P8TExODq1asYPHiwRWvk9lM3eHl5wdHR8YG76SsqKqDVaq1Ulf1xd3fHM888g+LiYmi1WrS0tKC6utpoDHvePfd797DnslarfeAG+La2Nty9e5e974aQkBB4eXmhuLgYAPv8qBYuXIiDBw/i+PHjGDhwoOFxc14rtFqtyef8/WP0t876bEpUVBQAGD2nLdVnhppucHZ2xqhRo3Ds2DHDY3q9HseOHUN0dLQVK7Mv9fX1uHr1Kvz8/DBq1Cg4OTkZ9byoqAg6nY4974bg4GBotVqjvtbW1iI3N9fQ1+joaFRXVyMvL88wJjMzE3q93vAiRo/u+vXrqKyshJ+fHwD22VwigoULF2Lfvn3IzMxEcHCw0XFzXiuio6Nx7tw5oxB55MgRaDQajBgxwjIXYuO66rMphYWFAGD0nLZYn3v0tuMn0HfffSdqtVrS0tLk4sWLMm/ePHF3dze6y5sezdKlSyUrK0tKSkrk559/ltjYWPHy8pJbt26JiEhSUpIEBgZKZmamnDlzRqKjoyU6OtrKVdu+uro6KSgokIKCAgEgmzZtkoKCAvnjjz9ERGT9+vXi7u4u6enpcvbsWZk2bZoEBwfLvXv3DHPEx8fLc889J7m5uXLy5EkZOnSozJw501qXZJMe1ue6ujpZtmyZ5OTkSElJiRw9elQiIyNl6NCh0tTUZJiDfe7a/Pnzxc3NTbKysqSsrMzw0djYaBjT1WtFW1ubhIaGysSJE6WwsFAOHTok3t7esnLlSmtckk3qqs/FxcXy8ccfy5kzZ6SkpETS09MlJCRExo8fb5jDkn1mqOkBmzdvlsDAQHF2dpaxY8fKqVOnrF2Sos2YMUP8/PzE2dlZBgwYIDNmzJDi4mLD8Xv37smCBQvEw8ND+vbtK6+++qqUlZVZsWJlOH78uAB44GP27Nki8r9/1v3hhx+Kr6+vqNVqiYmJkaKiIqM5KisrZebMmeLi4iIajUbmzJkjdXV1Vrga2/WwPjc2NsrEiRPF29tbnJycZNCgQTJ37twH/hLEPnfNVI8ByLfffmsYY85rRWlpqUyaNEn69OkjXl5esnTpUmltbbXw1diurvqs0+lk/Pjx4unpKWq1WoYMGSLLly+Xmpoao3ks1WfVX0UTERERKRrvqSEiIiK7wFBDREREdoGhhoiIiOwCQw0RERHZBYYaIiIisgsMNURERGQXGGqIiIjILjDUEBERkV1gqCEim5GYmAiVSgWVSgUnJyf4+vri5ZdfxjfffAO9Xm/2PGlpaXB3d++9QonIJjHUEJFNiY+PR1lZGUpLS5GRkYF//etfWLRoEaZMmYK2tjZrl0dENoyhhohsilqthlarxYABAxAZGYlVq1YhPT0dGRkZSEtLAwBs2rQJYWFh6NevHwICArBgwQLU19cDALKysjBnzhzU1NQYVn3Wrl0LANi+fTtGjx4NV1dXaLVazJo1y+idg4lI2RhqiMjmvfTSS4iIiMDevXsBAA4ODkhNTcWFCxewbds2ZGZmYsWKFQCAcePG4csvv4RGo0FZWRnKysqwbNkyAEBrayvWrVuH3377Dfv370dpaSkSExOtdVlE1MOesnYBRETmGD58OM6ePQsAWLx4seHxoKAgfPLJJ0hKSsJXX30FZ2dnuLm5QaVSQavVGs3x9ttvGz4PCQlBamoqxowZg/r6eri4uFjkOoio93ClhogUQUSgUqkAAEePHkVMTAwGDBgAV1dXvPnmm6isrERjY+ND58jLy8PUqVMRGBgIV1dXTJgwAQCg0+l6vX4i6n0MNUSkCJcuXUJwcDBKS0sxZcoUhIeHY8+ePcjLy8OWLVsAAC0tLZ2e39DQgLi4OGg0GuzYsQOnT5/Gvn37ujyPiJSD209EZPMyMzNx7tw5LFmyBHl5edDr9fjiiy/g4PC/v5ft2rXLaLyzszPa29uNHrt8+TIqKyuxfv16BAQEAADOnDljmQsgIovgSg0R2ZTm5maUl5fjxo0byM/Px6effopp06ZhypQpeOuttzBkyBC0trZi8+bNuHbtGrZv346tW7cazREUFIT6+nocO3YMd+7cQWNjIwIDA+Hs7Gw478cff8S6deusdJVE1BsYaojIphw6dAh+fn4ICgpCfHw8jh8/jtTUVKSnp8PR0RERERHYtGkTNmzYgNDQUOzYsQMpKSlGc4wbNw5JSUmYMWMGvL29sXHjRnh7eyMtLQ27d+/GiBEjsH79enz++edWukoi6g0qERFrF0FERETUXVypISIiIrvAUENERER2gaGGiIiI7AJDDREREdkFhhoiIiKyCww1REREZBcYaoiIiMguMNQQERGRXWCoISIiIrvAUENERER2gaGGiIiI7AJDDREREdmF/wcf8AKGQlI03AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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=11)\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",
   "id": "8d0e6ee5",
   "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",
   "id": "f5c1c351",
   "metadata": {},
   "source": [
    "$$\n",
    "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810a4087",
   "metadata": {},
   "source": [
    "where $\\overline{y}_{R_j}$  is the mean response for the training observations \n",
    "within box $j$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5629755a",
   "metadata": {},
   "source": [
    "## A top-down approach, recursive binary splitting\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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3102c6",
   "metadata": {},
   "source": [
    "## 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",
   "id": "f6e4959c",
   "metadata": {},
   "source": [
    "$$\n",
    "\\left\\{X\\vert x_j < s\\right\\},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f332ab",
   "metadata": {},
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f43761",
   "metadata": {},
   "source": [
    "$$\n",
    "\\left\\{X\\vert x_j \\geq s\\right\\},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b2f5b5",
   "metadata": {},
   "source": [
    "so that we obtain the lowest MSE, that is"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97388edc",
   "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",
   "id": "e2ed2edb",
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "433e9a7c",
   "metadata": {},
   "source": [
    "## Pruning the tree\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)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf126778",
   "metadata": {},
   "source": [
    "## Cost complexity pruning\n",
    "\n",
    "For each value of $\\alpha$  there corresponds a subtree $T \\in T_0$ such that"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd0b65f1",
   "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",
   "id": "3cb61a07",
   "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",
    "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 $\\alpha$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73748b5a",
   "metadata": {},
   "source": [
    "## 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",
    "4. Return the subtree from Step 2 that corresponds to the chosen value of $\\alpha$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b1da6d3",
   "metadata": {},
   "source": [
    "## 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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b48bdf",
   "metadata": {},
   "source": [
    "## Growing a classification tree\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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d679c00",
   "metadata": {},
   "source": [
    "## Classification tree, how to split nodes\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",
   "id": "04bae309",
   "metadata": {},
   "source": [
    "$$\n",
    "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b060012",
   "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",
   "id": "f56fef9d",
   "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",
   "id": "c1fac4cf",
   "metadata": {},
   "source": [
    "* Gini index $g$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c832a60f",
   "metadata": {},
   "source": [
    "$$\n",
    "g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90832db4",
   "metadata": {},
   "source": [
    "* Information entropy or just entropy $s$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0215b6b4",
   "metadata": {},
   "source": [
    "$$\n",
    "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8591f16d",
   "metadata": {},
   "source": [
    "## Visualizing the Tree, Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "113d51d1",
   "metadata": {},
   "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": "markdown",
   "id": "cc99e81f",
   "metadata": {},
   "source": [
    "## Visualizing the Tree, The Moons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4f56adb",
   "metadata": {},
   "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",
   "id": "bf0845ba",
   "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,
   "id": "f7344f0e",
   "metadata": {},
   "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": {},
     "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",
   "id": "1fb6fd52",
   "metadata": {},
   "source": [
    "## Printing out as text\n",
    "\n",
    "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,
