03_classification_own.ipynb

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   "id": "initial_id",
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     "end_time": "2023-12-03T12:21:48.192890082Z",
     "start_time": "2023-12-03T12:21:47.560811054Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/renan/.local/lib/python3.10/site-packages/sklearn/datasets/_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "mnist = fetch_openml('mnist_784', as_frame=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:21:57.829366881Z",
     "start_time": "2023-12-03T12:21:48.184019270Z"
    }
   },
   "id": "2059bb148f810588"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "X,y = mnist.data, mnist.target"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:21:57.831604838Z",
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    }
   },
   "id": "446ddb4fc8f3690d"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       ...,\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.]])"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ],
   "metadata": {
    "collapsed": false,
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     "end_time": "2023-12-03T12:21:57.842260245Z",
     "start_time": "2023-12-03T12:21:57.832282851Z"
    }
   },
   "id": "4b39196ed3fedee8"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "(70000, 784)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ],
   "metadata": {
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     "end_time": "2023-12-03T12:21:57.845463265Z",
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   },
   "id": "e576b39f54b187d3"
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  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_digit(image_data):\n",
    "    image = image_data.reshape(28,28)\n",
    "    plt.imshow(image,cmap='binary')\n",
    "    plt.axis('off')\n",
    "    \n",
    "some_digit = X[0]\n",
    "plot_digit(some_digit)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:21:57.931836518Z",
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    }
   },
   "id": "49ef67ab146916ef"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "'5'"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:21:57.932425894Z",
     "start_time": "2023-12-03T12:21:57.929880378Z"
    }
   },
   "id": "8ad8175c8aed401e"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:21:57.932636704Z",
     "start_time": "2023-12-03T12:21:57.930229225Z"
    }
   },
   "id": "aafca53eddb33c53"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Trainieren eines binären Klassifikators"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "83e799597a48cd7c"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == '5')  # True for all 5s, False for all other digits\n",
    "y_test_5 = (y_test == '5')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:21:57.932831131Z",
     "start_time": "2023-12-03T12:21:57.930516184Z"
    }
   },
   "id": "f351e2d709087816"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "SGDClassifier(random_state=42)",
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:22:10.657792059Z",
     "start_time": "2023-12-03T12:21:57.930778142Z"
    }
   },
   "id": "5f6d485dc6d31110"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "array([ True])"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:22:10.662928398Z",
     "start_time": "2023-12-03T12:22:10.657631654Z"
    }
   },
   "id": "d0818252db788a77"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.95035, 0.96035, 0.9604 ])"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring='accuracy')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:22:27.700824413Z",
     "start_time": "2023-12-03T12:22:10.660529223Z"
    }
   },
   "id": "1e927b600435f752"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy_clf = DummyClassifier()\n",
    "\n",
    "dummy_clf.fit(X_train, y_train_5)\n",
    "\n",
    "print(any((dummy_clf.predict(X_train))))  # Gibt False zurück, keine 5en gefunden"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:22:27.703607031Z",
     "start_time": "2023-12-03T12:22:27.693944106Z"
    }
   },
   "id": "74aeeb880e8ec62b"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.90965, 0.90965, 0.90965])"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(dummy_clf, X_train, y_train_5, cv=3, scoring='accuracy')\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:22:27.980032133Z",
     "start_time": "2023-12-03T12:22:27.694429858Z"
    }
   },
   "id": "6db69a7e593709d8"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95035\n",
      "0.96035\n",
      "0.9604\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3)  # add shuffle=True if the dataset is not\n",
    "                                       # already shuffled\n",
    "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    X_train_folds = X_train[train_index]\n",
    "    y_train_folds = y_train_5[train_index]\n",
    "    X_test_fold = X_train[test_index]\n",
    "    y_test_fold = y_train_5[test_index]\n",
    "\n",
    "    clone_clf.fit(X_train_folds, y_train_folds)\n",
    "    y_pred = clone_clf.predict(X_test_fold)\n",
    "    n_correct = sum(y_pred == y_test_fold)\n",
    "    print(n_correct / len(y_pred))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:22:45.056544133Z",
     "start_time": "2023-12-03T12:22:27.980431806Z"
    }
   },
   "id": "389ec1299606a5d4"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:23:02.130872311Z",
     "start_time": "2023-12-03T12:22:45.054857392Z"
    }
   },
   "id": "522bcc4bb1ac0cb2"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_train_5, y_train_pred)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:23:02.147298866Z",
     "start_time": "2023-12-03T12:23:02.133661745Z"
    }
   },
   "id": "bc87bde2abcfda09"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[53892,   687],\n       [ 1891,  3530]])"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:23:02.148263899Z",
     "start_time": "2023-12-03T12:23:02.146079174Z"
    }
   },
   "id": "74b9ae748a847b96"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Confusion Matrix\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c1b08bbd874c9ecb"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[53892,   687],\n       [ 1891,  3530]])"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_train_5, y_train_pred)\n",
    "cm"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:23:18.923150101Z",
     "start_time": "2023-12-03T12:23:02.149361341Z"
    }
   },
   "id": "b317e89b11a5f5cd"
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8370879772350012"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision_score(y_train_5, y_train_pred) # == 3530 / (687 + 3530)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:28:12.475936964Z",
     "start_time": "2023-12-03T12:28:12.446518138Z"
    }
   },
   "id": "f6736f7fcb4a8e29"
