IRIS_UWEIGHTED_AND_WEIGHTED ( reciprocal dist as weights ).ipynb

{"cells":[{"cell_type":"code","execution_count":null,"id":"b8e70c65","metadata":{"id":"b8e70c65"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n"]},{"cell_type":"code","execution_count":null,"id":"cd266943","metadata":{"id":"cd266943","outputId":"a43d63f2-fcbc-4816-ae36-17760bdc2b50"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_length</th>\n","      <th>sepal_width</th>\n","      <th>petal_length</th>\n","      <th>petal_width</th>\n","      <th>species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   sepal_length  sepal_width  petal_length  petal_width species\n","0           5.1          3.5           1.4          0.2  setosa\n","1           4.9          3.0           1.4          0.2  setosa\n","2           4.7          3.2           1.3          0.2  setosa\n","3           4.6          3.1           1.5          0.2  setosa\n","4           5.0          3.6           1.4          0.2  setosa"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["\n","df = sns.load_dataset('iris')\n","\n","df.head()\n"]},{"cell_type":"code","execution_count":null,"id":"afd0aaef","metadata":{"id":"afd0aaef","outputId":"79cc7bb7-f551-49de-ca53-fe30cc8ec678"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sepal_length = df['sepal_length']\n","sepal_width = df['sepal_width']\n","\n","# create a scatter plot of sepal length vs sepal width\n","plt.scatter(sepal_length, sepal_width)\n","plt.xlabel('Sepal Length')\n","plt.ylabel('Sepal Width')\n","plt.title('Iris Dataset: Sepal Length vs Sepal Width')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"id":"052ee09f","metadata":{"id":"052ee09f","outputId":"e07c0c80-7ec2-4d1a-8dbf-e88c56c0a418"},"outputs":[{"data":{"text/plain":["array([[5.1, 3.5],\n","       [4.9, 3. ],\n","       [4.7, 3.2],\n","       [4.6, 3.1],\n","       [5. , 3.6],\n","       [5.4, 3.9],\n","       [4.6, 3.4],\n","       [5. , 3.4],\n","       [4.4, 2.9],\n","       [4.9, 3.1],\n","       [5.4, 3.7],\n","       [4.8, 3.4],\n","       [4.8, 3. ],\n","       [4.3, 3. ],\n","       [5.8, 4. ],\n","       [5.7, 4.4],\n","       [5.4, 3.9],\n","       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[0,1]].values\n","X"]},{"cell_type":"code","execution_count":null,"id":"4d7b6f5e","metadata":{"id":"4d7b6f5e","outputId":"a04c11ac-b7d3-4383-e497-41246070e6e7"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAmQAAAGDCAYAAACFuAwbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAA4mElEQVR4nO3deXxcd33v/9dHmyV5kxdZ8hpnTyyTDWeFACGJRctaCJCwhTWltxdKWy6Ue38X2l5KWQoF2lvWQBKgWRroDbSA7YRAaEjiOAtJvMSx48S7LMe25EWytu/vjxk7imPLsqTRGcmv5+MxD82cc2bOeyxw3v5+zxIpJSRJkpSdkqwDSJIkHe8sZJIkSRmzkEmSJGXMQiZJkpQxC5kkSVLGLGSSJEkZs5BJKoiI+OuI+OEw7GduRKSIKMu//nVEfLDQ+x0OQ/ldIuKGiPjsUHyWpKFnIZM0IBGxp9ejJyLaer1+5xDv64aI6Dhkn78fyn0MVK9C+Mghy6fmMz/Tz88ZlgIrqThZyCQNSEpp3IEHsB54fa9lPyrALr/Ye58ppbMLsI/BqI6I+b1evwNYl1UYSSOLhUxSIVVExE0RsTsilkfEggMrImJGRPw4IpojYl1EfHQI93tyRCyNiNaIuCMiJvfa7xvyWXblpwTPzC9/X0T8rNd2T0XEv/V6vSEizuljnz8Aru31+j3ATb03ONJ3jojXAP8TePthRv9OiIh783+GiyNi6tG+S37duRHxcP59twKV/fqTk5QJC5mkQnoDcAtQA/wU+GeAiCgBfgb8HpgJXA58LCIah2i/7wHeD0wHuoCv5/d7GnAz8DGgFvg58LOIqAB+A1waESURMQOoAC7Ov+8kYBzwWB/7/CFwdUSURsS8/PYPHFjZ13dOKf0S+Bxw62FG/94BvA+Yls/08aN9l/z3+X/kSuJk4N+At/T/j0/ScLOQSSqk/0op/Tyl1E2uHBwoGucDtSmlv00pdaSUnga+A1zdx2d9PD8SdOBxYx/b/iCl9ERKaS/wv4G3RUQp8HbgP1NKS1JKncA/AFXAJfkMu4FzgFcAi4DNEXEG8Ergtymlnj72uRF4EriCXCH8wSHrB/KdAb6fUlqdUmoDbsvno6/vAlwElANfTSl1ppRuBx48yn4kZags6wCSRrWtvZ7vAyrzZ0OeAMyIiF291pcCv+3js/4hpfT/9XO/G3o9f5ZcOZkKzMi/BiCl1BMRG8iNWEFulOxVwCn557vIlbGL86+P5ibgveRK0aXAab3WDeQ7w4v/DMfln/f1XbqBTSml1Ou9zyKpaFnIJGVhA7AupXRqgT5/dq/nc4BOYDuwGXjJgRUREfltN+UX/QZ4PXAiuSnEXcA7yRWyf+7Hfn+c3+6hlNL6/LTiAUf7zukIy4+kr++SgJkREb1K2Rxg7THuQ9IwccpSUhaWArsj4pMRUZU/7mp+RJw/RJ//roiYFxHVwN8Ct+enTW8DXhsRl0dEOfCXwH7gd/n3/Qa4DKhKKW0kN3r1GmAK8MihOzlUfor01cDhrh12tO/cBMzNH2vWH319l/vIHTv30Ygoj4g3Axf083MlZcBCJmnY5cvR68gdD7WO3OjVd4GJfbztE4dch2x7H9v+ALiB3HRfJfDR/H6fBN4F/FN+n68nd7mOjvz61cAe8tOIKaVW4Gng3nzm/ny3ZSmlF41E9eM7Hzij87mIeLgf+znid8l/nzeTmz7dQe54s5/0J7+kbMQLDzGQJEnScHOETJIkKWMWMkmSpIxZyCRJkjJmIZMkScqYhUySJCljI/rCsFOnTk1z587NOoYkSdJRPfTQQ9tTSrWHWzeiC9ncuXNZtmxZ1jEkSZKOKiKOeAszpywlSZIyZiGTJEnKmIVMkiQpYxYySZKkjFnIJEmSMmYhkyRJypiFTJIkKWMWMkmSpIxZyCRJkjJmIZMkScqYhUySJCljFrI+tHd2s2RFEy1tnVlHkSRJo5iFrA8rtrTyoZuW8atVTVlHkSRJo5iFrA/nzKph2vgxLHrCQiZJkgrHQtaHkpJgYUMdv1ndTHtnd9ZxJEnSKGUhO4rGhnraOru5Z3Vz1lEkSdIoZSE7iotOmsKEyjIWLXfaUpIkFYaF7CjKS0u4/Mw67lrVRFd3T9ZxJEnSKGQh64fGhjp27etk6bodWUeRJEmjkIWsH15xWi1jykpYtHxr1lEkSdIoZCHrh+qKMl5xWi2LVzSRUso6jiRJGmUsZP3U2FDPlpZ2Ht/UknUUSZI0yljI+umKM6dRWhJOW0qSpCFnIeunmuoKLjxxspe/kCRJQ85CdgwaG+pZs20Pa5v3ZB1FkiSNIhayY3DlvDoApy0lSdKQspAdgxk1VZw1a6LTlpIkaUhZyI5RY0M9v9+wi60t7VlHkSRJo4SF7Bg1NuSmLRevcNpSkiQNDQvZMTpl2nhOqh3rcWSSJGnIWMgGoLGhnvuf3sGufR1ZR5EkSaOAhWwAGhvq6e5J3LVyW9ZRJEnSKGAhG4CzZk6kfkKl05aSJGlIWMgGoKQkWNhQxz1PNdPW0Z11HEmSNMJZyAaosaGe9s4efrO6OesokiRphCtYIYuI70XEtoh4oteyL0XEqoh4LCL+PSJqeq37VESsiYgnI6KxULmGygUnTmZiVTmLnbaUJEmDVMgRshuA1xyybAkwP6V0FrAa+BRARMwDrgYa8u/5l4goLWC2QSsvLeHyM6dx16ptdHb3ZB1HkiSNYAUrZCmle4AdhyxbnFLqyr+8H5iVf/5G4JaU0v6U0jpgDXBBobINlcaGelraOlm6bsfRN5YkSTqCLI8hez/wi/zzmcCGXus25pe9SERcFxHLImJZc3O2x2+94tRaKstLPNtSkiQNSiaFLCL+F9AF/OhY35tS+nZKaUFKaUFtbe3QhzsGVRWlvPK0WhYvb6KnJ2WaRZIkjVzDXsgi4r3A64B3ppQOtJhNwOxem83KLyt6jQ31bG1t57FNLVlHkSRJI9SwFrKIeA3wCeANKaV9vVb9FLg6IsZExInAqcDS4cw2UJefUUdpSThtKUmSBqyQl724GbgPOD0iNkbEB4B/BsYDSyLi0Yj4JkBKaTlwG7AC+CXwpymlEXHF1YnV5Vx00mQLmSRJGrCyQn1wSumawyy+vo/t/w74u0LlKaTGhno+fcdy1mzbzSnTxmcdR5IkjTBeqX8ILJxXD8Ci5U0ZJ5EkSSORhWwI1E+s5OzZNU5bSpKkAbGQDZHGhjoe29jC5l1tWUeRJEkjjIVsiDQ25KYtvbelJEk6VhayIXJy7ThOmTbO48gkSdIxs5ANocaGOpY+s4OdezuyjiJJkkYQC9kQamyop7sncedKR8kkSVL/WciG0EtmTmTGxEoWr7CQSZKk/rOQDaGIYGFDPfesbmZfR1fWcSRJ0ghhIRtiCxvq2N/Vwz2rm7OOIkmSRggL2RC7YO5kJlWXe7alJEnqNwvZECsrLeHyM+u4a2UTnd09WceRJEkjgIWsABob6mlt7+L+p5/LOookSRoBLGQFcOmpU6kqL/XelpIkqV8sZAVQWV7KK0+rZfHyJnp6UtZxJElSkbOQFUjj/Dq27d7Poxt3ZR1FkiQVOQtZgbz69DrKSsJpS0mSdFQWsgKZWF3OxSdPYfHyJlJy2lKSJB2ZhayAFjbUs277Xp7atifrKJIkqYhZyApo4bw6ABY94bSlJEk6MgtZAdVNqOTcOTUsWmEhkyRJR2YhK7DGhnqe2NTKxp37so4iSZKKlIWswBob6gFY7L0tJUnSEVjICuzEqWM5rW4ci522lCRJR2AhGwaNDfUsXbeDHXs7so4iSZKKkIVsGDQ21NOT4M6VTltKkqQXs5ANg4YZE5hZU8Vir9ovSZIOw0I2DCKChQ113PPUdvbu78o6jiRJKjIWsmGycF49HV09/GZ1c9ZRJElSkbGQDZPz505iUnW5NxuXJEkvYiEbJmWlJVxxZh2/WrWNjq6erONIkqQiYiEbRo0N9exu7+K+p5/LOookSSoiFrJh9PJTp1JdUeq0pSRJegEL2TCqLC/lVafXsmRFEz09Kes4kiSpSFjIhlljQz3Nu/fzyIadWUeRJElFwkI2zC47YxrlpcEibzYuSZLyLGTDbEJlORefPJVFy7eSktOWkiTJQpaJxoY6nn1uH0827c46iiRJKgIWsgxcOa+OCFj0hNOWkiTJQpaJaeMrOW/OJBav8PIXkiSpgIUsIr4XEdsi4oleyyZHxJKIeCr/c1J+eUTE1yNiTUQ8FhHnFSpXsWhsqGP55lY27NiXdRRJkpSxQo6Q3QC85pBlfwXclVI6Fbgr/xrgD4BT84/rgG8UMFdRaGyoB2DxCqctJUk63hWskKWU7gF2HLL4jcCN+ec3Am/qtfymlHM/UBMR0wuVrRicMGUsZ9SP96r9kiRp2I8hq0spbck/3wrU5Z/PBDb02m5jftmotrChnmXP7OC5PfuzjiJJkjKU2UH9KXcRrmO+EFdEXBcRyyJiWXNzcwGSDZ+F8+roSXDnSqctJUk6ng13IWs6MBWZ/7ktv3wTMLvXdrPyy14kpfTtlNKClNKC2tragoYttIYZE5hZU+VV+yVJOs4NdyH7KXBt/vm1wB29lr8nf7blRUBLr6nNUSsiaGyo57+e2s6e/V1Zx5EkSRkp5GUvbgbuA06PiI0R8QHg88CVEfEUcEX+NcDPgaeBNcB3gP9WqFzFprGhjo7uHn795LajbyxJkkalskJ9cErpmiOsuvww2ybgTwuVpZgtmDuZKWMrWLS8idedNSPrOJIkKQNeqT9jpSXBFWfWcfeqbezv6s46jiRJyoCFrAg0zq9jz/4ufrf2uayjSJKkDFjIisAlJ09lbEUpi71IrCRJxyULWRGoLC/lVWdMY8mKJrp7jvnSbJIkaYSzkBWJxoZ6tu/p4OH1O7OOIkmShpmFrEhcdnotFaUlTltKknQcspAVifGV5VxyyhQWLW8idxUQSZJ0vLCQFZHGhnrW79jHqq27s44iSZKGkYWsiFxxZh0RsMhpS0mSjisWsiJSO34MC06Y5M3GJUk6zljIiszCefWs3NLKhh37so4iSZKGiYWsyDQ21ANOW0qSdDyxkBWZOVOqOaN+vIVMkqTjiIWsCDU21LPs2Z00796fdRRJkjQMLGRFqLGhnpTgzpUe3C9J0vHAQlaEzpw+ntmTq5y2lCTpOGEhK0IRQeO8en635jl2t3dmHUeSJBWYhaxINc6vp6O7h7ufbM46iiRJKjALWZE6b84kpo6rcNpSkqTjgIWsSJWWBFfOq+PXq7bR3tmddRxJklRAFrIitrChnr0d3fxu7faso0iSpAKykBWxS06ewrgxZSz23paSJI1qFrIiNqaslMvOmMaSFU1096Ss40iSpAKxkBW5xoY6ntvbwUPP7sw6iiRJKhALWZF71enTqCgt8WxLSZJGMQtZkRs3poyXnTKFRcu3kpLTlpIkjUYWshGgsaGejTvbWLGlNesokiSpACxkI8AV8+ooCVjk2ZaSJI1K/SpkEfHyiHhf/nltRJxY2Fjqbeq4MSw4YTKLPY5MkqRR6aiFLCI+A3wS+FR+UTnww0KG0ostbKhj1dbdPPvc3qyjSJKkIdafEbI/At4A7AVIKW0GxhcylF6ssaEewLMtJUkahfpTyDpS7vS+BBARYwsbSYcze3I186ZP8DgySZJGof4Ustsi4ltATUR8CLgT+E5hY+lwGhvqeXj9Trbtbs86iiRJGkJ9FrKICOBW4Hbgx8DpwKdTSv80DNl0iMb5daQES1Y4SiZJ0mhS1tfKlFKKiJ+nlF4CLBmmTDqC0+vGc8KUahYtb+KdF56QdRxJkjRE+jNl+XBEnF/wJDqqiKCxoZ771m6ntb0z6ziSJGmI9KeQXQjcFxFrI+KxiHg8Ih4rdDAdXmNDHZ3dibtXbcs6iiRJGiJ9TlnmNRY8hfrt3NmTqB0/hsXLm3jjOTOzjiNJkobAUUfIUkrPAjXA6/OPmvwyZaCkJLhyXh2/fnIb7Z3dWceRJElDoD9X6v8z4EfAtPzjhxHxkUIH05E1NtSzt6Obe9dszzqKJEkaAv05huwDwIUppU+nlD4NXAR8qLCx1JeLT5rC+DFlXrVfkqRRoj+FLIDec2Pd+WUDFhF/HhHLI+KJiLg5Iioj4sSIeCAi1kTErRFRMZh9jGYVZSVcdsY07ly5ja7