   "id": "d0d96130",
   "metadata": {},
   "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",
   "id": "6574f042",
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ec4915",
   "metadata": {},
   "source": [
    "## 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",
   "id": "eff9d046",
   "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",
   "id": "2ec161df",
   "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 $max\\_leaf\\_nodes$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6284da65",
   "metadata": {},
   "source": [
    "## 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",
   "id": "af4b8a85",
   "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",
   "id": "75de258a",
   "metadata": {},
   "source": [
    "Here the MSE for a specific node is defined as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b537a97",
   "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",
   "id": "7778645a",
   "metadata": {},
   "source": [
    "with"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80609ac3",
   "metadata": {},
   "source": [
    "$$\n",
    "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dfdfc42",
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f109fa64",
   "metadata": {},
   "source": [
    "## Why binary splits?\n",
    "\n",
    "It is custom to split to a tree uising binary splits. The reason is\n",
    "that multiway splits fragment the data too quickly, leaving\n",
    "insufficient data at the next level down.  Multiway splits can be\n",
    "achieved by a series of binary split and this is normally preferred."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d922699",
   "metadata": {},
   "source": [
    "## Computing a Tree using the Gini Index\n",
    "\n",
    "Consider the following example with attributes/features and two\n",
    "possible outcomes (classes)  for each attribute.  Assume we wish to find some\n",
    "correlations between the average grade of a student as function of the\n",
    "number of hours studied and hours slept. We want also to correlate the\n",
    "grade in a given course with the general trend, whether the students\n",
    "recently has gotten grades below average or above.\n",
    "\n",
    "We have three features/attributes\n",
    "1. Trend of average grades before present course, classified as either below  or above  the average grade of the whole class  \n",
    "\n",
    "2. The number of hours studies, classified again as either higher (more than 3 hours per day) or lower . Here we have used a standard for one $ECTS$ which is scaled to 25-30 hours of work for a semester which lasts 18 weeks, with 15 weeks of lectures and 3 weeks for exams, assuming a total of 30 ECTS per semester. \n",
    "\n",
    "3. The number of hours slept as high for more than $8$ hours and below  for less than 8 hours of sleep, classified again as either high or low\n",
    "\n",
    "4. The final grade whether it is above or below average"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3caaf3",
   "metadata": {},
   "source": [
    "## The Table\n",
    "\n",
    "<table class=\"dotable\" border=\"1\">\n",
    "<thead>\n",
    "<tr><th align=\"center\">Grade Trend</th> <th align=\"center\">Hours slept</th> <th align=\"center\">Hours Studied</th> <th align=\"center\">Grade</th> </tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr><td align=\"center\">   Above          </td> <td align=\"center\">   Low            </td> <td align=\"center\">   High             </td> <td align=\"center\">   Above    </td> </tr>\n",
    "<tr><td align=\"center\">   Below          </td> <td align=\"center\">   High           </td> <td align=\"center\">   Low              </td> <td align=\"center\">   Below    </td> </tr>\n",
    "<tr><td align=\"center\">   Above          </td> <td align=\"center\">   Low            </td> <td align=\"center\">   High             </td> <td align=\"center\">   Above    </td> </tr>\n",
    "<tr><td align=\"center\">   Above          </td> <td align=\"center\">   High           </td> <td align=\"center\">   High             </td> <td align=\"center\">   Above    </td> </tr>\n",
    "<tr><td align=\"center\">   Below          </td> <td align=\"center\">   Low            </td> <td align=\"center\">   High             </td> <td align=\"center\">   Below    </td> </tr>\n",
    "<tr><td align=\"center\">   Above          </td> <td align=\"center\">   Low            </td> <td align=\"center\">   Low              </td> <td align=\"center\">   Below    </td> </tr>\n",
    "<tr><td align=\"center\">   Below          </td> <td align=\"center\">   High           </td> <td align=\"center\">   High             </td> <td align=\"center\">   Below    </td> </tr>\n",
    "<tr><td align=\"center\">   Below          </td> <td align=\"center\">   Low            </td> <td align=\"center\">   High             </td> <td align=\"center\">   Below    </td> </tr>\n",
    "<tr><td align=\"center\">   Above          </td> <td align=\"center\">   Low            </td> <td align=\"center\">   Low              </td> <td align=\"center\">   Below    </td> </tr>\n",
    "<tr><td align=\"center\">   Above          </td> <td align=\"center\">   High           </td> <td align=\"center\">   High             </td> <td align=\"center\">   Above    </td> </tr>\n",
    "</tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95856cd8",
   "metadata": {},
   "source": [
    "## Computing the various Gini Indices\n",
    "\n",
    "In computations we will translate all classes into numbers. Being\n",
    "these binary classes, they can easily be split into ones and zeros.\n",
    "\n",
    "**Gini index for Average trend.**\n",
    "\n",
    "[See handwritten notes November 3](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2022/NotesNov32022.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177279e1",
   "metadata": {},
   "source": [
    "## Computing the various Gini Indices, Hours slept\n",
    "\n",
    "**Gini index for hour slept.**\n",
    "\n",
    "[See handwritten notes November 3](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2022/NotesNov32022.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c2ba1c",
   "metadata": {},
   "source": [
    "## Computing the various Gini Indices, Hours studied\n",
    "\n",
    "**Gini index for hour studied.**\n",
    "\n",
    "[See handwritten notes November 3](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2022/NotesNov32022.pdf)\n",
    "\n",
    "For final tree, see the above handwritten notes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a317dc",
   "metadata": {},
   "source": [
    "## A possible code using Scikit-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e55268fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Grade Trend</th>\n",
       "      <th>Hours slept</th>\n",
       "      <th>Hours Studied</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "   Grade Trend  Hours slept  Hours Studied  Grade\n",