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "0.6511713705958311"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5,y_train_pred)  # == 3530 / (1891 + 3530)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:28:25.127558197Z",
     "start_time": "2023-12-03T12:28:25.058087306Z"
    }
   },
   "id": "aced81cdf380b060"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "0.7325171197343846"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(y_train_5, y_train_pred)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:31:57.951457706Z",
     "start_time": "2023-12-03T12:31:57.913367910Z"
    }
   },
   "id": "8cd24f8d5571c9e3"
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "array([2164.22030239])"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "y_scores"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:36:21.969326417Z",
     "start_time": "2023-12-03T12:36:21.919704128Z"
    }
   },
   "id": "c57e180601e2452e"
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "array([False])"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 3000\n",
    "\n",
    "y_some_digit_pred = y_scores > threshold\n",
    "y_some_digit_pred"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:38:00.695199542Z",
     "start_time": "2023-12-03T12:38:00.646343108Z"
    }
   },
   "id": "c61c79c672ac749a"
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(sgd_clf,X_train,y_train_5, cv=3, method='decision_function')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:39:38.825067609Z",
     "start_time": "2023-12-03T12:39:21.800821356Z"
    }
   },
   "id": "bc77572a5e9bbb20"
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:41:15.833314630Z",
     "start_time": "2023-12-03T12:41:15.810953072Z"
    }
   },
   "id": "96f06a5bad433560"
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x400 with 1 Axes>",
      "image/png": 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rB/PnZ73funXqPs88ow5toyhqwjpoUIGHbHOcndWB9NN7sO3niRNqx6QLFywWVpG0bBmUKweNG6vNH4TlSPIphLCqrjW7cn7weT5v9TlODuqj42R9MsP+GkbpKaXptrKbtAfNQmJiInXq1KFOnTokJiYWyDnCw9Ve5BqNaWYhgMuXTcno9u1q55kHzZ+vtlfs0EFdd3GR4WzyIm22pvSuX4fHHoNvv7V8PEXF1avq9/HAAbW5iLAcST6FEFbn5uTGJ898wrEBx6jlV8u4PdWQyvITy6k3px4X7160YoSFl6+vL75ZjUX0iKZNU2vdDh3K/P1t29QZh5yc1M5FaXbvVms3+/RRa/LEo3n7bXXWpXv31IQpfQJ/65bVwrJ5Msan9UjyKYQoNKr7Vudo/6NseWMLLSq2MG4/ffs0lb+tzOito0nQJVgxwsLFw8ODW7ducevWrXwZZun339WazLSxMhs3Bje3jOU0GvjkE3VcygoV1ATzzBk14VQUeOKJRw5FpKPRQOXK6lSd5cqpMyN162ZqyiDyRuZ1tx5JPoUQhYrWQUurSq0I6x3GhpANxkfxAJ9v/5z6c+pzLPKYFSMsWqKiTO04u3RRt7X/b/z//ftNjyP9/eHUKTW5NBjg888zTiUpLKN0aVi6VB0j9OmnrR2N7ZKpNa1Hkk8hRKHVtkpbDr1ziEo+lYzbzkWfo96ceny3/zsrRmb7vv1WTTgza0/YrBlEREDv3mrnFoNBXa9Rw+JhimyUKWM+K8/WreqsSqmp1ovJlqTVfHp6qi9hOZJ8CiEKtTql6nBhyAX+6fMPFbwrGLcPDB3IuLBxGJSC7+VdWCUlJRESEkJISAhJSUnZlk1JUQd7B7XWcsiQrMtOnap2LvLxUYdIssMJqGzOxx9D69awZIk6CoF4uLTkU2o9LU+STyGETWheoTnHBxynS40uxm1jt42l4Q8NSdGnWC8wK9Lr9SxZsoQlS5ag1+szLbNhg5o8urhAiRKg00GTJhnLdeqkdmZJTYWhQws2bpH/Klc2LffpA2vXWi8WWxAfbxqsX9p7Wp4kn0IIm1HMpRirX13N0KZDjdvCI8KpOqMqYZfCrBaXtTg7OzN16lSmTp2aYXrNffvUpDOt/SbAZ5+pPdTTPz5fskRtx/nHH2pnFq3WQsGLfPXSS+ofF6DW6L3wAuzYYd2YCjPp6W5dknwKIWyKRqNharupDG4y2Ljtyv0rtPqpFYuOLLJiZJbn5OTE0KFDGTp0qHF6TUVRk87MOgONHq223axY0dQzvUcPCwctCoSvb8aB0nv1goe0xrBb0tnIuiT5FELYpOntp7Ojzw78PUw9Zt5Y8wZzD861YlSWpddDWBj8+qv6NSoKHLL4rd61q/pI/bXXLBmhsKSnnlLb9ZYrp65fvAhff23dmAqrGjXUEQO++UatJRaW5WjtAIQQIq+eqvAUV4Zd4blFz7Ht8jYA3v7zbZy1zvQK6mXl6ArW6tUwZIjCtWum3kBly6qdTrZsMZU7eBAaNLBCgMIqfHzU9p5p93z8eHj3XbVmVJiUKqWOlSqsQ2o+hRA2zVnrzJ89/6Sefz3jtt6/9+bAjQNWjKpgrV4NL7+sjvOY3o0b5olnaqoknvbo8cehc2d1OTkZQkOtG48QD5LkUwhh8zydPTn8zmFq+Jp60gz/azhJqUWvwZterw6TpCgA5mMgpbX3LF9eTTyl85D9Sj+U1pkz1otDiMxI8imEKBIcNA7sfHMnzlq11/eOKzt4bfVrKGqWVmTs2JGxxjM9RVGHTJKezvatSRPYuROio+HLL60dTeGzdy8cOqT2ei9ivyJsgiSfQogio4RbCTa/sRkHjfqrbdWpVfRc3ZPk1GQrR5Z/Tp/OWbn0Q8kI+1OsGDz5JBQvbu1ICqe33oKGDc3HRxWWI8mnEKJIearCU0xoM8G4vvT4UoIXBZOgS7BiVPlj6VIYMCBnZWX4GJGeTLlpLm2opdKlZQYva5DkUwhR5HzU/CM+b/W5cf2fK//Q5uc2pBps93/ghIScjcmZ1ubz6acLPiZhG3Q6CA6G2bOtHUnhkJRkmmpW/kizDkk+hRBF0ifPfMLv3U2jbu+5toeRf4+0YkR5p9eDmxvMmGG+XaNRHlhXv06bJp2NhMlHH6kzW737LsyaBQZDwZwnMRFu3y6YY+dGVFT2Nb0REaZlST6tQ5JPIUSR9Xz155nTcY5xffLuySwMX2i9gPJg1Ci1XdrEieqMNV27wvHjsGpVxjmpy5WDlSvVMkKkuXTJtDxoELz9du72T001nxEoM/HxULMm+PlBUBCMGwdHjli+M88334C/P7Rtq9b4ZiZ9e2iZ1906JPkUQhRp7zR6h89afmZc7/9nf8Ijwq0XUC5s2qQOEn7lCpQsqXYiWbUKatdWE8zLlzVs3arOz751qzqjjSSe4kGLF0Pfvqb1efMgJARSUnK2f9eu6gQGo0ZlXebgQbh8WV0+cgTGjlWT0CpV4IMP4NatvEafO4sXq1+3bIEJEzIvI/O6W58kn0KIIu/TFp/yZtCbACTrk+m+sjt6g97KUWVv92547jnT+uTJGctotdCypdoWtGVLedQuMufhAXPnwscfm7YtWaL+3Dxs7ne9Hv74Q10ePx727Mm8nEaTeS3ihQswZQq88cbD41y5Uj1HwiP0DTx82LT8+ecQHp6xjCSf1ifJpxDCLkxrN43KxdVxVc7cOcPbf+Ty2aMFnT6tDpOT3vr1GcspisKtW7e4detWkRvPVOS/8eNh4UJwdVXXV6+GihVhw4acH2PcuMzbjD79NFy/bhpnduZMaNPG1A45LCz74169Cq+8otauvvRSzuN5UD3TRGekpkLv3hnjleTT+iT5FELYhWIuxfix84/G9fnh81l0ZJEVI8pcTIzadi69oUPhsccylk1ISKBUqVKUKlWKhEepLhJ2wcFBbTc8dappW1SUWkOYUxs2wHvvZV+mXDkYOBD+/hsCAtRtSUnZP3pP36Z0w4bM/9jKifRNCSpWVNuAOjyQ6UibT+uT5FMIYTdaVWrF4CaDjetvrX2Lf+/8a8WIzO3dC97e5tvmzjVPFoR4VP37qz9rbdqo6yPTDQKh16s1pJ99pnbYcXCAyEj49ltTme++U2sqcyKtFvPrr9XOSFlp2lRti5pmwACIi8vZOdJLn3wePQqtW2csc++eaVlqPq1Dkk8hhF2Z1m4anat1BkBn0NF9ZfdC88h69Gjz9bp1zTuKPMjDwwNFUVAUBQ8Pj4INThQpTZqotYszZ0LHjqbtGzeqj77HjIFPP1Ufm5cqpdZ2DhpkKvf33zk7z7ffqp3khg59eNk+faBVK3X58mX46qscX45RWvJZtix4eWVeZtUqtXf++fNqRz5heZJ8CiHsikajYWGXhfi5q9UwhyMOM37HeCtHpfr9d3jhBXV5wgS15kaIguLkpD4eTz/Dz5EjpuUHawV79jQtL1xo/t7ChfD882onppMnTds1GrW3vJOTefnQUDh3znybRgPff28q+803Dx/i6UHTpsH8+RkTV0WBf9M95HB3V4cwk9mNrEOSTyGE3SnhVoIlLy0xro8OG82FuxesEktqqvpYE9SOIMuXq7UyI0ZYJRxhx7780vwRfK1a5u8/8QRUq6Y+yn6wnejRo2qv+KVLzR9rPyg1Ff73P7W2tVevjOOAVq1qmkI2IUEdsik3XnpJrUENCTFt+/dfdeSIBg1y3lxAFCxJPoUQdim4cjC9g3oDYFAMPLfoOYvP/64o4O7uxKuvmrY5O6u1MjmRnJzM0KFDGTp0KMnJyQUTpLAbx4+br5cqBcnJag/3r76C336DXbtg82Z45hnzsvHxpuXsWoAkJalDKoF6rDVr1OULF2DwYPVVt67pkfm8eeo4oY8yN/2336rNBOLjYciQvB9H5B9JPoUQdmtq26kE+gQCcP7ueeYcmJP9DvlIr4cXX1SfsW/fDm++mftjpKamMn36dKZPn07qo/zvLARqrWN6pUpBbKxa+zhihPo4O6s2kuk7B2WXfHp6mo9ZO3KkOhTSjRvq9LEzZqhDjaUNaG8wwJkz4OiYp0sC4Isv1GsBNYEuV069ph078n5M8Wgk+RRC2C0fVx/mPW/qYjt512Rik2ML/Lx374Kbm3kjuOLFc38cJycn/ve///G///0Ppwcb1QmRSw8OoXT3rjp5QZq08UHT27NHrcFPX/Pp6Zn9eTp3No1je+aMmmymf/yu0ai1nV98oX4u0ve0z46iqDMtHTtmmm0JwMdHHeg+zfXram3u1q05O67If5J8CiHsWutKrXm28rMA3Iy7Sftf2pOiz+G8g3lUokTGben/c8wpZ2dnvvzyS7788kucnZ0fPTBh1/z84LXX1OUmTWDYMDhxwvS+i4t5+V9/hWbN1KGb0tpSajQP/0NKozGfvevfTEY7c3BQaz+vXIFKlczfW7NGfS8x0Xx7cjI0aqQONN+rl/l7ISGmnvRpZIxP65HkUwhh92a0n4GLVv2fdefVnUzZlYdMMIe6dzdf9/BQMnS6EMJa5syBtWvVgd5XrzZ/LyLCtHztmtqxB+CHH+DQIXW5fPmMSWpmqlUzLR86lLHmM82DtahxcWq70PHj1Z716fdLP8bngzFoNOr4pOnJGJ/WI8mnEMLuVfetzm/dfjOuf7L1E05Enchmj7y5fh2WLTOtOzgYuHs37201FUUhPj6e+Pj4QjNWqbBtHh7qY/HixdXlDh1M76UlmKC2m5yTSRPpypVzdp7GjU3L06apyWya7IY/2rnTlAT//rs6Zmea9MlnZg8CatRQxy8F0GohKChnsYr8J8mnEEIA7au2Z0hTtSusQTEQsjok3xO6Bx8Frl79xyMdLyEhAU9PTzw9PWV6TVEghg0zLacfhgnMB6dPk9k0sJmpUgX69VOXY2PV2sw02SWfbdvCEtMoaQweDPfvq8sPSz5BTT5Xr1bbqpYtm7NYRf6T5FMIIf4ztuVYnLXq/1pHIo+w4uSKfD1+kyam5fBwXb4eW4iCEBwMs2erMxSlJYtpMutYlNOaT4CJE01Tbt65Y9r+sIHfX3rJlPjevAmffKIu5yT51GjgxRfVtqHCeiT5FEKI//i4+jCz/Uzj+pTd+dv2c/x4tTfu3LkZB/DOC3d3d+Li4oiLi8M9p4ODCpFL/fvD1KkZOxK5uponir16qQPR51SJEuosRs2bq0Ms5ZRGo04LmvYjP2sW7N+vdjhKI/3vCjdJPoUQIp0+j/fB01mt0tl3fR8bz298pOPduKHO6HL3rrreoEH287XnhkajwcPDAw8PDzQyT6CwMI3GNKZnzZrqFJutW+fuGCEh6ji36f8Yy8mPcmCgafYjRYG331ZnREojyWfhJsmnEEKk4+jgyJTnTDWew/4ahkEx5OlYt2+r7comTIDFi/MrQiEKj7RH77duqRMn5JZGow6rVLKk2rmpfXuoXj1n+w4dqg6rBBAebj5cmSSfhZskn0II8YB+DfpRraQ6FszJWyeZvmd6ro+RnGxqzwZqx4ibN/MrQlVKSgqjRo1i1KhRpKQU7NikQmQmre3k7duwZUvej1O/PqxbB6GhGTvmZcXJCb7/3lRT+ssvpvck+Szc8pR8zpo1i8DAQFxdXWnatCn79u3Ltvy0adOoXr06bm5ulC9fnmHDhpGUlJSngIUQoqBpNBpmd5xtXB8TNobk1NzNnf7gbDCvv57/4wrqdDrGjx/P+PHj0emkA5OwvD591EHge/fGKuPVPvGE2ia1fHnznvk5GWtUWE+uk89ly5YxfPhwxowZw6FDh6hfvz5t27YlKioq0/JLlixhxIgRjBkzhlOnTjFv3jyWLVvG//73v0cOXgghCkrrSq1pU6kNALEpsXy27bMc77ttm/l6t27w88/5GZ3K0dGRIUOGMGTIEBwfZfJrIfKoUyd13vWFC83HAbWkiRPh5En46iv18f/16/Dxx9aJReRMrn9bffPNN/Tr148+/01tMGfOHNatW8f8+fMZMWJEhvK7du2iefPm9OzZE4DAwEB69OjB3r17szxHcnIyyem6rcXExADqX/n29Nd92rXa0zXbM7nfhc+4Z8ax+eJmACbvnszrdV/nseIPH8iwZUvzedYXLdLx4G3Nj/vt4ODApEmTMhxTFD5F9fOt0ah/bJ08qaF5cyXDz7kluLmZlr29TcvW/FYX1fv9MDm93lwlnykpKRw8eJCR6UaadXBwIDg4mN27d2e6z5NPPsnixYvZt28fTZo04cKFC4SGhvL6669neZ4JEyYwbty4DNs3btxol8OJbNq0ydohCAuS+124dPLtxJ+3/yRFn8Kw5cPoX75/tuV37CgLmAYRnDFjM6GhcVmWl/ttX4ry/f7rL2tHUPgU5fudmZxOdqFRcjGFx40bNyhbtiy7du2iWbNmxu0fffQR27Zty7I289tvv+WDDz5AURRSU1Pp378/s2fPzrQsZF7zWb58eW7fvo2Xl1dOw7V5Op2OTZs28eyzz+Lk5PTwHYRNk/tdON1JuEOlmZVISk3CycGJI28foUqJKpmWjYyE8uVN965fPz2zZmXeU17ut32R+13wtmzRsHevhtRU6N3bQPny1ovFXu93TEwMvr6+3L9/P9t8rcAbCYWFhTF+/Hi+++47mjZtyrlz5xgyZAiff/45n376aab7uLi44JJJa2EnJye7uolp7PW67ZXc78IlwDuA3vV7M+fgHHQGHZ9s+4RVr67KtOyDvX2//16LRqPN9viPcr/j4+Px/G+sm7i4ODzSBl0UhZZ8vgvOpEmwWW0lQ+nSWgYMsG48YH/3O6fXmqsOR76+vmi1WiIjI822R0ZGEhAQkOk+n376Ka+//jp9+/albt26vPjii4wfP54JEyZgMORt7DwhhLCkca1MzYDW/buO+JT4TMu98QYYDHDlCkRF5WywbCFE/ggPNy3v2mW1MEQO5Cr5dHZ2pmHDhmxO+9MCMBgMbN682ewxfHoJCQk4OJifRqtVawJy8cRfCCGsppRHKfoEqZ0sk/XJ2c75rtGow76kH+OzoLi7uxMVFUVUVJRdtocXIr309VkOMop5oZbr2zN8+HDmzp3LTz/9xKlTpxgwYADx8fHG3u9vvPGGWYekzp07M3v2bJYuXcrFixfZtGkTn376KZ07dzYmoUIIUdj1bWCaE3Pk5pHo9KZenamp1ohIHY/Uz88PPz8/mV5T2D1JPm1Hrtt8duvWjVu3bjF69GgiIiIICgpiw4YN+Pv7A3DlyhWzms5PPvkEjUbDJ598wvXr1/Hz86Nz5858+eWX+XcVQghRwJqVa8bjAY9zOOIwEXERLD2+lNfrv46iQKtWUK4cvP++acYXIYRlSfJpO/LU4WjQoEEMGjQo0/fCwsLMT+DoyJgxYxgzZkxeTiWEEIWCRqNh0rOTCF4UDMDM/TN5rd5rHDqk4Z9/1DInT6rtzixVCZmSkmIc5/PDDz/EWeYUFHZs8mR45x11Oe2rKJxkSgwhhMih1pVaU7dUXY5FHWPf9X0sO7GMye90N74/YIBlOxnpdDo++eQTAIYOHSrJp7Brb76pzrZUsiQ0aWLtaER2JPkUQogc0mg0fNn6S55f+jwA761/j9uHXwLU4UXatbNsPI6OjvTt29e4LIQ9c3RUE1BR+MlvKyGEyIXO1TvToHQDDt08xO2E21B5M5xTs87AQMvG4uLiwty5cy17UiGEeETSJFcIIXJpYOOBppW6SwD47+m3EEKIh5DkUwghcunV2q/i6fTf1HF1l1CmxjWkT6UQQuSMJJ9CCJFLns6eBN7ur6446Hk8ZCXWaHIZHx+Ph4cHHh4exMdnPuuSEEIUNpJ8CiFELp0+Dcd/7WFcP+w6BYNinemCExISSEhIsMq5hRAiLyT5FEKIXIqMBCKC4EZDAG7EX2PRkUUWj8PNzY2LFy9y8eJF3NzcLH5+IYTIC0k+hRAil8qW/W9hn6nj0aRdkyweh4ODA4GBgQQGBprNLCeEEIWZ/LYSQohcqlJFncpPf6gXZYupmeiJWyf45egvVo5MCCEKP0k+hRAihxQFFiyApUvVdQeNA+NajjO+PzpsNIqiWCwenU7HtGnTmDZtGjqdzmLnFUKIRyHJpxBC5NCnn6ozqBw5Aqmp6ra3GrzFE+WeAODC3QvsvrbbYvGkpKQwbNgwhg0bRkpKisXOK4QQj0KSTyGEyAG9Hr78Ul2eOBG2bDG993aDt43LH//9MXqD3iIxabVaevbsSc+ePdFqtRY5pxBCPCpJPoUQIgc2bjRff+450/Jr9V6jTLEyAPxz5R8WH11skZhcXV355Zdf+OWXX3B1dbXIOYUQ4lFJ8imEEDnw8sum5WeeAY3GtO6kdWJuZ9Mc69P3Trdo208hhLAlknwKIcRDREdD2jjuLi7w998Zy7Sv0p6GpdVxPw9HHGbLxS0ZCwkhhJDkUwghHmbZMtNyrVrg5JSxjEajYXiz4cb1aXunFXhc8fHx+Pn54efnJ9NrCiFshiSfQgjxEO++a1qeNy/rcq/UeoVyXuUA+PPfPzl161QBRwa3b9/m9u3bBX4eIYTIL5J8CiFENrZuNS37+8Pjj2dd1knrxOAmg43r74a+W6BtP93c3Dh+/DjHjx+X6TWFEDZDkk8hhMjGmDGm5datH16+f6P+lPYsDUDYpTA2X9xcQJGp02vWrl2b2rVry/SaQgibIb+thBAiG61amZYnTHh4+WIuxZgYPNG4PmX3lAKISgghbJckn0IIkY1x4+DiRdi7FypWzNk+3et0p6K3WnjDuQ1su7StQGLT6XTMnTuXuXPnyvSaQgibIcmnEEI8RGAgNGmS8/LOWmfGthxrXB+yYQgGxZDvcaWkpPD222/z9ttvy/SaQgibIcmnEEIUgNfqvUYN3xoAHIk8wsqTK/P9HFqtlhdeeIEXXnhBptcUQtgMST6FECITs2bBRx/BiRN529/RwZHPW31uXF90dFE+RWbi6urKmjVrWLNmjUyvKYSwGZJ8CiHEAxQFpk6FSZOgbl24eTNvx+lasyveLt4A7LyyE51e2mUKIYQkn0II8YAdO+D8eXW5dWsoXTpvx3HQONCqktpd/m7SXdafW59PEQohhO2S5FMIIR6wYIFp+c03H+1Yr9V9zbg8+8DsRzvYAxISEggMDCQwMJCEtMnnhRCikJPkUwgh0omNheXL1WVvb3jxxUc73gs1XjAbdunsnbOPGKGJoihcvnyZy5cvF+hMSkIIkZ8k+RRCiHSWL4e0SsSePeFRZ610dHBkUJNBxvXx/4x/tAOm4+rqyr59+9i3b590OBJC2AxJPoUQIp35803Lj/rIPU3voN54OnsC8PORn7l071K+HFer1dK4cWMaN24sQy0JIWyGJJ9CCPGfixdh1y51uW5daNgwf47r6+7LB80+AMCgGFh1clX+HFgIIWyQJJ9CCPGfd95Rv2o00KOH+jW/9Kjbw7i84uSKfDlmamoqv/zyC7/88gupqan5ckwhhChoknwKIcR/0tp3ajTQq1f+HrtayWrU868HwN7rezl56+QjHzM5OZnXXnuN1157jeTk5Ec+nhBCWIIkn0II8Z9B//ULCgiAMmXy//i96/c2Ls8Ln/fIx3NwcCA4OJjg4GAcHOTXuRDCNshvKyGE+M+zz4LBANevF8zxewf1xlnrDMBPR38iUZ/4SMdzc3Nj06ZNbNq0CbdH7ZYvhBAWIsmnEEKkk5/tPB9U3K04Pev2BCAmOYa/7vxVcCcTQohCSpJPIYTdS0qy3LneqPeGcfm3qN9I1D1a7acQQtgaST6FEHbPzU2t8SxWDO7cKdhzPV3xaR4r/hgA91Pvs+3KtjwfKyEhgdq1a1O7dm2ZXlMIYTMk+RRC2LXwcNNyXBwUL16w53N0cGRi8ETj+qMMu6QoCidPnuTkyZMyvaYQwmZI8imEsGuzZpmWX3sNLNFpvH2V9ni5eAGw7OQybsXfytNxXF1d2bp1K1u3bpXpNYUQNkOSTyGE3UpOhh9/NK3PmWOZ83o4e9A3qC8AKfoUlp9YnqfjaLVaWrZsScuWLWV6TSGEzZDkUwhht5YtMy0HBoKHh+XOndbrHWDJ8SWWO7EQQliZJJ9CCLuVfhajzz6z7LnrlapHBdcKAOy6uouLdy/m+hipqamsWbOGNWvWyPSaQgibIcmnEMIuXXwg1+vRI/NyBekpn6eMyz8e+jGbkplLTk7mxRdf5MUXX5TpNYUQNkOSTyGEXZo+3bT82GPg6Gj5GNqUaIOTgxMAcw7OQafX5Wp/BwcHnnzySZ588kmZXlMIYTPkt5UQwi4tXGhaXr3aOjGUdC5Jl+pdAIhOjGbzxc252t/NzY2dO3eyc+dOmV5TCGEzJPkUQtgdRYFffoHgYGjUCOrVs14sL9V8ybi84kTex/wUQghbIcmnEMLuaDTQsSNs2gT79lk3lraV2xrH/Fx6Yin3k+5bNyAhhChgknwKIeyaRmPd83s4e/Ba3dcASNAlsORYzoddSkxMpHHjxjRu3JjERJkjXghhG/LUxH7WrFlMmjSJiIgI6tevz4wZM2jSpEmW5e/du8eoUaNYvXo10dHRVKxYkWnTptGhQ4c8By6EEEXF2w3f5rsD3wGw+NhiBjQekKP9DAYDBw4cMC4LUdjp9Xp0utx1rLNFOp0OR0dHkpKS0Ov11g4n3zg5OeXLhBa5Tj6XLVvG8OHDmTNnDk2bNmXatGm0bduWM2fOUKpUqQzlU1JSePbZZylVqhQrV66kbNmyXL58GR8fn0cOXgghcmv1akhMhM6dwcvL2tGo6gfUp6ZvTU7dPsWuq7s4c/sM1X2rP3Q/FxcX/vzzT+OyEIWVoihERERw7949a4diEYqiEBAQwNWrV9FY+/FKPvPx8SEgIOCRrivXyec333xDv3796NOnDwBz5sxh3bp1zJ8/nxEjRmQoP3/+fKKjo9m1axdOTuqQIoGBgXkOWAgh8kpRYOxYOHYMXFzg6lXw87N2VKpe9XsxYrP6O3TizokseGHBQ/dxdHSkY8eOBR2aEI8sLfEsVaoU7u7uRS4he5DBYCAuLg5PT88iMwyaoigkJCQQFRUFQOnSpfN8rFwlnykpKRw8eJCRI0catzk4OBAcHMzu3bsz3Wft2rU0a9aMgQMH8vvvv+Pn50fPnj35+OOPs6y6TU5ONhswOSYmBlCrse2huj5N2rXa0zXbM7nfBe/wYTh2TP0jOCjIgI+PHmt9ux+832/Vf4svd3xJbEosS44t4csWX+LnUUgyY/HI7PnzrdfruXv3Ln5+fhQvXtza4ViEoiikpKTg4uJSpBJtFxcXDAYDt27donjx4hnyuJz+fOcq+bx9+zZ6vR5/f3+z7f7+/pw+fTrTfS5cuMCWLVsICQkhNDSUc+fO8e6776LT6RgzZkym+0yYMIFx48Zl2L5x40bc3d1zE3KRsGnTJmuHICxI7nfBGT68BeADQN26JwgNvWDVeMD8frfybsXaW2tJ0afw2arPaO/bPtt99Xo9x44dA6Bu3br50hZLFCx7/Hw7OjoSEBCAwWAwVibZi9jYWGuHkO8MBgOJiYls3rw5w7S+CQkJOTqGRlEUJacnvHHjBmXLlmXXrl00a9bMuP2jjz5i27Zt7N27N8M+1apVIykpiYsXLxp/MX7zzTdMmjSJmzdvZnqezGo+y5cvz+3bt/EqLI20LECn07Fp0yaeffZZY5MFUXTJ/S5Yt25B2bKm72tUlA5rNj3P7H7vurqLlotaAvBMhWfYFLIp21qT+Ph4Y03S3bt38fDwKPC4Rd7Y8+c7KSmJq1evEhgYiKurq7XDsQhFUYiNjaVYsWJFquYT1Pt56dIlypcvn+F+xsTE4Ovry/3797PN13JV8+nr64tWqyUyMtJse2RkJAEBAZnuU7p06Qy9o2rWrElERAQpKSk4Oztn2MfFxSXTxvNOTk5296EF+71ueyX3u2AsW2ZaDggAP7/C8T1Of7+fqfQMjxV/jPN3z7P9ynaO3T5GwzINs9zXxcWF+vXrG5fl56bws8fPt16