unqzjSJKkQepPIfs+8EBE/HVE/DVwP3D9QHcYETOBjwILUkrzgVLgauALwD+mlE4BdpIbmdMRNDbUs2NvB8ue3Zl1FEmSNEj9Oaj/K8D7gB35x/tSSl8d5H7LgKqIKAOqgS3Aq8ndEQDgRuBNg9zHqPaq02upKCtx2lKSpFGgPwf1XwQ8lVL6ekrp68DaiLhwoDtMKW0C/gFYT66ItQAPAbtSSl35zTYCh72mQ0RcFxHLImJZc3PzQGOMeGPHlHHpKVNZvLyJ3L3fJUnSSNWfKctvAHt6vd6TXzYgETEJeCNwIjADGAu8pr/vTyl9O6W0IKW0oLa2dqAxRoXGhno27Wpj+ebWrKNIkqRB6NdB/anXEExKqYf+XVD2SK4A1qWUmlNKncBPgJcBNfkpTIBZwKZB7OO4cPmZ0ygJnLaUJGmE608hezoiPhoR5fnHnwFPD2Kf64GLIqI6IgK4HFgB3A1cld/mWuCOQezjuDBl3BjOnzvZQiZJ0gjXn0L2YeASciNWm8jd2/K6ge4wpfQAuYP3HwYez2f4NvBJ4C8iYg0whUGcyXk8aWyoZ3XTHtZt35t1FEmSNED9OctyW0rp6pTStPzjHSmlQd3ZOqX0mZTSGSml+Smld6eU9qeUnk4pXZBSOiWl9NaU0v7B7ON4sbChDnDaUpKkkeyIhSwiPhQRp+afR0R8LyJaIuKxiDhv+CKqL7MmVTN/5gQLmSRJI1hfI2R/BjyTf34NcDZwEvAXwNcKG0vHonFePY+s30VTa3vWUSRJ0gD0Vci68mdBArwOuCml9FxK6U5yl6pQkWicXw/AkhVNGSeRJEkD0Vch64mI6RFRSe5MyDt7rasqbCwdi1OnjePEqWOdtpQkaYTqq5B9GlhGbtrypyml5QAR8UoGd9kLDbGIYGFDHfetfY6Wts6jv0GSJBWVIxaylNJ/ACcAZ6aUPtRr1TLg7YUOpmPT2FBPV0/i7lWDOgFWkiRloM/LXqSUulJKOw9ZtjeltOdI71E2zplVw7TxY5y2lCRpBOrPhWE1ApSUBFfOq+PXTzbT3tmddRxJknQMLGSjSGNDPW2d3fz2qe1ZR5EkScfgqDcJP8JFYFuAZ1NKXUMfSQN10UlTGF9ZxqLlW7lyXl3WcSRJUj8dtZAB/wKcBzwGBDAfWA5MjIg/SSktLmA+HYOKshIuP2Mad61soqu7h7JSB0AlSRoJ+vNf7M3AuSmlBSmllwLnkrvsxZXAFwsZTseusaGenfs6WfrMjqyjSJKkfupPITvtwDXIAFJKK4AzUkpei6wIvfL0WsaUlbB4uVftlyRppOhPIVseEd+IiFfmH/8CrIiIMYBXIS0y1RVlXHpqLYuXbyWllHUcSZLUD/0pZO8F1gAfyz+ezi/rBC4rTCwNRmNDHZtb2nl8U0vWUSRJUj8c9aD+lFIb8OX841BeILYIXXFmHaUlwaLlWzlrVk3WcSRJ0lEcdYQsIl4WEUsiYnVEPH3gMRzhNDCTxlZwwdzJLPI4MkmSRoT+TFleD3wFeDlwfq+HilhjQx1rtu1hbbODmJIkFbv+FLKWlNIvUkrbUkrPHXgUPJkGZWFDPYBnW0qSNAL0p5DdHRFfioiLI+K8A4+CJ9OgzKip4qxZE73ZuCRJI0B/rtR/Yf7ngl7LEvDqoY+jodTYUM+XFj3J1pZ26idWZh1HkiQdwVFHyFJKlx3mYRkbARbm72e5ZIWjZJIkFbMjjpBFxLtSSj+MiL843PqU0lcKF0tD4ZRp4zhp6lgWLW/i3RfPzTqOJEk6gr5GyMbmf44/wkNFLiJY2FDP/U8/R8s+b6ogSVKxOuIIWUrpWxFRCrSmlP5xGDNpCDU21PHN36zlrlVNvPm8WVnHkSRJh9HnMWQppW7gmmHKogI4e1YNdRPGeLalJElFrD9nWd4bEf8M3ArsPbAwpfRwwVJpyJSUBAvn1fNvD22graObqorSrCNJkqRD9KeQnZP/+be9lnnZixGksaGeH9z/LPc81Uxj/oKxkiSpePTn5uKXDUcQFc6FJ01mYlU5i5ZvtZBJklSE+nNz8bqIuD4ifpF/PS8iPlD4aBoq5aUlXH7GNO5auY3O7p6s40iSpEP059ZJNwCLgBn516uBjxUojwpkYUM9LW2dLF23I+sokiTpEP0pZFNTSrcBPQAppS6gu6CpNOReeVotleUlnm0pSVIR6k8h2xsRU8gdyE9EXAS0FDSVhlxVRSmvOLWWxcub6OlJWceRJEm99KeQ/QXwU+DkiLgXuAn4aEFTqSAaG+rZ2trO45vs05IkFZP+XPZiOfBK4HQggCfpX5FTkbn8zGmUlgSLlm/l7Nk1WceRJEl5/SlW96WUulJKy1NKT6SUOoH7Ch1MQ6+muoKLTprscWSSJBWZIxayiKiPiJcCVRFxbkScl3+8CqgeroAaWgvn1bO2eS9rtu3JOookScrra8qyEXgvMAv4MrnpSoDdwP8sbCwVysKGOj7z0+UsWr6VU6adknUcSZJEH4UspXQjcGNEvCWl9ONhzKQCmj6xirNnTWTx8q386WUWMkmSikF/jiGbFRETIue7EfFwRCwseDIVzMKGen6/sYUtLW1ZR5EkSfSvkL0/pdQKLASmAO8GPj+YnUZETUTcHhGrImJlRFwcEZMjYklEPJX/OWkw+9CRHbif5eLlTRknkSRJ0L9CduDYsT8EbkopLe+1bKC+BvwypXQGcDawEvgr4K6U0qnAXfnXKoBTpo3j5Nqxnm0pSVKR6E8heygiFpMrZIsiYjz52ygNRERMBF4BXA+QUupIKe0C3gjcmN/sRuBNA92Hjq6xoZ4H1u1g596OrKNIknTc608h+wC50arzU0r7gArgfYPY54lAM/D9iHgkf1zaWKAupbQlv81WoO5wb46I6yJiWUQsa25uHkSM41tjQz3dPYm7Vm3LOookSce9/hSylwPjgLMi4hVAA1AziH2WAecB30gpnQvs5ZDpyZRSIn/vzEOllL6dUlqQUlpQW1s7iBjHt7NmTWT6xEqnLSVJKgL9uXXS/+j1vBK4AHgIePUA97kR2JhSeiD/+nZyhawpIqanlLZExHTAoZsCiggWzqvjlgc3sK+ji+qK/vxPQZIkFcJRR8hSSq/v9bgSmA/sHOgOU0pbgQ0RcXp+0eXACnI3ML82v+xa4I6B7kP909hQz/6uHu5Z7dSvJElZGsiwyEbgzEHu9yPAjyKiAnia3DFpJcBtEfEB4FngbYPch47ighMnU1Ndznd/u45XnFbrKJkkSRk56n+BI+KfeP54rhLgHODhwew0pfQosOAwqy4fzOfq2JSVlvCZ18/jL2/7Pe/87gN8/73nU1NdkXUsSZKOO/0ZElnW63kXcHNK6d4C5dEw+6NzZ1FVXsZHb36Et3/rfm76wAXUTajMOpYkSceVyJ3QODItWLAgLVu27Ogb6qh+t2Y7H7ppGZPHVfCD91/I3Kljs44kSdKoEhEPpZQON0N45IP6I+LxiHjsMI/HI+KxwsVVFi45ZSr/+qGL2NPexVXfvI+VW1qzjiRJ0nHjiCNkEXFCX29MKT1bkETHwBGyobdm227eff1S9uzv4vvvPZ8FcydnHUmSpFFhQCNkQDkwK6X0bO8HMIuBnZ2pEeCUaeO5/U8uoXbcGN51/QPc/aSXg5MkqdD6KmRfBQ43b9WaX6dRamZNFbd9+GJOmTaOD924jDse3ZR1JEmSRrW+ClldSunxQxfml80tWCIVhanjxnDzhy7ipSdM4mO3PsoP7nsm60iSJI1afRWymj7WVQ1xDhWh8ZXl3Pj+C7j8jDr+9x3L+fpdTzGSz8qVJKlY9VXIlkXEhw5dGBEfJHcvSx0HKstL+ea7zuPN583kK0tW87f/sYKeHkuZJElDqa+D8z8G/HtEvJPnC9gCoAL4owLnUhEpKy3hH646m5qqCr537zpa9nXyhavOorz0qLdClSRJ/XDEQpZSagIuiYjLyN1QHOA/U0q/GpZkKiolJcH/ft2ZTKou58tLVtPa3sk/v+M8KstLs44mSdKId9TLV6SU7gbuHoYsKnIRwUcuP5WasRV8+o4neM/3lvLdaxcwobI862iSJI1ozjnpmL37ohP46tvP4eFnd3LNt+9n+579WUeSJGlEs5BpQN54zky+c+0C1jbv4W3fvI+NO/dlHUmSpBHLQqYBu+z0afzwAxeyfc9+3vrN+1izbXfWkSRJGpEsZBqUBXMnc+sfX0xnd+Kt37yP32/YlXUkSZJGHAuZBu3M6RP48Z9czLjKMt