       "0            1            0              1      1\n",
       "1            0            1              0      0\n",
       "2            1            0              1      1\n",
       "3            1            1              1      1\n",
       "4            0            0              1      0\n",
       "5            1            0              0      0\n",
       "6            0            1              1      0\n",
       "7            0            0              1      0\n",
       "8            1            0              0      0\n",
       "9            1            1              1      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1]\n",
      " [0 1 0]\n",
      " [1 0 1]\n",
      " [1 1 1]\n",
      " [0 0 1]\n",
      " [1 0 0]\n",
      " [0 1 1]\n",
      " [0 0 1]\n",
      " [1 0 0]\n",
      " [1 1 1]]\n",
      "Train set accuracy with Decision Tree: 1.00\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(\"grades.csv\"),'r')\n",
    "\n",
    "# Read the experimental data with Pandas\n",
    "from IPython.display import display\n",
    "grades = pd.read_csv(infile)\n",
    "grades = pd.DataFrame(grades)\n",
    "display(grades)\n",
    "# Features and targets\n",
    "X = grades.loc[:, grades.columns != 'Grade'].values\n",
    "y = grades.loc[:, grades.columns == 'Grade'].values\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/grade.dot\",\n",
    "    rounded=True,\n",
    "    filled=True\n",
    ")\n",
    "cmd = 'dot -Tpng DataFiles/grade.dot -o DataFiles/grades.png'\n",
    "os.system(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a5e24c",
   "metadata": {},
   "source": [
    "## Further example: Computing the Gini index\n",
    "\n",
    "The next 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 class=\"dotable\" 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>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "434a4333",
   "metadata": {},
   "source": [
    "## Simple Python Code to read in Data and perform Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "894efbe3",
   "metadata": {},
   "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": 7,
     "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",
   "id": "ce70cb1f",
   "metadata": {},
   "source": [
    "## Computing the Gini Factor\n",
    "\n",
    "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": 8,
   "id": "57fd167f",
   "metadata": {},
   "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",
   "id": "15373d99",
   "metadata": {},
   "source": [
    "## Regression trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41a7e5f3",
   "metadata": {},
   "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": 10,
   "id": "d7c9965b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 10,
     "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": "markdown",
   "id": "cb82e5ae",
   "metadata": {},
   "source": [
    "## Final regressor code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5810e45a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 12,
   "id": "cbba33cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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",
   "id": "5d0dc01f",
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875b55f8",
   "metadata": {},
   "source": [
    "## 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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ec4472",
   "metadata": {},
   "source": [
    "## Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods\n",
    "\n",
    "As stated above and seen in many of the examples discussed here about\n",
    "a single decision tree, we often end up overfitting our training\n",
    "data. This normally means that we have a high variance. Can we reduce\n",
    "the variance of a statistical learning method?\n",
    "\n",
    "This leads us to a set of different methods that can combine different\n",
    "machine learning algorithms or just use one of them to construct\n",
    "forests and jungles of trees, homogeneous ones or heterogenous\n",
    "ones. These methods are recognized by different names which we will\n",
    "try to explain here. These are\n",
    "\n",
    "1. Voting classifiers\n",
    "\n",
    "2. Bagging and Pasting\n",
    "\n",
    "3. Random forests\n",
    "\n",
    "4. Boosting methods, from adaptive to Extreme Gradient Boosting (XGBoost)\n",
    "\n",
    "We discuss these methods here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "532012c9",
   "metadata": {},
   "source": [
    "## An Overview of Ensemble Methods\n",
    "\n",
    "<!-- dom:FIGURE: [DataFiles/ensembleoverview.png, width=600 frac=0.8] -->\n",
    "<!-- begin figure -->\n",
    "\n",
    "<img src=\"DataFiles/ensembleoverview.png\" width=\"600\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
    "<!-- end figure -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d055f1bf",
   "metadata": {},
   "source": [
    "## Why Voting?\n",
    "\n",
    "The idea behind boosting, and voting as well can be phrased as follows:\n",
    "**Can a group of people somehow arrive at highly\n",
    "reasoned decisions, despite the weak judgement of the individual\n",
    "members?**\n",
    "\n",
    "The aim is to create a good classifier by combining several weak classifiers.\n",
    "**A weak classifier is a classifier which is able to produce results that are only slightly better than guessing at random.**\n",
    "\n",
    "The basic approach is to apply repeatedly (in boosting this is done in an iterative way) a weak classifier to modifications of the data.\n",
    "In voting we simply apply the law of large numbers while in boosting we give more weight to misclassified data in\n",
    "each iteration.  \n",
    "\n",
    "Decision trees play an important role as our weak classifier. They serve as the basic method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eadfafd",
   "metadata": {},
   "source": [
    "## Tossing coins\n",
    "\n",
    "The simplest case is a so-called voting ensemble. To illustrate this,\n",
    "think of yourself tossing coins with a biased outcome of 51 per cent\n",
    "for heads and 49% for tails. With only few tosses,\n",
    "you may not clearly see this distribution for heads and tails. However, after some\n",
    "thousands of tosses, there will be a clear majority of heads.  With 2000 tosses\n",
    "you should see approximately 1020 heads and 980 tails.\n",
    "\n",
    "We can then state that the outcome is a clear majority of heads. If\n",
    "you do this ten thousand times, it is easy to see that there is a 97%\n",
    "likelihood of a majority of heads.\n",
    "\n",
    "Another example would be to collect all polls before an\n",
    "election. Different polls may show different likelihoods for a\n",
    "candidate winning with say a majority  of the popular vote. The majority vote\n",
    "would then consist in many polls indicating that this candidate will\n",
    "actually win.\n",
    "\n",