vR6PR4ODgUGTaPz5M2ugTadddlDg4OKDRaDL9Wc7pz3auviPOzs40bNiQzZtNU8AZDAY2b95sVhOaXvPmzTl37pzZMCD//vsvpUuXzjTxFEKIgjB8uGn5+++tF0d2NBoN7zV5z7j+sDE/3dzcCA8PJzw8XKbXFELYjFyn48OHD2fu3Ln89NNPnDp1igEDBhAfH2/s/f7GG2+YdUgaMGAA0dHRDBkyhH///Zd169Yxfvx4Bg4cmH9XIYQQ2bh923y9UyfrxJETIfVCcNGqT37mh88nKTXJyhEJISxJo9GwZs2afC9bmOQ6+ezWrRuTJ09m9OjRBAUFER4ezoYNG4ydkK5cuWLWlrN8+fL89ddf7N+/n3r16jF48GCGDBmS6bBMQghREH7/3bRcoQIU5qdgvu6+dK3ZFYB7Sfc4cOOAlSMSwn717t0bjUaDRqPB2dmZKlWq8Nlnn2XoaJOfbt68Sfv22Xc2zEvZwiRPMxwNGjSIQYMGZfpeWFhYhm3NmjVjz549eTmVEEI8sg0bTMtLl1ovjpxqXak1vx7/FYCdV3byVIWnMi2XmJho/I9n/fr18uhdiALQrl07FixYQHJyMqGhoQwcOBAnJyezp7xAlv1YciurPjSPWrYwKcR//wshRP745Rf46y/44ANo2tTa0Txc+mQz9FxoluUMBgPbtm1j27ZtMr2mEAXExcWFgIAAKlasyIABAwgODmbt2rX07t2bLl268OWXX1KmTBmqV1dnJbt69SrdunWjYsWK+Pr68sILL3Dp0iWzY86fP5/atWvj4uJC6dKlzSr00j9KT0lJYdCgQZQuXRpXV1cqVqzIhAkTMi0LcOzYMVq3bo2bmxslS5bk7bffJi4uzvh+WsyTJ0+mdOnSlCxZkoEDB1p8/Nk81XwKIYQtcXaG555TX7aghm8NKhevzIW7F9h7bS8JugTcnTKOcezi4sLy5cuNy0LYmm++UV8P06ABrF1rvu355+HQoYfvO3y4eYfDR+Xm5sadO3cA2Lx5M15eXsbxW3U6HW3btuWJJ54gNDQUHx8fxo8fT7t27Th69CjOzs7Mnj2b4cOHM3HiRNq3b8/9+/fZuXNnpuf69ttvWbt2LcuXL6dChQpcvXqVq1evZlo2Pj6etm3b0qxZM/bv309UVBR9+/Zl0KBBLFy40Fhu69atlC5dmq1bt3Lu3Dm6detGUFAQ/fr1y79v0kNI8imEEIVQm0ptuHD3Asn6ZJafWE7voN4Zyjg6OvLKK69YPjgh8klMDFy//vBy5ctn3HbrVs72za9x7RVFYfPmzfz111+899573Lp1Cw8PD3788Ufj4/bFixdjMBiYO3cusbGxeHl5sWDBAnx8fAgLC+O5557jiy++4P3332fIkCHGYzdu3DjTc165coWqVavy1FNPodFoqFixYpbxLVmyhKSkJH7++WfjmL8zZ86kc+fOfPXVV8a+OcWLF2fmzJlotVpq1KhBx44d2bx5sySfQgiRHxQFbtyAUqXA1oZW7BPUh7mH5gLw/cHvM00+hbB1Xl5QtuzDy/llMtOsn1/O9n3UuWn+/PNPPD090el0GAwGevbsydixYxk4cCB169Y1a+d55MgRzp07h7e3t9kxkpKSOH/+PFFRUdy4cYM2bdrk6Ny9e/fm2WefpXr16rRr145OnTrxXBaPcE6dOkX9+vXNJpto3rw5BoOBM2fOGJPP2rVrm429Xrp0aeNMaZYiyacQosjq3h3+eyrNnTtQooR148mNJ8o9QT3/ehyNPMqea3s4GnmUev71zMro9XpjZ84nnnhCptcUNudRHok/+Bi+oLRq1YrZs2fj7OxMmTJlcHQ0pU4PzioWFxdHw4YNWbRoEXFxcXh6ehoHmffz88v1gPMNGjTg4sWLrF+/nr///ptXX32V4OBgVq5cmefreXAgeI1GY/E249LhSAhRZKUlngC2NsWyRqPhnYbvGNe/P5BxZPykpCSeeuopnnrqKZKSZDxQIQqCh4cHVapUoUKFCmaJZ2YaNGjA2bNnKVWqFJUrV6ZKlSrGl7e3N8WKFSMwMNBssp6H8fLyolu3bsydO5dly5axatUqoqOjM5SrWbMmR44cIT4+3rht586dODg4GDtDFRaSfAohiqQHOpeSTVOpQiukboixo9GS40syDDiv0WiM/7EVtfmjhbBFISEh+Pr68uKLL7Jr1y4uXrxIWFgYgwcP5tq1awCMHTuWKVOm8O2333L27FkOHTrEjBkzMj3eN998w6+//srp06f5999/WbFiBQEBAfj4+GR6bldXV3r16sXx48fZunUr7733Hq+//rrxkXthIcmnEKJI2rjRtPzFF9aL41F4u3rzcq2XAXXA+bVnzJ8zuru7c/bsWc6ePYu7e8be8EIIy3J3d2f79u2UL1+eN954g9q1a/PWW2+RlJSE13+NT3v16sW0adP47rvvqF27Np06deLs2bOZHq9YsWJ8/fXXNGrUiMaNG3Pp0iVCQ0MzfXzv7u7OX3/9RXR0NI0bN+bll1+mTZs2zJw5s0CvOS80iqIo1g7iYWJiYvD29ub+/fvGm2cPdDodoaGhdOjQIUMbDVH0yP3OXx06wPr16vKePYVvfM+c3u/NFzYTvCgYgE7VOvFHjz8sFaLIR/b8+U5KSuLixYtUqlQJV1dXa4djEQaDgZiYGLy8vHLdzrOwy+5+5jRfK1rfESGEQB2CJa3ms3x5yGIUE5vwTMVnKFtM7dL7579/cjzquJUjEkKIRyPJpxCiyFmxAvR6dblnz8I9l/vDOGmdeL/Z+8b1r3Z+ZVxOSkqiY8eOdOzYUTocCSFshg3/ShZCiMwNHGha7tHDenHkl34N+1HSrSQAi48u5sztM4A61FJoaCihoaHo07JtIYQo5CT5FEIUKXfvmpbd3KBevazL2gpPZ08GNTHN/fzLsV8AcHZ2ZsGCBSxYsMBsoGshhCjMJPkUQhQp6ed69veHojICUd8GfY3LS44twaAYcHJyonfv3vTu3dvuOrEIIWyXJJ9CiCKlTRt1QPnFiyE01NrR5J9yXuVoXak1AOfvnmfT+U1WjkgIIfJGkk8hRJHj6QkhIVCzprUjyV9vN3jbuDzn4Bz0ej3h4eGEh4dLm08hhM2Qud2FEMJGdKnRhRJuJYhOjOaPM3+w//J+mj3eDFDnlH5wnmkhhCiMpOZTCFEkKIo6k9FXX0FysrWjKRguji4Me2IYAHpFz5iwMZQpU4YyZcrI9JpCCJshyacQokjYuxc+/RR+/dW801FR8+GTH1LBuwIAG69uZOvRrVy/fl2m1xSiiNJoNKxZswaAS5cuodFoCA8Pt2pMj0qSTyFEkbBwofr1yBFYt86qoRQoF0cXBjY2DWQ6Y+8MK0YjRNHWu3dvNBoNGo0GJycnKlWqxEcffSSTOjwiST6FEDYvIUGt8Uzz8cfWi8US+jboi6ujOqfynINz+Hbvt4RdCkNvkE5HQuS3du3acfPmTS5cuMDUqVP5/vvvGTNmjLXDsmmSfAohbN4330BMjLrcpw8UK2bdeApaCbcStK/SHhRINaQyZMMQWv3UisDpgaw+tdra4QlRpLi4uBAQEED58uXp0qULwcHBbNqkDnVmMBiYMGEClSpVws3Njfr167Ny5Uqz/U+cOEGnTp3w8vKiWLFiPP3005w/fx6A/fv38+yzz+Lr64u3tzctWrTgUFFuN/Qf6e0uhLB5331nWn7rLevFYSmrT61mzek1GbZfj7nOy8tfZuWrK+las6vlAxMiFxr90IiIuAiLnzfAM4ADbx/I077Hjx9n165dVKxYEYAJEyawePFi5syZQ9WqVdm+fTuvvfYa69ev5/HHH+f69es888wztGzZki1btuDl5cXOnTtJTU0FIDY2ll69ejFjxgwURWHKlCl06NCBs2fPUqwI/xUtyacQwqbdvKm+0jz5pPVisQS9Qc+QDUNQUOCBDu4KCho0DN0wlBeqv4DWQWudIIXIgYi4CK7HXrd2GA/1559/4unpSWpqKsnJyTg4ODBz5kySk5MZP348f//9N82aqUOeVa5cmX/++YcffviB2bNn89133+Ht7c3SpUuNs5BVq1bNeOzWrVubneuHH37Ax8eHbdu20alTJ8tdpIVJ8imEsGkrVpiWP/mk6EynmZUdV3ZwLeZalu8rKFyNucqOKztoGdjScoEJkUsBngE2cd5WrVoxe/Zs4uPjmTp1Ko6Ojrz00kucOHGChIQEnn32WbPyKSkpPP744wCEh4fz9NNPZzn9bWRkJJ988glhYWFERUWh1+tJSEjgypUrebs4GyHJpxDCpq1O18Sxe3frxWEpN2NvPrxQLsoJYS15ffRtaR4eHlSpUgWA+fPnU79+febNm0edOnUAWLduHWXLljXbJy3ZdHNzy/bYvXr14s6dO0yfPp2KFSvi4uJCs2bNSElJKYArKTwk+RRC2Kw7d2DbNnW5ShWoXdu68VhC6WKl87WcECLnHBwc+N///sfw4cP5999/cXFx4cqVK7Ro0cKsnMFgICYmhnr16vHzzz+j0+kyrf3cuXMn3333HR06dADg6tWr3L592yLXYk3S210IYbOio6FTJ3B0hI4drR2NZTxd4WnKeZVD82CDz/9o0FDeqzxPV3jawpEJYR9eeeUVtFot33//PR988AHDhg3jp59+4vz58xw6dIgZM2bw008/ATBw4EBiYmLo3r07Bw4c4OzZsyxatIgzZ84AULVqVRYtWsSpU6fYu3cvISEhD60tLQok+RRC2KyqVWH5cpg5EwYNsnY0lqF10DK93XSATBNQBYVp7aZJZyMhCoijoyODBg3i66+/ZuTIkXz66adMmDCBmjVr0q5dO9atW0elSpUAKFmyJFu2bCEuLo4WLVrQsGFD5s6da6wFnTdvHnfv3qVBgwa8/vrrDB48mFKlSlnz8ixCoyiKYu0gHiYmJgZvb2/u37+Pl5eXtcOxGJ1OR2hoKB06dMiysbIoOuR+25dHvd+rT61myIYhGTofeTp7cmP4DYq5FN1hWmyRPX++k5KSuHjxIpUqVcLV1dXa4VhE2mN3Ly8vHByKVj1fdvczp/la0fqOCCGEnehasyuXhlxia6+tLOm6hNaB6pAtcSlxTPhngpWjE0KIrEnyKYSwOYoCixap02raM62DlpaBLelRtwffdfwOB436K33yrslcuV+0h2oRQtguST6FEDbn8GF44w0IDISff7Z2NNaTnJxM79696d27N4HFAvmg2QcA6Aw6FhxeYOXohBAic5J8CiFszqJF6tdbtyAx0bqxWFNqaio//fQTP/30E6mpqQxsMtDYCWl++Hz0Br2VIxRCiIwk+RRC2JTUVPj1V3XZ2RlefdW68ViTk5MTX3/9NV9//TVOTk5U8K5A+6rtAbhy/wqrT61+yBGEEMLyJPkUQtiUTZsgMlJd7twZihe3bjzW5OzszIcffsiHH36Is7MzAIMam8ac+mz7ZxgUg7XCE0KITEnyKYSwKWmP3AFef916cRRW7aq0o2HphgAcjzrOqpOrrByREEKYk+RTCGEzYmNhzRp1uWRJaN/equFYncFg4Pr161y/fh2DQa3h1Gg0jG8z3lhm3uF51gpPCCEyJcmnEMJmrFpl6mDUrZva5tOeJSYmUq5cOcqVK0diup5Xz1Z+loreFQHYdGETN2JvWCtEIYqUsLAwNBoN9+7ds+h5Fy5ciI+PzyMd49KlS2g0GsLDw7MsY6nrk+RTCGEzFi40Lcsjd5WjoyOOjo5m2zQaDSF1QwAwKAYm75psjdCEsHktW7Zk6NCh1g6jyJHkUwhhE06ehG3b1OVq1aBpU+vGUxh4eHig0+nQ6XR4eHiYvdezbk/j8rd7v+X07dOWDk8IAaSkpFg7hEJHkk8hhE3w8YFRo6BsWXj3XdBorB1R4Va7VG0GNh4IgF7RM+fAHCtHJIRt6d27N9u2bWP69OloNBo0Gg2XLl0C4ODBgzRq1Ah3d3eefPJJzpw5Y9xv7NixNGjQgJ9//pnHHnvMOP/5vXv36Nu3L35+fnh5edG6dWuOHDli3O/IkSO0atWKYsWK4eXlRcOGDTlw4IBZTH/99Rc1a9bE09OTdu3acfPmTeN7BoOBzz77jHLlyuHi4kJQUBAbNmzI9hpDQ0OpVq0abm5utGrVynh9BU2STyGETShTBr74Ai5fhgEDrB2Nbfi81ee4Oqr/8S0/sRxFUawckRDm4uPjiY+PN/vZTElJIT4+nuTk5EzLpnWuA9DpdMTHx5OUlJSjsrkxffp0mjVrRr9+/bh58yY3b96kfPnyAIwaNYopU6Zw4MABHB0defPNN832PXfuHGvXrmXlypXGNpavvPIKUVFRrF+/noMHD9KgQQPatGlDdHQ0ACEhIZQrV479+/dz8OBBRowYgZOTk/GYCQkJTJ48mUWLFrF9+3auXLnCBx98YBbvlClTmDx5MkePHqVt27Y8//zznD17NtPru3r1Kl27dqVz586Eh4fTt29fRowYkavvUV5J8imEsClarXQ0SpOcnMzAgQMZOHBghv+oAYq7FadVYCsAbsbdZOP5jZYOUYhseXp64unpye3bt43bJk2ahKenJ4MGDTIrW6pUKTw9Pbly5Ypx26xZs/D09OStt94yKxsYGIinpyenTp0ybluYvtF4Dnh7e+Ps7Iy7uzsBAQEEBASg1WoB+PLLL2nRogW1atVixIgR7Nq1yywBTklJYc6cOTz++OPUq1ePf/75h3379rFixQoaNWpE1apVmTx5Mj4+PqxcuRKAK1euEBwcTI0aNahatSqvvPIK9evXNx5Tp9MxZ84cGjVqRIMGDRg0aBCbN282vj958mQ+/vhjunfvTvXq1fnqq68ICgpi2rRpmV7f7Nmzeeyxx5gyZQrVq1cnJCSE3r175+p7lFeSfAohhI1KTU3lu+++47vvviM1NTXTMn2C+hiXx4SNkdpPIfJBvXr