04_2-LogisticRegression.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Boundaries"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s use the iris dataset to illustrate Logistic Regression. This is a famous\n",
    "dataset that contains the sepal and petal length and width of 150 iris flowers\n",
    "of three different species: Iris setosa, Iris versicolor, and Iris virginica"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s try to build a classifier to detect the Iris virginica type based only on\n",
    "the petal width feature. First let’s load the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data',\n",
       " 'target',\n",
       " 'frame',\n",
       " 'target_names',\n",
       " 'DESCR',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'data_module']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "list(iris.keys())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[\"data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[\"target_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[\"target\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 for setosa, 1 for versicolor and 2 for virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[\"feature_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris[\"data\"][:, 3:] # extracting petal width\n",
    "y = iris[\"target\"] == 2 # gives array of False and True\n",
    "y = y.astype(np.int) # converting true false to, 1 and 0. 1 for virginica and 0 for all other"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let’s train a Logistic Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X,y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s look at the model’s estimated probabilities for flowers with petal\n",
    "widths varying from 0 cm to 3 cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.linspace(0,3,1000).reshape(-1,1) # Numpy's reshape functions allows one dimension to be -1, which means \"unspecified\", whose value will be inferred from the length of the array and the remaining dimensions\n",
    "y_proba = log_reg.predict_proba(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.99250016e-01, 7.49984089e-04],\n",
       "       [9.99240201e-01, 7.59799387e-04],\n",
       "       [9.99230257e-01, 7.69743043e-04],\n",
       "       ...,\n",
       "       [3.08374822e-03, 9.96916252e-01],\n",
       "       [3.04400296e-03, 9.96955997e-01],\n",
       "       [3.00476842e-03, 9.96995232e-01]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_new, y_proba[:,1], \"g-\", label=\"Iris Virginica\")\n",
    "plt.plot(X_new, y_proba[:,0], \"b--\", label=\"Not Iris Virginica\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More fancier graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/lib/python3.10/site-packages/matplotlib/patches.py:1475: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  self.verts = np.dot(coords, M) + [\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decision_boundary = X_new[y_proba[:,1]>=0.5][0]\n",
    "\n",
    "plt.figure(figsize=(8,3))\n",
    "plt.plot(X[y==0], y[y==0], \"bs\")\n",
    "plt.plot(X[y==1], y[y==1], \"g^\")\n",
    "plt.plot([decision_boundary,decision_boundary], [-1,2], \"k:\", linewidth=2)\n",
    "plt.plot(X_new, y_proba[:,1], \"g-\", label=\"Iris Virginica\")\n",
    "plt.plot(X_new, y_proba[:,0], \"b--\", label=\"Not Iris Virginica\")\n",
    "plt.text(decision_boundary+0.02, 0.15, \"Decision Boundary\", fontsize=14, color=\"k\", ha=\"center\")\n",
    "plt.arrow(decision_boundary, 0.08, -0.3, 0, head_width=0.05, head_length=0.1, fc='b', ec='b')\n",
    "plt.arrow(decision_boundary, 0.92, 0.3, 0, head_width=0.05, head_length=0.1, fc='g', ec='g')\n",
    "plt.xlabel(\"Petal width (cm)\", fontsize=14)\n",
    "plt.ylabel(\"Probability\", fontsize=14)\n",
    "plt.legend(loc='center left', fontsize=14)\n",
    "plt.axis([0,3, -0.02, 1.02])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The petal width of Iris virginica flowers (represented by triangles) ranges\n",
    "from 1.4 cm to 2.5 cm, while the other iris flowers (represented by squares)\n",
    "generally have a smaller petal width, ranging from 0.1 cm to 1.8 cm. Notice\n",
    "that there is a bit of overlap. Above about 2 cm the classifier is highly\n",
    "confident that the flower is an Iris virginica (it outputs a high probability for\n",
    "that class), while below 1 cm it is highly confident that it is not an Iris\n",
    "virginica (high probability for the “Not Iris virginica” class). if we ask it to predict the\n",
    "class (using the predict() method rather than the predict_proba()\n",
    "method), it will return whichever class is the most likely. Therefore, there is\n",
    "a decision boundary at around 1.6 cm where both probabilities are equal to\n",
    "50%: if the petal width is higher than 1.6 cm, the classifier will predict that\n",
    "the flower is an Iris virginica, and otherwise it will predict that it is not\n",
    "(even if it is not very confident)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.66066066])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.predict([[1.7],[1.5]])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below graph shows the same dataset, but this time displaying two features:\n",
    "petal width and length. Once trained, the Logistic Regression classifier can,\n",
    "based on these two features, estimate the probability that a new flower is an\n",
    "Iris virginica. The dashed line represents the points where the model\n",
    "estimates a 50% probability: this is the model’s decision boundary. Note thatit is a linear boundary.16 Each parallel line represents the points where the\n",
    "model outputs a specific probability, from 15% (bottom left) to 90% (top\n",
    "right). All the flowers beyond the top-right line have an over 90% chance of\n",
    "being Iris virginica, according to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3W90r2+MEAiS8/4KgvlOR8Oo9rFo3gvhXGMI2HeDf/yNNMdfuIHj8QiS8/Yx8K2eg0E53lPXYBZtrDxHqqpmyMs7EBPYr9iWX7U3qiagrpzRy7KxgZmGN7pP/hwadVqBgyfowMbUAE4ul76eUpgC/a7h5YjzCgt+iQaeVaNZ7G1oPOYY3D/biznlXbYSvNfiyvROok78DX7b3uAOCY/21HRZBEARh5JA4EWolt7053L1qITosCXtnv0Wwf6zcdsGfY2Gb3wym5gK0GMRPle1YPxem7v0LJ1Z+woXN+vmhKYedNTpf0315EuTMgaSgENj064hC57eisPd+mJaWnVI78fM3CPPnAWduBptBXQEAFvWrw37vcvxeuRNhmw+m6ffnz58qj5UTCmXlaXIvnZYnoZAXT0lyP/WEERIifn2GZc78EJqYo0KdQQCAgiXqo3n/vXhybSWe/bdZMwHrCGYCc7hWO446+TtgQJlFsLMsqu2QCIIgCCOHSvUIjfD6XhgWdXmCtffrwq5o8jpPkln3xGKGLy+jsHO6H0IC4rDxSX2YmgkgEjHcPfMDPz7HotvUElo8g+wRExyJM04b8fPFd1gVtEGXGxN0rmwvKeQXTPLnASA77owlJYEzMQETi5Hw8h1Cp69AUsB3FH1yBpyZGZhIhKgzXkj6/BW5pw6T9rd//36MGzcOFy9eRMOGDVUeLxOJ+LK9cwcBExMUWHME1q26qfw46kZSrsfEYvwMeon/nZmOqLAA9J7xBEITM4jFInx8dgYRvz6jerOp2g5X44iZGAKOnvERBEEQmYPGOKUDiZPukxAngpmFEEmJYoSHJCBvIX6sR8rpyz+/iMSiLk9Qu31+jFpTQZvhqpzYEH7Mk1SevMcjdzl7bYeVBkkpmSgoBCaF+PiYSAROKATAT1/+vYszrNo3Rf418kvI9u/fj0GDBoExhpw5c8LDw0ON8jQYkecO6JU8MbEY0ZFByGlbCAAgFosgEPDX92fgC1zc2QUlKrZHo65rtBkmQRAEQegtNMaJ0GtMzfnb7XdQPHbO8MOdMz8A8NOXS/y9eCVrFHXMiQ9PImAgTi/FMn+KMU/fI3S2bI8TCJAUGIzQGSsRdeYqv00olP48zCuVhZljacQ/eSX3Z3Tt2jWpNAFAVFQU2rRpg9u3b6s+VqEQ9sv3wLpjfyApCUGTeyPyykmVH0fVRIcH4s7ZGfjw7AwAQCBIvr55C1VCHntHhHx9YnC/AwRBEAShz5A4ERpDUvoVHyvGW59wvLkXhrgYkfS9xAQ+02FmKcC3tzGICkuCWGxYHxyl8vRXIV6emqzHb78f2g4rDSwmFnE+zxF3zxfiGH5cGsdxYAkJ/L8tzZHw9jPEYREykx0AQKNGjdC+fXuZbVFRUWjdujXJ0x8SE2Pww98HQZ/vITEhBgB/fUVJ/PU1MbNEWPBbxMeGpbm+BEEQBEFoBxInQuM4lLVCL5eSuLDJH/8dD0JcdBIAwNRMgA++EfC5GIIarfPBOrcpBAJOy9GqHsv81uhyzTlZnppu0Dl5MitbAnlcRiFs0yFEHfeAOPrPh3szM8T7vkL0xZuwat0Iwty2aSY7MDMzw4kTJ9CxY0eZ7dHR0ZqTp0m9dFqectuVRc0WLnj+3ya8f3IcifHRAAChiRlCvvri88uLKFqhNSxy5FY4mQRBEARBEJrFRNsBEMZJi8EOCP0Wjy3jX+Htg3A4lLdCYrwYRxZ/QC57czTtx4/9SDlJgSEhkafTTpvw83kgTjfdoHNjnmwGd0XStx8IGb8IcQ+ewax8SbD4RPxavBkm9nlh3a8DAPk/IzMzMxw/fhw9evTAuXPnpNsl8nT58mWVj3mSyBM4DpFn9yNoUi9g7VGdHfNUoc5gRIV9w80T4/HjywPkti8PUVI8fDwXI4e1PcrV7AfAcH8HCIIgCELfoMkhCK1ydsMX3DryHW/uh4PjgKpOedFmpAMadC2g7dA0QmxIpFSechSwQdcbuiVPABC2YR8ij1xC3P2nAMchh1M92I7siZxdW2W4b0JCQhp5AgArKyt4eHigUaNGKo+XiUT4MWsIIs/uB4RCfsKI1t1VfhxV8fTmBrx7fARBX+6DAweHsk6o2GAkSlfpqu3QDBaaqY8gCMLwoVn10oHESX/5FRSPsB/xECUxFC5rhRzWfCLUWJ60x4ZG8bPt/ZGnLt7jkae8bslTUlAIRD9CwZJEMCtbHALrnACU+xklJCSgZ8+eOHv2rMx2tcuTy1BEntnHy9M/h2HdpofKj6MqoiOCEBP5A0yUhFx2ZWFmYQ3AeH4HNM2vuO+wNcsPoYCKLgiCIAwVEqd0IHHSX+R9ODS2D4y6Lk/Z/RnphDzpcOaJfgfUj0ichBvfD+Pqtz34HPUcBS1LoXyuuhhVgaZ8JwiCMES0Ph25u7s7atWqBWtra9jZ2aFz587w8/NLd58bN26A47g0rzdv3si0O3nyJBwdHWFubg5HR0ecPn0682dD6CXyPhwa2wdGy3w5+dn2KhdCTBA/YcSvN7ozYUR2f0ZmZmY4duwYOnfuLLM9Ojoabdq0wX///ZfdENPACYWwd98F684DAZGIn23P47jKj6MK6HdAvcSLYrH0aU+sfzkSUUm/0anYRFTK0wg+IZew6AmVRBKA10cvOG5yhNdHL22HojZUeY7GcL0IQh6ZEqebN2/C2dkZ9+7dw9WrV5GUlISWLVsiOjo6w339/Pzw/ft36atMmTLS9+7evYtevXphwIABePr0KQYMGICePXvi/v37mT8jgtBTLPPlRJdruitP2cXMzAxHjx5VKE/fvn1T+THTyNOUPjorT4R6iE6KwNT7DeAXdh9dik/BxvqP0afUbAwrtwKTKu2AX9h9/C/olLbDJLQIYwyu11zxOvQ1XK+5GuT6aao8R2O4XgShiEyJ0+XLlzF48GBUrFgRVapUwe7du+Hv749Hjx5luK+dnR0KFCggfQmFQul7a9euRYsWLeDi4oLy5cvDxcUFTk5OWLt2baZPiCD0GYk85atSmJenJusNUp66dOkis33+/PkoXLiwWo4placug5Ll6dIxtRyL0C2ikyIw26clohJ/o08pNwwuuwQAkCjm18sqZVMNAIeQuAAtRkloG88PnvAJ9AEA+AT6wPODp5YjUj2qPEdjuF4EoYhsTSsUHh4OAMiTJ0+GbatVq4aCBQvCyckJ3t7eMu/dvXsXLVu2lNnWqlUr3LlzR2F/8fHxiIiIkHkRhCFgmS8nOns58/L0I9Ig5enIkSNSeVq5ciWmTZum1mNyQiHsl+5MlqepfUmeDBwRE2HvW1f4R71E9xLT0a7oaOl2U4EZACAg+g2ik8KQw4TGxRorjDG4ebtByPEPc4WcEG7ebgaVRVHlORrD9SKI9MiyODHGMGXKFDRs2BCVKlVS2K5gwYLYtm0bTp48iVOnTqFcuXJwcnLCrVu3pG2CgoJgby87EN7e3h5BQUEK+3V3d4etra30VaRIkayeCkHoHHLl6bXi3wd9Q5J5On36tNqlSYJUnroOJnkyAn7EfILXt71o5TAc7YuOBcBPQy75wCdiItz8fgQJolgUt/5Lm6ESWkSSPRExEQD+vjC0LIoqz9EYrhdBpEeWxWncuHF49uwZDh8+nG67cuXKYcSIEahevTrq1auHzZs3o127dli1apVMu8zOKOXi4oLw8HDpKyCASi0Iw8IyX050lpTt/YjkxzwZkDyZmpqmGe+kbjihEPZLdsjK08WjGo2B0AzPf98EA0PHYuMBAEniRJm1mzwCtuHM5zXoVcoVZW1raitMQoukzp5IMKQsiirP0RiuF0FkRJbEafz48Th37hy8vb3h4OCQ6f3r1q2Ld+/eSb8vUKBAmuxScHBwmixUSszNzWFjYyPzIghDwzKvVVp5evVd22FphMePH+PmzZsq71ciTzbdhvDyNK0fyZMBYmuWHzlNcyNeFAMAMBGYSj/Ynf68FptfOaNhge5oWXioNsMktEjq7IkEQ8qiqPIcjeF6EURGZEqcGGMYN24cTp06hevXr6NEiRJZOuiTJ09QsGBB6ff16tXD1atXZdp4enqifv36WeqfIAyJNPLUbKPBy9Pjx4/RvHlztG3bFjdu3FB5/5xQCDuSJ4OmoGUpiFgSHoVeQXQiPx73U+QzbHrljO1vpuDvgr3Rq6QrCuTI2t8xQr+RZE8ECj4GCSDQ+yyKKs/RGK4XQShDppZNd3Z2xqFDh3D27FlYW1tLs0S2trawtLQEwJfQffv2Dfv27QPAz5hXvHhxVKxYEQkJCThw4ABOnjyJkydPSvudOHEiGjdujOXLl6NTp044e/YsvLy8cPv2bVWdJ0HoNRJ5OtN8I0J9v+FU043o6j0OeRwLZryzniGRpt+/fwMA2rVrh4sXL6JJkyYqPQ4nEMBuyQ4AQMTJ3Qia1g8AYN2ul0qPQ2iHYtYVMbzcKmx4OQr3gs8hOikMYfE/ECuKQt9Sc9GsUH8UsioNQLY0XMREaUqRCMMjQZQA/3B/iCGW+74YYgREBCBBlABzE3MNR6caVHmOxnC9CEIZOJaJxwOKxhzt3r0bgwcPBgAMHjwYnz9/lj4lXrFiBbZt24Zv377B0tISFStWhIuLC9q2bSvTx4kTJzBnzhx8/PgRpUqVwpIlS9C1q/ILE6pjdWBCv/jxOQZ5C1vAxDRbk0XqNLE/o6XylMPeGl2u6488saQkJAV8h2mJ9CdyGTZsGHbt2iWzLUeOHGqRJwBgYjGCZw9HxMndgECAAqsP6a08hYW8R678pbUdhk7xKNQTL3//h+8xH5DbrABaFxmBwjnKQCjgnxumlCbJv0VMhHvB5xCeEIKCOUqhWM6KyGNeQJunQaiBgPAAhMSEKHzfzsoODjaZH46gS6jyHI3hehGGhTrcIFPipMuQOBk3/q+jMKvpAzg2zI1Zh6sYjTxZ2lnrReaJJSUhqN9UxHrfR2Hv/TCvWEZh28TERPTu3RunTskuSmppaYlLly5pRp5WHYR1+94qP446+fLqMi7u6IwazWehTtv52g5HJxEzscwEESm/l2Savka/xVLfHvgc+RwAYGdZDGYCC8yvcQGFcpTSStwEQRBE5lGHGxjup0vCqAj+EovI34n438kfWNbnKZIS5ZcTGAKWea3Q2Wsc8ldzQGxwJE413YifL3V7zJM4KgaJ775AFPIL35oOQPzLdwrbmpqa4siRI2kyzrGxsWjbtm2adeBUgaRsz6bbEEAs5sc8XTii8uOok18/XkOUFI8Hlxfg/qX52g5HJxFwAnyN8sPnyBcAAA7JVRRCToiw+GDMedgK0YnhGFZuJQ43C8bMyodQwroyZvu0QGTib22FThAEQegAJE6EQVCzdX64na4OEzPOiOTJWSpPp5vptjwJc9mgsNcemFevmCxPL94qbC+Rp27duslsj42NRbt27dQrT92HppCn9Jdb0CWqNZ2MBp35ZR5InuQTFh+MtS+HY+z/KuNrlJ9MiZ6IiXDowyL8jAvEgDIL0a7oGNia5UPZXLXRu9QciJkId36cyuAIBEEQhCFD4kQYDLXb5ofbqWrJ8tTbsOXJIo+eyVOeXCh8dTfMqzny8tRsYIbydPjwYc3L0+LtKeSpv17JU/VmU9Gg00oAvDzduzSPZrlKQS5zO5SwroxqeVvAzrKYdDvHcRByQnyPeY/i1pXQvPBAWAhzIEmcCCEnRNGcjogTReNH7BctRk8QBEFoGxInwqCo3c4uOfN0ygjlqekG3ZenlJknJeWpe/fuMtsl8nT9+nWVx6j38uQ0TZp58rm8EPc95pM8pcDZcRMW1rwEM6GFzPa4pGgwMOQx58cLJojiYCIwBQD4hFxEZOIvFKQxTgRBEEYNiRNhcNRum9945SkkSj/k6eruTMnToUOH5MpT+/bt1SxPw/RTnppNJXlKB8l043FJ0XgTdh9xohhYmFjBqdBA+IRcwrOfN6RidS/4PLa9mQIzgQWKWJXXZtgEQRCEliFxIgyS2m3zY+4ZI5Sn6nokT5kc86QdedoGmx7DpfIUcf6Qyo+jLtLIE5XtpeFH3Bec/bIOvj+vgTGGpoX6olOxiZj7qC3mP+qIBY87YeHjTgiK+Yi+peeifK46cvvx+ugFx02O8PropeEzMG5W/G8FzBebY8X/Vmg7FIIgjAQSJ8JgqdVGVp7cez1FYoKBy9NVPZKn3LbJ8hT6m5en534K20vkqUePHjLbY2NjsXfvXrXEyAkEsFu0VZp5+jF9gN7JU8POqwEAPlcWkTyloohVefyK/45zX9bjR+xnAMCoCmswr/o52Jjlxf3g8wCA7iVmoEeJmdL9/KNew/fnddz5cQbvwx/D9ZorXoe+hus1V7q+GkIsFmPBzQVIECVgwc0FEIsN9/92giB0BxInwqCRyJOpuQB3TvOZJ6OTpxeB2g5LIVJ5qlGJl6dmAzOUp4MHD8rIU48ePbBjxw61xZg68/Rj+gBEnDuotuOpmmrNpqBhl38ASORpLn24/4OAE2ButTP4HvMR61+OxJnP6xAY8wGfIp/hWzSfAe1Xeh56lpwFjuPwKz4Ih94vwqS7teH2sDWWP+0DV5/WCBf6AAB8An3g+cFTm6dkNLjfdkdMYgwAICYxBu633bUcEUEQxgAtgEsYBQ8vh2Bh5ydIjBejfhd7zDpSBaZmhvvcIO53DM4034iQx19hmT8nulwfh7yVCmk7LIWIfofjW4shiH/0AsJ8uVH4+j6Y/1VOYfvExET069cPAHDw4EGYmpqqPUYmFiN47mhEHNsOCASwX7EPNh37qf24quKJ9xrcPj0FAFCr1RzUabtQOh23sfM7/ge2vp4Ev/D7+J3wA6acGQpblUW3EtPRqABfHvohwhf7382FT+gltCw8FA3su8LBqhy6n3aChe0nvP8KfA0WonrB6rg//D5dWzUiFothvcxaKk4AkMM0ByJnRUIgMNz/1wmCyBzqcAMSJ8JoSClP9TrbweVoVZInHSKNPF3bC/PKigfjJyYmAoBGpElCGnlavhc2nfpr7PjZJaU81Ww5G3XbLaIP+H9IEMUhXhyLwOh3sLMsBhOBGaxNcwMAPkU+x+ZX4/Ah4gnGVFiPevadkdM0F668v4LWB1ujfFHAOgfw6C0gFgOX+11Gq9KttHxGhsuSW0swx3tOmu2Lmy7G7MaztRARQRC6iDrcwHA/NRJEKmq2zo+5Z6rB1FyAu2eC4d7L17DL9nLnQGevcbCrUQSxIVE4pQ9le1d3w7zmn7I9p0GIf/ZGYXtTU9N0pUkdz4Q4gQB2C/+FTc8RfNnezEGIOHtA5cdRF9WaTpaW7T30XIJ7F92obO8PZkILWJvmRrlctZHb3B5WJrbS9/a8dcXnqOeYUeUgmhcehJymucAYg5v3bAg5IcQMyGEBgPEz9rl503VVF2KxGEtvL5X73tLbS2msE0EQaoXEiTAqjFGeOl11hl2NIogLjcapphsQ+lzH5clTeXlSRGhoKOrVq4dr166pPEapPPUaqbfy1KjLGgAkT+kh4Pg/j4c/LMHDkEsYXm4VauVrI83QXX7vAZ/ARxAxEfLaADFxgJgBIiaisU5qJOXYptTQWCfjYI/vHnALOOzx3aPtUNBkTxNwC9Sbtf8c9hncAg6DzwzOVj+6dN30GRInwuio2To/5p01Xnk63UwP5OnqnizLU2hoKJo1a4b79++jffv26pOnBVv0Vp6qNp2USp7mkDzJQSROwvNfN1ApT2PUzt8OQoHJn+0izLsxH0KOQ9kigJUl8P1n8n4CCCjrpAbSyzZJoKyT/iARgtYHWms1Dr9QP3ALOFTYVCHDtlOvTAW3gMM/d//RQGSELkLiRBglNVqlkqeeRiJPNYvqhzzlspGRp6/NBiolT79//4aTkxOeP38OAIiLi0P79u3h5aX69XUMS56WkjzJISYpAv5Rr+CYqwFym9sD4GUqSZyEgIgvKGLP4JAfCIsEgn4l7yeGGAERAUgQJWgpcsMkKiEKcYlx6baJS4pDVEKUhiIitEGX8l3w2vk1upTvopL+yuUrhwZFGuBN6BvcDbirsF2iKBEHnh+AqcAUAyoPAADs67IPr51fqyQORRS2LozXzq/h7pS9bKqqr5uxYqLtAAhCW0jkaUGnJ7h7lpcnl2OGO2GERe4c6OQ5FmdbbkbwQ3+cbrYBXa6NQ77KhbUdmlwk8vStxWDEP3yBr80GwuHaXphXUfxU0NraGhUqVMCzZ8+k2+Li4tChQwecP38ezZs3V2mMEnkCgIij2/BjxkAA0JsJI6o2nQRwHP47NQkPPfkn+XXbLaYJI/5gIjBDkZwVECuKgoiJIOSEEApMEJMUgdkt++Dq9+3IY+aA4dW2wfJva4iZWFriZ2dlB3MTcy2fgWFhY2GDO8Pu4P3v9wrblM1TFjYWNEGUIWNrYQtbC9uMG2aCYdWG4X8B/8OuJ7tQr0g9uW0uvL2A4OhgdKvQDfmt8gMAitoWVWkc8jAVmqJ8PsUTJSmLOq6bUcIMhPDwcAaAhYeHazsUQs94eDmYdTC/wlrDg83v+IglxIu0HZJaifsdzY7UXMnWYzzblm8WC3n6VdshpUvS73D2pVZX9hZl2Pu8tVic76t02ycmJrJevXoxADIvCwsLdvXqVbXEKBaJWJDbKPa2DNjbshwLP71PLcdRF0+817L148HWjwf73zkXJhaLtR2SznDm83rWxdOaeX3dx179vsue/rzBpt/7m3XxzMlm3m/K4pNiGWOMicSG/f8GQaiaT78/McwHa7W/lcz2QacHMcwH+/DrA/vnzj/McZMjM1tkxgadHsQYY2z3k90M88F2P9kts9+jwEes29FurMg/RZjZIjNmt9KO1d1Rl7n/555hLFHxUcx6qTWzXmrNohOi5bZpf6g9w3ywS28vSbf9vftvhvmyH6VTxnfB7wJruKshy7k0Jyu2ppjMufc83pPlXpabWS2xYo13N2Y3P99k87znMcwH8/7kneY6Sc4/9bETRYls4Y2FrPja4sxskRkrs74M2/RgU5r4FV03xhj7+OsjG3V+lLSP/Cvys793/y3TNj4pnq2/t5613N+SOfzjIG3X5UgX9jjwseKLq0XU4QaG+WidIDJBjVb5Me8cv0juvXPBWNrDsMv2zHPlQOerY5PL9pw2IvTZN22HpRBhLht+wohaf0H8MwxfnQYh/qni0ggTExMcOHAAvXr1ktkuyTxdvXpV5TFyAgHs5m+GTe9RAGN82d6Z/So/jrqo2mQiGndbBwB4dNUddy/MprK9P3QqNh6di0/Egffz4OLjhFkPmiIy8Rc6FZuIZbWvw0xoARETSTNNBEGohvEe47H4v8WoUbAGJtWZhMr2lRW29Q3yRf2d9eHx3gMNizbElLpT0LV8V5gKTLH98fYMj2VlZoVeFXshMiESJ16dSPN+UFQQLr+/DAcbB7Qs1VKp+I+/Oo7ORzsjX458GFtzLNqWaQsA+BbxDfV31sexl8dQr0g9TKgzAfly5EPL/S1x/9t9pfpOSZ+TfbD98Xa0LNkSw6oNw6/YX3C+5IztjzI+bwC4G3AX1bZWw7ZH21A+X3n+2lXoitikWKy7v07a7lfsL0y6MgnxSfFoW7otJtedjCbFm+DSu0uov6s+fL75ZDp2fYRK9QgCQI2W+TDvXHUs6PhYKk+uxw23bM8815+yvVabEezjj9PNNqLLdR0v2/PcjW8thyDe5zm+Og1Kt2xPIk8cx+HIkSPS7XFxcejYsSPOnTuHFi1aqDRGiTwBQMSRrfgxcxDAGGy6DFTpcdRFlb8nAABunZyIR1fdAcZQr8NSKtsDMLDMIjQq0APhCaHgwMHBqhzyWvBroomZGEJOqOUICcLwePbjGZ6MeqJUOdz+p/sRL4rH2R5n0bFcR5n3fsb8VLCXLMOqD8OOJzuw23c3BlaR/X9739N9SBInYXCVwRAKlPt993jnAc8BnmheUrZEfNa1Wfge9R0rW6zEtPrTpNv3+O7BkLNDlOo7JQHhAXgx9gVszPkS1Yl1JqLSlkpYfXc1RtQYke6+8Unx6HWCF8ZL/S6hdWnZiTq+RnyV/ju3RW74T/JHYRvZzwkvg1+i7s66cL3uiqsDVP9gUtcwzE+FBJEFJPJkZmEcmSeL3DnQ2fNP5ulnNE4305PMU+3KSmee9u/fj969e8tsl8iTOjNPtn1G85mnWYMRcXqfyo+jLqr8PSE58+S1jDJPKShhXRlV8zZDlbxNpdLEGKNME0Goien1p2d6DJGliWWabXlz5FVq37oOdeGY3xE3P9/Ex98fZd7b7bsbHDgMqaa82HQu3zmNNMUnxeP4y+Owt7LHhDoTZN4bVGVQlsYyuTu5S6UJSJ7swu+nHyLjI9Pd95zfOQREBKB/5f5ppAkAHGwcpP82NzFPI00AUNGuIpoWb4pbX24hUZSY6fj1DfofnyBSUKNlPsw9azzyJFO290eeQp7quDxd2ZUsT82yJ0+enqpfa4cTCJB/3iY9l6f1AKhsLyMoG0cQ6qN24dpKt+3u2B0CToDORztjyNkhOPT8EPzD/TN9zKFVh4KByax1dCfgDt6EvkGT4k1QMndJpfuSF7/fTz/Ei+JRs1BNmAnNZN7jOA71HORPTJEe1QtWT7NNIjxhcWHp7vvg2wMAQMuSypUf+gb5ou/Jvii6pijMFpmBW8CBW8Dh/NvzSBAlIDQmNHPB6yEkTgSRitSZpyXdnyAh3gjkqRYvT2ec9ECeJJmnX3/kyfeVwvbpyVOnTp3UK099x+ipPI2XlafzriRPBEFoFHsre6Xb1itSD9cHXkejoo1w+Plh9DvVD8XWFkPNbTXh/clb6X4GVhkIU4Ep9j7dCzHj/+7verILAD/zXnbjj4iPAADprHzK7JMR8mbKM5GsOcdE6e4rESt5maTU3Am4g7o76uLU61OoWqAqxtcej7mN52Le3/NQxb4KACBeFJ/J6PUPEieCkEP1FsnydP98CJb2MAJ58hwL+9rF9EOebK1l5clpsFLy1KdPH5ntkszTzZs3VR4jJxAg/9yNsvJ0aq/Kj6MuZOTJaxnunHcheSIIQmNkNqP7d/G/cbn/Zfye+Rveg7wxpe4UvAx5iXaH2uHDrw9K9ZHfKj86lOsA/3B/XPt4DTGJMTj28hhyWeRC1wpdsx2/pKQuJDpE7j4/on9k6hjZJZdFLgD8hBUZseS/JYgXxePawGs41+ccVrdajQVNF2B+k/kokLOAmiPVHUicCEIBqeXJGDJPna6M0Z/Mkxx5inuSvjzt27cvjTyVLl0aFSpkvGJ8VkgjTy5DEHFqj1qOpQ6q/D0ejbtvAAA89lpO8qQAr49ecNzkCK+P2VtoWVX96HJfuoquniPFlXksTS3RpHgTrG61Gq4NXRGbFJupOCWZpd2+u3H85XFEJkSib6W+sDRNO34qs5TLWw7mQnM8+v4ozQLZjDHc+3ov28fIDJJyQs+PGVdefPj1AXks86BB0QYy22MSY/D4+2OVxaTL9xZA4kQQ6VK9RT7MP8/L04MLxiFPna86J2eemm3QC3myqFMF4l9h+NY8c/JUsWJFXL9+HXZ2dmqLMU3ZnstQ/ZKnxuNk5encLJKnFDDG4HrNFa9DX8P1WtZLGlXVjy73pavo6jlSXMrz35f/pGVwKZFkcDIjPa1KtUJh68I4/eY0Njzg/+8bVj1zZXqKMDcxR3fH7giKCsL6++tl3tv3dB9ehyoes6sOOpbrCAcbBxx4dgBX3l9J837KTFSxXMXwO/Y3Xga/lG4TiUWY5jkNITHyM2iZRRfvrdSQOBFEBlRrnkqeuhm4PNlaotOVMbw8/Yrh5cn3a8Y7agmhrTUKXdmVLE9Og5SSJ1dXV7VLkwSO4/Renv7uvhEA8PjaCpKnFHh+8IRPIL9+iU+gDzw/ZG3MnKr60eW+dBVdPUeKS3lW312NgqsLov2h9pjgMQEzrs5A833NsclnE0rnKY0u5bso3ZdQIMTgqoMRlxSHR98foWqBqnInYMgq7k7usLeyx/Sr09HuUDvMvjYb3Y91x6gLo6Qz22lqtk5zE3Mc634MOc1yos3BNmh7sC1cvFzgfNEZDXY1QPvD7aVtx9ceDwaGhrsbYtT5UZjoMRHVt1XHsZfH0KR4E5XEo4v3VmpInAhCCWTk6aIRyNOfdZ6k8uS0UX/k6Xe4UpmnJUuWaESaJMiTp/CTuzV2/OxSubEzyVMqGGNw83aTruMk5IRw83bL9HVRVT+63JeuoqvnSHFljjE1x6C7Y3e8//Uee3z3YMvDLfge9R1zGs3BvWH3YG1unan+hlYbCg78GKWhVYeqNNYitkVwd9hd9HDsgf/5/w9r769FcHQwPAd4onTu0gAgM724uqlXpB4ej3yModWG4nnwc6y+uxonXp+AmdAMU+pOkbZrX7Y9TvQ4gZK5S+LA8wM49OIQyucrjwcjHqCYbbFsx6Gr91ZqOKZrEWWRiIgI2NraIjw8HDY2mrvhCOPiiVco5nd4jIQ4MWq3z4/ZJ6rBzNxwnz/Eh8fibMvN+PHgCyzy5EDna+OQv6pDxjtqCVF4JAJbD0PcPV8I8uRCYa89sKjmmOl+kpKS8OzZM1SvrrqnjBIYYwhZMA7hhzYDHAd7912w6TpY5cdRF89ubcLNE+MAANWdZqB+x2VGOy33lfdX0Ppg2rVPLve7jFalW2m8H13uS1fR1XOkuIyThrsa4u7XuwifFY6cZjm1HY5GUce9pQ43MNxPfAShBqo1z4f5F2oYV9le6szTkwBth6UQoa01Cl3eCYu6VZUa8ySPpKQkDBw4EPXr18fly5dVHiOfedoI275jKfOkx6R+Oiohs09JVdWPLvelq+jqOVJchs/3yO9pth18dhD/C/gfmpdsbnTSpE/3FokTQWSSak55Mf9CDZhbGknZnkSe6vyRp+abdF+eruxKlqcMxjylRCJNhw8fRnx8PDp37qxeeernDDCGYNdhCD+xS+XHURdp5WmmTv1h0wSSWvzU66SImEhubb6IibDuxQjcDjqZrX5UGZOm+tJVdPUcKS7Dp9KWSmi+rzkmeEzA5MuT0XRvU/Q/3R/WZtZY1WKVtsPTOPp0b2VKnNzd3VGrVi1YW1vDzs4OnTt3hp+fX7r7nDp1Ci1atED+/PlhY2ODevXq4coV2Zk79uzZA47j0rzi4uIyf0YEoQGqOeXFvPPJ8rS4qxHI0xU9kiebnMny9Ducl6fHLzPcb+fOnTh8+LD0e7XL09wNyfI0e7j+yVOPTQCAx9dW4n9nZxiNPEmejgoU/AkVQJDmKanXtz24/HUH3J/2kspTVvpRZUya6EtX0dVzpLiMg9E1RiM4Ohj7nu7DRp+N8Av1Q9+/+uLBiAf4y/4vbYenUfTt3sqUON28eRPOzs64d+8erl69iqSkJLRs2RLR0dEK97l16xZatGiBS5cu4dGjR2jatCk6dOiAJ0+eyLSzsbHB9+/fZV4WFhZZOyuC0AAp5cnnkvHJ02knPZOn5oMzlKfhw4ejf//+Mtvi4+PRqVMneHh4qDxGvZenRmOl8vTk+iqjkacEUQL8w/0hhvzfdzHECIgIkFmnpXnhwWhWqD/ETCSVp6z0o8qYNNGXrqKr50hxGQdLnJbg2ZhnCJsVhkS3RARODcTBrgdRPl95bYemcfTt3srW5BAhISGws7PDzZs30bhxY6X3q1ixInr16oW5c+cC4DNOkyZNQlhYWFZDockhCK3he/0n5rd/hPhYMWq1zY85pwx/wohzrbcg6N5nmOfOgS7XnJG/WhFth6UQUUQUP2HE3ScQ5LblJ4yoXlFxe5EIgwcPxoEDB2S2m5mZ4cyZM2jTpo3KY2SMIWTheIQf3ARwHOyW7IBtd9XO5KROnv+3BTeOjwUAVGs2FQ06rTT4CSMCwgPSXbvEzsoODjayE6mImAhrng/BtcD9EHBCzKpyBMUt62S6H1XGpIm+dBVdPUeKizA21HVvqcMNsiVO79+/R5kyZfD8+XNUqlRJqX3EYjGKFy+OGTNmYNw4fmamPXv2YPjw4ShcuDBEIhGqVq2KRYsWoVq1akrHQuJEaJM08nSyKswshBnvqKcYgzwNGTIE+/fvl9mudnlaNAHhBzYagDxNQ4NOKwxenrKCPHlqVKC7tsMiCIIwOHRqVj3GGKZMmYKGDRsqLU0AsHr1akRHR6Nnz57SbeXLl8eePXtw7tw5HD58GBYWFmjQoAHevXunsJ/4+HhERETIvAhCW1RtljxhhM+lECzu5ouEOFHGO+opkkVyC9QtjvjffNle8GMdL9u7vBMW9aopVbYnFAqxe/duDBgwQGZ7QkICOnfujEuXLqk8Ro7jkN9tPWz7j0ueMOL4TpUfR1381WgMmvTYDEBStjfdKMr2MouQE2LyX7vhVGggxEyEZU9747+gE9oOiyAIglCCLGecnJ2dcfHiRdy+fRsODsqlzw4fPozhw4fj7NmzaN68ucJ2YrEY1atXR+PGjbF+/Xq5bebPn48FCxak2U4ZJ0KbPPX+iXntjCfzlBARi7OtkjNPnb2cYVddxzNPbYYj7s5jpTNPQ4cOxb59+2S2m5mZ4fTp02jbtq3KY2SMIWTxRITv36Cfmafb/+LGsTEAjKdsLyvwmaehuBa470/m6TAaFeih7bAIgiAMBp3JOI0fPx7nzp2Dt7e30tJ09OhRDBs2DMeOHUtXmgBAIBCgVq1a6WacXFxcEB4eLn0FBOju027CeKjSNC8WXEzOPC3q+sSgM09mNn8yT/X4zNOZ5rqfeSp8eQcs6ldPnm3v0QvF7YVC7Nq1CwMHDpTZnpCQgC5duuDixYsqj5HjOOSfsw65Bk7Qz8xTw9Fo0nMLAODJ9dW4fWYaZZ7kwGeedqF5oUF/Mk998F/QcW2HRRAEQaRDpsSJMYZx48bh1KlTuH79OkqUKKHUfocPH8bgwYNx6NAhtGvXTqnj+Pr6omDBggrbmJubw8bGRuZFELpAlaZ5sfASL08PPUKNQ54u6488CaxzopDHH3kKi+DL9rIoT127dlWbPOWbvZaXJ4Cfbe/YDpUfR12klCdf739InhQg5ISY9NdOkieCIAg9IVPi5OzsjAMHDuDQoUOwtrZGUFAQgoKCEBsbK23j4uIi8wHj8OHDGDhwIFavXo26detK9wkPD5e2WbBgAa5cuYKPHz/C19cXw4YNg6+vL0aPHq2CUyQIzVO5SSp56kLypEvIZJ4yIU+DBg2S2Z6QkIChQ4emuyRDVkkjT3NG6J08Ne31L4A/8nR6KsmTHOTJ063vx7QdFkEQBCGHTInTli1bEB4ejiZNmqBgwYLS19GjR6Vtvn//Dn9/f+n3W7duRVJSEpydnWX2mThxorRNWFgYRo4ciQoVKqBly5b49u0bbt26hdq1a6vgFAlCO8jI02XjkaeC9Uvw8uS0EcGP/DPeUUsIrOXI08PnCtsLhULs3LlTRp5sbW1x4cIFWFlZqSVGufJ0dLtajqUOKjUYlSxPN9aQPCkgtTwtf9YXN78fzXhHI8froxccNznC66OXtkORQVfjIoiU0H2aNbI1HbkuQdORE7rKsxs/MbfdY8THiFCzdT64na5m8BNGnGvzL77f+QTzXJb8hBE1imo7LIWII6Pwrc1wxP3vMQS5bFD46m5Y1FS8crtIJMKwYcNw5swZXL16FbVq1VJ7jIwxhC6djLC96wAAdou2wbbXCLUfV1W8+N82eB8dBQCo2mQyGnZZTRNGyEHERFj3YjiuftsDASfEjMoH8XfBXtoOSydhjKHOjjrwCfRBrUK1cH/4fZ24p3Q1LoJIibHcpzozOQRBEMpTuUleLLxYHeY5hHh4ORQLOxt+5qmjx2g+8xQWy5ft6XrmyWMHLBr8yTy1GKJU5unhw4cakSbgT+bJdQ1yDeIz9cFuI/Us8zQSTXttBUCZp/QQckJMrLQDLQoPhpiJsOJZP8o8KcDzgyd8An0AAD6BPvD84KnliHh0NS6CSAndp1mHxIkgNIC0bC+HEI+uGIk8Scr2/sjTj4d6IE8Nayhdtle6dGkNRqhAno5s02gM2SGtPE0heZKDkBNiUqWdaFF4CMmTAhhjcPN2g5DjM/dCTgg3bzet30+6GhdBpITu0+xB4kQQGqLy33mMS56sLWTk6WwLPZCnS9t5eQqPzFCe0mP9+vW4cOGCiiNMIU+DJwEAgueO0j956s3H63tjLcmTAgScAJMq7UiWp6c05iklkqflIsb//yliIp14aq6rcRFESug+zR4kTgShQYxWnhqU1J/MU2p58nmWqT7Wrl2LiRMnomvXruqTJ5d/kGvwZAB6KE/1R8jI03+nJpM8yUFGniAmefpD6qflErT91FxX4yKIlNB9mn1InAhCw1T+Ow8WeRiZPHmMRsEGJZEQrofy1GKI0vK0du1aTJ7MC01iYiK6du2K8+fPqzxGXp5W62/mKYU8Pb25juRJASRPaUn9tFyCtp+a62pcBJESuk+zD4kTQWiBvxrLZp4WdHqM+FgjkyefL9oOSyFZkSfGGJ49k22TmJiIbt26qVGeUmeetqr8OOqiUv0RaNabn+CC5Ekx8uTpxvcj2g5LK0ielgsUfHQRQKCVp+a6GhdBpITuU9VA4kQQWiJl5umx508s7Gwk8tTwjzy12Kz7mSePHbBoVDNZnh48Vdie4zjs2LEDQ4YMkdmufnlanUKeRuuVPFWsPzyVPE2iP9pykMhTy8JDIYYYK5/2w43Aw9oOS+MkiBLgH+4PMcRy3xdDjICIACSIEigugkgF3aeqgdZxIggt8/zWL8xt+whx0SJUb5kXc89Uh7mlAa/zFBmHc23/xffbH2Fma4nOV8fCvlYxbYelEHFUNL61HYG4/x5CYGuNwp67YFG7iuL2YjGGDx+O3bt3y2w3NTXFiRMn0LFjR5XHyBhD6LJpCNv9DwDAbuG/sO09SuXHURcv7+7E9cPDAQBV/p6ARl3XGuSaItlFzMRY92IEPL/tggACTK98AE0K9dF2WBolIDwAITEhCt+3s7KDg42DBiPi0dW4CCIlxnafqsMNSJwIQgdIKU/VWuTFvLPGJU+dPMeiQG09kSebnPwiuTouT/kXbEGuPqNVfhx1QfKkHCRPBEEQykEL4BKEgfJXY75sz8JKiCdXfxrJmKcx0rK9sy03I+iBDo95ymmFwpe2w7JxLYgjojIs2xMIBArL9rp3745z586pPEaO45Bv1irkGjIFABAybwzCDv+r8uOoi4r1hqFZnx0Ax+HpzfW4dXIile3JQcAJMLHSdrRyGMaX7T3rD+/AQ9oOiyAIwiggcSIIHaFSIyOTp5zmeidPhS5uy7Q8DR06VGa7RJ7Onj2r8hil8jR0KgD9lCen3rw8Pbu1geRJAQJOgAkVt0nladWzASRPBEEQGoDEiSB0iEqN8mDR5ZrJ8tTROOSpUKNSvDy12KR/8nQ/fXnavn27XHnq0aOH+uRp5kpZeTq0ReXHUReO9YamkqcJJE9yIHkiCILQPCROBKFjVGqYOznz5MXLU1yMYctTh0ujeXmKiNO/zFPLIYi956u4vS7I0/yxeixPG0meFCCRp9YOw6XydD3woLbDIgiCMFhInAhCB5Ep2/P6iYWdjEie9DDzFNhqqFKZp2HDhslsT0pKwq9fv9QSo1Sehk0DQPJkqAg4AcZX3CrNPK1+NpDkiSAIQk2QOBGEjiIp27PMaWTy1PhP5kkf5OnSdlj+XVvpzNO2bduk8qRo3SdVwnEc8s1YIStPBzer7XiqxrHeUDj12SmVp5snxpM8ySF15knV8uT10QuOmxzh9dGL+tIwuhoXoR3oftA+NB05Qeg4L27/xtw2DxEbJUJVp7yYd646LHIY8FTlUfE43+5fBN76ADMbC36q8jrFtR2WQsTRMQhsNxKxNx9AYG2FQp67YVm3quL2YjHGjBmDOnXqpCnfUxeMMfxcORO/d6wEAOSftwm5+o3VyLFVwat7u3Ht8DCAMfzVyBl/d99