    "The example here shows how we can implement the coin tossing case,\n",
    "clealry demostrating that after some tosses we see the [law of large](https://en.wikipedia.org/wiki/Law_of_large_numbers)\n",
    "numbers kicking in."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd224ee4",
   "metadata": {},
   "source": [
    "## Standard imports first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d51d494",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common imports\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",
    "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')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d03687f5",
   "metadata": {},
   "source": [
    "## Simple Voting Example, head or tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "54b4f008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Common imports\n",
    "import numpy as np\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",
    "heads_proba = 0.51\n",
    "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
    "cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)\n",
    "plt.figure(figsize=(8,3.5))\n",
    "plt.plot(cumulative_heads_ratio)\n",
    "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n",
    "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n",
    "plt.xlabel(\"Number of coin tosses\")\n",
    "plt.ylabel(\"Heads ratio\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.axis([0, 10000, 0.42, 0.58])\n",
    "save_fig(\"votingsimple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9706454",
   "metadata": {},
   "source": [
    "## Using the Voting Classifier\n",
    "\n",
    "We can use the voting classifier on other data sets, here the exciting binary case of two distinct objects using the make moons functionality of **Scikit-Learn**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a175cf2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.896\n",
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.912\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(gamma=\"auto\", random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='hard')\n",
    "\n",
    "voting_clf.fit(X_train, y_train)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))\n",
    "\n",
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(gamma=\"auto\", probability=True, random_state=42)\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='soft')\n",
    "voting_clf.fit(X_train, y_train)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc53786",
   "metadata": {},
   "source": [
    "## Voting and Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1fff4626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n",
       "                             (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n",
       "                             (&#x27;svc&#x27;, SVC(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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n",
       "                             (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n",
       "                             (&#x27;svc&#x27;, SVC(random_state=42))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
       "                             ('svc', SVC(random_state=42))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "log_clf = LogisticRegression(random_state=42)\n",
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "svm_clf = SVC(random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='hard')\n",
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3a4bb0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.896\n",
      "SVC 0.896\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VotingClassifier 0.912\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fef61b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n",
       "                             (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n",
       "                             (&#x27;svc&#x27;, SVC(probability=True, random_state=42))],\n",
       "                 voting=&#x27;soft&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n",
       "                             (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n",
       "                             (&#x27;svc&#x27;, SVC(probability=True, random_state=42))],\n",
       "                 voting=&#x27;soft&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
       "                             ('svc', SVC(probability=True, random_state=42))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_clf = LogisticRegression(random_state=42)\n",
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "svm_clf = SVC(probability=True, random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='soft')\n",
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2cd36a0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.896\n",
      "SVC 0.896\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VotingClassifier 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f31db625",
   "metadata": {},
   "source": [
    "## Bagging\n",
    "\n",
    "The **plain** decision trees suffer from high\n",
    "variance. This means that if we split the training data into two parts\n",
    "at random, and fit a decision tree to both halves, the results that we\n",
    "get could be quite different. In contrast, a procedure with low\n",
    "variance will yield similar results if applied repeatedly to distinct\n",
    "data sets; linear regression tends to have low variance, if the ratio\n",
    "of $n$ to $p$ is moderately large. \n",
    "\n",
    "**Bootstrap aggregation**, or just **bagging**, is a\n",
    "general-purpose procedure for reducing the variance of a statistical\n",
    "learning method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce89108a",
   "metadata": {},
   "source": [
    "## More bagging\n",
    "\n",
    "Bagging typically results in improved accuracy\n",
    "over prediction using a single tree. Unfortunately, however, it can be\n",
    "difficult to interpret the resulting model. Recall that one of the\n",
    "advantages of decision trees is the attractive and easily interpreted\n",
    "diagram that results.\n",
    "\n",
    "However, when we bag a large number of trees, it is no longer\n",
    "possible to represent the resulting statistical learning procedure\n",
    "using a single tree, and it is no longer clear which variables are\n",
    "most important to the procedure. Thus, bagging improves prediction\n",
    "accuracy at the expense of interpretability.  Although the collection\n",
    "of bagged trees is much more difficult to interpret than a single\n",
    "tree, one can obtain an overall summary of the importance of each\n",
    "predictor using the MSE (for bagging regression trees) or the Gini\n",
    "index (for bagging classification trees). In the case of bagging\n",
    "regression trees, we can record the total amount that the MSE is\n",
    "decreased due to splits over a given predictor, averaged over all $B$ possible\n",