1jMulS5cGICoqyritYsWK+Pr6GtePHDlCXFwcJUuWNCbcnp6eXLx4kfPnzwMwfPhw+vbtS3BwMBMnTjRuT+Pu7s5jjz1mdt60c8bExHDjxg2aN29utk/z5s3NEvD0Tp06RdMHGs83a9Ysx9+DR+H48CJCCGE916/D11+rj9pr1LB2NIWLk5MTY8aMMS5n5sWaL1LTtyanbp9i7/W9HIs6Rj3/epmWFcLS4uLiADWxSvPhhx8ydOjQDKM4pCVabm5uxm0DBw6kX79+xhrJNGltF9OXzc9avfSfN81/DdDTP+J/sANgXFwcpUuXJiwsLMOx0oZQGjt2LD179mTdunWsX7+eMWPGsHTpUl588cUM50w7r63+MSk1n0KIQm3uXPj2W6hZE+bJeOlmnJ2dGTt2LGPHjjVOr/kgRwdHY8cjQGY8EoWKh4cHHh4exgQO1J9rDw8PXFxcMi3r4GBKXZycnPDw8DB26nlY2dxydnZGr9fner8HNWjQgIiICBwdHalSpYrZK30NabVq1Rg2bBgbN26ka9euLFiwIEfH9/LyokyZMuzcudNs+86dO6lVq1am+9SsWZN9+/aZbduzZ08uryxvJPkUQhRaSUkwbpxp/bnnrBeLLetSo4txeflJ6XgkRE4FBgayd+9eLl26xO3bt81qN3MjODiYZs2a0aVLFzZu3MilS5fYtWsXo0aN4sCBAyQmJjJo0CDCwsK4fPkyO3fuZP/+/dSsWTPH5/jwww/56quvWLZsGWfOnGHEiBGEh4czZMiQTMv379+fs2fP8uGHH3LmzBmWLFmS63axeSXJpxCi0PrjD9Ny27bwX0dT8R9FUbh37x737t3LNqEs61WWpys8DcDp26fZe32vpUIUwqZ98MEHaLVaatWqhZ+fn1lnp9zQaDSEhobyzDPP0KdPH6pVq0b37t25fPky/v7+aLVa7ty5wxtvvEG1atV49dVXad++PePS//X9EIMHD2b48OG8//771K1blw0bNrB27VqqVq2aafkKFSqwatUq1qxZQ/369ZkzZw7jx4/PtGx+0yg28CdwTEwM3t7e3L9/Hy8vL2uHYzE6nY7Q0FA6dOiQp8cFwrbI/c6oRw9YulRdXrpUnVKzqMiP+x0fH4+npyegtil7sJ1ZenMPzuXtP98G4IXqL7Cm+5o8nVPkjT1/vpOSkrh48SKVKlXK8Hi8qDIYDMTExODl5WX26L8oyO5+5jRfK1rfESFEkZGcDOvXq8ve3tC1q3XjsXW9gnpR2lPtlfv7md85F33OyhEJIexVnpLPWbNmERgYiKurK02bNs3QYDUrS5cuRaPR0KVLl7ycVghhR7Zsgfv31eWOHcHOKotyxN3dnZSUFFJSUsx6C2fGWevM0CeGGtd/PPRjAUcnhBCZy3XyuWzZMoYPH86YMWM4dOgQ9evXp23btmbjW2Xm0qVLfPDBBzz99NN5DlYIYT9mzjQtv/KK9eIozDQaDU5OTjg5OZn1Fs5Kr/q90GrUIWl+OPgD8SnxBR2iEEJkkOvk85tvvqFfv3706dOHWrVqMWfOHNzd3Zk/f36W++j1ekJCQhg3bhyVK1d+pICFEEXfxYumR+4VKkCHDtaNp6jw9/TnldpqJn836S5rTq+xbkBCCLuUq0HmU1JSOHjwICNHjjRuc3BwIDg4mN27d2e532effUapUqV466232LFjx0PPk5ycbDZVXExMDKA22M7t3Ky2LO1a7ema7Zncb5NffnFAUdQaur599Wg0BoratyU/7ndKSgqjR48G1N+zWY31mV6/oH4sPa724pq8azKv1HglR7Wm4tHY8+c7NTUVRVHQ6/V5HqrI1qT15VYUpchds16vR1EUUlNTM/w85/TnO1fJ5+3bt9Hr9fj7+5tt9/f35/Tp05nu888//zBv3jzCw8NzfJ4JEyZkOrzAxo0bH9quqSjatGmTtUMQFiT3G2rXhtGjS7FxY0XKlTtKaGjGecuLike530lJSXzzzTcANGnSJEc9iRVFobJbZS4kXiA8MpyJyydSv1j9h+4n8oc9fr41Gg2lS5cmOjqaYsWKWTsci4qNjbV2CPkuNjaW+Ph4tmzZkmGIt4SEhBwdo0Cn14yNjeX1119n7ty5ZiP4P8zIkSMZPny4cT0mJoby5cvz3HPP2d1QS5s2beLZZ5+1u6E57JHcb3OdOsEnnwC0sXYoBSI/7ndKSorxd2WnTp1yVPMJkFApgZA1IQAccz7GyA4jH7KHeFT2/vmOjIwkJiYGV1dX3N3di3xtu6IoxMfHZ5i9yZYpikJCQgKxsbGULl2aoKCgDGXSnlQ/TK6ST19fX7RaLZGRkWbbIyMjCQgIyFD+/PnzXLp0ic6dOxu3pVU/Ozo6cubMGR577LEM+7m4uGSYVgswNqy3N/Z63fZK7rd9eZT77eTkxJQpU3K938t1XmboxqHcSrjFmjNriE6Oxt/T/+E7ikdmr5/vsmXLotVquX37trVDsQhFUUhMTMTNza3IJJ9pihcvTkBAQKbXldOf7Vwln87OzjRs2JDNmzcbh0syGAxs3ryZQYMGZShfo0YNjh07Zrbtk08+ITY2lunTp1NepisRQqSj14NWa+0oij5nrTNvPv4mX+38ilRDKtP2TGNC8ARrhyWKsLRH76VKlbKLdq86nY7t27fzzDPPFKk/NpycnNDmwy/pXD92Hz58OL169aJRo0Y0adKEadOmER8fT58+fQB44403KFu2LBMmTMDV1ZU6deqY7e/j4wOQYbsQQnz+Ofz9NwwfDi+8IInow6Q1+gf1aVJualjefPxNvt75NQoKM/bNYMgTQwjwzPgES4j8pNVq8yV5Key0Wi2pqam4uroWqeQzv+R6qKVu3boxefJkRo8eTVBQEOHh4WzYsMHYCenKlSvcvHkz3wMVQhRtiYnw3Xewcye8+ipcv27tiAq/hIQEnJ2dcXZ2znFD/zTVSlbj3cbvAhCvi2fyrskFEaIQQmSQpw5HgwYNyvQxO0BYWFi2+y5cuDAvpxRCFHFLlsCtW+ryK6+o43uKgjXq6VF8f/B7Ug2pLDm2hK+Cv0LrUPRrpYQQ1iVzuwshCoXvvzctDxtmvThsibu7O3fv3uXu3bt5GoaudLHSdKzaEYCbcTfZfHFzfocohBAZSPIphLC6Awdg/351OSgIGje2ajg2Q6PR4OPjg4+PT5571L5e73Xjssz3LoSwBEk+hRBWN2uWaXnAAChiI5MUah2qdsDP3Q+AFSdXcPDGQStHJIQo6iT5FEJY1eXLsHixuuztDSEh1o3HlqSkpDB27FjGjh1LSkpKno7h5uTGp898alz/etfX+RWeEEJkSpJPIYRVff45/DdaEIMHg4eHdeOxJTqdjnHjxjFu3LhHGjuxX8N+eLt4A7D8xHL+vfNvfoUohBAZSPIphLCaixfhp5/UZS8vGDLEuvHYGkdHR959913effddHB3zPluyq6Mrw54w9fL6cNOH+RGeEEJkSpJPIYTVREVBy5bqYPJDh0LJktaOyLa4uLgwa9YsZs2alemUxLkx5IkhlClWBoC1Z9ay59qe/AhRCCEykORTCGE1TZvCpk0QEQEffGDtaOybj6sPY1uMNa5P3TPVesEIIYo0ST6FEFbn6wvFilk7CvF6/dcp6aZWPy8/sZwjEUesHJEQoiiS5FMIIWxUfHw8Tk5OODk5ER8f/8jHc3V0Nev5PuGfCY98TCGEeJAkn0IIi1u8GH78EfI4OpBIJzU1ldS04QLywdsN36aURykAlp1YxslbJ/Pt2EIIAZJ8CiEsLCkJPvoI+vWD6tUhNtbaEdkuNzc3rl27xrVr13Bzc8ufYzq5mfV877WmV74cVwgh0kjyKYSwqPnz4eZNdfnxx6Wt56NwcHCgbNmylC1bFgeH/Pt1PqDRABwd1KGbDtw4wPbL2/Pt2EIIIcmnEMJidDoYONC0/skn1otFZM3b1ZsxLcYY19/f+D5JqUlWjEgIUZRI8imEsJjvvzctN2sGDRpYL5aiICUlhUmTJjFp0qQ8T6+ZlRFPjaCWXy1Arf0cs3XMQ/YQQoickeRTCGERycnwYbqJc8aNs14sRYVOp+Ojjz7io48+eqTpNTPj6ODIvOfnGR+/f7vvW24n3M7Xcwgh7JMkn0IIi5gzR+1sBFCuHDz7rHXjKQocHR3p1asXvXr1eqTpNbPyRLknGNhYbSeRlJrE9we+f8geQgjxcJJ8CiEKXHQ0fP65aX3tWuvFUpS4uLiwcOFCFi5c+MjTa2ZlcNPBaNAA6rif12OuF8h5hBD2Q5JPIUSBGzMG7txRl3v0UHu5C9tQuXhletTtAUC8Lp6P/v7IyhEJIWydJJ9CiAIXEACuruDhAZMmWTsakVvftvuWEm4lAFhybAnhEeHWDUgIYdMk+RRCFLhRo+DUKVi0CMqWtXY0RUd8fDw+Pj74+Pjky/SaWSnpXpLRz4w2rr+2+jXiUwrufEKIok2STyGERQQGwosvWjuKouf+/fvcv3+/wM/zTqN3qFqiKgAnbp1g4j8TC/ycQoiiSZJPIYSwUW5ubvz777/8+++/+Ta9ZlZcHV1Z+epK49BLE/6ZwLWYawV6TiFE0STJpxCiQEydCkOGQAE+DbZ7Dg4OVK1alapVq+br9JpZqedfj9fqvQaAXtHT749+KIpS4OcVQhQtknwKIfLd7dswciR8+y0EBUFCgrUjEvll8rOTKe1ZGoAN5zYw99BcK0ckhLA1knwKIfLdM8+oMxoBtGgB7u7Wjaeo0ul0zJo1i1mzZuX7DEdZKelekh+f/9G4Pvyv4UTFR1nk3EKIokGSTyFEvtqzR+3ZDqDRmA8uL/JXSkoKgwYNYtCgQfk+t3t2OlTtwJtBbwLq2J8LDi+w2LmFELZPkk8hRL7R62HAANN6x45QurT14inqtFotL7/8Mi+//DJardai5x7ebLhx5qNpe6dhUAwWPb8QwnZJ8imEyDfTp0N4uLocFAS//WbNaIo+V1dXVqxYwYoVK3B1dbXouWuXqk3bKm0BiIiLkHnfhRA5JsmnECJfXL8OY8eqyxoNzJgBjo5WDUkUsGFPDDMuf7b9M+4k3LFiNEIIWyHJpxDikSkKDBwIsbHq+ptvwlNPWTcmUfCee+w52j5mqv0csG7AQ/YQQghJPoUQ+SA0FH7/XV3295f52y0lISGBsmXLUrZsWRKsNJ7V9HbT0WrU9qYrTq5g2fFlVolDCGE7JPkUQjyydu1g9mwoXhxmzlS/ioKnKAo3btzgxo0bVhvsvbpvdSY9a/prY9D6QaToLdfzXghheyT5FEI8Mq0W+veHixfh5ZetHY39cHV15fDhwxw+fNjiHY7SG/rEUDpV6wTA7YTbbL+83WqxCCEKP0k+hRD5xtvb2hHYF61WS1BQEEFBQRYfaik9jUZDSN0Q4/rUPVNl2k0hRJYk+RRC5MmZM7B2rbWjEIVFh6od8HH1ASD0bCjT9063bkBCiEJLkk8hRK4lJMArr8ALL8A770B8vLUjsk86nY6FCxeycOFCi02vmRUvFy++72Qa6/ODjR+w99peK0YkhCisJPkUQuSKoqizGB07pq7v2GHdeOxZSkoKffr0oU+fPhadXjMrr9Z+lb6P9wVAr+jpubonOr11k2IhROEjyacQIle++AJ+/lld9vCAVavUr8LytFotHTp0oEOHDlZt85netHbTqOVXC4ALdy+w6OgiK0ckhChsJPkUQuTYxIkwerRpfd48qFnTevHYO1dXV9atW8e6deus2ts9PQ9nD37o9INxfeI/E2XedyGEGUk+hRA58u+/MHKkab1jR+jWzXrxiMKreYXmtApsBcDZ6LP8duo3K0ckhChMJPkUQjxUXBy8+KJpvWxZ6ekusje46WDj8oB1A4iMi7RiNEKIwkSSTyFEthQF+vaFkyfV9Vq14PRpcJDfHlaXkJBA1apVqVq1qtWm18zKC9VfoHO1zgDcSrjFgHUDZOxPIQQgyacQ4iFu3YIDB9RlLy9YvRo8Pa0bk1ApisK5c+c4d+5coUvsNBoN856fh5+7HwC/nf6NZSdk3nchhCSfQoiHKFUK9u6F1q3hp5+genVrRyTSuLq68s8///DPP/8Umg5H6fl5+DGj/Qzjeo9VPYiIi7BiREKIwkCSTyHEQ5UsCX//DV26WDsSkZ5Wq6V58+Y0b9680Ay19KBXa79Kg9INjOtjto6xYjRCiMJAkk8hRAabNqmdjNLTaKwTi7BtGo2G5S8vx93JHYB5h+dx9s5ZK0clhLAmST6FEGbWroX27eG55+DePWtHI7KTmprKihUrWLFiBampqdYOJ0uPlXiMdxq+A6gzHz294GmORx23clRCCGuR5FMIYfT99+p87Xo97N4Ns2dbOyKRneTkZF599VVeffVVkpOTrR1Otj588kMCfQIBiIyP5IWlLxCXEpf9TkKIIkmSTyEEAH/+Cf37m9Z79oSPP7ZePOLhHBwcaNGiBS1atMChkI99VbpYacJ6heHt4g2oU2++suIVklKTrByZEMLS8vTbatasWQQGBuLq6krTpk3Zt29flmXnzp3L008/TfHixSlevDjBwcHZlhdCWN6vv5oPIt+5MyxcKGN5FnZubm6EhYURFhaGm5ubtcN5qIo+FdnRZwdujmqsG85t4PXfXrdyVEIIS8v1fy3Lli1j+PDhjBkzhkOHDlG/fn3atm1LVFRUpuXDwsLo0aMHW7duZffu3ZQvX57nnnuO69evP3LwQohH9+OPEBICaU0GS5SA5cvBycm6cYmiqa5/Xf7s+adxfeXJlaw9I9NlCWFPcp18fvPNN/Tr148+ffpQq1Yt5syZg7u7O/Pnz8+0/C+//MK7775LUFAQNWrU4Mcff8RgMLB58+ZHDl4IkXeKApMmQb9+6jLA229DVBQUwiEjRRHSulJr3m/2vnH9haUvcOHuBStGJISwJMfcFE5JSeHgwYOMHDnSuM