AU5XLQczE2PByNC5/3Q4BBJhaeS+aFeqfrT4ZY6izow58An1Qq1At3B9+P8vX3hj6UiW6GhehHeh+yDw0HTlBGCGVGubGQg8+8+R7zUjGPF1MkXlquRlB9z9rOyyFCKxy8GV7f9eGODIagUpknrZu3aoxaQL4zFPe6cuRe/h0AEDIAmeEHdikseNnF8e6Q6SZp+f/baLMkwL4sr1/0dphxJ/M0yBcDzyQrT49P3jCJ9AHAOAT6APPD57Ul4bQ1bgI7UD3g25A4kQQekClhrmlZXvGIk8dL6WSp3uftB2WQqTy1KSOUvKUEWFhYSqLTYJUnkbMAACELBynd/LUvO+uZHk6Po7kSQ6qlCfGGNy83SDk+Ay3kBPCzdstS9fdGPpSJboaF6Ed6H7QHUicCEJPqNjAuOTJ1CqVPLXaovvydGGr0pknRdy8eRMlSpTA6dOnVR4jx3HIO22Z3spThTqDkzNPtzeTPClAIk9tHEb+mW1vYJbkSfKEW8T4/2dETJTlJ93G0Jcq0dW4CO1A94PuQOJEEHpEanma3+GRUchT4b9LIyEiDmf0MfN094nS+9+8eRNt27ZFWFgYevbsSfIkB5nM0+3NuHncmeRJDgJOgHEVt6CNw0gwsEzLU+on3BKy8qTbGPpSJboaF6Ed6H7QLUicCELPSClPT6//Mgp56nBxFAr/XRqJkfH6k3mSyFOroUrJ05s3b9C2bVvExMQA4KcqJ3mST4U6g1PI0xaSJwXIk6dr3/YrtW/qJ9wSsvKk2xj6UiW6GhehHeh+0C1InAhCD6nYIDcWX0khT+2NR570JvN0YSssm9ZVWp7Kli2L/v1lZ0CTyNOpU6dUHmOyPM0EoK/ytJvkKQNSy9Pq54MylCfJE26Bgo8IAgiUftJtDH2pEl2Ni9AOdD/oHiROBKGnONb/I0/WQjz1NiJ5asJnnvRCns7/KytPdx4rbi8QYMuWLRg1apTM9qSkJPTq1UuN8uQuK0/7N6r8OOqiQp1BaeVJLNZ2WDqHRJ7aFhmllDwliBLgH+4PMeRfSzHECIgIQIIoIcNjG0NfqkRX4yK0A90Puget40QQes6rO78xp/VDxEaKUKVpHsw/Xx0WVibaDkttJEbH43z7rfh24z1Mrc3R6cpYFKxXQtthKUQcE4vA9qMQ632PX+fp8k5Y1q+uuL1YjLFjx2Lr1q0y201MTHD06FF07dpV5TEyxvBztSt+b1sGAMg/Zz1yDRyv8uOoi9cP9sHr4GB+naeGY/B3943gBPRcMDViJsamV864FPAvOHCY8tceNC88UG7bgPAAhMSEKOzLzsoODjYOSh3XGPpSJboaF6Ed6H7IOupwAxIngjAASJ70R564nFYofIXkSdWklKdKDUajSY9NJE9yyIw8EQRB6DO0AC5BEHJxrJ8bS1KW7XV4jLjoJG2HpTZMrczR4cIoODQt82fCiM34fuejtsNSiCCHJT/mqVldsKhofGs1LMOyvc2bN2P06NEy2yVleydPnlR5jBzHIe/Upcg9ygUAELJ4AsL2bVD5cdRFhdoD0aLfXoDj8OJ//+IGle3JRcAJ4Oy4CW2LjAYDwz/PB+Pqt73aDosgCEIvIHEiCAOhQr3cWHw5WZ7mtTd8eWp/fmQKedqi+/J0np8wQipP/3ukuL1AgE2bNsmVp969e6tPnqYsQe6RswBI5Gm9yo+jLsrXHoDm/fakkKexJE9ykMhTuyJjwMCw5vkQkieCIAglIHEiCAMiZebp2Q0jkSdJ5ikqHmdb64E8pcw8tR6ulDyNGTNGZrva5Ukm8zRRr+RJNvO0leRJAQJOgLGOG0meCIIgMgGJE0EYGBXqGZk85TBLlqdIPZGn81th6VRPaXnauHGjXHnq1asXHj58qPIYpZknPZWn8rUHkDwpgdzM09c92g6LIAhCZyFxIggDpEK93FjiWct45UkfyvbO/SsrT7cVC5CizNOwYcNQvbriSSayg1SeRrsC4OXp9951ajmWOihfewBa9N8nlSfvY2NInuTAcZysPL0YSvJEEAShgEyJk7u7O2rVqgVra2vY2dmhc+fO8PPzy3C/mzdvokaNGrCwsEDJkiXx77//pmlz8uRJODo6wtzcHI6Ojjh9+nRmQiMIIhUV6ubCEs9ayGFjgmc3fmFuu0fGIU/NyvJle/oiT83r8/LUZkS68sRxnIw8jRw5Elu2bIFAjTPHcRyHvJMXI/eY2QCA0CWTtCpPXh+94LjJEV4fvZRqX75Wf6k8vbyzTUaeMtuXKuNSdz+ZheSJIGTR1u+iJjGGc1QHmfqLe/PmTTg7O+PevXu4evUqkpKS0LJlS0RHRyvc59OnT2jbti0aNWqEJ0+ewNXVFRMmTJCpzb979y569eqFAQMG4OnTpxgwYAB69uyJ+/fvZ/3MCIJAhbq5sPhKTeSwMcHzm7+NQ57Oj5SRp8D/6bg8nd2SaXk6fPiw2qUp5THzTlokK0971qr9uKlhjMH1miteh76G6zVXKLuShkSeOE4glSexSJSlvlQZl7r6ySoSeWpfdKxUnjy/7tZoDAShC2j7d1ETGMM5qotsreMUEhICOzs73Lx5E40bN5bbZubMmTh37hxev34t3TZ69Gg8ffoUd+/eBQD06tULERER8PDwkLZp3bo1cufOjcOHDysVC63jRBCKeX0vDHNaPURMRBL++js3Fl6sYdjrPMUk4EKHbfh6/S1Mc5qjo8doFGpYStthKUQcE4vATmMQ63UHnFUOFL68A5YNa2o7LBkYY/i51g2/tywBAORzXYPcgydp7PhX3l9B64Otpd9f7ncZrUq3Unr/Nz4H4HVgEBgTw7ZiGwz+7gHGZa0vVcal6n6yC2MMm1+PwwX/zeDAYWKlHWjlMFTjcRCEttCV30V1YgznCOjgOk7h4eEAgDx58ihsc/fuXbRs2VJmW6tWrfDw4UMkJiam2+bOnTsK+42Pj0dERITMiyAI+fBle8mZJ7e2RpJ5cuIzT+fa/Kv7mSdJ2V50TIZjntIjICAA586dU3GEcjJPSydrLPPEGIObtxuEnBAAIOSEcPN2y9RT0pSZp/CXHugdzYFjWetLlXGpsh9VwHEcxlbYKM08rXsxnDJPhNGgS7+L6sIYzlGdZFmcGGOYMmUKGjZsiEqVKilsFxQUBHt7e5lt9vb2SEpKQmhoaLptgoKCFPbr7u4OW1tb6atIkSJZPRWCMArK10ku23txy0jk6VwKeWq9BYG3P2g7LIUILC2Sy/Yk8vSfT6b6CAgIQJMmTdC1a1ccO3ZM5TFqS548P3jCJ9AHIiYCAIiYCD6BPvD84JmpfsrV6gf7FtMgBlAvnqFnNCAWZ60vVcalqn5UhUSeOhR1BgPD2hfDSJ4Io0DXfhfVgTGcozrJsjiNGzcOz549U6qUjuM4me8lVptyu7w2qbelxMXFBeHh4dJXQEBAZsInCKMkZeZJIk+xUYYvT0Wal0vOPOmyPKXOPLUZobQ8SaTp48ePEIlE6Nu3r1rlKc/YOQD+yNPuNSo/joTUT0clZOUpKWMMK79742BODmIADeKBXtGACQRZ6ksVcany/FQJx3EYU2EDOhQdJ5WnK193aSUWgtAEuvq7qEqM4RzVTZbEafz48Th37hy8vb3h4OCQbtsCBQqkyRwFBwfDxMQEefPmTbdN6ixUSszNzWFjYyPzIggiY8rXkZWnue0MX57anR2hP/JkaZEledqzZw8+fkwuR5TI09GjR1UeI8dxyDNxIfI4uwEAQt2nqE2eUj8dlZCVp6SSvnzMGfbnBMQA6scD3aPEePgta31lNy5Vnp+q4eVpvTTztO7FcJInwmDR5d9FVWEM56huMiVOjDGMGzcOp06dwvXr11GiRIkM96lXrx6uXr0qs83T0xM1a9aEqalpum3q16+fmfAIglCS8nVyYenVFPJkBJknGXnSh7K9c/8iR4sGyfJ0K315mjNnDpydnWW2iUQi9OvXT33yNGGBrDzt+kelx5A8HRUo+FMlyESmKHVfj8whI0+9owG363Oy1FdW41Ll+amL1JknkifCENGH38XsYgznqAkyJU7Ozs44cOAADh06BGtrawQFBSEoKAixsbHSNi4uLhg4cKD0+9GjR+PLly+YMmUKXr9+jV27dmHnzp2YNm2atM3EiRPh6emJ5cuX482bN1i+fDm8vLwwadKk7J8hQRByKVc7hTz9Zxzy1P7cH3mKTtALeSp4dkuyPLVNX544jsOGDRu0JE9zAQChy6aqVJ4SRAnwD/eHGPIXrhVDjICIACSIErLUV0p5qhcP/PX5JeIT4zQWlyrPT50kZ55IngjDRF9+F7ODMZyjJsjUdOSKxhzt3r0bgwcPBgAMHjwYnz9/xo0bN6Tv37x5E5MnT8bLly9RqFAhzJw5E6NHj5bp48SJE5gzZw4+fvyIUqVKYcmSJejatavSJ0LTkRNE1vB7EAbXFvxU5ZUa5cbCSzVgmdNwpypPik3AhY7bEeDlB1MrM3TwGIPCjXR4qvLYOHzvNAYxV//HT1V+aTssG9dS2J4xhgkTJmDjxo0y2wUCAQ4dOoRevXqpPEbGGH6tn49fmxYCAPLNXIXcw6aqpO+A8ACExIQofN/Oyg4ONumXjGfUV8hLD7w9PxdgYjjWHYZmvbeBy2CNLFXFpcrzUzeMMfz7eiLO+W8AAEyqtAOtHIZpOSqCUA369LuYVYzhHFOiDjfI1jpOugSJE0FkHb8HYZjd8iGiw5NQsWFuLPIwMnm6NBqFG5fWdlgKEcfG4XvnsYjxvA0uhyUKe+wwKnnSBG8fHYbnvv5gmZAnYyS1PE2suB2tiwzXclQEQRBp0bl1nAiCMAzK1eYnjLCyNcHL27/h1sawy/ZMLP+U7bXgy/bOt/0X3/7T8bK9M5uRo2VDsJhYfGszPMOyvfXr12P8+PEy28ViMfr27YsjR46oPEaO45B3YoqyveXT8HvnapUfR12UrdEHLQceAMcJ8OreTlw/MgJMLL+kxZjhOA6jK6xDx6L8vbXu5Qhc+bpTy1ERBEFoBhIngiAAyJenmEgDl6ezKeSpzRZ8u/Ve22EpRJ48xdx8oLA9x3FYt26dXHnq16+fWuQJAPJMmG8g8rSL5EkBEnnqVGwCAGDti+G4HLBDy1ERBEGoHxIngiCklKudC0uu1pLK09y2xiFPRVuWT8486YM8tWoEFhOLwLYjdE6epJmncfMASORplcqPoy5Sy9O1w8NJnuTAcRxGlV+LTsUmAuAzTyRPBEEYOiROBEHIUK6WrdHJU7szww1eniZMmCCzXSwWY+PGjRCrSQryTpifQp6m66E8HQTHCfD6/m6SJwXw8rRGmnla93IEPAK2azkqgiAI9UHiRBBEGlLLk1ubh0YlT+fa6Lg8WZinlacb9xW25zgOa9eulZGnunXr4uLFixCocQKENPK0Y6XajqVqytboTfKkBKkzT+tfjiR5IgjCYCFxIghCLuVq2WKpVy3kzGWCV/8LMx55alUeSTF/5OnmO22HpRCpPLX+I0/tRiotT3Xr1sXly5dha2ur9jjzTpiPPOPnAwBCV8zQGXny+ugFx02O8PropbBN2Rq90XLQIXACYbrytOJ/K2C+2Bwr/rdCnSFnCmXOT1VIMk+di00CwMvTpYBtaj8uoT00eX8RhC5B05ETBJEubx+Gw7W5D6LDk+DYIBcWedREDmsDnqo8LhEXO2+H/5U3MMlhho6XRqHw32W0HZZCxHHx/FTlV/4Dl8MShS5uQ44mdRS2Z4whNjYWOXLk0GCUwM8NC/Brw3wAQN7py5FnxAyNHj8ljDHU2VEHPoE+qFWoFu4Pv69wnUIAePv4KDz39gVjYlSoMwROfXZIpyoXi8WwXmaNmMQY5DDNgchZkWrN4ilDZs9Plcfd9mYKznxZCwAYX3Er2hYZqfbjEppFW/cXQWQWmo6cIAiNU7amkWWeLEzR7syI5MxT260Gl3lKT5rU9Swt7/h50szTz5UztZp58vzgCZ9Afjp3n0AfeH7wTLd92eq9UmWehkEsFgEA3G+7IyYxBgAQkxgD99vu6g1eCTJ7fqqC4ziMLP+PNPO04eUoyjwZINq6vwhCFyBxIggiQ8rW5Mc8SeWpNcmTLiGwMEfB0ynkKYMxT4qIi4tDhw4dcOjQITVE+UeeJiwAwJft/dqu+dI2xhjcvN0g5IQAACEnhJu3W4bCWLZ6L37Mk0CI1/f34Prh4UhKSsTS20tl2i29vVRtE24oQ1bPT1VI5KlLsckASJ4MDW3fXwShbUicCIJQChl5usPLU3SEEchT6wpSefp6Q0/kKTaOlyfve0rvHxcXhy5duuDixYsYMGCA+uRp3FypPP1cOVPj8iR5Wi5ifMZIxERKPzVPLU/r1tVBbEKMTBttZ52yc36qguM4jCi/Wlae/Ldq7PiE+tCF+4sgtAmJE0EQSiNTtncnDHPbGIE8nR4ulafz7fREnto05uWp3Uil5CkhIQFdu3bF5cuXAfDjdgYMGICDBw+qJc408rRtuVqOk5rUT8slZOapednqvdBqIF+2Z/blCfpGA1yq3bSVdVLF+amKNPL0ajTJk56jS/cXQWgLEieCIDJFmRokTzovT6c2ZUqeTE1NUb58eZltYrEYAwcOVK88TVwIAPi5apZG5Cn103IJmX1qXqZ6T0TX6g4RgDrxSCNP2so6qer8VIU8ebro/69GYyBUh67dXwShDUicCILINFJ5ym1qPGV7p4ejWBtHXp7a/qt/8nT9rsL2HMdh9erVmDx5ssx2tcuTs5usPG1dppbjAMlPywUK/uwJIFD6qblYLMbCL+exLycUypOms06qPD9VIpGnrsWnAAA2vhpDmSc9RFfvL4LQNCROBEFkiTI1bOHuVRM5c5vi9V3jkKe2p4bxmafYRF6evN9qOyyFSOWp7d+8PLUflS15OnDggFrizOvshryTFgEAfq52UZs8JYgS4B/uDzHky4wYYgREBCBBlJBhX1EJUYhLjMMTc2CvAnmKS4pDVEKUCs8gfVR5fqqG4zgML7dKKk+UedI/dPn+IghNQus4EQSRLd4/DodL84eI+p2I8nVtsfhKLVjZGPY6T5e67sQXj1cwsTRFh4uj4NC0rLbDUog4Lh7fu41DzKWb4CwtUOjCVuRoVk9he8YYpk2bhn/++Udmu0AgwN69e9G/f3+1xPlz0yL8WjcXAJB3qjvyjJql8mMEhAcgJCZE4ft2VnZwsHFQqq/7X+/j/e/3AICEj/cRdX0TwMQwK9MQVo1Holy+8qhVuJZK4lYWVZ6fOmCMYYffdJz6vBoAMM5xC9oVHa21eIjMoev3F0GkRh1uQOJEEES2SSlPFerlwqLLNQ1fnrrtxJdLeiJP8Qn43tU5WZ7O/4scTvUVtteWPP3avBg/17oBUJ88qYv3T07g8t7eYGIRytUagOb9dkMgEGa8o5GRWp6cHTejfdExWo6KIAhDhBbAJQhCJyldXbZsb04rH8Mv2zs5DMXaOvJle+226nbZnrmZbNleh9GIuXZHYXuO47Bq1SpMmTJFZrtYLMagQYPUVraXZ+wc2bK9f7W/mKyylK7WHa0HHQEnEMLPZz+8Dg6RLpJLJMOX7a1Et+LTAACbXo3FBf8tWo6KIAhCOUicCIJQCSnl6c29cF6ewhO1HZbaMLEwRbtTsvIUcF2P5Kn9qCzLkzrHPMnI0z+u+LVlaQZ76A5p5OnAYJInOXAch2HlVpA8EQShd5A4EQShMkpXt4X7tVrJ8tT6oUHLk9BcVp4utNcTeWrXBCwuXml5mjp1qsx2xhgGDhyII0eOqCXOPGPnIO/kxQCAn2tm6588DT7Ky9PDAyRPCpAvT5u1HBVBEET6kDgRBKFSSlezgfu1WrDOw8vT7FZGKE/X/LQdlkIE5mYoeHIjrNo3TZYnr/8pbM9xHFauXJlGnuzs7FClShW1xZlnzGzknbwEgB7KU9VuJE9KIJGn7iWmAwA2vXImeSIIQqchcSIIQuWUrmaDpV68PPndN0J56rBN5+WpwIkNyZmnDqMzJU/29vbw9vZGhQoV1BpnnjGuqeRpiVqPp0rSytMgkic5cByHoWWXkzwRBKEX0Kx6BEGojfdPIuDa3AeRvxJRro4tllypCStbU22HpTZE8Ym42DV5tr3250eiiFM5bYelEHF8AoK6j0f0BW9wFub8bHvNGyhszxjD4sWL0b1793Slyd8fCA1VfNx8+YCiRZWP89eWpfi5ZjYAIO/kJcgzxlX5nbXMe9+TuLKnN8TiJJSr2R/N+++h2fbkwBjDrrczceLTSgDA2Aob0aGYs5ajIghCn6FZ9QiC0CtSlu353Q/H7JbGkXkq3q4iP2FEe/3IPFl1aKZ05snNzS1DaSpXDqhR489rwgrUOG3Of/2zrVw5vp2y5BnjirxT+FK9n2tm48ys1srvrAG8PnrBcZMjvD56pXmvdNVuaDXkKAQCE8o8AVjxvxUwX2yOFf9bIbM9OfM0AwCw+fU4nP+yKd2+0rvu2oTi0g6Gfn6EbkDiRBCEWilVNYU8PTCOsr22J4eieLuKEMXpiTwdXy8jT9FXFctTejDGcPv2E8TFSbaIgb8XACYJ/FeIAQBxcelnpOSRa+RMrK/OZysrnrqCn5sWZSlGVcMYg+s1V7wOfQ3Xa66QV8RRukrXFPJ00GjlSSwWY8HNBUgQJWDBzQUQi8Uy7/PytEwpeVLmumsDiks7GPr5EboDiRNBEGpHRp6MJPPU9uRQFG+vn/L0vWPm5Ykxhnnz5qF//xoA9vIbG7oDZjH8v81i+O+ziPttd2yskIjVf+aj+LVuLn5tXpzl/lSF5wdP+AT6AAB8An3g+cFTbrvU8nR1/0CIRYa71pk83G+7IyaRvx9iEmPgfjvt/SCRpx4lZgJQLE/KXndNQ3FpB0M/P0J3IHEiCEIjpMk8tXyIqDADl6cTsvLk72WY8iSRpkWLFv150jsEwC6g8VJA8uCXgf8eYoX9KEIsFmPpbb5Ub2slYFVVfvvPtW5azTwxxuDm7QYhx49ZEnJCuHm7KXzaXbpKV7QecgwCgQnePjqEqwcGGY08pfwZSlh6e2marBPAy9OQsu7oWWIWgLTylNnrrikoLu1g6OdH6BYkTgRBaIxSVW2w7HqyPM0xhrK9FPJ0ocM2+F99o+2wFCIwN0PBE5mXJy8vLyxalFJgGIDhwMsYgPuziUOWs04pMxUAsK1isjz9WjdXa/IkecotYnzZnYiJMnzaXapKF6OUp9Q/Q0Bx1gng5Wlw2aUymadzXzYCyNp11wQUl3Yw9PMjdAsSJ4IgNErJKrLyZGyZpwsdt+u0PHFmqeSpwyhEe95Od5/mzZtj5syZqbYy4CyAJ7KbMpt1kpepAHh5WvdnzJM25Cn1U24JyjztTitPhl22p+hnCCjOOgHJmSeJPG15PR5nP6/P8nVXJ9m5H4wxLlVh6OdH6B4kTgRBaByJPNnkTc48GYM8lehQSf/kKT4B3zuNSVeeOI6Du7u7HHmCrDxlIeskL1MhYVOFRDzr2gKA5uUp9VNuCco+7ZaVp8MGLU/p/QzTyzoBacv2/n0zET9EWb/u6iK794OxxaUqDP38CN2DxIkgCK1Qsgo/5kkiT8aQeWpzfIj+yVNHJz7zpKQ8DRqUgTz9yTopyjKkJL1MhYSBNv9Dnqn8B29NyZPkKbdAwZ9QAQRKPe0uVaULWg89btDypMzPML2sE5C2bM+xOFDUPm07Za+7qlHV/WAscakKQz8/QjfJtDjdunULHTp0QKFChcBxHM6cOZNu+8GDB4PjuDSvihUrStvs2bNHbpu45DltCYIwQFLK01sfI5Unz9faDkshnJkZCh5fB6tOTsljnjKQp2mzXQB5a+ieBfAYfNbJJA6WuaIyPH5UQhTiEtP/OxCXFAfTQWORd/pyAH/kaePCDPvODgmiBPiH+0OsoORQDDECIgKQIErIsK9SlTsbtDwp+zOMSkj/fuA4Dn1LzkdQSA4AvDwVSyVPmbnuqkSV94MxxKUqDP38CN2EY5lUcQ8PD/zvf/9D9erV0a1bN5w+fRqdO3dW2D48PByxsbHS75OSklClShWMHz8e8+fPB8CL08SJE+HnJzvjVIECBZSOSx2rAxMEoRk+PYvErGYPEPEzEWVr2WKJZ03kzGWq7bDUhighCR7dd+HT+RcQWpii/dnhKNpS8aKy2oYlJOB7z4mIPnsNnLkZCp77F1YtGypsf8bnHtasWIpbJ87LbOc4Dp3GDcfQQSPQoUYtpY59/+t9vP/9XuH7ZfOURa3CfF+/tq/Az5V8ViLP+PnIO36eUsfICgHhAQiJCVH4vp2VHRxsHJTu7+Ozs/DY1R1icRLKVO+NlgP2QyA0UUWoWiczP8OM8A/zx6GP83EzeDcAoF2haaifv4/0/cxed1Wh6vtBVehqXKrC0M+PyB7qcINMi5PMzhyXoTil5syZM+jatSs+ffqEYsWKAeDFadKkSQgLC8tqKCROBKHnpJSnMjVtsPRqLcOXpx678OmcnsrT2S2watVIcXvGMHv2bLi7y45f4TgOO3bswNChQ9USpyblSdUYsjypEsYY9r6bg6Mf+RLA0eXXoVPxCVqOiiAIXUMdbqDxMU47d+5E8+bNpdIkISoqCsWKFYODgwPat2+PJ0+eKOiBJz4+HhERETIvgiD0lxKVrbHsem3Y5DPFu4cRcG3hg8jfBly2Z2aCNseHokSnv/SnbO/YOlh1bp48YcTlW4rbcxyWLFkCFxcXme2MMQwfPhy7du1SS5x5RsxAvhkrAAC/NszHz/Xz1XIcdVCycie0GXoCAqEp3j0+As/9/Q2qbE9VcByHQWUWo1dJVwD8hBFnPq/TclQEQRgDGhWn79+/w8PDA8OHD5fZXr58eezZswfnzp3D4cOHYWFhgQYNGuDdu3cK+3J3d4etra30VaRIEXWHTxCEmilR2RrLriXL0+yWPoY95snMBG2ODUGJjpUgik/ChY7b8eWKjsvT0bXJ8tR5LKKv/Ke4/R95cnV1ldnOGMOYMWMQEBCgljhzD5+eLE8bF+ifPA05/keejsJz/wCSJzmklqetbyaRPBEEoXY0Wqrn7u6O1atXIzAwEGZmZgrbicViVK9eHY0bN8b69evltomPj0d8fLz0+4iICBQpUoRK9QjCAPj0/E/ZXihftrfEsxascxtJ2Z65CdqfG6H7ZXu9JiH6jBdftndmM6xaN1bcnjHMmTMHS5fypVUmJiY4duwYunTpotY4f+9YidAVMwAAecbNQ94J89V6PFXy8dlZeOzuAbEoEWWq90LLAQeobE8Oqcv2RpVfi87FJ2o5KoIgdAG9LtVjjGHXrl0YMGBAutIEAAKBALVq1Uo342Rubg4bGxuZF0EQhkGJv2TL9ma3NKKyPX3KPHVpkZx5yqBsb/HixXB1ddWYNAF/Mk8zVwLQ08yTtGzvKJXtKUCSeepdcjYAPvN0+vNa7QZFEITBojFxunnzJt6/f49hw4Zl2JYxBl9fXxQsWFADkREEoYukkSdjGPN0bIhUni522o4vl19pOyyFcGZmKHhkTablydfXVyPSJCH3sGky8vTCZT4eP4bcl7+/xsJSipJ/dSR5UgKO4zCwzCKpPG17M5nkiSAItZBpcYqKioKvry98fX0BAJ8+fYKvry/8//zFcXFxwcCBA9Pst3PnTtSpUweVKlVK896CBQtw5coVfPz4Eb6+vhg2bBh8fX0xevTozIZHEIQBISNPjwxPnrw+esFxkyO8PnoBSJankp0r8/LUeYfuy1MmM08p1/DTFLmHTYNw1CoAgPnJBdjTYj5q1ECaV7lyui9PV/b1I3mSg0Se+pSaA0AiT2u0HBVBEIZGpsXp4cOHqFatGqpVqwYAmDJlCqpVq4a5c+cC4CeA8E/1lyc8PBwnT55UmG0KCwvDyJEjUaFCBbRs2RLfvn3DrVu3ULt27cyGRxCEgVHiL2ss9zY8eWKMwfWaK16HvobrNVfp6vZCMxO0PjpYf+TJ1FRWnjqNQbTHzSz1dfbsWezYsUPFEfKENZ2KZSG8PI3PuwAT8swDIDvENy4OCA1Vy+GzRcm/OqLt0JMQCE3x/skxkicFcByHAaUXok8pNwDAtjdTSJ4IglAp2ZocQpegdZwIwrD5/CISM5vyE0aUrm6DpV76PWHElfdX0Ppga+n3l/tdRqvSraTfixKScLn3Hnw8/QxCcxO0OzMcxVo7aiNUpWCJifyEEaevgjMz5SeMaPO30vufO3cO3bt3R2JiIrZv355m9tXs8vgxn1Uamms1ZuWfBgDY+HMu1v+aD4CTtnv0CKheXaWHVhmfnp/HpV3dIBYlonTVHmg16BBNGCEHxhj2v5+Hwx8WAQBGlv8HXYpP1nJUBEFoGr2eHIIgCCI7FK+UnHl6/zgCrs19EPkrQdthZQnGGNy83SDkhAAAISeEm7cbUj7HEpqZoPWRwSjZRc8yT11bgiUk8mV7l24otW9KaQKAESNGYPv27WqJc1dYcuZpXN6FcjNPukqJvzokZ558j+PK3r4QifQ/+6pq+MzTApnM06lP/2g5KoIgDAESJ4Ig9AaJPNnmN+PlqcVDvSzb8/zgCZ9AH4iYCAAgYiL4BPrA84OnTLvU8nSh03Z89tBxeTqyJlmeujgrVbb35MkTqTRJGDlypFrlyT1kNQBgXN5F+idPw05J5clzL5XtySO1PG33m0ryRBBEtiFxIghCryheyRrLrtdKlic9yzylzjZJkJd1AiRjnoagZJfKECeIcLGznsmTEpmnuXPnws3NLc12dcrT7rAp+itPldrLyBNlnuQjkae+pfgx2CRPBEFkFxIngiD0jjTy1OKh3shT6myTBEVZJwAQmgr1Up5ydmuVnHlKR544jsOCBQsUytO2bdvUEufusClYGsJ/kJbKk54M+02WJzNp5onkKS0cx6F/6fky8nTy02otR0UQhL5C4kQQhF7Cl+0ly5NLc92XJ0m2SaDgv14BBHKzToACebr0Ut0hZxnO1BQFDv+TaXmSzNCaklGjRqlNnvaETZaRJ7Pj8+Ref12El6eTJE8ZkFqedvhNI3kiCCJLkDgRBKG3FKvIy1MuOzN8eKL78pQgSoB/uD/EEMt9XwwxAiICkCCSfw4SeSrVtQovT1126Ic8dW+dLE8XvRW35zjMnz9fLfKULx9gYSH/vT1hk7EkhJ+22vzUIvxaN1eP5YnK9uTBcRwGlFmAfqXmASB5Iggia9B05ARB6D1fXkZiVjMfhAUnoFQ1G7h71YR1HjNthyWXgPAAhMSEKHzfzsoODjYO6fYhShThSp89+HDyKQRmQrQ7PRzF22p+YVllYYmJCOo7FVEnLoMzM0WBkxuRs31Txe0Zw/z587Fw4cI07/37778YNWpUluLw909/naZcN9dCtIWftjr3mNnIO2kROI5TvIMO8enlRVza0RViUQJKVemGVoMPQyjU3+n61cmBd/Nx8MMCAMCwcivRvcQ0LUdEEIQ6UIcbkDgRBGEQ6JM8qQJRoghXeu/Bh1O8PLU9NRwl2pE8ZZffe9YidCnJk6FD8kQQhg+t40QQBKGAYhWtscy7tt6U7WUXoakQrY4MRqlufNnepa478OmijpftHVqNnD3agCUkIqjbOERdSL9sb8GCBZg3b16a90aPHo2tW7eqJc7cgychnytftvd7yxL8XCt/zJkuUqJiO7QbfhoCoRk+PD2JK3v6UNmeAvqXmS8t29vpNx0nPq3SckQEQegDJE4EQRgMxRxzysjTLCcfRPw0cHk6rGfydHCVVJ6+d01fngBg/vz5cuXJ399fXWHy8jR7LYA/8rRmjt7IU/GKbUmelCStPK3UckQEQeg6JE4EQRgUKeXpo28kXJobhzyV7l41WZ4uvNB2WApJKU9IzJo8ubi4YPHixWqNM/egicny9O9SPZen3iRPCuhfZj76l54PANjpN4PkiSCIdKExTgRBGCRfXkXBpdkD/P6RgJJVreHuVQs2eQ17zJNn3714f8KXH/N0chhKtK+k7bAUwhITEdRvGqKOewCmpih4Kv0xTwAvUAkJCViyZInGxh2F7VuPkMUTAQC5R7si7+TFejPm6fMrD1zc3vnPmKeuaDX4CI15UsDB9wtw4P18AMDQssvRo+QM7QZEEES2ockh0oHEiSCI1OiiPGU0s1u+fEDRolnrW0aeTIVoe0p5eVJnXIpgSUn8hBESeTq5ATk7NEt/H8Y0Li4y8jTKBXmnLEFAAKfx65UVSJ6U5+D7hTjwns9sDim7DD1LztRyRARBZAcSp3QgcSIIQh7+r6Mwq+kfeapiDfdr2pMnf3+gXDkgLk5xGwsLwM8vm/LUby/eH1c+86SJuBTBkpIQ1G8qoo4pL0+KiIiIUNv//2H7NiBk8QQAgLCPCyquWIK4OMUCp67rlRVSylPJyl3QeshRkicFpJSnrGaevD56YYLHBKxvsx7NSzZXdYgEQSgJzapHEASRSYpWyAn367WR294MH59GwsXJB+Gh2hnzFBqavpwA/PvpZTIyQmgqRMuDg1C6h2TM084MxzxpIi5FcCYmKHBwNXL2/DPmqdt4RJ27lul+/Pz8UKFCBWzevFn1QQLINXA88s9ZBwAQHXaHs5UrAMXPHdV1vbJCccc2aDfiDIQm5vj47DQu7+5FY54U0K/0XAwozU+Bv+vtTBz7uDxT+zPG4HrNFa9DX8P1mqvejIsjCEI5SJwIgjB4JBNGSOTJtbn25EkTyMhTonLypE3SyFP3CZmSJz8/PzRt2hSBgYFwdnZWozxNkMrTqDzLMDVv+vKkSxR3bIN2w1PJU5Lh/g5kh76l3aTytPvtrEzJk+cHT/gE+gAAfAJ94PnBUy0xEgShHUicCIIwCopWMD55anVID+WpV9tMydPPnz/RtGlTfP/+XbpN3fIUN3g9AP2Tp2KOrWXlaU9vkicFpJWnZRnuwxiDm7cbhJwQACDkhHDz1p91wAiCyBgSJ4IgjIaiFXJi+Q3dKNvTBAITOfJ0/rm2w1IIZ2KCAgdWycrTWa9098mbNy9Gjx6dZruzszM2bdqkljgTW4/HwuANAHh5mpbXBforT5R5UkTf0m4YWGYRAGD3W5cM5UmSbRIxEQBAxESUdSIIA4PEiSAIo6JI+T/yVMAcn54ZkTz1rMbLU7dd+iFPvdvx8tRjYobyNHfuXCxcuDDN9nHjxqlNng6Ej5PK08g8y/VPnkac/SNPZ0ie0qFPqTlKyVPqbJMEyjoRhGFB4kQozefPAMcBgwdrOxJg/nw+lhs31Huc4sX5V3bQpetG8BQpnxPLvWsZlzwdHCgjTx/P6bg87V+ZKXlyc3PTijwtCN4IQA/lqUIrkiclSS1PRz+4p2mTOtskgbJOBGFYkDipAckHZY4D2reX3+bGDf59ORUmmaJJE76fzFK8OD9drjYRiQAHB8DEBPj2Lf2258/z59mxo2ZiIwwfY5WnMr2qQ5wogkd3kidVcDDcOZU8zYK+ypPH7p4kTwrg5WkxAGDPO1cZeZJkmwQKPlIJIKCsE0EYCCROaubiReDWLW1HoRoKFwZevwbc0z5syxJCIZ+FEYmAffvSb7trF/912DD+67hxfCy1a6smFkVcu8a/soOqrxuhOoqUz4kVN5LlaVazB2qVp3z5Mn5gYWHBt1MHAhMhWh4YkEaetB2XItLIkxJjntzc3LBo0aI021UpT6mvl6w8rcC0vLNgYcE0fr2yQkp5+vT8LMlTOvQpNVuuPCWIEuAf7g8xxHL3E0OMgIgAJIjouhKEvkML4KqBz5+BEiX4rI6/P//h/u5d2TY3bgBNmwKjRgH//pv1YzVpAty8CWT2p1i8OBAUlPHaLerm40egdGn+9fat/DbBwXxmKk8e4OtXPkNFEKok4E0UZjb1we+geBT/Kyfcr9VGrvzqWSTX3z/99X3y5VP/oqniJBE8++/Hu6OPITAVos2JoTCp+pfW41IES0pC0MAZiDp8ATAxQcHj65Czc4t091m8eDHc3NzSbN+4cSOcnZ2zHZO8n6PplU2w2D0OACDsNQMlFi4Dl5WSAC3w5fUVXNzeCaKkeJT4qxPaDDkGoYl2ForWdY58WIq972YDAAaXWYpepVwQEB6AkJgQhfvYWdnBwcZBUyESBAE1uQEzEMLDwxkAFh4eru1Q2KdPjAGMtWrF2KBB/L9PnpRt4+3Nbx81Ku3+X74wNnQoY4UKMWZqyljhwvz3/v6y7XhdSvsaNCjjGIsVY8zcXHbb7t38/rt3M3bhAmMNGzKWMyffNuV5pe4/MJCxCRMYK12aMQsLxnLnZqxSJcbGjGFMmR9H06Z8v//9J//9Vav492fMSN42bx6/zds7eVvK+F6/ZqxLF8by5uW3ffrEt4mOZmz6dMYcHPjzr1iRsW3bkn8e8+alvU6S85d37KNHGatWjT/vAgUYGz+esZgY2faKrhtjjEVEMLZgAWN//cVYjhyM2dgwVrUqY3PmMJaQkNzu1CnGevdmrFQpxiwt+XYNGzJ24oT8a0ZknoA3kaxPgeusNTzY6L/+Y7+D47UdkloRJSYxj9672XqMZxtNJ7EPZ55qO6R0EScmssA+k9lblGFvTSqwyNOeGe6zePFiBr5uTua1ceNGtcX5e/9G9rYM2NsyYCHLpzOxWKy2Y6maL6+usE2Tzdn68WDnt3ViSYmG/TuQHQ6/X8Jae4C19gA7/H6JtsMhCEIO6nADKtVTMwsXAubmgKsrX5KWEe/eAbVq8aVpNWoAU6cC1avz39esCbx/n9x23jygWLHkf0tenTtnL+bjx/k+8uUDxo4F2rZV3DYmBmjQANiwAShVChg/Hhg4kM+47dkD/PqV8fEk5XeScrzU7N7Nfx06VLn4378H6tYFfvwABg3iywHNzPjr3749sHIlkDcvMHEiUK8ef43/+Ue5vlOyaRMfU4UKwJgxQO7c/HUYPly5/UND+TjnzePLFkeP5vsrUABYvhyIjk5u6+ICvHwJNGzIx92jB+DnB3Tvzh+TyD4O5fiyvTwFzfH5eRRcnB4gLMRwS2sEJkK03D8AZXr/KdvrsRsfzz7TdlgK4UxMUGDfClj37QAkJfFjns5cTXef2bNnY/HixWm2jxs3DmfOnFFLnLn6OyP/PL4k8PeOlfi5cqbejG0pWqEl2o84B6GpBV+2t6sHle0poHcpVwwqswQAsPfdbBz+sETLEREEoRFUpmBaRlczTowxNmUK//3WrcltFGWcmjVL25Yx/nuAMScn2e1//81vzyzpZZw4jrGrV9PuIy9zcu4cv23y5LTtIyIYi1figWVsLGO5cvHZrago2ffu3+f7b9hQdnt6GSeAMTe3tMfZsYN/r2NHxkSi5O2vX/MZo8xmnGxtGXvzJnl7TAxjZcvy1+/bt7Rxpc449ejBb3d1TRtrUBBjiYnJ33/4kLZNZCSfqbK15TNphGoIeBPJ+hb8k3mqRJknXUOcmMi+952SnHk6dSXDfZYsWSKTcWrZsiWLSZ0aVjG/D2ySZp6Cl03Tv8zTFAs+87S1I2We0uHI+6XSzNOh94u1HQ5BECmgjJOeMns2YGsLLFjAZ2gUERAAXL8OODoCI0bIvjdiBJ/ZuHaNb6dOOncGmjfP3D6Wlmm3WVvzmZ6MsLAA+vQBoqKAY8dk30s9KYQyFCgAzJmTdvuBA/zXRYsAQYo7v3x5PjOVWSZOBMqVS/7e0pI/D8aAR4/S3/fHD+DECT5LN39+2vft7WXHcpUsmbZNzpx8Ni08HPDxyXz8hHwcyvGz7eUpaI7PL6Lg0sw4Mk9l+9SQThjx4YxuZ57s9y5Pzjz1nISo0+lP9ezq6oolS/iMQMuWLXHmzBlYyvtPS4Xk6jcW+edvBgCE7VyF0BUz9DPz9OIcPHZ1p8yTAnqVcsHgMksBAPvezcHhD2kznARBGA4kThogTx5g5kwgMBBYu1ZxuydP+K9//512inGOAxo35v/99KlawpSSmZnqGjfmRcXdHWjXDti8GXj2LPOTVUjESFKWBwCxscCRI7yA9eihfF9VqsgXtqdPASsroHLltO/Vr5+5eAG+hDI1Dn/G/oaFpb/vw4f8NWraFDA1zfhYwcHAlCm8POfIkTzd/dSp/PuBgZkKncgAh3L8IrnGJE8t9vXn5SlJjMs99ECeUpbtKSlPhw8f1og0ScjVd4z+ylP5Fink6TzJUzrIypMbyRNBGDA0P5mGmDQJ2LgRWLGCn0lPHhER/Fd7e/nvFyjAfw0PV3l4Mig6vjxsbfkZA+fN49daunSJ3+7gwI/LGTtWuX5q1OCF57//+DFKpUsDJ0/y5zp8OC882Y0/IgIoUiRz+6SHrW3abZIsUUbj2SRiVbhwxsf59Ysf9+bvz48na94cyJWLHxfl6wucPQvEx2cicB1GF2ack+BQ1grLvWthZlMfqTy5X6uFXHbm2ep32bL0M5I1agCzZinXlyqv19dAIfJN6o9fv4DQK4/g0X0Xyi0bgrzNqmS6L1Vy9y4/+2ZahECr5agcClh4nsf3npNQ8Oga5OzaSmFfvXv3VlucisjVdwwAIGT+WITtXAUwhnwzV+rFbHsSebqwvaNUntoMOQ6hafZ+BwyRXqVcwHEcdr91wb53/GyOfUrJKX0gCEKvyXTG6datW+jQoQMKFSoEjuMyHGB748YNcByX5vXmzRuZdidPnoSjoyPMzc3h6OiI06dPZzY0ncbSki/JCg8Hli6V30YyU+KPH/Lfl2xX92zrmf17Xrw4sHcvEBLCZ82WL+ezKc7OwOHDyveTOusk+ZqZMj1Acfw2NnyM8lB0zdVFrlz814wW/gWAnTv5D8iLFwO3b/OTQSxaxN9PdeuqM0rN4u/Plz7WqKH4Va4c305TpMk8OfkgLDjrlrpsGf9A4cQJxS8XF75dRqjyekn6qllHiP5X+uMhaoCJxHg5fTcG13imlWsP8NJUvz7Qv7+C1yATVPVcgbiWfzJPvSYj6tQVpfr2+ugFx02O8PrIrwsVGRmJY6lrhZUkdV+pydV3DPIv2AIACNu1GqHLp+tv5ml3D4gSDeRJjYrpWXIWhpTl13aizBNBGCaZFqfo6GhUqVIFGzduzNR+fn5++P79u/RVpkwZ6Xt3795Fr169MGDAADx9+hQDBgxAz549cf/+/cyGp9MMHcqPp9m0Sf4HkKpV+a+3bqUtdWOMz8akbAfwWQdAuRn71I1QyMc2Y0ayMJ07p/z+/frxMxDu2wd8+AB4e/PjvVQlB1Wq8DPVPZNTgXTnjmqOoSw1a/LjrLy9gcTE9Nt++MB/7dgx7XuSe8IQCA3NeF2xuLj0MyzqwKGsFZbfqI28hXh5mtUs6/KU0di3zLRT5fVK2ZcYQhwAL09CiDEUu1AZT7Vy7eVnmmQRQ4hn/VfAul9HqTxFnkxfnhhjcL3mitehr+F6zRURERFo3bo1evXqhfXr12cqxtR9KRKiXH1G67c8jTxP8qQEvDzxTz72vXPDofdpF2MmCEJ/ybQ4tWnTBosXL0bXrl0ztZ+dnR0KFCggfQkln/gBrF27Fi1atICLiwvKly8PFxcXODk5YW16A4L0EKGQzzbFx/PTlKemaFF+zMvLl2mn5t61i9/erJlsuVmePPzXr1/VF3d6vHgBfPmSdrskg5OZoQR58gBduvDnMmgQL4uZzTalR79+/Fc3N0CcYoH3N2/4jJkmsbcHunXjpWjBgrTvBwcDSUn8vyVTzt++Ldvm0KHk0khCvTiUtcIyb16evrzMnjzpA6nlaQh2ozLUPLgyOwiE/IQR/Xl5Cuqdvjx5fvCETyA/o4rPJx/Ua1oPd/48PZk4cSLWrVun9KFl+gr0gecHxWOt0sjTsmn6I0/lmsvI06Vd3UmeFNCz5EypPO1/P5fkiSAMCI1NDlGtWjUULFgQTk5O8Pb2lnnv7t27aNmypcy2Vq1aSf+QySM+Ph4REREyL32gSxd+7SBJFiE1W7bwYwlGjOBnt3N15b+OGAHkz8+/n5JmzfivPXrwpVvLlgEXL6rxBFLh5cXP+Na4MTByJF9m1KsXMGAAL03jxmWuP4ko/e9//KQJAwaoLtYhQ/g4z51LHkcyciQ/GUaLFnwbgcZ+I/iJNCpUAJYs4SeamDaNn+yhQwdejqOi+HYDBvDjqcaPB3r2BKZPB1q14rdn8vkFkQ1SZp6MRZ72Y4CMPP28rrvyxAmFsN+TsTwxxuDm7QYhxz+84/w4vHr8SqbNpEmTlJKn1H0JOSHcvN3SlaFcfUbDbuG/AICw3f/onTx1GHkBQlMLfH55geQpHVLL08H3cp6WEgShd6j9Y2LBggWxbds2nDx5EqdOnUK5cuXg5OSEW7duSdsEBQXBPtXofHt7ewQFBSns193dHba2ttJXEUWj/nWQ5csVv1euHD/j2uDBwIMH/GKtDx7w3/v4AGXLyrYfMYIvjfvxg/8A7uLCL2CrKVq14j/QR0UBp04Ba9bw8ffuzZcbyZt5Lj2cnPgxUwAvEPnzqy5WoZDP0Eydymd01q7lBW316uTpyNU9fiwl+fIB9+7xGbDYWH7ykJ07+YzbrFnJE2I4OAA3b/LXxssL2LqVz1p6evLXiNAchcsYlzwxCLAfA+CDmhBCDL9Zu/H+lB7JU69JaeRJkiESMb6+mVVmgFPaviZNmpRh1UPqvkRMlGHWCQBse4/SW3kqUs4plTx1I3lSQM+SMzG0LP8H/8D7eSRPBGEAcCwb/1tzHIfTp0+jc+fOmdqvQ4cO4DgO5/4MgDEzM8PevXvRp08faZuDBw9i2LBhiFNQxB8fH4/4FFOJRUREoEiRIggPD4eNJj/9EgbBnDm8eF66BLRpo+1ojJPHj/lMYEZkRchVzbd30ZjZ9AF+fotHUUd+3SdlZtvr0YOfACIjunfP+AGIKq9XRn1xEKM/DqAWHkJgIkCrI4NRulvVjA+eTQ4e5CeByIgDB5JLcQGAiUT4MWQWIvefBYRCFDi6FtbdWoExhjo76uDx98dS2QH4TFFB34L4ejptzfOaNWswadKkNNvT66t6weq4P/x+hjPnhR/ZhuC5/DSruQZPRj6X1Xox2x4ABPhdw4VtHZCUGIviFduh7dCTNNueAo5/XIFdb2cCAPqXno9+pedpOSKCMA4iIiJga2urUjfQyjpOdevWxbt376TfFyhQIE12KTg4OE0WKiXm5uawsbGReRFERnz/nnbbq1fA+vX8THd//63xkAg9pHAZKyz/M+bJ/1UUZjY1/MzTAfRH/jY1IU4S40rvPXh/0lfbYSmEEwphv3sZn3kSiaSZp9QZIgkiJsLXKl8xdNrQNH1NnjwZa9asSbM9vb6UyToBgG3vkbBbuBUAELZnDULdp+pV5kky5unzy4uUeUqHHiVnpMg8zcfB93IGthIEoRdoRZyePHmCggULSr+vV68erl69KtPG09MT9bOyKilBpMOYMfzMfyNH8osSd+8OVKvGlxr+8w+/uCxBKIO0bK9wsjz9/mG4HxwZBCizoD/K9ePl6XIvPZAnSdneH3k6v2omBAr+7AkgwPOyz+Hu7p7mvSlTpsjIk2RsU3p9ZTTWSUJaeZqiV/LUYeQFmJha4vPLi7i4syvJkwJ6lJyBYeVWACB5Igh9JtPiFBUVBV9fX/j6+gIAPn36BF9fX/j/mV/bxcUFAwcOlLZfu3Ytzpw5g3fv3uHly5dwcXHByZMnMS7FrAETJ06Ep6cnli9fjjdv3mD58uXw8vKSWx5BENmhRw/A2pofj/XPP8C1a3yWycODnzyC0B758gEWFum3sbDg2+kK0szTH3ma1Sx9eVKmtE7Zdqq8Xsr2ld9egOZ7+6Nc/5pgIvVnnkqWzF47qTwN6ASIRHDeHo0WL+U/HRFDjICIAEyeNhnL5QxETSlPCaIE+If7QwxxmnYp+0oQJSgVv6w8rdU7eWo/8jxMTC3x5dUlkqd06F5iOskTQeg5mR7jdOPGDTRt2jTN9kGDBmHPnj0YPHgwPn/+jBs3bgAAVqxYgW3btuHbt2+wtLRExYoV4eLigrZt28rsf+LECcyZMwcfP35EqVKlsGTJkkxNea6OOkaCIDSLv3/6awXly8dP269rpB7ztOx6LeS2lz/eY9my9Ndpksz4qAyqvF6Z6UssEsNr8AH4HXgITihA66PqG/N092766zmVLMnPVJoeKcc8MaEAcVsmIKl92gXi7Kzs4GDjAID/2zVz5sw0bVavXo0pU6YgIDwAITEKVtRO1ZeyyIx5GjQR+VzX6M+Yp7fXcWFreyQlxqKYY1u0G3aKxjwp4MSnldjpNwMA0K/UPPQvM1+7ARGEgaION8jW5BC6BIkTQRDaJPB9NGY04eWpSAU+E6VIngwBsUgMryEH4bffR+3ypAqYSIQfQ10Que8MP2HE4X9g3SP9mWAykid1EH50O4LdRgLQf3lqO+wkTEwzSGUaKSRPBKF+DGZyCIIgCEOjUGkrrPgz5ingNZ+BMuQxTwKhAM1390O5AbXARH/GPJ14ou2wFMIJhbDf5Q7rQV34MU99piDyuEe6+8yYMQMrVqxIs33jxo2Ikiy0pmJse42A3eLtAICwvesQunSy/pTtlW2GDqMuSsv2Lu3shqRE+TPjGjt82d5KAMDBDwtw4N187QZEEIRSkDgRBEGoCHny9CvI8OWp/MDavDz13qv78rRzKawHdk6Wp2OX0t1n+vTpMvJUokQJeHt7I2fOnGqL07bncL2VJ4eyTVPJU1eSJwV0LzGN5Ikg9AwSJ4IgCBUikad8DhYIeB2NWc0MX56cdvVNzjz13ot3x3VcnlJmnvpOVVqeJNJUrFgxtceZRp6WTNJTefIgeUqH1PK0/908vfk5E4QxQmOcCIIg1EDg+2jMbOqD0K9xKFLBCsuu10aeAoY95una0EN4s+8BOKEArQ4PQpke1bQdlkKYSIQfw1wRufc0P+bp0GpY92yb7j6RkZGwtrbWUIQ84cd2IHjOCABAroETkG/2Wr0Z8/T1rTfOb233Z8xTG7QddorGPCng5KfV2OE3DQDQt9Rc9C89X29+zgShq9DkEOlA4kQYA/o665y+k9WZ3QI/xGBmkwe8PJW3wjJv1cqTKu8HVcxeJxaJcW3YIbzZ+wAQClBuySDka5FWnnTlPk0jTwdXwbpXu6z1xZjaPugaijwVrdAa7YafJnlSAMkTQagWEqd0IHEiDB1/f6BcOSAunYoXCwvAz083PpQaCnfvAsqsxX3njmblSZX3Q3bPMSVfPokxt/Qh1BQ/gAgC7MUg+EJWnnTpPmUiEX4Mn43IPaeyLE9isRjjxo1DyZIlMW3aNLXEGX58J4JnDwcA2A4Yj/xz1unNh+qv727g/L9tVS5PXh+9MMFjAta3WY/mJZurIFLtc+rTP9juNxUA0KeUGwaUXqA3P2eC0DVoVj2CMGJCQ9P/kAzw76eXgSAyT3pZGGXaFSqVA8tv1Eb+IhYIeBONWU0f4Nf37I/3UOX9kN1zTMnP3wLsF/fFfdSGEGIMwl5Uw+MsxaUJOKEQ9juWwGZIN37MU79piDx6Uen9GWMYN24ctmzZgunTp2PVqlVqidO2xzDYLdkBcBzC929AyKIJejMWxqFME3QYfQkmZjng//oyLu7oku0xT4wxuF5zxevQ13C95qo31yIjupaYgpHl/wEAHP6wCPvf05gngtAlSJwIgiDUTKFSObDMO4U8NfNRiTzpKgwCHEJf3EMdCCHGQOxLI0+6BCcUwi6lPPWdqpQ8pZQmCdOnT8fKlSvVEqeMPB3YqH/yNOpisjxt75wtefL84AmfQB8AgE+gDzw/eKoqVK3TpfhkjCi3GoBEnubqzc+ZIAwdEieCIAgNkCbzZATydBh99EeeBIJkeRKLeXk6ciH9fTgOpUqVSrN9xowZ6pOn7kP1Wp46jvqTeXpzJcvyxBiDm7cbhJwQACDkhHDzdtOb66AMspmnxSRPBKEjkDgRBEFoiIIlZeVpZlOSJ10ijTz1