    "trees. A large value indicates an important predictor. Similarly, in\n",
    "the context of bagging classification trees, we can add up the total\n",
    "amount that the Gini index  is decreased by splits over a given\n",
    "predictor, averaged over all $B$ trees."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cdece01",
   "metadata": {},
   "source": [
    "## Making your own Bootstrap: Changing the Level of the Decision Tree\n",
    "\n",
    "Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with\n",
    "a decision tree wth different depths and perform a bootstrap aggregate (in this case we perform as many bootstraps as data points $n$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "324a964b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial degree: 1\n",
      "Error: 0.06380941468319971\n",
      "Bias^2: 0.05160313473529168\n",
      "Var: 0.01220627994790804\n",
      "0.06380941468319971 >= 0.05160313473529168 + 0.01220627994790804 = 0.06380941468319971\n",
      "Polynomial degree: 2\n",
      "Error: 0.043464037468677004\n",
      "Bias^2: 0.02659851591375224\n",
      "Var: 0.01686552155492476\n",
      "0.043464037468677004 >= 0.02659851591375224 + 0.01686552155492476 = 0.043464037468677\n",
      "Polynomial degree: 3\n",
      "Error: 0.020716391693769383\n",
      "Bias^2: 0.01159033914386312\n",
      "Var: 0.00912605254990626\n",
      "0.020716391693769383 >= 0.01159033914386312 + 0.00912605254990626 = 0.02071639169376938\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial degree: 4\n",
      "Error: 0.02063627410934057\n",
      "Bias^2: 0.0117496656370668\n",
      "Var: 0.008886608472273775\n",
      "0.02063627410934057 >= 0.0117496656370668 + 0.008886608472273775 = 0.020636274109340574\n",
      "Polynomial degree: 5\n",
      "Error: 0.02087627881701288\n",
      "Bias^2: 0.01349183949256158\n",
      "Var: 0.007384439324451296\n",
      "0.02087627881701288 >= 0.01349183949256158 + 0.007384439324451296 = 0.020876278817012876\n",
      "Polynomial degree: 6\n",
      "Error: 0.02069601123831537\n",
      "Bias^2: 0.013918526350129823\n",
      "Var: 0.0067774848881855445\n",
      "0.02069601123831537 >= 0.013918526350129823 + 0.0067774848881855445 = 0.020696011238315368\n",
      "Polynomial degree: 7\n",
      "Error: 0.022964339924731444\n",
      "Bias^2: 0.01550381208433455\n",
      "Var: 0.007460527840396904\n",
      "0.022964339924731444 >= 0.01550381208433455 + 0.007460527840396904 = 0.022964339924731455\n",
      "Simple tree: 0.5148389267750961\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.utils import resample\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "n = 100\n",
    "n_boostraps = 100\n",
    "maxdepth = 8\n",
    "\n",
    "# Make data set.\n",
    "x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
    "error = np.zeros(maxdepth)\n",
    "bias = np.zeros(maxdepth)\n",
    "variance = np.zeros(maxdepth)\n",
    "polydegree = np.zeros(maxdepth)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
    "\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",
    "\n",
    "# we produce a simple tree first as benchmark\n",
    "simpletree = DecisionTreeRegressor(max_depth=3) \n",
    "simpletree.fit(X_train_scaled, y_train)\n",
    "simpleprediction = simpletree.predict(X_test_scaled)\n",
    "for degree in range(1,maxdepth):\n",
    "    model = DecisionTreeRegressor(max_depth=degree) \n",
    "    y_pred = np.empty((y_test.shape[0], n_boostraps))\n",
    "    for i in range(n_boostraps):\n",
    "        x_, y_ = resample(X_train_scaled, y_train)\n",
    "        model.fit(x_, y_)\n",
    "        y_pred[:, i] = model.predict(X_test_scaled)#.ravel()\n",
    "\n",
    "    polydegree[degree] = degree\n",
    "    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )\n",
    "    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )\n",
    "    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n",
    "    print('Polynomial degree:', degree)\n",
    "    print('Error:', error[degree])\n",
    "    print('Bias^2:', bias[degree])\n",
    "    print('Var:', variance[degree])\n",
    "    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
    " \n",
    "mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**2))\n",
    "print(\"Simple tree:\",mse_simpletree)\n",
    "plt.xlim(1,maxdepth)\n",
    "plt.plot(polydegree, error, label='MSE')\n",
    "plt.plot(polydegree, bias, label='bias')\n",
    "plt.plot(polydegree, variance, label='Variance')\n",
    "plt.legend()\n",
    "save_fig(\"baggingboot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33074a97",
   "metadata": {},
   "source": [
    "## Random forests\n",
    "\n",
    "Random forests provide an improvement over bagged trees by way of a\n",
    "small tweak that decorrelates the trees. \n",
    "\n",
    "As in bagging, we build a\n",
    "number of decision trees on bootstrapped training samples. But when\n",
    "building these decision trees, each time a split in a tree is\n",
    "considered, a random sample of $m$ predictors is chosen as split\n",
    "candidates from the full set of $p$ predictors. The split is allowed to\n",
    "use only one of those $m$ predictors. \n",
    "\n",
    "A fresh sample of $m$ predictors is\n",
    "taken at each split, and typically we choose"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8140eb76",
   "metadata": {},
   "source": [
    "$$\n",
    "m\\approx \\sqrt{p}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba57277",
   "metadata": {},
   "source": [
    "In building a random forest, at\n",
    "each split in the tree, the algorithm is not even allowed to consider\n",
    "a majority of the available predictors. \n",
    "\n",
    "The reason for this is rather clever. Suppose that there is one very\n",
    "strong predictor in the data set, along with a number of other\n",
    "moderately strong predictors. Then in the collection of bagged\n",
    "variable importance random forest trees, most or all of the trees will\n",
    "use this strong predictor in the top split. Consequently, all of the\n",
    "bagged trees will look quite similar to each other. Hence the\n",
    "predictions from the bagged trees will be highly correlated.\n",
    "Unfortunately, averaging many highly correlated quantities does not\n",
    "lead to as large of a reduction in variance as averaging many\n",
    "uncorrelated quantities. In particular, this means that bagging will\n",
    "not lead to a substantial reduction in variance over a single tree in\n",
    "this setting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7dce23d",
   "metadata": {},
   "source": [
    "## Random Forest Algorithm\n",
    "The algorithm described here can be applied to both classification and regression problems.\n",
    "\n",
    "We will grow of forest of say $B$ trees.\n",
    "1. For $b=1:B$\n",
    "\n",
    "  * Draw a bootstrap sample from the training data organized in our $\\boldsymbol{X}$ matrix.\n",