3BwYHg4GB2796do2MkJCSg0+koUaJElmWSk5PNGs/HxMQAoNPp0Ol0uQnZpqVdqz1dsz2z9P1et07DRx+ZfgUMHarnq68MGAxgMFgkBLuWH/c7MTGRp59+GoAdO3bYxKP3NF+2/JKjEUfZdHETAF2WdmHvm3txdMjVf0s2Q36f2xd7vd85vd5cfcpv376NXq/H39/fbLu/vz+nT5/O0TE+/vhjypQpQ3BwcJZlJkyYwLhx4zJs37hxI+7u7rkJuUjYtGmTtUMQFmSp+60o0KxZY3bvLkOPHqdo0eJf1q+3yKlFOo9yv5OSkjh69CgA69evL5SzHGWnq2tXNqFe/7GoYwxeNJhOfp2sHFXBkt/n9sXe7ndCQkKOyln0T8yJEyeydOlSwsLCsv0lOXLkSIYPH25cj4mJMbYV9fLyskSohYJOp2PTpk08++yzOEkDvCLPGve7ZUvYvDmVF16oAlSxyDmFKj/ut16vJzQ0FIBWrVoV2lmOslO2XlmeX/Y8AEuilvB2u7cJ8g+yblAFQH6f2xd7vd9pT6ofJlfJp6+vL1qtlsjISLPtkZGRBAQEZLvv5MmTmThxIn///Tf16tXLtqyLiwsuLi4Ztjs5OdnVTUxjr9dtrwrifhsM8NVX8Pjj0K6daXvx4vDyy/l6KpFLj3K/nZycaN++fT5HZFmda3Smf8P+zDk4hwRdAq1+bsWhdw5RrWQ1a4dWIOT3uX2xt/ud02vNVYcjZ2dnGjZsaNZZKK3zULNmzbLc7+uvv+bzzz9nw4YNNGrUKDenFEI8ouhoeP55+N//1LE7L160dkRCmJv03CTj/O/xunh6rOoh438KUYTlurf78OHDmTt3Lj/99BOnTp1iwIABxMfH06dPHwDeeOMNsw5JX331FZ9++inz588nMDCQiIgIIiIiiHtw4mghRL7btw8aNoR169T1e/dABpooOlJTU1m3bh3r1q0r1NNrPoynsyfrQ9YT4Kk+QTt08xCjt462clRCiIKS6zaf3bp149atW4wePZqIiAiCgoLYsGGDsRPSlStXzGbamD17NikpKbz8wLO9MWPGMHbs2EeLXgiRqdRU+OwzGD9enSoToGRJWLzY/LG7sG3Jycl06qR20ImLi8PR0XZ7ipfyKMUfPf7gyXlPojPomLRrEjq9jqntplo7NCFEPsvTb6pBgwYxaNCgTN8LCwszW7906VJeTiGEyKNTp9SxO3fuNG174gl14Pjy5a0Xl8h/Dg4OxqZMhX16zZxoVKYRY1uOZdSWUQBM2zsNf09/Rjw1wsqRCSHyk+3/thJCABAfDx98APXqmRJPR0cYNw62b5fEsyhyc3Nj//797N+/36bG+MzOiKdG0Duot3F95OaR7Lm2x3oBCSHynSSfQhQhy5ebpsmsVAm2bYPRo2WqTGE7HDQOLHhhAR80+8C47d1175JqsN02rUIIc5J8CmGj0qbETOPhAVOngouLmnCeOAFPPmmd2IR4VF+0/oJAn0AADkccpu/avhgUmXpLiKJAkk8hbIyiwOrVUL8+HDtm/l7XrnD+vPqovYg8hRXZSExMpHnz5jRv3pzExERrh5OvXBxdmNVhlnH9pyM/MWT9ECtGJITIL5J8CmEjFAXWr4dGjeCll9TEc8wY8zIaDZQta534hOUZDAZ27drFrl27MBiKXq1gh6odmPf8PLQadeammftnMv/wfCtHJYR4VLY7LocQdkJRICwMPv3UvAc7QEQEJCZKLae9cnFx4bfffjMuF0VvPv4mGjS8ufZNAN5a+xb3ku4xvNnwh+wphCispOZTiEIqJQWWLIFmzaB1a/PEMygI/vxT3SaJp/1ydHSkS5cudOnSxabH+HyYPo/3oV+Dfsb19ze+z6srXkV5sOGzEMImSPIpRCF08yZUrAghIbB3r2l7zZqwciUcPAgdO6qP2YWwB1PbTsXfw9+4vuLkCr7e+bUVIxJC5JUkn0IUQgEB5m0369WDX35R23m+9BIUgfHERT7Q6/WEhYURFhaGPm0qqyLKw9mDK8OuMLTpUOO2kZtHMnv/bOsFJYTIk6L7nEaIQk5RYP9+dcrLAwfUdp1pNBoYNgzWrIH33oOnn5ZaTpFRUlISrVq1AtTpNT08PKwcUcFy1joztd1UvF29GbdtHAoK74a+S1mvsjxf/XlrhyeEyCFJPoWwsOPH4ddfYelSuHDBtH3fPvPsMiREfQmRFY1GQ61atYzL9mJMizHcT7rPtL3TABi8fjCtK7XG09nTuoEJIXJEkk8hClhqKuzaBX/8ob7OnMlYxsVFHRQ+IMDy8Qnb5e7uzokTJ6wdhsVpNBqmtJ3Cvhv72HV1F5fvX6bv2r78+tKvdpWEC2GrpOWYEAWsXj1o0QImTzZPPB0coE0b+PFHiIyEN9+UnrtC5JSDxoGfuvyEu5M7AMtOLGP01tFWjkoIkROSfAqRD65dUx+ljxuX8b0nnjAtOzio7Te//RauX4e//4a33gJvb8vFKkRRUaVEFeY/bxp0/osdXzAuLJMPoRCiUJHH7kLkksGg1mDu2gU7dsD27XDxovqeRgODB0Px4qbyL78MCQnQqRO0bw8lS1onblH0JCYm8vzzakebtWvX4maHg752q9ONS/cuMWLzCADGbhtLwzIN6VStk5UjE0JkRZJPIXLg/n2YOhX27FHH3bx3L/NyigLbtkGXLqZtHTqoLyHym8Fg4O+//zYu26uPn/qYFH0Ko8PUx+49V/UkvH84lYtXtnJkQojMSPIpxH9SU+HsWbU3uq8v/DeCDQDOzvDll2qZB7m4qI/Wn34annkGnnrKcjEL++bi4sLixYuNy/bsk2c+IfRcKHuu7SE2JZbGcxszq8Msutfpbu3QhBAPkORT2J2UFHWIo7Nn4eRJdeD248fh1Cn1PVAHck+ffLq5QZ06EB4O/v7qlJdPPAHNm0PjxmoCKoSlOTo6EiLjcQFqD/g13dbQ8qeWnL59mujEaHqs6oEGDd3qdLN2eEKIdCT5FEWOoqiPxS9fhvr1zQdnHzcOPvtMbbeZnePHM2774Qfw81OnvZTRXIQofPw9/dneezuN5jbiyv0rAPRc3RNnrTMv1nzRytEJIdJI8ilsjsGgzn1+8ybcuKH2Gr94Ec6dg/Pn1eXYWLXs9etQpoxpX1/fzBNPrRaqV1drN+vWVYdHelDjxgVzPULklV6v59ChQwA0aNAArVZr5Yisz8/Dj3PvnaPt4rZsvbQVg2Kg6/Ku/N79d5kFSYhCQpJPUSjo9XDnjjre5a1bEBVl+vrMM/Dss6ayN29CuXI5O+758+bJZ+3a0LAhVK0KVapAjRpqwlmjhjw6F7YnKSmJJk2aAPYxvWZOOWmd+Ou1vwheFMz2y9sB6LqsK+81eY9Jz03C0UH+6xPCmuQTKPJVaqraM/zePTWZvHcPYmLA0dG8BzjAwIGwdauaZEZHZ/0o/KOPzJNPf391vMzMyjs6qo/FAwPV14PjZ7Zsqc6jLkRRoNFoqFixonFZmDhpnfizx5+8vOJlNp7fiF7RM23vNP749w9md5zNs489+/CDCCEKhCSfAoNBTf7i4kyv2NjM14cMMR+nctkytQ1lWsIZH5/5OR57LGPyee2a2snnYSIjzdcdHeH118HdXa3VLF0aKlRQazLLl1ffF8IeuLu7c+nSJWuHUWgVcylGaM9QRm8dzfh/xgNw/u55nlv8HN+2+5b3mr5n5QiFsE/y33QhExurJnLJyWqyd+GCN3v3atDr1W1pr+LF1akZ0/vuO7hyBRITISlJ/Zr+lZCgfh06FHr3Nu13/bqavOXEq6+aJ58JCWqP8Ye5fz/jNl9f8PBQO/GULKnWaKa9/PzUV6lSalL5oIULcxavEMK+aR20fNnmS5577Dla/tTSuH3whsFcvn+Z8W3G46x1tlp8Qtgjm0o+jx9Xa7v0evVlMJgvOzmpQ9+kt2ePmpDp9eoj4ay+1qkDzz1nvu/o0Wqylln59K/334f/ml0BcOQI9O8POl3Wr5QU9RUdDa6upn0nTFBfKiegZabfi6efzph8zpsH//U9yNa1a+bruWkmltaRJ423t7q/tzf4+Kgvb2/1VbKkaT2zWX1++EGNWQghClqLwBYkjkpk6IahfH/wewCm7J5C6NlQ/uz5pwxIL4QF2VTy+WBi+aBy5eDqVfNtkyfDqlUPP/Zbb2VMPqdOVWsfH+bll82Tz/h4NenNiZQU8+TTOYd/gCcnZ9yW/jhZcXVVE+j0PD3VaR89PaFYMfXl6Wl6pV+vUcN83xdfzNn3KDPSMVeIR5OUlET37uog6kuXLsU1J78E7JiroyuzO87Gz92PiTsnkmpI5dTtUzSb14yNr22kfkB9a4cohF2wqeTzYR5MqiDn7f8ym7kmp/vqdObrTk7qV61WXc7s5eys9q5+MObatdUBzl1dwcnJQETEZapWrYCbmxYXF4yvzHp7T5umPgZ3dVVfbm7qV3d3ddnNTe2o8yBnZwgNzdm1Pkj6OAhhPXq9nt9//924LB5Oo9HweevP6Vy9M73W9OL07dNExUfR5uc2rA9ZT+OyMqaaEAXNppLPXr1MCZRWq77Slh0cMvZsBnjtNbVW0tFRLZfV10qVMu77xx/q1/Tl0hJKR0fTNj8/8/0aNVKTyswSvYd55RX1BaDT6QkNPUqHDuVwcnp4NaGMQymEfXF2duaHH34wLouca1K2CTvf3EmHXzqw9/pe7iTeoeVPLZn//Hxerf2qjB4gRAGyqeTz22/Byyt3+3TqlPfz5XWObo1GagSFEAXPycmJfv36WTsMm1XCrQR/9PiDJ+Y9wYW7F0jQJdB9VXem7J7C4q6LqVaymrVDFKJIykPdnBBCCFE0+Hn4caT/Eer7m9p77r+xn6A5Qfxw8AcrRiZE0SXJpxBC2CiDwcCJEyc4ceIEhqxmaRAP5ensyaF3DrHoxUVUL1kdgMTURN758x1m7ptp5eiEKHok+RRCCBuVmJhInTp1qFOnDomJidYOx6Y5aBx4rd5rHHj7AAMaDTBuf2/9e7y8/GVORJ2wYnRCFC2SfAohhA3z9fXF19fX2mEUGZ7OnnzX8TtGNB9h3Lbq1Crqzq5L95XdiYyLzGZvIUROSPIphBA2ysPDg1u3bnHr1i08cjNbhHio8W3G812H7/BzV4czUVBYdmIZ1WdWZ9LOScSl5HGAYyGEJJ9CCCHEgzQaDQMaD+DikItMfnYyLloXAO4n3+ejvz8iYHIAH278kOjEaCtHKoTtkeRTCCGEyIKHswfvP/k+RwccpVVgK+P2eF08k3dPptL0SgxeP5jY5NhsjiKESE+STyGEsFFJSUmEhIQQEhJCUlKStcMp0qqVrMaWXls42v8o/Rr0M9aExiTHMGPfDKrNrMaSY0tQFMXKkQpR+EnyKYQQNkqv17NkyRKWLFki02taSF3/uvzQ+QfODT7H89WfN26PiIsgZHUIHZZ0YM3pNSToEqwYpRCFm03NcCSEEMLE2dmZqVOnGpeF5ZTzKsfv3X/nXPQ5hm4Yyrqz6wDYcG4DG85twMfVh/4N+zOs2TBKeZSycrRCFC5S8ymEEDbKycmJoUOHMnToUJycnKwdjl2qUqIKf/T4g5WvrDRLMu8l3WPizomU/aYsvdf05kjkEStGKUThIsmnEEII8Qg0Gg0v1XqJy0Mvs67nOnoH9cZBo/73mmpI5acjP9FkXhOmX57OvaR71g1WiEJAkk8hhLBRBoOBS5cucenSJZlesxBwdXSlQ9UOLHhhAacHnubj5h/j4aSOv6qgsPXuVur/UJ8PNn7AgRsHrBytENYjyacQQtioxMREKlWqRKVKlWR6zUKmasmqTAyeyNVhVxnTYowxCb0Zd5Mpu6fQeG5jHD9zZPyO8Vy+d9nK0QphWZJ8CiGEDXN3d8fd3d3aYYgsFHcrztiWYzny9hGaejc1e0+v6Bm1ZRSB0wNpubAlY7aOYdP5Tej0OitFK4RlSG93IYSwUR4eHsTHx1s7DJEDFbwrMLLSSBo+05BPt3/Knmt7OH37tPH9bZe3se3yNgC8XLx4tvKzvFD9BbrW7IqHs0ydKooWST6FEEIIC/H39GfBCwsAOHXrFIuOLmLVqVX8e+dfY5mY5BhWnVrFqlOr6PN7HzpW60ij0o0IrhxMk7JN0DporRW+EPlCHrsLIYQQVlDTrybj24zn9MDTnBp4isUvLqZHnR6UcCthLKNX9Kw9s5bRYaN5cv6TlJtajo83fcz2y9vl8bywWVLzKYQQNio5OZlBgwYBMHPmTFxcXKwckcgLjUZDDd8a1PCtQUi9EPQGPWGXwvj56M+sOrmKeJ2paUVEXARf7/qar3d9jYvWhaCAIBqXaUxw5WBaBrbE29XbilciRM5I8imEEDYqNTWVH3/8EYBp06ZJ8llEaB20tKnchjaV2zDv+Xmcjz7Pzqs7WXlyJRvObUBBnT8+WZ/M3ut72Xt9LzP3z8TRwRE/dz8mtJnA46Ufp06pOsbxRoUoTCT5FEIIG+Xk5MQXX3xhXBZFj6ODI9V9q1PdtzpvPv4mt+JvsfbMWjZf3My+6/s4f/e8sWyqIZWbcTfp/XtvAPzc/Xi51sv0rNuThqUb4ubkZqWrEMKcJJ9CCGGjnJ2dGTVqlLXDEBbk5+HHWw3e4q0GbwFwP+k+2y9v54NNH5h1WgK4lXCL2QdmM/vAbAB83X3xdfeluGtxSriVoLhbcUq4/vfVrQTFXYvj5+FH9ZLVKedVDiet/EEjCkaeks9Zs2YxadIkIiIiqF+/PjNmzKBJkyZZll+xYgWffvoply5domrVqnz11Vd06NAhz0ELIYQQArxdvelcvTOdq3cmPiWezRc3c/LWSXZf282m85tITDVNPnA74Ta3E27n6LgaNPi6+1K6WGkCPAMo7fnA1/+2+3v44+XihUajKahLFEVQrpPPZcuWMXz4cObMmUPTpk2ZNm0abdu25cyZM5