m5ahPE2ZMgWrV69Os53kST6Fy/ydbXmSZJtETAQAEDGRwWWdAD7zRPJEELoFiRNBEIQGSSlPX/2MT57kjXnSJbIqT//880+a7TNmzJBZPFeVpJGnheP15kN1duQpdbZJgiFmnYC08rTvneGdI0HoEyROBEEQGsaY5UkAhkHYixBPw5KnyZMny5WnmTNnqleelu7k5engJqOQp9TZJgmGmnUCJPK0BgBw5OMSkieC0CIkTgRBEFpAIk92RY1Tnt7O3ou3R/VMng7roDx1G5KcedJ7eeqEpIRYhe0l2SaBgo8uAggMMusEAF2KTyJ5IggdgMSJIPSEfPn49W/Sw8KCb0eojpIlVdsuJQVL8rPtSeRpRpMH+BmonDyp8n5Q5TmmF5dEnnyEdQAxg2dfPZGnod15eeqfPXlavny5WuJMk3laME5vPlQXLvM3Oo72+CNPnri4o7NCeUoQJcA/3B9iiOW+L4YYAREBSBAlqDNkrZFanva+m6M3P2eCMBRoAVyC0CP8/dNf/yZfPt1YVNTQuHs3/TWMSpbMeGHY9Aj6xC+SG+wfh8Jlc2C5d23kLZTxAqGqvB9UeY4ZxZU3jxjvFh7G6933wQk4tDw4EGV711Cucy3AxGIEj5yDiJ0nAIEA9vtXwqZvh3T3Wbt2LSZPnpxm+7JlyzBz5ky1xBlxag9+uAwFGINt37HIP2+j3iye+u39LZz/ty0SE6JRpFwLtB9xFiZmlmnaBYQHICQmRGE/dlZ2cLBxUGeoWufM53XY+mYSAKBXSVcMKrNYb37OBKFJ1OEGJE4EQRA6QFblSV9hYjGujziCV7vuGZ08rVy5EtOmTVNLnLLyNAb5523Smw/VysoTkVqeXDCozBK9+TkThKZQhxtQqR5BEIQOUKBE8pinb29jMLOp8mV7+ggnEKDZ9t5wHFoXTMzw5B9viEXyS7B0AU4ggN22xbAZxpft/RgwHRGHzqe7z6RJk7BmzRqZbZaWlqhevbra4rTpOhj27rsAjkPkxSNI+vZFbcdSNYVLN0aH0ZdgamaFAL+ruJDBmCdjpnPxiRhVfi0A4OhHd+x9N5vK9ghCA1DGiSAIQocI+sRLU/CXOBQuw8uUoWeeHi7zwl+jG8Aij5W2w8kQJhYjeJQbInYcVzrztG7dOkyaNAmWlpa4cOECmjVrpvY4I84egFlpR1hUVJ+kqYtvH/7D+S1tKPOkBGc/r8e/byYCoMwTQaSGSvXSgcSJIAhD4cfnGMxo8keejKBsT99II0/7VsCmX8d099m4cSMqVKgAJycnDUWp38jKU3O0H3GO5EkBKeWpZ4lZGFx2KckTQYDEKV1InAiCMCRk5MkIMk/6RlbkicgcqeWp3YizMDXLoe2wdJKzXzbg39cTAJA8EYQEGuNEEARhJNgXz4EVN2rDrpgFvr3jJ44I/Wa4Y570DU4ggN3WRbAZ0ZMf8zRwBiIOnM1yfy9evFBhdIZB4VKN0GGMx58xT164uL0TEhNitB2WTtKp2HiMrrAeAHDs0zLseetKY54IQg1QxokwGGiq7sxx+jTw8qXi9ytWBLp0Ua4vVU1lrcopsTU5vXZm7q3M9pU687TMuzbyFbZQeVxE1mBiMYJHz0XE9mN85mnvctj075SpPvbt24fBgwdj8eLFcHV1VVOk+kvgh9s4t6U1ZZ6UIGXmqUeJmRhS1p0yT4TRQqV66UDiZNz4+wPlygFx6TyQt7AA/PzogyTAS1PXrhm3O3UqY3m6exeoXz/jvu7cSV9UVNWPqvtS5b2V1b5+fI7BzKY++PE5FoVK82V7MSILuud1BCYWI3jMPERsO5ppeZJIk+RP8eLFizF79mx1hquXpJQnh7JOaD/yHMmTAs592Ygtr8cDALqXmIGhZZeRPBFGCZXqEYQCQkPT/wAJ8O+n93TemEgv05TZdulldTLTTlX9qLovVd5bWe3LvngOLPeuBfvilgh8z5ftfXgVR/e8jsAJBLDbsgA2I3tlqmzPz88PQ4YMkSmpmjNnDpYsWaLOcPWSQqUaouOYyzA1z4mvb6/hwraOVLangI7FxmFMhQ0AgBOfVmDX21lUtkcQKoLEiSAIQg9ILU87Rz6AOWjMkzyYWPPrQcnIE2O8PO0/k+4+5cqVw9q1a9NsnzNnDhYvXqyeQLOBNq5rSgqVaoiOoz1SyFMHkicFkDwRhHogcSIIgtAT7IvzZXr2xS3xMyAGtUHyJI+YH5EQJ4k0flypPI3qzcvToJkZZp7Gjx+P9evXp9nu5uamc/IkCv0BlpSk1RhkM0/XKfOUDh2LjcPYChsBSORpJskTQWQTEieCIAg9wr6YJZbfqI3chSxhBZInCeIkEd7sf4DTThtxuMpynGy0Dv9NPqXxODiBAHab5yfLkxJle+PHj8eGDRvSbNcFeWJJSYg4sx9fBzrBv2MVfO3bCCFLJ2s1pkIlG6Qq26PMkyI6FHNOIU8rSZ4IIpuQOBEEQegZ9sUsMWxbbcSA5AkAkmIT4NFzN66PPIr43zGoMvFvFGpUCp8vvcLFrjs0Ho9cecqgbG/cuHEK5WnRokVqijR9xHGx+D6xJ4LdRkIc8Ru5Bk2EZc1GiL55CYHOSswuo0Zk5ek6yVM6kDwRhOogcSIIgtBDche0xAOQPCVExOJEg7X4cf8Lqk1pit6PZ6DW7FZosKITmu3ogx/3v+D9qacaj0siT7aj+ySX7e07ne4+48aNw8aNG9Nsnzt3rsblSRQVga+9GyDu6X3kGjIFRc88Rp4xs5FvxgrYL96BuKf3EXVF8xm9lKSRp63tSZ4U0KGYM5wdNwEgeSKI7EDiRBAEoafEIa08WSBW22FpjISIWJxpuRnxv2NQy60V6i1pDwAQJfDjcPJXcwA4ICrgt1bi4wQC5N80L1meBs/KUJ6cnZ0VytPChQvVFaoMoqgIBA5pCVHEb+RxdkO+KfwsfywhAQBg7lgNHMchMShAI/GkR6GSDdBpzBVent55kzylQ/uiY2UyTzv9ZpA8EUQmybQ43bp1Cx06dEChQoXAcRzOnDmTbvtTp06hRYsWyJ8/P2xsbFCvXj1cuXJFps2ePXvAcVyaV1xGc+0SxB/y5ePXrEkPCwu+HcEvbquqdiVLKtdXRu1U1Y+q+1LlvaWOvlLLUy34SDNPhnzPi0Vi3HG9gF8vg1BtuhP+Gt1Qul1oZgIA+P3mBxLCYmFmk8FFVyNSeRrTN9vyNG/ePLXLExOJ8HO1K+Lfv0TuYdORq89o6XbOzAwAkPDxDUSRYRDk1I01EwuWrJ9WnuKjtR2WTpIy83Ty8yqSJ4LIJCaZ3SE6OhpVqlTBkCFD0K1btwzb37p1Cy1atMDSpUuRK1cu7N69Gx06dMD9+/dRrVo1aTsbGxv4+fnJ7GuR0ScMgvhD0aL8Qp/prVmTLx8tBCqhSxd+cdv01mmqWDHjxW8BfgHZO3fSXxOpZMmMF5pVVT+q7kuV95b6+rJE2Pfa2DHqAfAtBt0cHmD4ttooVdHCYO/5iE8/8WbvA1QcXg+VxzYCwE+XLRDyzwPFIjHeHXmMpNhE5P2rkDZD5eVp41wAQPiWQ/gxeBYAwGag4l8wZ2dnAHz5XkrmzZuHFi1aoJ4yN28WSPz6CRFn9sK2x3Dk6jcWAH9dOaGQ/7dIhMiLR8DiYmFe9i+1xJAVJPJ0dktrXp62dUD7URdokVw5tC/K/1w3vXLGyc+rAADDyq2gRXIJQhlYNgDATp8+nen9HB0d2YIFC6Tf7969m9na2mYnFBYeHs4AsPDw8Gz1QxAEoa/8+BLDBpe4wVrDgw0pdZMF+8doOyS18WLHHbbFaioL+xDCGGMsKSFJ5v1nm2+xDYIJ7O6cC9oITy5ikYj9GDOPvUUZ9pYry8L3nMxwn02bNjEA0teqVavUGmPYsR3sXRUrlvDlAx9zQoLM+78PbmZvywlY6Jo5ao0jqwR++B/bMs2arR8PdnJdE5YQF6XtkHSW8182s9YeYK09wLa9nsrEYrG2QyIIlaION9D4GCexWIzIyEjkyZNHZntUVBSKFSsGBwcHtG/fHk+ePEm3n/j4eERERMi8CIIgjBm7ovxU5QVKWuL7hxjMbOqDkADDHPNkmT8nzHPnQFIMP+5GaCqUlhz5rvXGDecTKNW9KioMravNMGVIU7Y3xAURe9OfYGHs2P+3d99hUR1tG8DvQy8K0osC1thrFCU27DXRaCzJayW2iIod0SRiNKLG2A2WGEti1NhLYqxgSTQae9fYQAURFBBQ2s77x8ICAkvbZXfh/l0X1/dxzpzdZyfzJnszc+aMxsqV8qVVCxcuxKRJk9Rao761HfQtrCB7K79PSDI0VPTrqw1L8GKWN8p0+gQWvb3UWkdhOVX+AD1GH4KhcVk8/S8Y+7lsL1fdXb+Ad60fAAC7Hn2PH+9M5rI9ojwUe3D6/vvvER8fj759+yqO1ahRAxs2bMC+ffuwZcsWmJiYoHnz5rh3716urxMQEABLS0vFj4uLS3GUT0Sk1exdTTE/KCM8TfU8VyLDk2UVW8hSZHh86DYSY+SfL/LqMwR7/4ZTE/fgvf6N0Hh6R1hWstFwpVlJkpQ9PG3IOzxdvnxZ7aEJAAxdq0CkpiDh1CGkvo4BACTdvooIf29EBkxEmW79YT1qOgxdKqm9lsJyquTB8JRP3V2/wJhagQCAXY8WMTwR5UESRfhfiCRJ2L17N3r27Jmv9lu2bMGwYcOwd+9etG/fPtd2MpkMjRo1QqtWrXJ8ojogn3FKTExU/B4bGwsXFxfExMTAwkI7blglItKUiJA38G1zDuEP3sCxsikWBLvDzsVU02Wp1J3N5xE0chvs3ndBYvQbvHn+GslxiWg4uS2qD2iCclXtAABCiBzv34h5GAWjssYwtS1T3KVDCIEX3rMQE/grIElw+CkAFkM0+2ykdLH7NiPi65Ewqf0+UmOjkRr1HLKEOFh5TUbZHgNg5FYVQO79mi6v8+oW9vAM9v7QCcmJr1G+amt8OPJ3GBqba6webfZ7yCqsuPkFAKBXxYkYVn0h73kinRcbGwtLS0uVZoNiC07btm3D0KFDsX37dnTr1i3P9sOHD8eTJ09w8ODBfNWijs4hItJlL0LfYKpnyQ5PIYdv4dmpB4i5Hwkzx7KoPfwDlKtmBz2DtM0McgtN91/g2qq/EHU9DC0X94J1DYfiLl0ensbMQswPRQtPFy5cwNGjR+Hr66uy2uJPH8bbf08hKeQ+DGwdYdl3OAwrVoNkYKCoPb1fRWqqYvMIQP7gXD0T7Rhn4Q/PYs8PHRme8uGPkNVYflO+iyLDE5UE6sgGBd5VrzC2bNkCLy8vbNmyJV+hSQiBy5cvo25d7dmxh0gbhISobudAVb6WqmhjTYD21pUXOxd5WEoPT1M9z2F+kDvsXbN/qdXVz+jasSZcO9YEkLb7m17GCvR3f8/MsoodHNzdEHM/Envar0DvE+NgWcWuWGpOJ0kS7FbMBADE/PArnnv5QQgBy6F571ib7sKFC2jfvj2io6Px5s0b+Pv7q6Q28xYdYd6iIwDl/SpSUiAZGECWlIi4g9vx5uxxJD95CMnQCGbNO6Bs989g4KC5XQ0dKzVDz9GHseeHjnj63wnsX92N4SkXXV1HAgCW3xyFXY8WQQiB4TW+Z3giyqTAM05xcXH477//AAANGzbEokWL0KZNG1hbW8PV1RV+fn54+vQpNm3aBEAemgYNGoSlS5eiV6+Mv6SZmprC0tISADBr1iw0a9YM1apVQ2xsLJYtW4aff/4Zf/31F9zd3fNVF2ecqKQLCQGqVweUPd7MxES+RXVeX3BV+Vqqoo01aXNdBfEi9A1825xH2P0EOFY2zRaeSsJnTPfqznPIklNhU8c519kmWWrGtuVxz2JwetJuRF5+ik/+ngATq+LfvloIgRdjv0HMys2AJMF+3dx8haeLFy+iffv2ePUq4wG/M2fOVFl4yizpwR2IlGQYv1dH0a/pM02pLyMRNr4v3l4+Cz2zMij70f+QHPoAstcxSIl4hgpbTsPAxl7lNRVE5pkn5yqt8NGoPxiecsGZJyop1JENCrw5xL///ouGDRsqnsE0ceJENGzYEF9/LX9GRVhYGEJCQhTtV69ejZSUFHh7e8PJyUnx4+Pjo2gTHR2NESNGoGbNmujYsSOePn2KkydP5js0EZUGkZHKv9gC8vPKZg3U8Vqqoo01AdpbV0HYuZhiflATOFUxQ/gD+b1PESEZG0aUhM8IAAkRr3Fs2Bb8Wm8+Xt15nvuXvbS/FwohYO5kAfeZXaBnoIfrq/8qxmozSJIEu+Vfw9L7f4AQiPh8OmLW78zzusuXL2cJTYD8D5GqDk4pURF4PmMYQj6sh6QHd+ShKe3ZTsnhTxD6aXO8+ScYpk3bwGXXv7CbvhjOgXthP3sNjKrUxNMh7SF7q9kNStJnnoxMLPDs/knsX92NG0bkoqvrSIyttQqAfMOItbcnccMIojQFDk6enp4QQmT72bBhAwBgw4YNCA4OVrQPDg5W2h4AFi9ejMePHyMxMRERERE4dOiQ2h7uR0RUGmULT55Zw1NJYGZfFrb1nOHaoTrKulrl2CYh4jWOev2K16GvIEkSJEmS39+kJ+HlzfBirjhDjuHppx1Kr/Hy8sKqVauyHVd1eDKwsYdx9Xowa94BBuXd5PXq6SE5/AnCvuiB5Ef3oF/OBolXz+HF3AmI/nkFAMDQrSqsx/pD38IKsbs3qqyewnKs1Aw90sJT+rI9hqecdXUdibG1VwMAdj9ejDW3JzI8EUED25ETEZFm2LnIn/PkVMUM4Q9LZnjyXNkXH/4xCgamRgCA1ORUAPL7cgDAqKwxRKoMvzVZiKgbYRAyGcL+foDY+5EwMDWELFWmsdoV4WnMAHl4GjYjz/A0cuTIXMPTzJkzVfZl195/JZx/PAg9YxOI1FSIpCREb1yKxJuXYOrRDs5rDsBhwSaYNmiGF9/64PXv2yDp6cG4am2U7TEQxu9pxz3LjhWbZglP+1Z1ZXjKRVeXEYrwtOfxEoYnIjA4ERGVKnYVTLKFp1dhJSs8pd+/9PJmGIJGbEVizBvFZgYGpkZouehjlK1ojV2ey7D1/e+wt1MgDMyN4dalluJaTZEkCXbLvipweFq9enW249988w38/f1V9mU3vQ8lfX0IWSriD++CaZNWKL/uT5jUbwrz1l1gOXAsLD75HNEblyAl8jkkIyNYfDwYpu83BwCt+OKdOTw9u38S+1Z1RVJinKbL0kpdXUZgXO01AOThafXtCVrxz5BIUxiciIhKmXfD07qR52CCkhWeAODV3ReIvPoUz07dVxwTQsDMwQLv9W8Ey8q2aPJlJ7T4vic6bxuCKj3rabDaDIUJTyNGjCiW8JQu8fYVJD95CIt+IyDp60OWJH+uop6xCfTKWiL5WQgkYxN5Y4OMDXy1ZZOBd8PT/lXdGJ5y0cVluCI87X28lDNPVKoxOBERlULp4cm5qhlePX0Dd5S88FT5ozowtSuDS98fx+vQV/INDdK+uEt6enh5KxzOLaugzojmqOBZTXHdy1vhCD1+F/f3XMWLS6EaqV0RnsYOzLjnad12pdcoC0+qXLYHAPrlbGDgWAEiUb6riJ6RMYRMhtTYaCSH3IckSZDFxSo+CwAk/XcLCWeOI+7IHry9eUlltRRW9vDEmafcZA5PnHmi0ozBiYiolLKrYIJ5Qe6wcTGDGUpeeJL09NDlt6GIffgSJ7y3I+zvhwCAZ389wL3tl2BqY47MEyDxYTE4N/tP/Ob+PfZ1DsShTzfiQI8f8ZfvXs3UL0mwW/qlPDwB8pmnQoan2bNnqzQ8GTi7wrBCJcQd2onkp48BAKkvXyBm2xrEH90Di/4jYejkAgBIeRGOqJWzEdLHHU+HdUb4xE8R9kUPRH6nugf2FpZjxabo4X0kLTydYnhS4t2ZJ4YnKo2K5QG4RFR0trbyZ+nk9awdW9vifS1V0caaAO2tS1XsKphgyg53jGt2DmYiAe44h3Nwx1tkfUiurn5GIwtT9DkzAfu7rcaxz7cgOT4RKQnJSIx+g46/DISpXVkAQMTFUJybdRCPfr+JWl7NUKVXPVjVcMCzU/dxctxOmDtbooGPZ7HXnx6eACBm+c+IGDYDAGD5eZ9crxkxYgQA+b1Pmc2ePRuAfOOIoi6Z0zMyhuPirQjt3QThEz+FZGQM2ZsEJF47j7I9B8Gyv/y9316/gJcrv0F88O+w6O2FMh17wahyDbz59xRezBkHfXtnWA32yePd1MvRzR09vI9g78oOivD04ag/YGRcRqN1aaMuLsMhQcLSG8Ox9/FSAMDIGou1ZgkmkboV+AG42ooPwKXSICRE+bN0bG3z/4BSVb6WqmhjTYD21qVKV869xXefnENUaAKsnE3x+Rp3WDllhCdd/4wJEa8Rcvg2wk4/gKQnoVr/RijfqioA4MWVpzjlsxMRF0PRetknqNyzLozLyR+EK4TAqQm7EHUtDN33j4CBqaFGviQKIRA5/ltEL5M/XN5+7RxYDuur9Jq1a9cqQlS6Pn36YMuWLdDX11dJXclhoYj5ZSUSb1+BSEmGRc9BMGvREQZ2jki8dwMv/Ecj8eYl2H21DObte0LfolzG55k7AYl3rsF59X5IJqYa//Id/vgc9v7QEUlvYuBcpSXDkxJ/hv6IpTeGAwB6uI3DyBpLNP7Pj+hd6sgGDE5ERAQAiHz6FtPanMPTewlwqCh/7pNDRTNNl6VyslRZlt3z9n+4GuF/P0T7Df9Dxe51FF8AU5NToW+oj7+m7cO9rRcx4NZ0xTbnmlDU8NSnTx/8+uuvMDBQ7WITIZNB0tODLCkRekbGiuNPh3fD2ytn4TBvA8zbdFf0q0hOhmRoiMjvpuH1H1vhdvAW9ExMc3v5YvX88Xns+aEDw1M+HHqyDkuuDwMAfOQ6FqNqLmV4Iq2ijmzAe5yIiAgAYFtefs9T+WpmeP7oDXzbnMfzRwmaLktl0v9OmDk0nf/2EB7/cRPNv+sBty61FF/8ZKky6BvqQ5Yqw5Njd2FZ2UajoQmQL9uzXTID5XwGAwAihn+JmB9/U3rN8OHDsWbNGvTr108toSmtMPn/Mczon5c/zEHCqT9hO/U7mLfqkhGaUlMhGRpCpKYi4cwxGLpU1prQBAAObk3Qc/QRGJla4tn9U9gX2IX3POWiU4XPMb7OjwCAfSHLseqWD+95ohKPwYmIiBTeDU9TPc+VmPD07l/DZSmpeBr8H5xbVUHFbrWhZyBfviZk8hmp1KQUnJlxAK/uPEe1T99XnNMkSZJgu3h61vC0dpvSa4YPH44tW7aoJzQho18V4SglBQnnTsC0SSuYe3aDlPa+QiaTPwMqKQlRi2Yg+eEdlO32qeKctsgcnsIenJaHp7evNV2WVkoPTxIkhicqFRiciIgoi8zhKeLx2xIVnjJLin2LlzfD4dS8Mswc5Ms4ZCmpkPT0kPImCVdXnMTNtX/DyaMSqvVpACDjIbCalC08jfgKMWu25nlNbuLj41X6ZVcWF4uk/27CpFFzGNg6AJCHKUlPD7K3bxD9ywrEbF8Lk4YeKNNFvsmFNvRrZtnC0yqGp9x0qvA5fBieqJTQrn9TERGRVigN4UnPyABWNR2QHJcIWap8xkPPQB9vX8bj34Aj+GfmQZRxsULnbUNgXM5Mq2ZFFOFp/BAAQMTIr/OcecpJZGQkPDw88OWXX6rsy65kaASjKjUhEuIgUlPlxwwMkBr9Eq9WByBq+UwYOLrAcck26FuU06p+zSxrePoL+1Z1ZXjKRacKXlnCU+CtcQxPVCJxO/I0pWHXLNKMkr4THpVctuXlD8n19ZRvGDHV8xwWBLuXmA0jjMoYo3KPujgz4wDs33dBuWp2SE1MwT8zDyLiQigcmrjiwz9GwcDEULEBgjaRJAm2i/wAANFLNiBixFeAELAc0T9f10dGRqJt27a4du0arl27BgCYM2dO0bcqNy+DMu16IGrxDBjXfh+GFatBJCXi5bKZeHvjAkzqNoHz2j+gZ2yilf2amYNbE/T0Poo9K9srZp4+GnUQRiZlNV2a1ulUwQsAsPT6MOwPWQEA+KLmMm4YQSUKd9WD/Mto9ep5P6flzh1+KaWCUeXY4jglTYl69ha+bc7h6d0E2LuZlKjwBABnv/oddzb/i4Tnr5HyJhk2dZxQ6aM68JjTHUD2Xfi0jRACkZMCEL14AwDAfvU3eYan5ORkNGnSBFeuXMlyfPr06SoJTwAQteQrxO7bjNSo5xBv38DovTowb/sRbCfMkdedmgpJRduiq9vzkH+xd2UHJL6JhlPl5gxPShx68hOWXh8GAYEPXccwPJHGcDtyJYrSORcvAu+/n3e7CxeARo0KWSCVSqocWxynpElZwpOrfCbKsVLJCU+RV5/iTWQ8JAkoV90BZZwtAUDrZ0TSZQtPq76B5Ujl4emnn37CsGHDsi2p8vPzw7fffquSL7uJt68i9VUkIEkwqlQdBg7O8np1pF8zY3jKv8NP1mPJ9c/TwpM3vqi5nOGJih23IyciIo2wcTbB/CB3lH/PDBEhb+HreQ7hD0vOPU+29crDpe17qNDmvYzQJITOfLmXJAm23/uh3IQhAICIUV8jZrXyDSO8vLzw448/ZvtCGxAQgBkzZqjkHhXjGvVg5tEWZs3aZIQmHerXzBxcG6On91EYm5ZLu+eJG0bkpmOFoRhfZx0kSNgfshKBt8bynicqEXTv31xERKQRNs7yZXolNTy9S9f+Qq6t4SmnOnWVvev7WcNTYGeGp1y8G55+uDWG4Yl0HoMTERHlm7VT9pmnsAclNzzpGkV4mjgUgDw8Ra/aovQaTYQnXZYlPD38Wx6e3sRquiyt1LHCUEyo8xMkSDgQ8gPDE+k8BiciIiqQd2eeprVheNImkiTBduE0RXh68cXMfIWndevW5Riepk+fzi+778gWnlZ1YXjKRYcKQxieqMRgcCIiogKzdpKHpwrVzRmetJAiPE2SbxGdn/A0dOjQHMPTvHnzGJ5yoAhPZlYMT3l4NzytvOnN8UQ6icGJiIgKRb5srwlcaphz2Z4WkiQJtt/5Fjg8/fTTTwxP+fRueNrLZXu56lBhCCbUXQ8JEn4PDWR4Ip3E4AT5Q0NNTJS3MTGRtyMqCFWOLY5T0kbWTiaYd1wenl6EMjxpG0V4mvw5gLTwFPir0muGDBmSa3hatmyZ2mrVVfYujRThKfzRGYYnJTqUH5wtPMmETNNlEeUbn+OUJiQEiIzM/bytLR8qSoWjyrHFcUra6mXYW0xrex6ht+Nh52KCeUHucK5Scp7zpOuEEIicugDRC9cBAOx+8Ee5Lz5Tes3GjRsxdOhQxaxAnTp1cOzYMdjb26u9Xl0UEXoRe1a2R2LCKzhW9ECPL/6Ekalqnh1T0hx9ugmLrg2BgEA3ly8wutYK6En8Wz6pFh+Aq4Q6OoeIiPLvZXgiprU5x/CkpbKFp5UzUW70/5Rekx6eateuzdCUDy9CL2H3ynZp4akZenxxiOEpF5nDU1eXUfCutZLhiVSKD8AlIiKtZe1ojHlB7lmW7T27X7qW7cWHxWi6hFxJkgTbBVMzlu15z0L0yl+UXjN48GDs2LFDo6EpJSoCIiVFI+9dUHYuDTMt2zuLvYGdkPhGe8eEJrUvPwgT626ABAl/hK7CypujuWyPtB6DExERqUzm8BT5pHSFp9Djd7Gp6mxcCzyl6VJylR6erKYMAwC8GPNNnuGpV69emgtN4U/xpH9zhE8dpDPhyd6lET72PqYIT/sCOzM85aJ9+UGYVHdjWnhazXueSOsxOBERkUqV1vD05NhdpCQkIXj0dq0PTzbzpxQoPOUmJSUFW7ZsUdvuaIl3riL56SPEHdiiU+HJzqUhPvY+BhMza4anPLQrPzBTeOLME2k3BiciIlI5RXiqmSk8/Rev6bLUqtmcbmg4uS0AIHj0dlz9QQfC09ThAAoXnlJSUjBw4EB89tln8PX1VUt4Mm/dBU