7xnfu5d832rCNJkjSiWMg0JE6YMpbbP3wJsyZV877vP8gvn9iadSRJkkYMC5mGTN2ESm7944uYP3MC/+1HD3HbgxuyjiRJ0ohgIdOQqqmu4IcfvJCXnTKVT/z4Mb59z9qsI0mSVPQsZBpy1RVlXH/t+bz2rOl87uer+MIvV3lTckmS+nDUK/VLA1FRVsLXrz6XiVXlfOPXa9m1r4PPvukllJZE1tEkSSo6FjIVTGlJ8Hdvms+k6nL+791raW3r4itvP5sxZd7/UpKk3ixkKqiI4H80nsGk6go++58raW3v5Jvveiljx/g/PUmSDvAYMg2LD156El+66izuXbOdd373AXbt68g6kiRJRcNCpmHz1gWz+ca7XsqKLa287Vv3sbWlPetIkiQVBQuZhlVjQz03vO98Nu1s46pv/o5ntu/NOpIkSZmzkGnYXXLyVG6+7iL2dXRz1TfvY8Xm1qwjSZKUKQuZMnHWrBpu++OLKS8N3v7t+3jwmR1ZR5IkKTMWMmXmlGnjuP1PLqF2/Bjeff0D3L1qW9aRJEnKhIVMmZpZU8W//fHFnDJtHB+6aRl3PLop60iSJA07C5kyN2XcGG7+0EUsmDuJj936KDfd90zWkSRJGlYWMhWF8ZXl3PC+C7j8jDo+fcdyvnbnU97/UpJ03LCQqWhUlpfyzXedx1vOm8U/3rmav/nZCnp6LGWSpNHP+9eoqJSVlvClq86iprqc6/9rHS1tnXzxqrMoL/XfDpKk0ctCpqJTUhL8f689k0nV5fzD4tW0tnXyf995HpXl3pRckjQ6OeygohQR/PdXn8r/edN8fvXkNt5z/VJa2zuzjiVJUkFYyFTU3n3RCXzt6nN5eP1Orv7W/TTv3p91JEmShpyFTEXvDWfP4LvXLuDp7Xt427fuY8OOfVlHkiRpSFnINCK86vRp/OiDF/Lcnv289Zv38VTT7qwjSZI0ZDIrZBFRGhGPRMR/5F+fGBEPRMSaiLg1Iiqyyqbi9NITJnPrH19Md0q89Vv38eiGXVlHkiRpSGQ5QvZnwMper78A/GNK6RRgJ/CBTFKpqJ05fQK3f/hiJlSW847v3M9/PbU960iSJA1aJoUsImYBrwW+m38dwKuB2/Ob3Ai8KYtsKn4nTBnL7R++mDmTq3n/DQ/yyye2ZB1JkqRByWqE7KvAJ4Ce/OspwK6UUlf+9UZgZga5NEJMm1DJrdddzPyZE/hvP3qYWx9cf+SNe3qOvE6SpCIw7IUsIl4HbEspPTTA918XEcsiYllzc/MQp9NIMrG6nB9+8EIuPbWWT/74cb71m7Uv3ui552D2bNixY/gDSpLUT1mMkL0MeENEPAPcQm6q8mtATUQcuHPALGDT4d6cUvp2SmlBSmlBbW3tcORVEauuKOM771nA686azt//YhV//4uVL7wp+U9/Cps3535KklSkhr2QpZQ+lVKalVKaC1wN/Cql9E7gbuCq/GbXAncMdzaNTBVlJXzt6nN554Vz+NZvnuZTP3mc7gM3Jb/hhhf+lCSpCBXTvSw/CdwSEZ8FHgGuzziPRpDSkuCzb5rP5LEV/NOv1tDS1slX//Bkxtx/f26D+++H1laYMCHboJIkHUamhSyl9Gvg1/nnTwMXZJlHI9jOncT69fxlHUw8dyKffWQru59cy7fGTmBsx3aoqIDvfhcuv/yF75szByZNyiazJEl58YLjbUaYBQsWpGXLlmUdQ8Xgk5+EL34RKiuhooLbT7+UT77qOuZvXcMN//bXTGrf/cLRsY4OaG+HT3wCvvCF7HJLko4bEfFQSmnB4dZ56ySNDn//9/C5z0EEtLZy1YP/yTf+/XOsnHYif/TuL/PDc/6A3e2duWnL1tbcdp/7XO59kiRlzBEyjS5Ll8Ib3wi7dkF7Ow/MauAzV36YVdNOpKqjndevvper1y/l3O9+hbjAGXJJ0vDpa4TMQqbRp7UVzjkH1q0DIAG/n34aN5/dyM/mvYp95WM4vW4811wwmz86dxYTq8szjStJOj5YyHR8aW/PHajf3v6iVbsnTuZn/7mUmx/ewuObWhhTVsIfvmQ611wwh/PnTiJ3Fy9JkoZeX4WsmC57IQ2NJUtyZ1W2t0N1NXR1QVkZ7NvH+NTFO3au5B0feR1PbGrhlgfX8/8e2cy/P7KJk2vHcvX5c3jLS2cxeWxF1t9CknQc8aB+jT4/+EFu2rK6Gj7wgdztk97/fqiqyi3/wQ8AmD9zIp9900tY+r8u54tXncXEqnL+7ucruehzd/Hf//Vh7l2znZ6ekTuCLEkaOZyy1OjS0ZGbriwrg9tug8bG59f98pfw9rdDdzfs3AnlLz527Mmtu7nlwfX85OFNtLR1csKUat5+/myueukspo2vHMYvIkkabTyGTMePtjb4y7+Ez3wG6upevL6pCf7mb+DLX86NmB1Be2c3v3xiKzcvXc8D63ZQVhJccWYdV18wm0tPraW0xGPNJEnHxkImDcLa5j3c+uAGbn9oIzv2djCzpoq3nz+bty6YxfSJRy51kiT1ZiGThkBHVw9LVjRx89L1/Nea7ZQEXHb6NK6+YA6XnV5LWamHZEqSjsxCJg2x9c/t49Zl67lt2Uaad++nbsIY3rZgNm9bMJvZk6uzjidJKkIWMqlAOrt7+NWqbdyydD2/Xt0MwKWn1nLN+bO5Yl4d5Y6aSZLyLGTSMNi0q43bHtzAbcs2sKWlnanjKnjLS2dx9flzOHHq2KzjSZIyZiGThlF3T+Ke1c3869L1/GrVNrp7EhefNIWrL5jNa+bXM6asNOuIkqQMWMikjDS1tnP7Qxu55cH1bNjRxqTqct583iyuuWA2p0wbn3U8SdIwspBJGevpSfxu7XPcvHQ9i1dspbM7seCESVxzwRz+8CXTqapw1EySRjsLmVREtu/Zz08e3sgtSzfw9Pa9jK8s44/OncnV589h3owJWceTJBWIhUwqQiklHli3g1uWrufnT2ylo6uHs2fXcM35s3n92TMYO6Ys64iSpCFkIZOK3K59Hfzk4U3c8uB6VjftYWxFKW84ZybXXDCbl8ycSIS3apKkkc5CJo0QKSUeXr+Lm5eu5z8e20x7Zw/zpk/gmgvn8MZzZjCh8sU3RJckjQwWMmkEam3v5I5HN3PzA+tZsaWVqvJSXnvWdK65YA7nzalx1EySRhgLmTSCpZR4fFMLNy/dwE8f3cTejm5OqxvH1efP4c3nzaSmuiLriJKkfrCQSaPE3v1d/Oz3m7n5wQ38fsMuKspK+IP59bz+rBm8/NSpVJZ7+QxJKlYWMmkUWrmllVuWruffH9lEa3sXVeWlXHrqVBY21PPqM6YxeawjZ5JUTCxk0ijW0dXDA+ueY/HyJu5c2cSWlnZKAhbMnczCeXVccWYdc72XpiRlzkImHSdSSjyxqZUlK7ayeEUTq7buBuDUaeNY2FDHlfPqOWvmREpKPCFAkoabhUw6Tm3YsY8lK5pYsqKJpc/soLsnMW38GK6YV8eV8+q45OQp3uxckoaJhUwSu/Z1cPeT21iyoonfPNnM3o5uxlaU8srTa7lyXh2vPr2OidVe50ySCsVCJukF2ju7ue/p51iyook7VzSxbfd+SkuCC+ZO5sr86NnsydVZx5SkUcVCJumIenoSj21qYcmKrSxZ0cTqpj0AnFE/noXzcsedzZ85wQvRStIgWcgk9duzz+1lyYomFq9oYtkzO+hJMH1iJVecmRs5u+ikKVSUlWQdU5JGHAuZpAHZsbeDX63axpIVW7ln9XbaOrsZP6bs4HFnrzp9GhOrPO5MkvrDQiZp0No7u/mvp7azZEUTd61qYvueDspKgotOmnLwuLMZNVVZx5SkomUhkzSkunsSj27YyeL8JTWebt4LQMOMCQfL2bzpHncmSb1ZyCQV1NrmPQevd/bw+p2kBDNrqrhyXh0L59Vx/omTKS/1uDNJxzcLmaRh07x7P79alStnv31qO/u7ephQWcarz5jGlfPqecVpUxlf6XFnko4/FjJJmdjX0cVv88ed/WrVNnbs7aCitISLT37+uLO6CZVZx5SkYWEhk5S57p7EQ8/uPHi9s2ee2wfA2bMm5stZPafVjfO4M0mjloVMUlFJKbFm256DJwU8umEXAHMmVx8cOVtwwiTKPO5M0ihiIZNU1La1tnPnym0sXrGV3615jo7uHmqqy3n1GdNYOK+Oi0+e6vXOJI14RVXIImI2cBNQByTg2ymlr0XEZOBWYC7wDPC2lNLOvj7LQiaNPnv2d3HP6uaDx521tHUCUDt+DCdNHcvJ08Zxcu04Tq4dy8m145hRU0VpidOckopfsRWy6cD0lNLDETEeeAh4E/BeYEdK6fMR8VfApJTSJ/v6LAuZNLp1dvew7JmdPLZxF2ub97C2eS9rm/ewa1/nwW3GlJVw4tSxz5e0fGE7cepYxo4pyzC9JL1QX4Vs2P+2SiltAbbkn++OiJXATOCNwKvym90I/Bros5BJGt3K82dkXnzylBcs37G3I1fQtu1hbfMenm7ey/LNLfziiS309Po35vSJlQeL2km1+ZG1aWOpn1DpyQOSikqmx5BFxFzgHmA+sD6lVJNfHsDOA68Pec91wHUAc+bMeemzzz47XHElFbn9Xd2sf27f86Np2/awdvtent62h937uw5uV11Rykm1B0bVxh18fuLUsVSWl2b4DSSNZkU1ZXlwxxHjgN8Af5dS+klE7OpdwCJiZ0ppUl+f4ZSlpP5IKdG8ez9r8qNpvQvb5pY2Dvw1GAGzJlVx0tTnR9MOFLbacWMcVZM0KEU1ZQkQEeXAj4EfpZR+kl/cFBHTU0pb8seZbcsim6TRJyKYNqGSaRMqueTkqS9Y19bRzbrtew9OfebK2h6WrttBW2f3we3GV5a9aETtlGljmTN5LBVlXp5D0uAMeyHLT0deD6xMKX2l16qfAtcCn8//vGO4s0k6/lRVlDJvxgTmzZjwguU9PYmtre29jlXby9Pb93Dvmu38+OGNB7crLQnmTK4+eNZn76nQSWMrhvvrSBqhsjjL8uXAb4HHgZ784v8JPADcBswBniV32YsdfX2WU5aSsrBnfxfreo2mHRhde3r7Xjq6eg5uN3lsRe5SHS+Y/hzH7ElVXvRWOg4V5TFkQ8FCJqmYdPckNu1s61XUDkyF7mH7no6D25WXBnOnPD+idlLtOGbWVDGjppL6iZWMKfPEAmk0KrpjyCRpNCotCeZMqWbOlGouO2PaC9a17Otk7fbc9OfT23MnFDy1bTd3rmyiq+eF/zCuHT+GGTVVzJhYmftZU8XMmkqmT8w9nzquwhMMpFHGQiZJw2BidTnnzZnEeXNeePJ4Z3cPm3a2sXlXG5t2tbGlpf3g89VNu/n1k80vOLkAoKKshBkTny9oM2ueL24z8sXNi+JKI4v/j5WkDJWXljB36ljmTh172PUpJVraOtm0q43Nu3JlbXPL889/t3Y7Ta3tHDLIRk11OTMm5gra82Xt+VG3aePHeBybVEQsZJJUxCKCmuoKaqoraJgx8bDbdHX30LR7f66s9S5uu9rYuLONB5/ZefCeoAeUlgT1EyqZfoRp0Zk1VUyoKnNqVBomFjJJGuHKSkuYmS9RR7JnfxdbDjMtunlXG7/fuItfPrGVju6eF7xnbEUp03uVtRkTq/KvK5lZU+UJCNIQspBJ0nFg3JgyTq0bz6l14w+7vqcnsX3vfjbvaj9Y3A6MtG1paWPF5la279n/ovdNHTfmBcewTZ9YmT9jNPeYMraCkhJH2aSjsZBJkigpCaaNr2Ta+ErOmV1z2G3aO7vZ2tL+gmPYcse0tfPUtj38ZnUz+zoOOQGhtITpNZVMHlvBxKryg4+aqnIm9Ho9saqcidXl1FTltqssL3G6VMcVC5kkqV8qy0v7dQJC75MPNu1qY8uudnbu62DH3g7Wbd/Lrn2dtLZ30tdlMCtKS/KFrSxX4KqfL3QTDil2E6tfWOy8QbxGIguZJGlI9D4B4dBbUR2qpyexe38XrW2dtLR1smtf7ucLHx0Hn2/b3c5T23bTsq+T1vauPj+7oqzk+bLW63GgyNUcUuB6r7fMKSsWMknSsCspiYNFaPYxvre7J7G7/YXlrXehaz1k+ZaWdlZt3U1rWye79/dd5irLSw5T1npPt5YdMiL3/DpvMq/BsJBJkkaU0pLnR+KOVVd3D63tXYcZjeukZV/Hi5Zt3Jk7oaGlrZO9hxwfd6jK8hImVOZG2iZUluV/ljOhqqzX8kNfP7+dhe74ZiGTJB03ykpLmDy2gsljj73MdXb30NrWya62w4zG7es8OAXb2t5Ja1sXO/Z28Mz2vQcLYPehV+89xGAK3fjKMi9BMsJZyCRJ6ofy0hKmjBvDlHFjjvm9KSXaOrtpbevKF7bni9vzr49c6FrbOl90z9ND9S504ysPPwp3pNG5CVUWuqxZyCRJKrCIoLqijOqKMuonVh7z+/tT6Ha3d71g2a59Hazfse/gKN7RCt2YspLDFLXnXx/uRIgDJ0OMH1Pm9eYGyUImSVKRG4pC197Z0+/RuWMtdCUB4ysPX9aOVOQOXHvOMpdjIZMkaZSLCKoqSqmqKKVuwsBH6Fp6HTN32BMjej02t7QdLHOd3UcucxEwYSBlLj81O1rKnIVMkiT1qfcI3fSJR75n6uEcrcy1HqbMbWlpo6UtN2p36D1WX5gLxo859FIkRy50Nb0uU1JsZc5CJkmSCmawZa69syd3Tbm2jn6Vua0t7QMqc284ewbXveLkwX7dAbOQSZKkotR7qvVYj53rXeaO9Ohd6LI+y9RCJkmSRp3BlLkseFlgSZKkjFnIJEmSMmYhkyRJypiFTJIkKWMWMkmSpIxZyCRJkjJmIZMkScqYhUySJCljFjJJkqSMWcgkSZIyZiGTJEnKmIVMkiQpYxYySZKkjEVKKesMAxYRzcCzWecYBaYC27MOoUHxdziy+fsb+fwdjnzD8Ts8IaVUe7gVI7qQaWhExLKU0oKsc2jg/B2ObP7+Rj5/hyNf1r9DpywlSZIyZiGTJEnKmIVMAN/OOoAGzd/hyObvb+TzdzjyZfo79BgySZKkjDlCJkmSlDEL2XEqImZHxN0RsSIilkfEn2WdSQMTEaUR8UhE/EfWWXTsIqImIm6PiFURsTIiLs46k45NRPx5/u/RJyLi5oiozDqT+hYR34uIbRHxRK9lkyNiSUQ8lf85aTgzWciOX13AX6aU5gEXAX8aEfMyzqSB+TNgZdYhNGBfA36ZUjoDOBt/lyNKRMwEPgosSCnNB0qBq7NNpX64AXjNIcv+CrgrpXQqcFf+9bCxkB2nUkpbUkoP55/vJvcfgZnZptKxiohZwGuB72adRccuIiYCrwCuB0gpdaSUdmUaSgNRBlRFRBlQDWzOOI+OIqV0D7DjkMVvBG7MP78ReNNwZrKQiYiYC5wLPJBxFB27rwKfAHoyzqGBORFoBr6fn3b+bkSMzTqU+i+ltAn4B2A9sAVoSSktzjaVBqgupbQl/3wrUDecO7eQHeciYhzwY+BjKaXWrPOo/yLidcC2lNJDWWfRgJUB5wHfSCmdC+xlmKdJNDj544zeSK5czwDGRsS7sk2lwUq5S1AM62UoLGTHsYgoJ1fGfpRS+knWeXTMXga8ISKeAW4BXh0RP8w2ko7RRmBjSunA6PTt5AqaRo4rgHUppeaUUifwE+CSjDNpYJoiYjpA/ue24dy5hew4FRFB7riVlSmlr2SdR8cupfSplNKslNJccgcR/yql5L/MR5CU0lZgQ0Scnl90ObAiw0g6duuBiyKiOv/36uV4YsZI9VPg2vzza4E7hnPnFrLj18uAd5MbVXk0//jDrENJx6GPAD+KiMeAc4DPZRtHxyI/unk78DDwOLn/rnrV/iIXETcD9wGnR8TGiPgA8Hngyoh4itzI5+eHNZNX6pckScqWI2SSJEkZs5BJkiRlzEImSZKUMQuZJElSxixkkiRJGbOQSSoKEZEi4su9Xn88Iv66APu5OSIei4g/P8y690TEExHxeP5WRh/PL78hIq4awL7mRsQ7hiK3pNHNQiapWOwH3hwRUwu1g4ioB85PKZ2VUvrHQ9b9AfAxYGFK6SXARUDLIHc5FzimQpa/QbWk44yFTFKx6CJ3Qc3DjVzNjYhf5Ue27oqIOX19UERURsT3e410XZZftRiYmb8Q8qWHvO1TwMdTSpsBUkr7U0rfOcxnP3OgNEbEgoj4df75K3tdZPmRiBhP7sKSl+aX/XlElEbElyLiwfx3+eP8e18VEb+NiJ8CKyJibET8Z0T8Pj9i9/b+/zFKGon8l5ikYvJ/gcci4ouHLP8n4MaU0o0R8X7g68Cb+vicPyV3f+CXRMQZwOKIOA14A/AfKaVzDvOe+cBgbtT+ceBPU0r3RsQ4oJ3cjcI/nlJ6HUBEXAe0pJTOj4gxwL0RsTj//vOA+SmldRHxFmBzSum1+fdNHEQuSSOAI2SSikZKqRW4CfjoIasuBv41//wHwMuP8lEvB36Y/8xVwLPAaUOX9LDuBb4SER8FalJKXYfZZiHwnoh4FHgAmAKcml+3NKW0Lv/8cXK3cPlCRFyaUhrs1KmkImchk1Rsvgp8ABg7zPtdDry0H9t18fzfnZUHFqaUPg98EKgiN/J1xmHeG8BHUkrn5B8nppQOjJDt7fVZq8mNmD0OfDYiPn3M30bSiGIhk1RUUko7gNvIlbIDfgdcnX/+TuC3R/mY3+a3Iz9VOQd48ijv+XvgS/kD/4mIioj44GG2e4bni9tbDiyMiJNTSo+nlL4APAicAewGxvd67yLgTyKi/EC2iHhR8YyIGcC+lNIPgS+RK2eSRjGPIZNUjL4M/Pderz8CfD8i/gfQDLwPICI+DJBS+uYh7/8X4BsR8Ti5Ea33ppT2R8QRd5hS+nlE1AF3Rm7DBHzvMJv+DXB9RPwf4Ne9ln8sf/JAD7nRtl/kn3dHxO+BG4CvkTvz8uH8Ppo5/LFwLyFXDnuATuBPjhhc0qgQKaWsM0iSJB3XnLKUJEnKmIVMkiQpYxYySZKkjFnIJEmSMmYhkyRJypiFTJIkKWMWMkmSpIxZyCRJkjL2/wMVS4p1zmVYLQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["from