QqVSpD+V27dtGjRw8mTJhAp06dWLJkCV26dOHQoUPUqVMnXy5CCCHskaIo3L6tJhO+vr6SANg5D2cPnq/+PM9Xfx5Qh2z6/sD3HI44zOX7l7kec53oxGhiU2IfeiwFhVsJt7iVcIujkUezLeusdaakW0lcHV3ROmjRarQ4aBzQOqhfUw2pOGgcqFKiCm6Obrg5uuHq6Iqbk7rs4uiCq6MrLtr/vqZbd9I64ejgiFajReugNS47Ojgaz5W27OjgiKODI94u3jhpnYz7pMUjn4/CQ6MoipKbHZo2bUrjxo2ZOXMmoM4tXL58ed577z1GjBiRoXy3bt2Ij4/nzz//NG574oknCAoKYs6cOTk6Z0xMDN7e3ty/fx8vL6/chGvTdDodoaGhdOjQQdpz2QG53/YlP+53fHw8np6eAMTFxeHhIYORF1aF6fOt0+u4l3SP6MRo7ibdVb8m3uV2wm3ORZ/jasxVIuIiuBl3k4i4CFL0KVaNN784aBwyJMZpCWr69zQaDRo0mX4Fsnwv/VdFUYiPj6eYZzEcHBxyfbw06RPmR9men8fKbHurwFaMfHpkjvO1XNV8pqSkcPDgQUaOHGnc5uDgQHBwMLt37850n927dzN8+HCzbW3btmXNmjVZnic5OZnk5GTj+v379wGIjo5Gp7Ofcc10Oh0JCQncuXPH6r+sRMGT+21f8uN+p5/d6M6dOyQlJeVXeCKfFbbPtwMO+Gp88XXzhWz6ISmKwr2ke0TERxAVH0VkfKTZ16j4KG7E3iAxNZFkfTIGxYDeoFe/KnoSdYlZH9zCDP/9s6h7lj2dtRQzFONOrTvExqq16g+r18xV8nn79m30ej3+/v5m2/39/Tl9+nSm+0RERGRaPiIiIsvzTJgwgXHjxmXYXqlSpdyEK4QQdqNixYrWDkEIYadW//cvTWxsLN7eWY85Wyh7u48cOdKsttRgMBAdHU3JkiXtqs1GTEwM5cuX5+rVq3bV3MBeyf22L3K/7Yvcb/tir/dbURRiY2MpU6ZMtuVylXz6+vqi1WqJjIw02x4ZGUlAQECm+wQEBOSqPICLi0uGwZJ9fHxyE2qR4uXlZVc/vPZO7rd9kfttX+R+2xd7vN/Z1XimydXUB87OzjRs2JDNmzcbtxkMBjZv3kyzZs0y3adZs2Zm5QE2bdqUZXkhhBBCCFF05fqx+/Dhw+nVqxeNGjWiSZMmTJs2jfj4ePr06QPAG2+8QdmyZZkwYQIAQ4YMoUWLFkyZMoWOHTuydOlSDhw4wA8//JC/VyKEEEIIIQq9XCef3bp149atW4wePZqIiAiCgoLYsGGDsVPRlStXcHAwVag++eSTLFmyhE8++YT//e9/VK1alTVr1sgYnzng4uLCmDFjZL5mOyH3277I/bYvcr/ti9zv7OV6nE8hhBBCCCHyKldtPoUQQgghhHgUknwKIYQQQgiLkeRTCCGEEEJYjCSfQgghhBDCYiT5tKDk5GSCgoLQaDSEh4ebvXf06FGefvppXF1dKV++PF9//XWG/VesWEGNGjVwdXWlbt26hIaGmr2vKAqjR4+mdOnSuLm5ERwczNmzZ83KREdHExISgpeXFz4+Prz11lvExcXl+7Xaq0uXLvHWW29RqVIl3NzceOyxxxgzZgwpKSlm5eR+27dZs2YRGBiIq6srTZs2Zd++fdYOSTxgwoQJNG7cmGLFilGqVCm6dOnCmTNnzMokJSUxcOBASpYsiaenJy+99FKGSVWuXLlCx44dcXd3p1SpUnz44YekpqaalQkLC6NBgwa4uLhQpUoVFi5cmCEe+ZmxrIkTJ6LRaBg6dKhxm9zvfKQIixk8eLDSvn17BVAOHz5s3H7//n3F399fCQkJUY4fP678+uuvipubm/L9998by+zcuVPRarXK119/rZw8eVL55JNPFCcnJ+XYsWPGMhMnTlS8vb2VNWvWKEeOHFGef/55pVKlSkpiYqKxTLt27ZT69esre/bsUXbs2KFUqVJF6dGjh0Wu3x6sX79e6d27t/LXX38p58+fV37//XelVKlSyvvvv28sI/fbvi1dulRxdnZW5s+fr5w4cULp16+f4uPjo0RGRlo7NJFO27ZtlQULFijHjx9XwsPDlQ4dOigVKlRQ4uLijGX69++vlC9fXtm8ebNy4MAB5YknnlCefPJJ4/upqalKnTp1lODgYOXw4cNKaGio4uvrq4wcOdJY5sKFC4q7u7syfPhw5eTJk8qMGTMUrVarbNiwwVhGfmYsa9++fUpgYKBSr149ZciQIcbtcr/zjySfFhIaGqrUqFFDOXHiRIbk87vvvlOKFy+uJCcnG7d9/PHHSvXq1Y3rr776qtKxY0ezYzZt2lR55513FEVRFIPBoAQEBCiTJk0yvn/v3j3FxcVF+fXXXxVFUZSTJ08qgLJ//35jmfXr1ysajUa5fv16vl6vMPn666+VSpUqGdflftu3Jk2aKAMHDjSu6/V6pUyZMsqECROsGJV4mKioKAVQtm3bpiiK+nlzcnJSVqxYYSxz6tQpBVB2796tKIr6e9/BwUGJiIgwlpk9e7bi5eVl/Px/9NFHSu3atc3O1a1bN6Vt27bGdfmZsZzY2FilatWqyqZNm5QWLVoYk0+53/lLHrtbQGRkJP369WPRokW4u7tneH/37t0888wzODs7G7e1bduWM2fOcPfuXWOZ4OBgs/3atm3L7t27Abh48SIRERFmZby9vWnatKmxzO7du/Hx8aFRo0bGMsHBwTg4OLB37978u2Bh5v79+5QoUcK4LvfbfqWkpHDw4EGz++bg4EBwcLDxvonC6f79+wDGz/LBgwfR6XRm97JGjRpUqFDB7DNYt25d4yQsoH6OY2JiOHHihLFMdp91+ZmxrIEDB9KxY8cM90Tud/6S5LOAKYpC79696d+/v1kSkF5ERITZDytgXI+IiMi2TPr30++XVZlSpUqZve/o6EiJEiWMZUT+OnfuHDNmzOCdd94xbpP7bb9u376NXq/P9r6JwsdgMDB06FCaN29unJ0vIiICZ2dnfHx8zMo++BnM62c9JiaGxMRE+ZmxoKVLl3Lo0CHj9ODpyf3OX5J85tGIESPQaDTZvk6fPs2MGTOIjY1l5MiR1g5ZPIKc3u/0rl+/Trt27XjllVfo16+flSIXQjyqgQMHcvz4cZYuXWrtUEQBuXr1KkOGDOGXX37B1dXV2uEUebme212o3n//fXr37p1tmcqVK7NlyxZ2796dYX7XRo0aERISwk8//URAQECGHnNp6wEBAcavmZVJ/37attKlS5uVCQoKMpaJiooyO0ZqairR0dHG/UXmcnq/09y4cYNWrVrx5JNP8sMPP5iVk/ttv3x9fdFqtdneW1G4DBo0iD///JPt27dTrlw54/aAgABSUlK4d++eWW3Yg5/TB3sp5/Sz7uXlhZubG1qtVn5mLODgwYNERUXRoEED4za9Xs/27duZOXMmf/31l9zv/GTtRqdF3eXLl5Vjx44ZX3/99ZcCKCtXrlSuXr2qKIqpA0pKSopxv5EjR2bogNKpUyezYzdr1ixDB5TJkycb379//36mHVAOHDhgLPPXX39JB5R8du3aNaVq1apK9+7dldTU1Azvy/22b02aNFEGDRpkXNfr9UrZsmWLVGeCosBgMCgDBw5UypQpo/z7778Z3k/rgLJy5UrjttOnT2faASV9L+Xvv/9e8fLyUpKSkhRFUTug1KlTx+zYPXr0yNABRX5mClZMTIzZ/9XHjh1TGjVqpLz22mvKsWPH5H7nM0k+LezixYsZervfu3dP8ff3V15//XXl+PHjytKlSxV3d/cMQ+84OjoqkydPVk6dOqWMGTMm06F3fHx8lN9//105evSo8sILL2Q69M7jjz+u7N27V/nnn3+UqlWrytA7+ejatWtKlSpVlDZt2ijXrl1Tbt68aXylkftt35YuXaq4uLgoCxcuVE6ePKm8/fbbio+Pj1kPWWF9AwYMULy9vZWwsDCzz3FCQoKxTP/+/ZUKFSooW7ZsUQ4cOKA0a9ZMadasmfH9tKF3nnvuOSU8PFzZsGGD4ufnl+nQOx9++KFy6tQpZdasWZkOvSM/M5aXvre7osj9zk+SfFpYZsmnoijKkSNHlKeeekpxcXFRypYtq0ycODHDvsuXL1eqVaumODs7K7Vr11bWrVtn9r7BYFA+/fRTxd/fX3FxcVHatGmjnDlzxqzMnTt3lB49eiienp6Kl5eX0qdPHyU2Njbfr9NeLViwQAEyfaUn99u+zZgxQ6lQoYLi7OysNGnSRNmzZ4+1QxIPyOpzvGDBAmOZxMRE5d1331WKFy+uuLu7Ky+++KLZH5qKoiiXLl1S2rdvr7i5uSm+vr7K+++/r+h0OrMyW7duVYKCghRnZ2elcuXKZudIIz8zlvdg8in3O/9oFEVRLPykXwghhBBC2Cnp7S6EEEIIISxGkk8hhBBCCGExknwKIYQQQgiLkeRTCCGEEEJYjCSfQgghhBDCYiT5FEIIIYQQFiPJpxBCCCGEsBhJPoUQQgghhMVI8imEEJkICwtDo9Fw7949i5534cKF+Pj4PNIxLl26hEajITw8PMsy1ro+IYSQ5FMIYXc0Gk22r7Fjx1o7RCGEKLIcrR2AEEJY2s2bN43Ly5YtY/To0Zw5c8a4zdPTkwMHDuT6uCkpKTg7O+dLjEIIUVRJzacQwu4EBAQYX97e3mg0GrNtnp6exrIHDx6kUaNGuLu78+STT5olqWPHjiUoKIgff/yRSpUq4erqCsC9e/fo27cvfn5+eHl50bp1a44cOWLc78iRI7Rq1YpixYrh5eVFw4YNMyS7f/31FzVr1sTT05N27dqZJcwGg4HPPvuMcuXK4eLiQlBQEBs2bMj2mkNDQ6lWrRpubm60atWKS5cuPcq3UAgh8kySTyGEyMaoUaOYMmUKBw4cwNHRkTfffNPs/XPnzrFq1SpWr15tbGP5yiuvEBUVxfr16zl48CANGjSgTZs2REdHAxASEkK5cuXYv38/Bw8eZMSIETg5ORmPmZCQwOTJk1m0aBHbt2/nypUrfPDBB8b3p0+fzpQpU5g8eTJHjx6lbdu2PP/885w9ezbTa7h69Spdu3alc+fOhIeH07dvX0aMGJHP3ykhhMghRQgh7NiCBQsUb2/vDNu3bt2qAMrff/9t3LZu3ToFUBITExVFUZQxY8YoTk5OSlRUlLHMjh07FC8vLyUpKcnseI899pjy/fffK4qiKMWKFVMWLlyYZTyAcu7cOeO2WbNmKf7+/sb1MmXKKF9++aXZfo0bN1beffddRVEU5eLFiwqgHD58WFEURRk5cqRSq1Yts/Iff/yxAih3797NNA4hhCgoUvMphBDZqFevnnG5dOnSAERFRRm3VaxYET8/P+P6kSNHiIuLo2TJknh6ehpfFy9e5Pz58wAMHz6cvn37EhwczMSJE43b07i7u/PYY4+ZnTftnDExMdy4cYPmzZub7dO8eXNOnTqV6TWcOnWKpk2bmm1r1qxZjr8HQgiRn6TDkRBCZCP943CNRgOobS7TeHh4mJWPi4ujdOnShIWFZThW2hBKY8eOpWfPnqxbt47169czZswYli5dyosvvpjhnGnnVRQlPy5HCCGsTmo+hRAiHzVo0ICIiAgcHR2pUqWK2cvX19dYrlq1agwbNoyNGzfStWtXFixYkKPje3l5UaZMGXbu3Gm2fefOndSqVSvTfWrWrMm+ffvMtu3ZsyeXVyaEEPlDkk8hhMhHwcHBNGvWjC5durBx40YuXbrErl27GDVqFAcOHCAxMZFBgwYRFhbG5cuX2blzJ/v376dmzZo5PseHH37IV199xbJlyzhz5gwjRowgPDycIUOGZFq+f//+nD17lg8//JAzZ86wZMkSFi5cmE9XLIQQuSOP3YUQIh9pNBpCQ0MZNWoUffr04datWwQEBPDMM8/g7++PVqvlzp07vPHGG0RGRuLr60vXrl0ZN25cjs8xePBg7t+/z/vvv09UVBS1atVi7dq1VK1aNdPyFSpUYNWqVQwbNowZM2bQpEkTxo8fn6HnvhBCWIJGkYZEQgghhBDCQuSxuxBCCCGEsBhJPoUQQgghhMVI8imEEEIIISxGkk8hhBBCCGExknwKIYQQQgiLkeRTCCGEEEJYjCSfQgghhBDCYiT5FEIIIYQQFiPJpxBCCCGEsBhJPoUQQgghhMVI8imEEEIIISzm//TVafcvxuylAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))  # extra code – it's not needed, just formatting\n",
    "plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "plt.vlines(threshold, 0, 1.0, \"k\", \"dotted\", label=\"threshold\")\n",
    "\n",
    "# extra code – this section just beautifies and saves Figure 3–5\n",
    "idx = (thresholds >= threshold).argmax()  # first index ≥ threshold\n",
    "plt.plot(thresholds[idx], precisions[idx], \"bo\")\n",
    "plt.plot(thresholds[idx], recalls[idx], \"go\")\n",
    "plt.axis([-50000, 50000, 0, 1])\n",
    "plt.grid()\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.legend(loc=\"center right\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:41:36.469544729Z",