5LtwMGBjoZnnp6H1WEp70/cNlebrKGp9UMT6S1uDlEKXHmDPDgQe7nK1cGPDyKr5502lpXadi9jrv9UXF49TwRvm3OIfRWPGwryJ/75FzVXNNlqY0QAn/77sPF744BAFqv7IN6o1tquKrcCSEQNW0hXi1YCwCwW/E1ynkPyPO69NC0detWxbHJkydjwYIF2bYxV4W4o3sRNu4TICUFZbp/CscFmyAZGKj8fdThRegl7FnZHm8TXsLBrSl6jD4EY1NLTZellY4/+wULrw5K2zBiJLxr/cANI6jQ1JINRAGdOHFCdO/eXTg5OQkAYvfu3XleExwcLBo1aiSMjY1FpUqVRGBgYLY2O3bsEDVr1hRGRkaiZs2aYteuXQWqKyYmRgAQMTExBbquNPj7byGAvH/+/pt1CSHE48dCmJgor8nERN5OV6nyM5aG/qKieRn+VgyveVJ0xkExoEKQeHovTtMlqZVMJhOnp+wRyzBWLMNYcWXFCU2XpJRMJhMvfBeIu6gm7qKaeLXi5zyvCQ4OFpIkCQBZfiZPnixkMpla6nx9ZI+4W8tQ3K0G8Wx8fyFLTlbL+6hDROglscbXWiwbC7FtYVPxNiFa0yVprWNPfxZdD+qJzgchll4bIVJlqZouiXSUOrJBgWN8fHw86tevjxUrVuSr/cOHD9G1a1e0bNkSly5dwvTp0zFu3Djs3LlT0ebMmTPo168fBg4ciCtXrmDgwIHo27cv/vnnn4KWRzlQNqNTmHaqoq11RUYCb98qb/P2rfIZFm2nys9YGvqLisbKwRjzMy3bm1rCl+1JkoQP5n+ERlPaAQBOjNmBqytPariq3EmSBJuAyVmX7a34Wek1rVu3xsaNG7PNLi1cuBBTpkxRy7K9Mu17ZCzb+30rwicP0J1lexUaoOcY+T1Pzx//w2V7SrR1HoBJ9eTL9g4+WYPlN0Zx2R5pjQIHpy5dumDOnDno1atXvtqvWrUKrq6uWLJkCWrWrIlhw4bBy8sLCxcuVLRZsmQJOnToAD8/P9SoUQN+fn5o164dlixZUtDyiIhIC6WHJ9daZRD1NJHhSctIkgSbeZNh5ZsWnsbOzjM8DRw4MMfw9P3336s3PC3bARgaIu6PbToenjoyPOWirfMATK63CXrQw59P1jI8kdZQ+8LRM2fOoGPHjlmOderUCf/++y+Sk5OVtvn7779zfd3ExETExsZm+SEiIu1l5WCMecebwKWmeekKT1N1KDwFqC48TZ48meHpHVnD0zmGJyXkM08MT6Rd1B6cwsPD4eDgkOWYg4MDUlJSEJm2die3NuHh4bm+bkBAACwtLRU/Li4uqi+eiIhUKqeZp6f3Snh4mvcRGvm2B6BD4WnaCABp4Wn5JqXXDBw4EJs2bcoWnhYtWqS+8NTuo5IVnhKiNV2WVmrr/D+GJ9IqxbJVybv/Mk3/l2jm4zm1UbYzj5+fH2JiYhQ/oaGhKqyYiIjUJX3mKT08+bYpBeEp4EPdCk9zJ8HKbyQA4MW4OXmGpwEDBmDTpk3Q08v6taJYw9Ok/+lWeBp7HCbmNvLwFNiJ4SkX2cPTSIYn0hi1BydHR8dsM0cREREwMDCAjY2N0jbvzkJlZmxsDAsLiyw/RESkG+QzT5nCU2mYeXonPF1ZoeXh6duJGTNP+QxPGzduzDE8TZo0SX3haflOeXg6+Jtuhafy9eUzT2nhaQ9nnnLV1vl/mFzv57Tw9CPDE2mM2oOTh4cHjhw5kuXY4cOH0bhxYxgaGipt88EHH6i7PCIi0pBy9vLw5Fa7DKKelZ7w9P40eXg6OVYHwtPcSVnD07LChafFixerLzy1/bBEhKeIkPMMT0q0cf6M4Yk0rsDBKS4uDpcvX8bly5cByLcbv3z5MkJCQgDIl9ANGjRI0X7UqFF4/PgxJk6ciFu3buGnn37CunXrMHnyZEUbHx8fHD58GPPnz8ft27cxf/58HD16FOPHjy/apyMA8ofIqrKdqmhrXba2gImJ8jYmJvJ2ukqVn7E09BepTzl7+bK90hSePOa+E56Wn9BwVbnLtmzPJ3/hKadlez/99JPiu4KqlWn7IZxW7MoUnj7TqfD08ZjjDE/58G54WnZjBMMTFStJFPDPP8HBwWjTpk2244MHD8aGDRswZMgQPHr0CMHBwYpzJ06cwIQJE3Djxg04OzvD19cXo0aNynL9jh078OWXX+LBgweoUqUKvv3223xveQ6o6enAJciZM8qfh1S5MuDhUXz1pNPWukJClD93yNYWcHUtvnrUQZWfsTT0F6lXdEQiprU9j8c34mDjbIx5Qe6o8J65pstSGyEEzkzfjwvzjgIAWi3tjfrjWmu4qtwJIRA1YxFeBawGANgumQErn8FKr9m8eTMGDRoEmUwGS0tLHD16FI0bN1ZrnXHH9yNsbG8gORlluvSB48LNkNJWt2i7F0+vYM+KdngbHwV71yboOfowjM3KabosrRT07FcsvDoQMsjQqcLnGFd7DfSkYrltn3SIOrJBgYOTtmJwIiLSbaUyPM04gAsB8qXqrZb1Rv2xWh6evlyMV3NXAch/ePLx8cGff/6p9tCULi7oAMLH9IZITtK58BT59Cp2r2ibFp4ao+foIwxPuQh+tgXfXR0AGWToWN4LPnXWMjxRFgxOSjA4ERHpPoYnHQtPi6fDavwQpdfExMTA0tKyGKrLEBd0AGFjeunkzJM8PLXD2/hI2Ls2Ro/Rh2FiZqXpsrQSwxMpo45swNFFRERaI/2ep4p1Mu55enK3hN/z9G13NJ4ufwj8yXE7cWWZlt/zNGcCrGZ8AQCInDAXr5ZsUHpNcYcmACjTpjucVuyCZGiEuIPb5fc8JScXex2FYVu+Hj4ecwwm5raICPkXe3/oiLcJrzRdllbydP4UU+pvhh70cPjpT1h6fTjveSK1YnAiIiKtUs7eGAHH3VGxThm8DCsd4anZnG4Z4clnJy4vDdZsUUpIkgSb2eMLFJ5ys2TJEowfP149u+216Q7HFTvl4enPHboXnsYeZ3jKB0+n/gxPVGwYnIiISOuUszPKHp7uxGm6LLV5NzydGr+rxIenJUuWYMKECVi6dKlaw5Ni5knXwpNz3azhaWUHhqdcvBuellwfxvBEasHgREREWkkRnuqmhac250tHeJqhW+HJ+svRANLC0+L1+bp26dKlmDBhguL3ZcuWqS08mbfpljU8TfxUN8NT6AWGJyU8nfpjav1foQc9HHm6nuGJ1ILBiYiItFY5OyMEHMs081QawtPsd8LTkiANV5U7SZJg/Y1PRniaGJCv8OTg4JDtOU9qD08rd8vD06GdOheeeo0NYnjKh9ZO/RieSK0YnIiISKtlW7ZX2sLThN0lLjz1798fmzdvzjE8+fj4qCc8eXbNmHnSsfBk41yHM0/5pAhPkn5aePocqSJV02VRCcHgREREWq9ULtub3Q1NvuwEQIfC01feANLC06KflF6TW3havny5+sKTjs88ZQ5Pe1a0Z3jKRWunfphab3NaeNqApdeHMTyRSjA4ERGRTlAs20sLT1M9zyP0dskOT02/6apb4WnWuIzwNGkeXn2/Tuk1/fv3x6+//lq84cmza5bwFDahv06Fp15jg2Baxg4vnlyUh6f4l5ouSyu1duoH33q/MjyRSjE4ERGRzsgcnl6Fy2eeSlt4urRYB8LT12nhafL8PMNTv379NBqe4g/v0qnwZONcBx+POZ4RnlZ2YHjKRSunvgxPpFIMTkREpFPK2Rlh3nF3VKpXtnSFp6/k4en0xN24tOi4hqvKnSRJsPYvXHjS19fPcnz58uUYN26c+sLTD3sywtP4fgxPJVArp76YVn+LIjwtucZ7nqjwGJyIiEjnWNoaIeBYk4yZJ89zJT88zcoUnibtKZHhafPmzdnC04oVK9QXnlp3yQhPR3breHjisr3ctHTsowhPR59tZHiiQmNwIiIinWRpm2nm6XlSqQxPF7/X7vBkM8sH1jPHAEgLTwsLH57Gjx+vljoV4cnIOCM8JSWp5b1ULWt4usTwpERLxz6KZXtHn23E4mteDE9UYAxORESks9JnnjKHp5BbpSA8fd0ZAPDXZO0OTwBg4z8uIzxNyV94enfZniRJaNCggdpqzBaeJvTXrfCk2DCC4UmZzMv2jj3bxPBEBcbgREREOu3d8DStTSmYefLvUqLDU9++fRXhSZIkrFu3DkOHDlVrjeatOutueHKqzfCUT5mX7TE8UUFJQh2LhjUgNjYWlpaWiImJgYWFhabLISKiYhYTmYTp7c/jwZXXsHIwwrwgd7jWLKPpstTqH/8/cG7WnwCA5gt7otGkthquSLko/2V4OWsFAMB2wVRYTRmmtP327dvx+vVreHl5FUd5AID4k38ibHRPiKREmLfvCacl2yAZGRXb+xdFVNgN7F7RFm9eR8CuQkP09D4KE3NrTZellU6Fb8e8K59CJlLRznkgJtRdD31JP+8LSWeoIxswOBERUYnxbngKOO4Ot1qlKDx91wONJrfTcEXKZQ5PNvOnwHrqcA1XlF1JCU+25Rug55ijMDW30XRZWulU+A7Mu9Kf4amEUkc24FI9IiIqMdKX7VWuL1+259f2HB7fLLnL9gCgqX9XuM9MW7Y3ZS8uLjym4YqUs/EfB2v/sQCAKN/v8HLB2kK/1p07d9Sz216rznAK3Ctftnd0D8J8+urUsr1eY4JgWtYekU8vY8+K9ngTH6XpsrRSS8dPMK3+1rRlez9j8bWhXLZHSjE4ERFRiWJhkxaeGmTc81TqwtN3Wh6eZo6F9axxAAofnoKDg9GoUSN4e3urJzy17JQRno7t1anwZO1Ui+Epn1o6fgK/+tsU4WnRtSEMT5QrBiciIipxLGyMEHBUPvMUHVGKwpN/FwDAX1N1IDx9PabQM0/BwcHo1q0bEhISEBgYyPCUA4an/Gvh2FsRno4/+4XhiXLF4ERERCVS5pmn6IhSsmxvZpeMmSddCE+FmHl69eoVevTogYSEBMWx9PAkk8lUXqN5y05wWrVPp8OTWVkHhqc8MDxRfjA4ERFRiaWYeSpty/YyzTxdWHBUwxUpZ/P1mKzhaf4ape2trKywbt26bA/JDQwMxJgxY9QTnlp01Onw9PGY45nCUzuGp1ykhyd9yQDHn/2C768OZniiLBiciIioREsPT1UaWmQs27vxWtNlqVXTmV0U4elv3326EZ6+8QEARE1biJfzVitt/8knn2Dr1q0aDk99dDQ8XZGHp7hITZellVo49sa0+luhLxkgKGwzwxNlweBEREQlnjw8Nc4IT23Pl4rw1HSWDoWnr7wzwpPf9/kKT9u2bcsxPKlt2V56eDI2QfyxfboXnsYGZYSnle0ZnnKReeaJ4YkyY3AiIqJSoax11vDk26bkhyf3r7ug6TddAaSFp/lHNFyRcjZfecNm9ngAaeEpYJXS9r1798a2bdtgYGCQ5fiqVavUG54C92aEp3GfQJaUqPL3UQdrx5pZwtNuzjzlqrljL/g1+E0RnhZeHcTwRDDIu4luSN9NJzY2VsOVEBGR1jIA/Ha9h1k9LiH6eQIS3r5GbGyJeA58rmr4fIC4t/H4d+4RvEGS1v930mDcABi+fYNX3/4AI8hgkEe9HTp0wPr16zFkyBCkpmZ8sV21ahUSExOxaNEi6Omp+O/E9ZrB/PstCB/fH0JIeP06DpKhboQnA7PyaDf0AA6s6orEZD28jotHskw3Hu5b3OqatsO4yuvx/bUhSE2Q8Dr2NfQkzjnoivR/16lyx01JqGP/Tg148uQJXFxcNF0GERERERFpifv376Ny5coqea0SE5xkMhmePXuGsmXLQpKkIr1WbGwsXFxcEBoaCgsLCxVVSHlhv2sO+14z2O+aw77XDPa75rDvNYP9rjkxMTFwdXXFq1evUK5cOZW8ZolZqqenp4cKFSqo9DUtLCw4yDWA/a457HvNYL9rDvteM9jvmsO+1wz2u+aocqkuF2oSERERERHlgcGJiIiIiIgoDwxOOTA2NsbMmTNhbGys6VJKFfa75rDvNYP9rjnse81gv2sO+14z2O+ao46+LzGbQxAREREREakLZ5yIiIiIiIjywOBERERERESUBwYnIiIiIiKiPDA4ERERERER5aHUBafAwEDUq1dP8SAyDw8PHDx4UOk1J06cwPvvvw8TExNUrlwZq1atKqZqS46C9ntwcDAkScr2c/v27WKsuuQJCAiAJEkYP3680nYc86qXn77nuFcNf3//bH3o6Oio9BqO+aIraL9zvKvW06dPMWDAANjY2MDMzAwNGjTAhQsXlF7DcV90Be13jnvVqFixYo796O3tnes1qhjvBkUpWhdVqFAB8+bNQ9WqVQEAGzduRI8ePXDp0iXUrl07W/uHDx+ia9euGD58OH755Rf89ddfGD16NOzs7NC7d+/iLl9nFbTf0925cyfLk7bt7OzUXmtJdf78eaxZswb16tVT2o5jXvXy2/fpOO6Lrnbt2jh69Kjid319/VzbcsyrTkH6PR3He9G9evUKzZs3R5s2bXDw4EHY29vj/v37KFeuXK7XcNwXXWH6PR3HfdGcP38eqampit+vX7+ODh06oE+fPjm2V9l4FySsrKzEjz/+mOO5qVOniho1amQ5NnLkSNGsWbPiKK1EU9bvQUFBAoB49epV8RZVQr1+/VpUq1ZNHDlyRLRu3Vr4+Pjk2pZjXrUK0vcc96oxc+ZMUb9+/Xy355hXjYL2O8e76vj6+ooWLVoU6BqO+6IrTL9z3KuHj4+PqFKlipDJZDmeV9V4L3VL9TJLTU3F1q1bER8fDw8PjxzbnDlzBh07dsxyrFOnTvj333+RnJxcHGWWOPnp93QNGzaEk5MT2rVrh6CgoGKqsOTx9vZGt27d0L59+zzbcsyrVkH6Ph3HfdHdu3cPzs7OqFSpEvr3748HDx7k2pZjXnUK0u/pON6Lbt++fWjcuDH69OkDe3t7NGzYEGvXrlV6Dcd90RWm39Nx3KtOUlISfvnlF3h5eUGSpBzbqGq8l8rgdO3aNZQpUwbGxsYYNWoUdu/ejVq1auXYNjw8HA4ODlmOOTg4ICUlBZGRkcVRbolRkH53cnLCmjVrsHPnTuzatQvVq1dHu3btcPLkyWKuWvdt3boVFy9eREBAQL7ac8yrTkH7nuNeNZo2bYpNmzbh0KFDWLt2LcLDw/HBBx8gKioqx/Yc86pR0H7neFedBw8eIDAwENWqVcOhQ4cwatQojBs3Dps2bcr1Go77oitMv3Pcq96ePXsQHR2NIUOG5NpGVeO91N3jBADVq1fH5cuXER0djZ07d2Lw4ME4ceJErl/i302vQogcj5NyBen36tWro3r16orfPTw8EBoaioULF6JVq1bFWbZOCw0NhY+PDw4fPgwTE5N8X8cxX3SF6XuOe9Xo0qWL4v+vW7cuPDw8UKVKFWzcuBETJ07M8RqO+aIraL9zvKuOTCZD48aNMXfuXADy2YwbN24gMDAQgwYNyvU6jvuiKUy/c9yr3rp169ClSxc4OzsrbaeK8V4qZ5yMjIxQtWpVNG7cGAEBAahfvz6WLl2aY1tHR0eEh4dnORYREQEDAwPY2NgUR7klRkH6PSfNmjXDvXv31FhhyXPhwgVERETg/fffh4GBAQwMDHDixAksW7YMBgYGWW6sTMcxrxqF6fuccNwXnbm5OerWrZtrP3LMq0de/Z4TjvfCcXJyyvZHyJo1ayIkJCTXazjui64w/Z4TjvvCe/z4MY4ePYphw4Ypbaeq8V4qZ5zeJYRAYmJijuc8PDywf//+LMcOHz6Mxo0bw9DQsDjKK7GU9XtOLl26BCcnJzVWVPK0a9cO165dy3Js6NChqFGjBnx9fXPc8YpjXjUK0/c54bgvusTERNy6dQstW7bM8TzHvHrk1e854XgvnObNm+POnTtZjt29exdubm65XsNxX3SF6feccNwX3vr162Fvb49u3bopbaey8V7wfSt0m5+fnzh58qR4+PChuHr1qpg+fbrQ09MThw8fFkIIMW3aNDFw4EBF+wcPHggzMzMxYcIEcfPmTbFu3TphaGgoduzYoamPoJMK2u+LFy8Wu3fvFnfv3hXXr18X06ZNEwDEzp07NfURSox3d3bjmC8+efU9x71qTJo0SQQHB4sHDx6Is2fPiu7du4uyZcuKR48eCSE45tWloP3O8a46586dEwYGBuLbb78V9+7dE5s3bxZmZmbil19+UbThuFe9wvQ7x73qpKamCldXV+Hr65vtnLrGe6kLTl5eXsLNzU0YGRkJOzs70a5dO8WXdyGEGDx4sGjdunWWa4KDg0XDhg2FkZGRqFixoggMDCzmqnVfQft9/vz5okqVKsLExERYWVmJFi1aiN9//10DlZc8735555gvPnn1Pce9avTr1084OTkJQ0ND4ezsLHr16iVu3LihOM8xrx4F7XeOd9Xav3+/qFOnjjA2NhY1atQQa9asyXKe4149CtrvHPeqc+jQIQFA3LlzJ9s5dY13SYi0O6OIiIiIiIgoR6VycwgiIiIiIqKCYHAiIiIiIiLKA4MTERERERFRHhiciIiIiIiI8sDgRERERERElAcGJyIiIiIiojwwOBEREREREeWBwYmIiLSep6cnJEkq9vcNDg6GJEnw9/fP9zWFqbVixYqoWLFiwYojIqJixeBERFRKPXr0CJIkZfkxMjKCi4sLPvvsM1y9erXQr71hwwZIkoQNGzaoruB8OH36NCRJwocffpjj+ZEjR0KSJDRs2DDH87Nnz4YkSViwYIFK6/L394ckSQgODlbp6xIRUfEx0HQBRESkWVWqVMGAAQMAAHFxcTh79iy2bNmCXbt24fjx4/jggw80XGH+NW3aFObm5jh58iRSU1Ohr6+f5Xz6DNKVK1fw8uVLWFtbZzsPAG3atAEAuLu749atW7C1tS2W+omISHtxxomIqJSrWrUq/P394e/vj4ULF+L06dOYMWMGEhMTMWPGDE2XVyCGhoZo3rw5YmNjcfHixSznwsLCcPfuXXz88ccQQuDEiRNZziclJeHMmTOwsLBAo0aNAABmZmaoUaMGgxMRETE4ERFRdmPHjgUAnD9/PsvxvXv3ol27drCysoKJiQnq1KmDhQsXIjU1VdFmyJAhGDp0KABg6NChWZYCprtw4QLGjBmDOnXqwNLSEqampqhbty7mzZuH5OTkItWePlv07rK49N8nTZqEMmXKZDv/zz//4M2bN2jVqpVipkrZPU6nT59G69atYW5uDhsbG/Tr1w+hoaHZ2nl6emLWrFmK2tL7Iqd7muLj4zFx4kSUL18exsbGqFevHnbs2FGwDiAiIrXgUj0iIsomp80Npk+fjoCAAFSoUAG9e/eGhYUFTp48iSlTpuCff/7B9u3bAQA9e/ZEdHQ09u7dix49eqBBgwbZXmvt2rXYv38/WrVqha5duyIhIQHBwcHw8/PD+fPnsXPnzkLXnh6cgoKCMGXKFMXxoKAglC1bFu7u7mjevDmCgoKyXJf+e/r1yhw7dgxdunSBnp4e+vXrB2dnZxw7dgzNmzeHlZVVlrZDhgwBAJw4cQKDBw9WBKZy5cplaZecnIyOHTvi5cuX6NWrFxISErB161b07dsXf/75Jzp27FiQbiAiIlUTRERUKj18+FAAEJ06dcp2bsaMGQKA8PT0FEIIcfjwYQFAdOnSRcTHxyvayWQyMWrUKAFA7NixQ3F8/fr1AoBYv359ju/96NEjkZKSkuWYTCYTXl5eAoA4ffp0lnOtW7cW+f1PVkpKiihbtqwoW7asSE5OVhyvVq2a6Ny5sxBCiLlz5wpJksSLFy8U59u0aSMAiIsXLyqOBQUFCQBi5syZimOpqamicuXKQpIkcerUqSz1f/bZZwJAtlpnzpwpAIigoKAca3ZzcxMARI8ePURiYqLi+NGjR3P9Z0RERMWLS/WIiEq5//77T3GP0+TJk9GiRQt8++23MDExwdy5cwEAK1asAACsXr0aZmZmimslScK8efMgSRK2bNmS7/d0c3PLtnGDJEnw9vYGABw9erTQn0dfXx8tW7bE69evceHCBQDAs2fPcO/ePbRu3RoA0Lp16yz3OSUlJeHs2bOwsrJC/fr1lb7+6dOn8eDBA3Tv3h0tWrTIUv/cuXOzfa6CWLx4MYyMjBS/t2vXDm5ubtmWTBIRUfHjUj0iolLu/v37intwDA0N4eDggM8++wzTpk1D3bp1AQBnz56Fubk51q1bl+NrmJqa4vbt2/l+z6SkJKxYsQJbt27F7du3ERcXByGE4vyzZ8+K8Inky+3++OMPBAUFoWnTpor7mTw9PQEATZo0gZmZGYKCgtC7d2+cPXsWb968QefOnaGnp/xvileuXAEAtGzZMts5Nzc3uLi44NGjRwWuuVy5cqhUqVK24xUqVMCZM2cK/HpERKRaDE5ERKVcp06d8Oeffypt8/LlS6SkpCgCVk7i4+Pz/Z6ffPIJ9u/fj/feew/9+vWDvb09DA0NER0djaVLlyIxMTHfr5WTtm3bApBv7jBt2jQEBQXB3NwcjRs3BiAPiB4eHopAVZD7m2JiYgAA9vb2OZ53cHAoVHCytLTM8biBgQFkMlmBX4+IiFSLwYmIiPJkYWEBSZIQGRlZ5Nc6f/489u/fj06dOuH333/PsrTt7NmzWLp0aZHfo0GDBrCyssLp06eRkpKC4OBgNG/eHAYGGf/Z8/T0xFdffYWIiIhsz29SJj3gRERE5Hj++fPnRa6fiIi0D+9xIiKiPDVt2hRRUVG4d+9evtqnh6HM25Snu3//PgCgW7du2e4HOnXqVBErldPT00OrVq0QHx+PPXv24L///lPc35Qu/ffDhw/j7NmzsLOzQ+3atfN87fR7oHKq9fHjxzluSa6sP4iISDcwOBERUZ7GjRsHAPDy8kJUVFS28+Hh4bh165bid2trawDAkydPsrV1c3MDIN9kIbMbN24gICBAZTWnzx6lLy9Mv78pnbu7O0xMTDB//ny8ffsWnp6eOW7D/q4WLVqgUqVKOHDgQJbPIITA9OnTcwxHyvqDiIh0A5fqERFRnjp37oyvvvoKs2fPRtWqVdG5c2e4ubkhKioK//33H06dOoU5c+agZs2aAAAPDw+YmppiyZIliI2NhZ2dHQBg2rRpcHd3h7u7O3777TeEhYWhWbNmCAkJwb59+9CtWzeVPfA1PThdv34dZmZmaNKkSZbzxsbGaNasWYGW6QHy2aw1a9aga9euaN++veI5TsePH0dYWBjq1auHq1evZqtFkiTMmDEDt2/fhqWlJSwtLfHFF18U/YMSEVGx4IwTERHlyzfffIMjR46gZcuWOHbsGBYtWoQDBw4gMTER/v7++N///qdoa21tjR07dqBatWoIDAyEn58f/Pz8AMiXrR04cABeXl64f/8+li9fjps3b2LhwoVYsGCByuqtW7cubG1tAQAffPABDA0Ns7XJvHwvv8EJANq3b49jx46hadOm2L59O9asWQM3NzecPn062wNwAaBWrVpYv349rK2tsXjxYvj5+WH+/PmF+FRERKQpksi8/ysRERERERFlwxknIiIiIiKiPDA4ERERERER5YHBiYiIiIiIKA8MTkRERERERHlgcCIiIiIiIsoDgxMREREREVEeGJyIiIiIiIjywOBERERERESUBwYnIiIiIiKiPDA4ERERERER5YHBiYiIiIiIKA8MTkRERERERHlgcCIiIiIiIsrD/wEh/W6Uvjhu7gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = iris[\"data\"][:,2:] # petal length and petal width\n",
    "y = iris[\"target\"] == 2 \n",
    "y = y.astype(np.int64)\n",
    "\n",
    "log_reg = LogisticRegression(random_state=42)\n",