    "\n",
    "  * We grow then a random forest tree $T_b$ based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached\n",
    "\n",
    "1. we select $m \\le p$ variables at random from the $p$ predictors/features\n",
    "\n",
    "2. pick the best split point among the $m$ features using for example the CART algorithm and create a new node\n",
    "\n",
    "3. split the node into daughter nodes\n",
    "\n",
    "4. Output then the ensemble of trees $\\{T_b\\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4e3455f",
   "metadata": {},
   "source": [
    "## Random Forests Compared with other Methods on the Cancer Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f530afd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(426, 30)\n",
      "(143, 30)\n",
      "Test set accuracy Logistic Regression with scaled data: 0.96\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy SVM with scaled data: 0.96\n",
      "Test set accuracy with Decision Trees and scaled data: 0.87\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.93333333 0.73333333 0.93333333 1.         1.         0.92857143\n",
      " 1.         0.92857143 0.92857143 0.92857143]\n",
      "Test set accuracy with Random Forests and scaled data: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "from sklearn.ensemble import BaggingClassifier\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",
    "#define methods\n",
    "# Logistic Regression\n",
    "logreg = LogisticRegression(solver='lbfgs')\n",
    "# Support vector machine\n",
    "svm = SVC(gamma='auto', C=100)\n",
    "# Decision Trees\n",
    "deep_tree_clf = DecisionTreeClassifier(max_depth=None)\n",
    "#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)))\n",
    "\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_validate\n",
    "# Data set not specificied\n",
    "#Instantiate the model with 500 trees and entropy as splitting criteria\n",
    "Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion=\"entropy\")\n",
    "Random_Forest_model.fit(X_train_scaled, y_train)\n",
    "#Cross validation\n",
    "accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']\n",
    "print(accuracy)\n",
    "print(\"Test set accuracy with Random Forests and scaled data: {:.2f}\".format(Random_Forest_model.score(X_test_scaled,y_test)))\n",
    "\n",
    "\n",
    "import scikitplot as skplt\n",
    "y_pred = Random_Forest_model.predict(X_test_scaled)\n",
    "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
    "plt.show()\n",
    "y_probas = Random_Forest_model.predict_proba(X_test_scaled)\n",
    "skplt.metrics.plot_roc(y_test, y_probas)\n",
    "plt.show()\n",
    "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813c8183",
   "metadata": {},
   "source": [
    "Recall that the cumulative gains curve shows the percentage of the\n",
    "overall number of cases in a given category *gained* by targeting a\n",
    "percentage of the total number of cases.\n",
    "\n",
    "Similarly, the receiver operating characteristic curve, or ROC curve,\n",
    "displays the diagnostic ability of a binary classifier system as its\n",
    "discrimination threshold is varied. It plots the true positive rate against the false positive rate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0723ecc",
   "metadata": {},
   "source": [
    "## Compare  Bagging on Trees with Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ea6c0862",
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n",
    "    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "851aee9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9790209790209791"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n",
    "rnd_clf.fit(X_train, y_train)\n",
    "y_pred_rf = rnd_clf.predict(X_test)\n",
    "np.sum(y_pred == y_pred_rf) / len(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287af61b",
   "metadata": {},
   "source": [
    "## Boosting, a Bird's Eye View\n",
    "\n",
    "The basic idea is to combine weak classifiers in order to create a good\n",
    "classifier. With a weak classifier we often intend a classifier which\n",
    "produces results which are only slightly better than we would get by\n",
    "random guesses.\n",
    "\n",
    "This is done by applying in an iterative way a weak (or a standard\n",
    "classifier like decision trees) to modify the data. In each iteration\n",
    "we emphasize those observations which are misclassified by weighting\n",
    "them with a factor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fa95d1",
   "metadata": {},
   "source": [
    "## What is boosting? Additive Modelling/Iterative Fitting\n",
    "\n",
    "Boosting is a way of fitting an additive expansion in a set of\n",
    "elementary basis functions like for example some simple polynomials.\n",
    "Assume for example that we have a function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0843532",
   "metadata": {},
   "source": [
    "$$\n",
    "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0036ded",
   "metadata": {},
   "source": [
    "where $\\beta_m$ are the expansion parameters to be determined in a\n",
    "minimization process and $b(x;\\gamma_m)$ are some simple functions of\n",
    "the multivariable parameter $x$ which is characterized by the\n",
    "parameters $\\gamma_m$.\n",
    "\n",
    "As an example, consider the Sigmoid function we used in logistic\n",
    "regression. In that case, we can translate the function\n",
    "$b(x;\\gamma_m)$ into the Sigmoid function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b3db1f",
   "metadata": {},
   "source": [
    "$$\n",
    "\\sigma(t) = \\frac{1}{1+\\exp{(-t)}},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b53e923c",
   "metadata": {},
   "source": [
    "where $t=\\gamma_0+\\gamma_1 x$ and the parameters $\\gamma_0$ and\n",
    "$\\gamma_1$ were determined by the Logistic Regression fitting\n",
    "algorithm.\n",
    "\n",
    "As another example, consider the cost function we defined for linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe6aab6",
   "metadata": {},
   "source": [
    "$$\n",
    "C(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f7e570",
   "metadata": {},
   "source": [
    "In this case the function $f(x)$ was replaced by the design matrix\n",
    "$\\boldsymbol{X}$ and the unknown linear regression parameters $\\boldsymbol{\\beta}$,\n",
    "that is $\\boldsymbol{f}=\\boldsymbol{X}\\boldsymbol{\\beta}$. In linear regression we can \n",
    "simply invert a matrix and obtain the parameters $\\beta$ by"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5428ac",
   "metadata": {},
   "source": [
    "$$\n",
    "\\boldsymbol{\\beta}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a66005",
   "metadata": {},
   "source": [
    "In iterative fitting or additive modeling, we minimize the cost function with respect to the parameters $\\beta_m$ and $\\gamma_m$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819e870d",