     "start_time": "2023-12-03T12:41:36.373381440Z"
    }
   },
   "id": "9832fcab07346b18"
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as patches  # extra code – for the curved arrow\n",
    "\n",
    "plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting\n",
    "\n",
    "plt.plot(recalls, precisions, linewidth=2, label=\"Precision/Recall curve\")\n",
    "\n",
    "# extra code – just beautifies and saves Figure 3–6\n",
    "plt.plot([recalls[idx], recalls[idx]], [0., precisions[idx]], \"k:\")\n",
    "plt.plot([0.0, recalls[idx]], [precisions[idx], precisions[idx]], \"k:\")\n",
    "plt.plot([recalls[idx]], [precisions[idx]], \"ko\",\n",
    "         label=\"Point at threshold 3,000\")\n",
    "plt.gca().add_patch(patches.FancyArrowPatch(\n",
    "    (0.79, 0.60), (0.61, 0.78),\n",
    "    connectionstyle=\"arc3,rad=.2\",\n",
    "    arrowstyle=\"Simple, tail_width=1.5, head_width=8, head_length=10\",\n",
    "    color=\"#444444\"))\n",
    "plt.text(0.56, 0.62, \"Higher\\nthreshold\", color=\"#333333\")\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.axis([0, 1, 0, 1])\n",
    "plt.grid()\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:43:56.818506514Z",
     "start_time": "2023-12-03T12:43:56.706092531Z"
    }
   },
   "id": "ae2ceaf883047bec"
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "3370.0194991439557"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_for_90_precision = (precisions >= 0.90).argmax()\n",
    "threshold_for_90_precision = thresholds[idx_for_90_precision]\n",
    "threshold_for_90_precision"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:47:29.842292970Z",
     "start_time": "2023-12-03T12:47:29.788188237Z"
    }
   },
   "id": "de2ae84b3afa89fc"
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores >= threshold_for_90_precision)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:51:27.028152751Z",
     "start_time": "2023-12-03T12:51:26.940569381Z"
    }
   },
   "id": "2c5ce7b949524595"
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9000345901072293"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_90)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:51:28.574518785Z",
     "start_time": "2023-12-03T12:51:28.476533158Z"
    }
   },
   "id": "48214931b06f15b4"
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "0.4799852425751706"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_at_90_precision = recall_score(y_train_5, y_train_pred_90)\n",
    "recall_at_90_precision"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:51:30.409766950Z",
     "start_time": "2023-12-03T12:51:30.307507403Z"
    }
   },
   "id": "2d62db161ef7b336"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Die ROC-Kurve"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c6b0675ef3da17d6"
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "(60000,)"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train_5,y_scores)\n",
    "y_train_5.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:54:30.734244763Z",
     "start_time": "2023-12-03T12:54:30.647030206Z"
    }
   },
   "id": "70094827818bcc5c"
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x500 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_for_threshold_at_90 = (thresholds <= threshold_for_90_precision).argmax()\n",
    "tpr_90, fpr_90 = tpr[idx_for_threshold_at_90], fpr[idx_for_threshold_at_90]\n",
    "\n",
    "plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting\n",
    "plt.plot(fpr, tpr, linewidth=2, label=\"ROC curve\")\n",
    "plt.plot([0, 1], [0, 1], 'k:', label=\"Random classifier's ROC curve\")\n",
    "plt.plot([fpr_90], [tpr_90], \"ko\", label=\"Threshold for 90% precision\")\n",
    "\n",
    "# extra code – just beautifies and saves Figure 3–7\n",
    "plt.gca().add_patch(patches.FancyArrowPatch(\n",
    "    (0.20, 0.89), (0.07, 0.70),\n",
    "    connectionstyle=\"arc3,rad=.4\",\n",
    "    arrowstyle=\"Simple, tail_width=1.5, head_width=8, head_length=10\",\n",
    "    color=\"#444444\"))\n",
    "plt.text(0.12, 0.71, \"Higher\\nthreshold\", color=\"#333333\")\n",
    "plt.xlabel('False Positive Rate (Fall-Out)')\n",
    "plt.ylabel('True Positive Rate (Recall)')\n",
    "plt.grid()\n",
    "plt.axis([0, 1, 0, 1])\n",
    "plt.legend(loc=\"lower right\", fontsize=13)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:54:56.069224906Z",
     "start_time": "2023-12-03T12:54:55.950468411Z"
    }
   },
   "id": "c20f90691d845481"
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9604938554008616"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_train_5, y_scores)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T12:57:41.108606640Z",
     "start_time": "2023-12-03T12:57:41.000441080Z"
    }
   },
   "id": "444ffa743c3fdc05"
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(random_state=42)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T13:00:17.868535476Z",
     "start_time": "2023-12-03T13:00:17.758914619Z"
    }
   },
   "id": "c308728c36046407"
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3, method='predict_proba')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T13:03:24.791799999Z",
     "start_time": "2023-12-03T13:02:46.240594940Z"
    }
   },
   "id": "ade45245c6ac52cc"
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_digit(X[1])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T13:03:52.766855189Z",
     "start_time": "2023-12-03T13:03:52.681597782Z"
    }
   },
   "id": "408d75a855ba6003"
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x500 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1]\n",
    "precisions_forest, recalls_forest, thresholds_forest = precision_recall_curve(\n",
    "    y_train_5, y_scores_forest)\n",
    "\n",
    "plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting\n",
    "\n",
    "plt.plot(recalls_forest, precisions_forest, \"b-\", linewidth=2,\n",
    "         label=\"Random Forest\")\n",
    "plt.plot(recalls, precisions, \"--\", linewidth=2, label=\"SGD\")\n",
    "\n",
    "# extra code – just beautifies and saves Figure 3–8\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.axis([0, 1, 0, 1])\n",
    "plt.grid()\n",
    "plt.legend(loc=\"lower left\")\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T13:06:57.641045295Z",
     "start_time": "2023-12-03T13:06:57.544827064Z"
    }
   },
   "id": "6b46dd0082fa1897"
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9274509803921569"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_forest = y_probas_forest[:, 1] >= 0.5  # positive proba ≥ 50%\n",
    "f1_score(y_train_5, y_train_pred_forest)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-03T13:14:15.065979109Z",
     "start_time": "2023-12-03T13:14:14.971708876Z"
    }
   },
   "id": "d6db3b1736c9b509"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "8ba1af2f3849c4bb"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}