sklearn.cluster import KMeans\n","clustering_score = []\n","for i in range(1, 11):\n","    kmeans = KMeans(n_clusters = i, init = 'random', random_state = 42)\n","    #kmeans = KMeans(n_clusters=i, init ='k-means++', max_iter=300,  n_init=10,random_state=0 )\n","    kmeans.fit(X)\n","    clustering_score.append(kmeans.inertia_) # inertia_ = Sum of squared distances of samples to their closest cluster center.\n","\n","plt.figure(figsize=(10,6))\n","plt.plot(range(1, 11), clustering_score)\n","plt.scatter(3,clustering_score[2], s = 200, c = 'red', marker='*')\n","plt.title('The Elbow Method')\n","plt.xlabel('No. of Clusters')\n","plt.ylabel('Clustering Score')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"id":"06899330","metadata":{"id":"06899330"},"outputs":[],"source":["kmeans= KMeans(n_clusters = 3, random_state = 42)\n","\n","# Compute k-means clustering\n","kmeans.fit(X)\n","\n","# Compute cluster centers and predict cluster index for each sample.\n","pred = kmeans.predict(X)\n","\n"]},{"cell_type":"code","execution_count":null,"id":"e0f6fbd8","metadata":{"id":"e0f6fbd8","outputId":"9e3d9a87-b1bd-4303-be7f-60457f049ba7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of data points in each cluster= \n"," 2    53\n","0    50\n","1    47\n","Name: Cluster, dtype: int64\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_length</th>\n","      <th>sepal_width</th>\n","      <th>petal_length</th>\n","      <th>petal_width</th>\n","      <th>species</th>\n","      <th>Cluster</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>setosa</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>setosa</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>setosa</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>setosa</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>setosa</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>6.7</td>\n","      <td>3.0</td>\n","      <td>5.2</td>\n","      <td>2.3</td>\n","      <td>virginica</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>6.3</td>\n","      <td>2.5</td>\n","      <td>5.0</td>\n","      <td>1.9</td>\n","      <td>virginica</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>6.5</td>\n","      <td>3.0</td>\n","      <td>5.2</td>\n","      <td>2.0</td>\n","      <td>virginica</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>6.2</td>\n","      <td>3.4</td>\n","      <td>5.4</td>\n","      <td>2.3</td>\n","      <td>virginica</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>5.9</td>\n","      <td>3.0</td>\n","      <td>5.1</td>\n","      <td>1.8</td>\n","      <td>virginica</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 6 columns</p>\n","</div>"],"text/plain":["     sepal_length  sepal_width  petal_length  petal_width    species  Cluster\n","0             5.1          3.5           1.4          0.2     setosa        0\n","1             4.9          3.0           1.4          0.2     setosa        0\n","2             4.7          3.2           1.3          0.2     setosa        0\n","3             4.6          3.1           1.5          0.2     setosa        0\n","4             5.0          3.6           1.4          0.2     setosa        0\n","..            ...          ...           ...          ...        ...      ...