   "metadata": {},
   "source": [
    "## Iterative Fitting, Regression and Squared-error Cost Function\n",
    "\n",
    "The way we proceed is as follows (here we specialize to the squared-error cost function)\n",
    "\n",
    "1. Establish a cost function, here $C(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f_M(x_i))^2$ with $f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m)$.\n",
    "\n",
    "2. Initialize with a guess $f_0(x)$. It could be one or even zero or some random numbers.\n",
    "\n",
    "3. For $m=1:M$\n",
    "\n",
    "a. minimize $\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2$ wrt $\\gamma$ and $\\beta$\n",
    "\n",
    "b. This gives the optimal values $\\beta_m$ and $\\gamma_m$\n",
    "\n",
    "c. Determine then the new values $f_m(x)=f_{m-1}(x) +\\beta_m b(x;\\gamma_m)$\n",
    "\n",
    "We could use any of the algorithms we have discussed till now. If we\n",
    "use trees, $\\gamma$ parameterizes the split variables and split points\n",
    "at the internal nodes, and the predictions at the terminal nodes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b40fd6",
   "metadata": {},
   "source": [
    "## Squared-Error Example and Iterative Fitting\n",
    "\n",
    "To better understand what happens, let us develop the steps for the iterative fitting using the above squared error function.\n",
    "\n",
    "For simplicity we assume also that our functions $b(x;\\gamma)=1+\\gamma x$. \n",
    "\n",
    "This means that for every iteration $m$, we need to optimize"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afdbae5d",
   "metadata": {},
   "source": [
    "$$\n",
    "(\\beta_m,\\gamma_m) = \\mathrm{argmin}_{\\beta,\\lambda}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2=\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta(1+\\gamma x_i))^2.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b5a9a94",
   "metadata": {},
   "source": [
    "We start our iteration by simply setting $f_0(x)=0$. \n",
    "Taking the derivatives  with respect to $\\beta$ and $\\gamma$ we obtain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071ce45d",
   "metadata": {},
   "source": [
    "$$\n",
    "\\frac{\\partial {\\cal C}}{\\partial \\beta} = -2\\sum_{i}(1+\\gamma x_i)(y_i-\\beta(1+\\gamma x_i))=0,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b64e74a0",
   "metadata": {},
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006f6950",
   "metadata": {},
   "source": [
    "$$\n",
    "\\frac{\\partial {\\cal C}}{\\partial \\gamma} =-2\\sum_{i}\\beta x_i(y_i-\\beta(1+\\gamma x_i))=0.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a6cb700",
   "metadata": {},
   "source": [
    "We can then rewrite these equations as (defining $\\boldsymbol{w}=\\boldsymbol{e}+\\gamma \\boldsymbol{x})$ with $\\boldsymbol{e}$ being the unit vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9792edc",
   "metadata": {},
   "source": [
    "$$\n",
    "\\gamma \\boldsymbol{w}^T(\\boldsymbol{y}-\\beta\\gamma \\boldsymbol{w})=0,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad4e3e77",
   "metadata": {},
   "source": [
    "which gives us $\\beta = \\boldsymbol{w}^T\\boldsymbol{y}/(\\boldsymbol{w}^T\\boldsymbol{w})$. Similarly we have"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4422c86",
   "metadata": {},
   "source": [
    "$$\n",
    "\\beta\\gamma \\boldsymbol{x}^T(\\boldsymbol{y}-\\beta(1+\\gamma \\boldsymbol{x}))=0,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9095127",
   "metadata": {},
   "source": [
    "which leads to $\\gamma =(\\boldsymbol{x}^T\\boldsymbol{y}-\\beta\\boldsymbol{x}^T\\boldsymbol{e})/(\\beta\\boldsymbol{x}^T\\boldsymbol{x})$.  Inserting\n",
    "for $\\beta$ gives us an equation for $\\gamma$. This is a non-linear equation in the unknown $\\gamma$ and has to be solved numerically. \n",
    "\n",
    "The solution to these two equations gives us in turn $\\beta_1$ and $\\gamma_1$ leading to the new expression for $f_1(x)$ as\n",
    "$f_1(x) = \\beta_1(1+\\gamma_1x)$. Doing this $M$ times results in our final estimate for the function $f$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7e4c3c3",
   "metadata": {},
   "source": [
    "## Iterative Fitting, Classification and AdaBoost\n",
    "\n",
    "Let us consider a binary classification problem with two outcomes $y_i \\in \\{-1,1\\}$ and $i=0,1,2,\\dots,n-1$ as our set of\n",
    "observations. We define a classification function $G(x)$ which produces a prediction taking one or the other of the two values \n",
    "$\\{-1,1\\}$.\n",
    "\n",
    "The error rate of the training sample is then"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e78fb3",
   "metadata": {},
   "source": [
    "$$\n",
    "\\mathrm{\\overline{err}}=\\frac{1}{n} \\sum_{i=0}^{n-1} I(y_i\\ne G(x_i)).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8efd60b4",
   "metadata": {},
   "source": [
    "The iterative procedure starts with defining a weak classifier whose\n",
    "error rate is barely better than random guessing.  The iterative\n",
    "procedure in boosting is to sequentially apply a  weak\n",
    "classification algorithm to repeatedly modified versions of the data\n",
    "producing a sequence of weak classifiers $G_m(x)$.\n",
    "\n",
    "Here we will express our  function $f(x)$ in terms of $G(x)$. That is"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df99945c",
   "metadata": {},
   "source": [
    "$$\n",
    "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc416763",
   "metadata": {},
   "source": [
    "will be a function of"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389d2723",
   "metadata": {},
   "source": [
    "$$\n",
    "G_M(x) = \\mathrm{sign} \\sum_{i=1}^M \\alpha_m G_m(x).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1eec60f",
   "metadata": {},
   "source": [
    "## Adaptive Boosting, AdaBoost\n",
    "\n",
    "In our iterative procedure we define thus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de59939",
   "metadata": {},
   "source": [
    "$$\n",
    "f_m(x) = f_{m-1}(x)+\\beta_mG_m(x).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ffc9c90",
   "metadata": {},
   "source": [
    "The simplest possible cost function which leads (also simple from a computational point of view) to the AdaBoost algorithm is the\n",
    "exponential cost/loss function defined as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b8ad6ea",
   "metadata": {},
   "source": [
    "$$\n",
    "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}\\exp{(-y_i(f_{m-1}(x_i)+\\beta G(x_i))}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290ecb27",
   "metadata": {},
   "source": [
    "We optimize $\\beta$ and $G$ for each value of $m=1:M$ as we did in the regression case.\n",
    "This is normally done in two steps. Let us however first rewrite the cost function as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b835bd84",
   "metadata": {},
   "source": [
    "$$\n",