    "log_reg.fit(X,y)\n",
    "\n",
    "x0,x1 = np.meshgrid(\n",
    "    np.linspace(2.9,7,500).reshape(-1,1),\n",
    "    np.linspace(0.8,2.7,200).reshape(-1,1)\n",
    ")\n",
    "\n",
    "X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "y_proba = log_reg.predict_proba(X_new)\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(X[y==0,0], X[y==0,1], \"bs\") # X has 2 columns, one for petal length and one for petal width. y==0 will take all non virginca flowers and then from X takes 0th index which means petal length and then it is plotted against petal width (X[1])\n",
    "plt.plot(X[y==1,0], X[y==1,1], \"g^\") # all virginica\n",
    "\n",
    "zz = y_proba[:,1].reshape(x0.shape)\n",
    "contour = plt.contour(x0,x1,zz, cmap=plt.cm.brg)\n",
    "left_right = np.array([2.9,7])\n",
    "boundary = -(log_reg.coef_[0][0] * left_right + log_reg.intercept_[0]) / log_reg.coef_[0][1]\n",
    "\n",
    "plt.clabel(contour, inline=1, fontsize=12)\n",
    "plt.plot(left_right, boundary, \"k--\", linewidth=3)\n",
    "plt.text(3.5,1.5, \"Not Iris Virginica\", fontsize=14, color=\"b\", ha=\"center\")\n",
    "plt.text(6.5,2.3, \"Iris Virginica\", fontsize=14, color=\"g\", ha=\"center\")\n",
    "plt.xlabel(\"Petal Length\", fontsize=14)\n",
    "plt.xlabel(\"Petal Width\", fontsize=14)\n",
    "plt.axis([2.9,7,0.8,2.7])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.77762524, 2.38552012]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.coef_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like the other linear models, Logistic Regression models can be\n",
    "regularized using ℓ1 or ℓ2 penalties. Scikit-Learn actually adds an ℓ2 penalty\n",
    "by default."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Softmax regression"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s use Softmax Regression to classify the iris flowers into all three\n",
    "classes. Scikit-Learn’s LogisticRegression uses one-versus-the-rest by\n",
    "default when you train it on more than two classes, but you can set the\n",
    "multi_class hyperparameter to \"multinomial\" to switch it to SoftmaxRegression.You must also specify a solver that supports Softmax\n",
    "Regression, such as the \"lbfgs\" solver (see Scikit-Learn’s documentation\n",
    "for more details). It also applies ℓ2 regularization by default, which you can\n",
    "control using the hyperparameter C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#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>LogisticRegression(C=10, multi_class=&#x27;multinomial&#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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=10, multi_class=&#x27;multinomial&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=10, multi_class='multinomial')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = iris[\"data\"][:,(2,3)] # petal length and petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "softmax_reg = LogisticRegression(multi_class=\"multinomial\", solver=\"lbfgs\", C=10)\n",
    "softmax_reg.fit(X,y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the next time you find an iris with petals that are 5 cm long and 2 cm\n",
    "wide, you can ask your model to tell you what type of iris it is, and it will\n",
    "answer Iris virginica (class 2) with 94.2% probability (or Iris versicolor\n",
    "with 5.8% probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softmax_reg.predict([[5,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.38014896e-07, 5.74929995e-02, 9.42506362e-01]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softmax_reg.predict_proba([[5,2]])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below graph shows the resulting decision boundaries, represented by the\n",
    "background colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "x0, x1 = np.meshgrid(\n",
    "        np.linspace(0, 8, 500).reshape(-1, 1),\n",
    "        np.linspace(0, 3.5, 200).reshape(-1, 1),\n",
    "    )\n",
    "X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "\n",
    "y_proba = softmax_reg.predict_proba(X_new)\n",
    "y_predict = softmax_reg.predict(X_new)\n",
    "\n",
    "zz1 = y_proba[:, 1].reshape(x0.shape)\n",
    "zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(X[y==2, 0], X[y==2, 1], \"g^\", label=\"Iris virginica\")\n",
    "plt.plot(X[y==1, 0], X[y==1, 1], \"bs\", label=\"Iris versicolor\")\n",
    "plt.plot(X[y==0, 0], X[y==0, 1], \"yo\", label=\"Iris setosa\")\n",
    "\n",
    "custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "\n",
    "plt.contourf(x0, x1, zz, cmap=custom_cmap)\n",
    "contour = plt.contour(x0, x1, zz1, cmap=plt.cm.brg)\n",
    "plt.clabel(contour, inline=1, fontsize=12)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"center left\", fontsize=14)\n",
    "plt.axis([0, 7, 0, 3.5])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the decision boundaries between any two\n",
    "classes are linear. The figure also shows the probabilities for the Iris\n",
    "versicolor class, represented by the curved lines (e.g., the line labeled with\n",
    "0.450 represents the 45% probability boundary). Notice that the model can\n",
    "predict a class that has an estimated probability below 50%. For example, at\n",
    "the point where all decision boundaries meet, all classes have an equal\n",
    "estimated probability of 33%"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "exercies"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implement Batch Gradient Descent with early stopping for Softmax\n",
    "Regression (without using Scikit-Learn)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by loading the data. We will just reuse the Iris dataset we loaded earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris[\"data\"][:,(2,3)] # petal length , petal width\n",
    "y = iris[\"target\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to add the bias term for every instance ($x_0$=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_with_bias = np.c_[np.ones([len(X),1]), X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1. , 1.4, 0.2],\n",
       "       [1. , 1.4, 0.2],\n",
       "       [1. , 1.3, 0.2],\n",
       "       [1. , 1.5, 0.2],\n",
       "       [1. , 1.4, 0.2],\n",
       "       [1. , 1.7, 0.4],\n",
       "       [1. , 1.4, 0.3],\n",
       "       [1. , 1.5, 0.2],\n",
       "       [1. , 1.4, 0.2],\n",
       "       [1. , 1.5, 0.1],\n",
       "       [1. , 1.5, 0.2],\n",
       "       [1. , 1.6, 0.2],\n",
       "       [1. , 1.4, 0.1],\n",
       "       [1. , 1.1, 0.1],\n",
       "       [1. , 1.2, 0.2],\n",
       "       [1. , 1.5, 0.4],\n",
       "       [1. , 1.3, 0.4],\n",
       "       [1. , 1.4, 0.3],\n",
       "       [1. , 1.7, 0.3],\n",
       "       [1. , 1.5, 0.3],\n",
       "       [1. , 1.7, 0.2],\n",
       "       [1. , 1.5, 0.4],\n",
       "       [1. , 1. , 0.2],\n",
       "       [1. , 1.7, 0.5],\n",
       "       [1. , 1.9, 0.2],\n",
       "       [1. , 1.6, 0.2],\n",
       "       [1. , 1.6, 0.4],\n",
       "       [1. , 1.5, 0.2],\n",
       "       [1. , 1.4, 0.2],\n",
       "       [1. , 1.6, 0.2],\n",
       "       [1. , 1.6, 0.2],\n",
       "       [1. , 1.5, 0.4],\n",
       "       [1. , 1.5, 0.1],\n",
       "       [1. , 1.4, 0.2],\n",
       "       [1. , 1.5, 0.2],\n",
       "       [1. , 1.2, 0.2],\n",
       "       [1. , 1.3, 0.2],\n",
       "       [1. , 1.4, 0.1],\n",
       "       [1. , 1.3, 0.2],\n",
       "       [1. , 1.5, 0.2],\n",
       "       [1. , 1.3, 0.3],\n",
       "       [1. , 1.3, 0.3],\n",
       "       [1. , 1.3, 0.2],\n",
       "       [1. , 1.6, 0.6],\n",
       "       [1. , 1.9, 0.4],\n",
       "       [1. , 1.4, 0.3],\n",
       "       [1. , 1.6, 0.2],\n",
       "       [1. , 1.4, 0.2],\n",
       "       [1. , 1.5, 0.2],\n",
       "       [1. , 1.4, 0.2],\n",
       "       [1. , 4.7, 1.4],\n",
       "       [1. , 4.5, 1.5],\n",
       "       [1. , 4.9, 1.5],\n",
       "       [1. , 4. , 1.3],\n",
       "       [1. , 4.6, 1.5],\n",
       "       [1. , 4.5, 1.3],\n",
       "       [1. , 4.7, 1.6],\n",
       "       [1. , 3.3, 1. ],\n",
       "       [1. , 4.6, 1.3],\n",
       "       [1. , 3.9, 1.4],\n",
       "       [1. , 3.5, 1. ],\n",
       "       [1. , 4.2, 1.5],\n",
       "       [1. , 4. , 1. ],\n",
       "       [1. , 4.7, 1.4],\n",
       "       [1. , 3.6, 1.3],\n",
       "       [1. , 4.4, 1.4],\n",
       "       [1. , 4.5, 1.5],\n",
       "       [1. , 4.1, 1. ],\n",
       "       [1. , 4.5, 1.5],\n",
       "       [1. , 3.9, 1.1],\n",
       "       [1. , 4.8, 1.8],\n",
       "       [1. , 4. , 1.3],\n",
       "       [1. , 4.9, 1.5],\n",
       "       [1. , 4.7, 1.2],\n",
       "       [1. , 4.3, 1.3],\n",
       "       [1. , 4.4, 1.4],\n",
       "       [1. , 4.8, 1.4],\n",
       "       [1. , 5. , 1.7],\n",
       "       [1. , 4.5, 1.5],\n",
       "       [1. , 3.5, 1. ],\n",
       "       [1. , 3.8, 1.1],\n",
       "       [1. , 3.7, 1. ],\n",
       "       [1. , 3.9, 1.2],\n",
       "       [1. , 5.1, 1.6],\n",
       "       [1. , 4.5, 1.5],\n",
       "       [1. , 4.5, 1.6],\n",
       "       [1. , 4.7, 1.5],\n",
       "       [1. , 4.4, 1.3],\n",
       "       [1. , 4.1, 1.3],\n",
       "       [1. , 4. , 1.3],\n",
       "       [1. , 4.4, 1.2],\n",
       "       [1. , 4.6, 1.4],\n",
       "       [1. , 4. , 1.2],\n",
       "       [1. , 3.3, 1. ],\n",
       "       [1. , 4.2, 1.3],\n",
       "       [1. , 4.2, 1.2],\n",
       "       [1. , 4.2, 1.3],\n",
       "       [1. , 4.3, 1.3],\n",
       "       [1. , 3. , 1.1],\n",
       "       [1. , 4.1, 1.3],\n",
       "       [1. , 6. , 2.5],\n",
       "       [1. , 5.1, 1.9],\n",
       "       [1. , 5.9, 2.1],\n",
       "       [1. , 5.6, 1.8],\n",
       "       [1. , 5.8, 2.2],\n",
       "       [1. , 6.6, 2.1],\n",
       "       [1. , 4.5, 1.7],\n",
       "       [1. , 6.3, 1.8],\n",
       "       [1. , 5.8, 1.8],\n",
       "       [1. , 6.1, 2.5],\n",
       "       [1. , 5.1, 2. ],\n",
       "       [1. , 5.3, 1.9],\n",
       "       [1. , 5.5, 2.1],\n",
       "       [1. , 5. , 2. ],\n",
       "       [1. , 5.1, 2.4],\n",
       "       [1. , 5.3, 2.3],\n",
       "       [1. , 5.5, 1.8],\n",
       "       [1. , 6.7, 2.2],\n",
       "       [1. , 6.9, 2.3],\n",
       "       [1. , 5. , 1.5],\n",
       "       [1. , 5.7, 2.3],\n",
       "       [1. , 4.9, 2. ],\n",
       "       [1. , 6.7, 2. ],\n",
       "       [1. , 4.9, 1.8],\n",
       "       [1. , 5.7, 2.1],\n",
       "       [1. , 6. , 1.8],\n",
       "       [1. , 4.8, 1.8],\n",
       "       [1. , 4.9, 1.8],\n",
       "       [1. , 5.6, 2.1],\n",
       "       [1. , 5.8, 1.6],\n",
       "       [1. , 6.1, 1.9],\n",
       "       [1. , 6.4, 2. ],\n",
       "       [1. , 5.6, 2.2],\n",
       "       [1. , 5.1, 1.5],\n",
       "       [1. , 5.6, 1.4],\n",
       "       [1. , 6.1, 2.3],\n",
       "       [1. , 5.6, 2.4],\n",
       "       [1. , 5.5, 1.8],\n",
       "       [1. , 4.8, 1.8],\n",
       "       [1. , 5.4, 2.1],\n",
       "       [1. , 5.6, 2.4],\n",
       "       [1. , 5.1, 2.3],\n",
       "       [1. , 5.1, 1.9],\n",
       "       [1. , 5.9, 2.3],\n",
       "       [1. , 5.7, 2.5],\n",
       "       [1. , 5.2, 2.3],\n",
       "       [1. , 5. , 1.9],\n",
       "       [1. , 5.2, 2. ],\n",
       "       [1. , 5.4, 2.3],\n",
       "       [1. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_with_bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(2042)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ratio = 0.2\n",
    "validation_ratio = 0.2\n",
    "total_size = len(X_with_bias)\n",
    "\n",
    "test_size = int(total_size * test_ratio)\n",
    "validation_size = int(total_size * validation_ratio)\n",
    "train_size = total_size - test_size - validation_size\n",