\n","145           6.7          3.0           5.2          2.3  virginica        1\n","146           6.3          2.5           5.0          1.9  virginica        2\n","147           6.5          3.0           5.2          2.0  virginica        1\n","148           6.2          3.4           5.4          2.3  virginica        1\n","149           5.9          3.0           5.1          1.8  virginica        2\n","\n","[150 rows x 6 columns]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["df['Cluster'] = pd.DataFrame(pred, columns=['cluster'] )\n","print('Number of data points in each cluster= \\n', df['Cluster'].value_counts())\n","df"]},{"cell_type":"code","execution_count":null,"id":"e3a24c14","metadata":{"id":"e3a24c14","outputId":"3ce84898-62e5-4a25-d8f2-7557c9637690"},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Clusters representation')"]},"execution_count":8,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.figure(figsize=(6,4))\n","plt.scatter(X[pred == 0, 0], X[pred == 0, 1], c = 'brown' , label = 'Cluster 0')\n","plt.scatter(X[pred == 1, 0], X[pred == 1, 1], c = 'green', label = 'Cluster 1')\n","plt.scatter(X[pred == 2, 0], X[pred == 2, 1], c = 'blue', label = 'Cluster 2')\n","plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:, 1],s = 300, c = 'yellow', label = 'Centroid', marker='*')\n","plt.xlabel('sepal.length')\n","plt.ylabel('sepal.width')\n","\n","plt.legend()\n","plt.title('Clusters representation')"]},{"cell_type":"code","execution_count":null,"id":"e4ae5329","metadata":{"id":"e4ae5329"},"outputs":[],"source":["import numpy as np\n","\n","def clustering(X, k, maximum_iter=100):\n","\n","    # Initialize centroids randomly\n","    centroids = X[np.random.choice(X.shape[0], k, replace=False)]\n","    for i in range(maximum_iter):\n","        #  distances between each data point and the centroids\n","        distances = np.array([np.linalg.norm(X - centroid, axis=1) for centroid in centroids])\n","        w = 1/distances\n","        #print(distances)\n","        \n","        # s=np.sum(w, axis=0)\n","        # Normalize\n","        w = w/np.sum(w, axis=0)\n","        \n","        #print(w)\n","        #  data points --> cluster with the highest weight\n","        clusters = np.argmax(w, axis=0)\n","\n","        # Update centroids\n","        new_centroids = np.array([X[clusters == j].mean(axis=0) for j in range(k)])\n","        #new_centroids = np.array([np.average(X[clusters == j], axis=0, weights=w[clusters == j]) for j in range(k)])\n","        # Check \n","        if np.all(centroids == new_centroids):\n","            break\n","        centroids = new_centroids\n","    return clusters, centroids"]},{"cell_type":"code","execution_count":null,"id":"43a8cbd9","metadata":{"id":"43a8cbd9","outputId":"4d50e8ab-115d-4027-e9c0-1db14a488f41"},"outputs":[{"name":"stdout","output_type":"stream","text":["[2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 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 1 1 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0\n"," 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 1 1 1 1\n"," 1 1 0 0 1 1 1 1 0 1 0 1 0 1 1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1\n"," 1 0]\n","[[5.77358491 2.69245283]\n"," [6.81276596 3.07446809]\n"," [5.006      3.428     ]]\n"]},{"name":"stderr","output_type":"stream","text":["c:\\users\\anves\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:10: RuntimeWarning: divide by zero encountered in true_divide\n","  # Remove the CWD from sys.path while we load stuff.\n","c:\\users\\anves\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in true_divide\n","  from ipykernel import kernelapp as app\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["X = df[[\"sepal_length\",\"sepal_width\"]].values\n","# Define the number of clusters\n","k = 3\n","\n","# Run the clustering function\n","clusters, centroids = clustering(X, k)\n","\n","# Print the cluster assignments\n","print(clusters)\n","\n","# Print the final centroids\n","print(centroids)\n","\n","import matplotlib.pyplot as plt\n","\n","# Plot the data points and the centroids\n","plt.scatter(X[:, 0], X[:, 1], c=clusters)\n","plt.scatter(centroids[:, 0], centroids[:, 1], c='red', marker='x')\n","plt.xlabel(\"SL\")\n","plt.ylabel(\"SP\")\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"id":"3635a22c","metadata":{"id":"3635a22c","outputId":"1fb23dae-c291-4cae-f714-3cf6633aaa09"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[-0.90068117  1.01900435]\n"," [-1.14301691 -0.13197948]\n"," [-1.38535265  0.32841405]\n"," [-1.50652052  0.09821729]\n"," [-1.02184904  1.24920112]\n"," [-0.53717756  