    "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}w_i^{m}\\exp{(-y_i\\beta G(x_i))},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b38ce14f",
   "metadata": {},
   "source": [
    "where we have defined $w_i^m= \\exp{(-y_if_{m-1}(x_i))}$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37dc59e",
   "metadata": {},
   "source": [
    "## Building up AdaBoost\n",
    "\n",
    "First, for any $\\beta > 0$, we optimize $G$ by setting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ea786a",
   "metadata": {},
   "source": [
    "$$\n",
    "G_m(x) = \\mathrm{sign} \\sum_{i=0}^{n-1} w_i^m I(y_i \\ne G_(x_i)),\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a81aa0",
   "metadata": {},
   "source": [
    "which is the classifier that minimizes the weighted error rate in predicting $y$.\n",
    "\n",
    "We can do this by rewriting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75be4ffe",
   "metadata": {},
   "source": [
    "$$\n",
    "\\exp{-(\\beta)}\\sum_{y_i=G(x_i)}w_i^m+\\exp{(\\beta)}\\sum_{y_i\\ne G(x_i)}w_i^m,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f28a57",
   "metadata": {},
   "source": [
    "which can be rewritten as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b446ff3f",
   "metadata": {},
   "source": [
    "$$\n",
    "(\\exp{(\\beta)}-\\exp{-(\\beta)})\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i))+\\exp{(-\\beta)}\\sum_{i=0}^{n-1}w_i^m=0,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d10f84",
   "metadata": {},
   "source": [
    "which leads to"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda97977",
   "metadata": {},
   "source": [
    "$$\n",
    "\\beta_m = \\frac{1}{2}\\log{\\frac{1-\\mathrm{\\overline{err}}}{\\mathrm{\\overline{err}}}},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c96cfaa",
   "metadata": {},
   "source": [
    "where we have redefined the error as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "399edb04",
   "metadata": {},
   "source": [
    "$$\n",
    "\\mathrm{\\overline{err}}_m=\\frac{1}{n}\\frac{\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i)}{\\sum_{i=0}^{n-1}w_i^m},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20bfce2f",
   "metadata": {},
   "source": [
    "which leads to an update of"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "282c44ac",
   "metadata": {},
   "source": [
    "$$\n",
    "f_m(x) = f_{m-1}(x) +\\beta_m G_m(x).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a8ddb16",
   "metadata": {},
   "source": [
    "This leads to the new weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bf9bda1",
   "metadata": {},
   "source": [
    "$$\n",
    "w_i^{m+1} = w_i^m \\exp{(-y_i\\beta_m G_m(x_i))}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b681cd",
   "metadata": {},
   "source": [
    "## Adaptive boosting: AdaBoost, Basic Algorithm\n",
    "\n",
    "The algorithm here is rather straightforward. Assume that our weak\n",
    "classifier is a decision tree and we consider a binary set of outputs\n",
    "with $y_i \\in \\{-1,1\\}$ and $i=0,1,2,\\dots,n-1$ as our set of\n",
    "observations. Our design matrix is given in terms of the\n",
    "feature/predictor vectors\n",
    "$\\boldsymbol{X}=[\\boldsymbol{x}_0\\boldsymbol{x}_1\\dots\\boldsymbol{x}_{p-1}]$. Finally, we define also a\n",
    "classifier determined by our data via a function $G(x)$. This function tells us how well we are able to classify our outputs/targets $\\boldsymbol{y}$. \n",
    "\n",
    "We have already defined the misclassification error $\\mathrm{err}$ as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb4a855",
   "metadata": {},
   "source": [
    "$$\n",
    "\\mathrm{err}=\\frac{1}{n}\\sum_{i=0}^{n-1}I(y_i\\ne G(x_i)),\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "238ee6db",
   "metadata": {},
   "source": [
    "where the function $I()$ is one if we misclassify and zero if we classify correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd0b8fe3",
   "metadata": {},
   "source": [
    "## Basic Steps of AdaBoost\n",
    "\n",
    "With the above definitions we are now ready to set up the algorithm for AdaBoost.\n",
    "The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.\n",
    "1. We start by initializing all weights to $w_i = 1/n$, with $i=0,1,2,\\dots n-1$. It is easy to see that we must have $\\sum_{i=0}^{n-1}w_i = 1$.\n",
    "\n",
    "2. We rewrite the misclassification error as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ae4017",
   "metadata": {},
   "source": [
    "$$\n",
    "\\mathrm{\\overline{err}}_m=\\frac{\\sum_{i=0}^{n-1}w_i^m I(y_i\\ne G(x_i))}{\\sum_{i=0}^{n-1}w_i},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a963b267",
   "metadata": {},
   "source": [
    "1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n",
    "\n",
    "a. Fit then a given classifier to the training set using the weights $w_i$.\n",
    "\n",
    "b. Compute then $\\mathrm{err}$ and figure out which events are classified properly and which are classified wrongly.\n",
    "\n",
    "c. Define a quantity $\\alpha_{m} = \\log{(1-\\mathrm{\\overline{err}}_m)/\\mathrm{\\overline{err}}_m}$\n",
    "\n",
    "d. Set the new weights to $w_i = w_i\\times \\exp{(\\alpha_m I(y_i\\ne G(x_i)}$.\n",
    "\n",
    "5. Compute the new classifier $G(x)= \\sum_{i=0}^{n-1}\\alpha_m I(y_i\\ne G(x_i)$.\n",
    "\n",
    "For the iterations with $m \\le 2$ the weights are modified\n",
    "individually at each steps. The observations which were misclassified\n",
    "at iteration $m-1$ have a weight which is larger than those which were\n",
    "classified properly. As this proceeds, the observations which were\n",
    "difficult to classifiy correctly are given a larger influence. Each\n",
    "new classification step $m$ is then forced to concentrate on those\n",
    "observations that are missed in the previous iterations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22277568",
   "metadata": {},
   "source": [
    "## AdaBoost Examples\n",
    "\n",
    "Using **Scikit-Learn** it is easy to apply the adaptive boosting algorithm, as done here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2c8adda4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada_clf = AdaBoostClassifier(\n",
    "    DecisionTreeClassifier(max_depth=1), n_estimators=200,\n",
    "    algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42)\n",
    "ada_clf.fit(X_train, y_train)\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada_clf = AdaBoostClassifier(\n",
    "    DecisionTreeClassifier(max_depth=1), n_estimators=200,\n",
    "    algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42)\n",
    "ada_clf.fit(X_train_scaled, y_train)\n",
    "y_pred = ada_clf.predict(X_test_scaled)\n",
    "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
    "plt.show()\n",
    "y_probas = ada_clf.predict_proba(X_test_scaled)\n",
    "skplt.metrics.plot_roc(y_test, y_probas)\n",
    "plt.show()\n",
    "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}