    "\n",
    "random_indices = np.random.permutation(total_size)\n",
    "\n",
    "X_train = X_with_bias[random_indices[:train_size]]\n",
    "y_train = y[random_indices[:train_size]]\n",
    "X_valid = X_with_bias[random_indices[train_size:-test_size]]\n",
    "y_valid = y[random_indices[train_size:-test_size]]\n",
    "X_test = X_with_bias[random_indices[-test_size:]]\n",
    "y_test = y[random_indices[-test_size:]]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The targets are currently class indices (0, 1 or 2), but we need target class probabilities to train the Softmax Regression model. Each instance will have target class probabilities equal to 0.0 for all classes except for the target class which will have a probability of 1.0 (in other words, the vector of class probabilities for ay given instance is a one-hot vector). Let's write a small function to convert the vector of class indices into a matrix containing a one-hot vector for each instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_one_hot(y):\n",
    "    n_classes = y.max() + 1\n",
    "    m = len(y)\n",
    "    Y_one_hot = np.zeros((m,n_classes))\n",
    "    Y_one_hot[np.arange(m),y] = 1\n",
    "    return Y_one_hot"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test this function on the first 10 instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 1, 1, 0, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_one_hot(y_train[:10])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Looks good, so let's create the target class probabilities matrix for the training set and the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_one_hot = to_one_hot(y_train)\n",
    "y_valid_one_hot = to_one_hot(y_valid)\n",
    "y_test_one_hot = to_one_hot(y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's implement the Softmax function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(logits):\n",
    "    exps = np.exp(logits)\n",
    "    exps_sum = np.sum(exps, axis=1, keepdims=True)\n",
    "    return exps/exps_sum"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are almost ready to start training. Let's define the number of inputs and outputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = X_train.shape[1] # == 3 (2 features plus the bias term)\n",
    "n_outputs = len(np.unique(y_train)) # == 3 (3 iris classes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now here comes the hardest part: training! Theoretically, it's simple: it's just a matter of translating the math equations into Python code. But in practice, it can be quite tricky: in particular, it's easy to mix up the order of the terms, or the indices. You can even end up with code that looks like it's working but is actually not computing exactly the right thing. When unsure, you should write down the shape of each term in the equation and make sure the corresponding terms in your code match closely. It can also help to evaluate each term independently and print them out. The good news it that you won't have to do this everyday, since all this is well implemented by Scikit-Learn, but it will help you understand what's going on under the hood.\n",
    "\n",
    "So the equations we will need are the cost function:\n",
    "\n",
    "$J(\\mathbf{\\Theta}) = - \\dfrac{1}{m}\\sum\\limits_{i=1}^{m}\\sum\\limits_{k=1}^{K}{y_k^{(i)}\\log\\left(\\hat{p}_k^{(i)}\\right)}$\n",
    "\n",
    "And the equation for the gradients:\n",
    "\n",
    "$\\nabla_{\\mathbf{\\theta}^{(k)}} \\, J(\\mathbf{\\Theta}) = \\dfrac{1}{m} \\sum\\limits_{i=1}^{m}{ \\left ( \\hat{p}^{(i)}_k - y_k^{(i)} \\right ) \\mathbf{x}^{(i)}}$\n",
    "\n",
    "Note that $\\log\\left(\\hat{p}_k^{(i)}\\right)$ may not be computable if $\\hat{p}_k^{(i)} = 0$. So we will add a tiny value $\\epsilon$ to $\\log\\left(\\hat{p}_k^{(i)}\\right)$ to avoid getting `nan` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 5.173284880908112\n",
      "500 0.8258143504756524\n",
      "1000 0.6740383508681776\n",
      "1500 0.5891518016822948\n",
      "2000 0.5353052890403674\n",
      "2500 0.4975988211901051\n",
      "3000 0.469220320068328\n",
      "3500 0.4467104744290491\n",
      "4000 0.4281482798645294\n",
      "4500 0.4123865613153482\n",
      "5000 0.3986986115898958\n"
     ]
    }
   ],
   "source": [
    "eta = 0.01\n",
    "n_iterations = 5001\n",
    "m = len(X_train)\n",
    "epsilon = 1e-7\n",
    "\n",
    "Theta = np.random.randn(n_inputs, n_outputs)\n",
    "\n",
    "for iteration in range(n_iterations):\n",
    "    logits = X_train.dot(Theta)\n",
    "    Y_proba = softmax(logits)\n",
    "\n",
    "    if iteration % 500 == 0:\n",
    "        loss = -np.mean(np.sum(y_train_one_hot * np.log(Y_proba + epsilon), axis=1))\n",
    "        print(iteration, loss)\n",
    "    error = Y_proba - y_train_one_hot\n",
    "    gradients = 1/m * X_train.T.dot(error)\n",
    "    Theta = Theta - eta * gradients"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that's it! The Softmax model is trained. Let's look at the model parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.96506788, -1.31523785, -2.75290651],\n",
       "       [-0.37388472,  1.16174385,  0.478946  ],\n",
       "       [-1.0446009 , -0.97856165,  2.04772298]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Theta"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make predictions for the validation set and check the accuracy score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits = X_valid.dot(Theta)\n",
    "Y_proba = softmax(logits)\n",
    "y_predict = np.argmax(Y_proba, axis=1)\n",
    "\n",
    "accuracy_score = np.mean(y_predict == y_valid)\n",
    "accuracy_score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, this model looks pretty good. For the sake of the exercise, let's add a bit of $l_2$\n",
    " regularization. The following training code is similar to the one above, but the loss now has an additional $l_2$\n",
    " penalty, and the gradients have the proper additional term (note that we don't regularize the first element of Theta since this corresponds to the bias term). Also, let's try increasing the learning rate eta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3.3146667923694895\n",
      "500 0.5378947790755032\n",
      "1000 0.5046573947978396\n",
      "1500 0.49505389686784396\n",
      "2000 0.4914430657465934\n",
      "2500 0.4899610932392464\n",
      "3000 0.48932601099063844\n",
      "3500 0.48904708136462205\n",
      "4000 0.4889227267817381\n",
      "4500 0.4888667580914424\n",
      "5000 0.48884141287504024\n"
     ]
    }
   ],
   "source": [
    "eta = 0.1\n",
    "n_iterations = 5001\n",
    "m = len(X_train)\n",
    "epsilon = 1e-7\n",
    "alpha = 0.1 # regularization hyperparameter\n",
    "\n",
    "Theta = np.random.randn(n_inputs, n_outputs)\n",
    "\n",
    "for iteration in range(n_iterations):\n",
    "    logits = X_train.dot(Theta)\n",
    "    Y_proba = softmax(logits)\n",
    "\n",
    "    if iteration % 500 == 0:\n",
    "        xentropy_loss = -np.mean(np.sum(y_train_one_hot * np.log(Y_proba + epsilon), axis=1))\n",
    "        l2_loss = 1/2 * np.sum(np.square(Theta[1:])) # not adding regularization to first theta i.e bias term\n",
    "        loss = xentropy_loss + alpha * l2_loss\n",
    "        print(iteration, loss)\n",
    "    error = Y_proba - y_train_one_hot\n",
    "    gradients = 1/m * X_train.T.dot(error) + np.r_[np.zeros([1,n_outputs]), alpha*Theta[1:]] \n",
    "    \"\"\"\n",
    "        By adding alpha * Theta[1:] to the gradients, the regularization term is included in the gradient calculation for all weights except the bias term (Theta[0]). The np.zeros([1,n_outputs]) term is added to the regularization term to account for the bias term in the weights, as it is not regularized. \n",
    "    \"\"\"\n",
    "    Theta = Theta - eta * gradients"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the additional $l_2$\n",
    " penalty, the loss seems greater than earlier, but perhaps this model will perform better? Let's find out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits = X_valid.dot(Theta)\n",
    "Y_proba = softmax(logits)\n",
    "y_predict = np.argmax(Y_proba, axis=1)\n",
    "\n",
    "accuracy_score = np.mean(y_predict == y_valid)\n",
    "accuracy_score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool, perfect accuracy! We probably just got lucky with this validation set, but still, it's pleasant."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's add early stopping. For this we just need to measure the loss on the validation set at every iteration and stop when the error starts growing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1.2164162761242936\n",
      "249 0.7624795705167464\n",
      "250 0.7624807731610082 early stopping!\n"
     ]
    }
   ],
   "source": [
    "eta = 0.1\n",
    "n_iterations = 5001\n",
    "m = len(X_train)\n",
    "epsilon = 1e-7\n",
    "alpha = 0.1 # regularization hyperparameter\n",
    "best_loss = np.infty\n",
    "\n",
    "Theta = np.random.rand(n_inputs,n_outputs)\n",
    "\n",
    "for iteration in range(n_iterations):\n",
    "    logits = X_train.dot(Theta)\n",
    "    Y_proba = softmax(logits)\n",
    "    error = Y_proba - y_train_one_hot\n",
    "    gradients = 1/m * X_train.T.dot(error)\n",
    "    Theta = Theta - eta * gradients\n",
    "\n",
    "    logits = X_valid.dot(Theta)\n",
    "    Y_proba = softmax(logits)\n",
    "    xentropy_loss = -np.mean(np.sum(y_valid_one_hot * np.log(Y_proba + epsilon), axis=1))\n",
    "    l2_loss = 1/2 * np.sum(np.square(Theta[1:]))\n",
    "    loss = xentropy_loss + alpha * l2_loss\n",
    "    if iteration % 500 == 0:\n",
    "        print(iteration,loss)\n",
    "    if loss < best_loss:\n",
    "        best_loss = loss\n",
    "    else:\n",
    "        print(iteration-1, best_loss)\n",
    "        print(iteration, loss, \"early stopping!\")\n",
    "        break\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits = X_valid.dot(Theta)\n",
    "Y_proba = softmax(logits)\n",
    "y_predict = np.argmax(Y_proba, axis=1)\n",
    "\n",
    "accuracy_score = np.mean(y_predict == y_valid)\n",
    "accuracy_score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still perfect, but faster."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot the model's predictions on the whole dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "x0,x1 = np.meshgrid(\n",
    "    np.linspace(0,8,500).reshape(-1,1),\n",
    "    np.linspace(0,3.5,200).reshape(-1,1)\n",
    ")\n",
    "\n",
    "X_new = np.c_[x0.ravel(),x1.ravel()]\n",
    "X_new_with_bias = np.c_[np.ones([len(X_new),1]), X_new]\n",
    "\n",
    "logits = X_new_with_bias.dot(Theta)\n",
    "Y_proba = softmax(logits)\n",
    "y_predict = np.argmax(Y_proba, axis=1) # axis = 1 means row wise\n",
    "\n",
    "zz1 = Y_proba[:,1].reshape(x0.shape)\n",
    "zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(X[y==2,0], X[y==2,1], \"g^\", label=\"Iris Virginica\")\n",
    "plt.plot(X[y==1,0], X[y==1,1], \"bs\", label=\"Iris Versicolor\")\n",
    "plt.plot(X[y==0,0], X[y==0,1], \"yo\", label=\"Iris Setosa\")\n",
    "\n",
    "\n",
    "custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "\n",
    "plt.contourf(x0,x1,zz, cmap=custom_cmap)\n",
    "contour = plt.contour(x0,x1,zz1, cmap=plt.cm.brg)\n",
    "plt.clabel(contour, inline=1, fontsize=12)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 7, 0, 3.5])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let's measure the final model's accuracy on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits = X_test.dot(Theta)\n",
    "Y_proba = softmax(logits)\n",
    "y_predict = np.argmax(Y_proba, axis=1)\n",
    "\n",
    "accuracy_score = np.mean(y_predict == y_test)\n",
    "accuracy_score"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}