1.93979142]\n"," [-1.50652052  0.78880759]\n"," [-1.02184904  0.78880759]\n"," [-1.74885626 -0.36217625]\n"," [-1.14301691  0.09821729]\n"," [-0.53717756  1.47939788]\n"," [-1.26418478  0.78880759]\n"," [-1.26418478 -0.13197948]\n"," [-1.87002413 -0.13197948]\n"," [-0.05250608  2.16998818]\n"," [-0.17367395  3.09077525]\n"," [-0.53717756  1.93979142]\n"," [-0.90068117  1.01900435]\n"," [-0.17367395  1.70959465]\n"," [-0.90068117  1.70959465]\n"," [-0.53717756  0.78880759]\n"," [-0.90068117  1.47939788]\n"," [-1.50652052  1.24920112]\n"," [-0.90068117  0.55861082]\n"," [-1.26418478  0.78880759]\n"," [-1.02184904 -0.13197948]\n"," [-1.02184904  0.78880759]\n"," [-0.7795133   1.01900435]\n"," [-0.7795133   0.78880759]\n"," [-1.38535265  0.32841405]\n"," [-1.26418478  0.09821729]\n"," [-0.53717756  0.78880759]\n"," [-0.7795133   2.40018495]\n"," [-0.41600969  2.63038172]\n"," [-1.14301691  0.09821729]\n"," [-1.02184904  0.32841405]\n"," [-0.41600969  1.01900435]\n"," [-1.14301691  1.24920112]\n"," [-1.74885626 -0.13197948]\n"," [-0.90068117  0.78880759]\n"," [-1.02184904  1.01900435]\n"," [-1.62768839 -1.74335684]\n"," [-1.74885626  0.32841405]\n"," [-1.02184904  1.01900435]\n"," [-0.90068117  1.70959465]\n"," [-1.26418478 -0.13197948]\n"," [-0.90068117  1.70959465]\n"," [-1.50652052  0.32841405]\n"," [-0.65834543  1.47939788]\n"," [-1.02184904  0.55861082]\n"," [ 1.40150837  0.32841405]\n"," [ 0.67450115  0.32841405]\n"," [ 1.2803405   0.09821729]\n"," [-0.41600969 -1.74335684]\n"," [ 0.79566902 -0.59237301]\n"," [-0.17367395 -0.59237301]\n"," [ 0.55333328  0.55861082]\n"," [-1.14301691 -1.51316008]\n"," [ 0.91683689 -0.36217625]\n"," [-0.7795133  -0.82256978]\n"," [-1.02184904 -2.43394714]\n"," [ 0.06866179 -0.13197948]\n"," [ 0.18982966 -1.97355361]\n"," [ 0.31099753 -0.36217625]\n"," [-0.29484182 -0.36217625]\n"," [ 1.03800476  0.09821729]\n"," [-0.29484182 -0.13197948]\n"," [-0.05250608 -0.82256978]\n"," [ 0.4321654  -1.97355361]\n"," [-0.29484182 -1.28296331]\n"," [ 0.06866179  0.32841405]\n"," [ 0.31099753 -0.59237301]\n"," [ 0.55333328 -1.28296331]\n"," [ 0.31099753 -0.59237301]\n"," [ 0.67450115 -0.36217625]\n"," [ 0.91683689 -0.13197948]\n"," [ 1.15917263 -0.59237301]\n"," [ 1.03800476 -0.13197948]\n"," [ 0.18982966 -0.36217625]\n"," [-0.17367395 -1.05276654]\n"," [-0.41600969 -1.51316008]\n"," [-0.41600969 -1.51316008]\n"," [-0.05250608 -0.82256978]\n"," [ 0.18982966 -0.82256978]\n"," [-0.53717756 -0.13197948]\n"," [ 0.18982966  0.78880759]\n"," [ 1.03800476  0.09821729]\n"," [ 0.55333328 -1.74335684]\n"," [-0.29484182 -0.13197948]\n"," [-0.41600969 -1.28296331]\n"," [-0.41600969 -1.05276654]\n"," [ 0.31099753 -0.13197948]\n"," [-0.05250608 -1.05276654]\n"," [-1.02184904 -1.74335684]\n"," [-0.29484182 -0.82256978]\n"," [-0.17367395 -0.13197948]\n"," [-0.17367395 -0.36217625]\n"," [ 0.4321654  -0.36217625]\n"," [-0.90068117 -1.28296331]\n"," [-0.17367395 -0.59237301]\n"," [ 0.55333328  0.55861082]\n"," [-0.05250608 -0.82256978]\n"," [ 1.52267624 -0.13197948]\n"," [ 0.55333328 -0.36217625]\n"," [ 0.79566902 -0.13197948]\n"," [ 2.12851559 -0.13197948]\n"," [-1.14301691 -1.28296331]\n"," [ 1.76501198 -0.36217625]\n"," [ 1.03800476 -1.28296331]\n"," [ 1.64384411  1.24920112]\n"," [ 0.79566902  0.32841405]\n"," [ 0.67450115 -0.82256978]\n"," [ 1.15917263 -0.13197948]\n"," [-0.17367395 -1.28296331]\n"," [-0.05250608 -0.59237301]\n"," [ 0.67450115  0.32841405]\n"," [ 0.79566902 -0.13197948]\n"," [ 2.24968346  1.70959465]\n"," [ 2.24968346 -1.05276654]\n"," [ 0.18982966 -1.97355361]\n"," [ 1.2803405   0.32841405]\n"," [-0.29484182 -0.59237301]\n"," [ 2.24968346 -0.59237301]\n"," [ 0.55333328 -0.82256978]\n"," [ 1.03800476  0.55861082]\n"," [ 1.64384411  0.32841405]\n"," [ 0.4321654  -0.59237301]\n"," [ 0.31099753 -0.13197948]\n"," [ 0.67450115 -0.59237301]\n"," [ 1.64384411 -0.13197948]\n"," [ 1.88617985 -0.59237301]\n"," [ 2.4920192   1.70959465]\n"," [ 0.67450115 -0.59237301]\n"," [ 0.55333328 -0.59237301]\n"," [ 0.31099753 -1.05276654]\n"," [ 2.24968346 -0.13197948]\n"," [ 0.55333328  0.78880759]\n"," [ 0.67450115  0.09821729]\n"," [ 0.18982966 -0.13197948]\n"," [ 1.2803405   0.09821729]\n"," [ 1.03800476  0.09821729]\n"," [ 1.2803405   0.09821729]\n"," [-0.05250608 -0.82256978]\n"," [ 1.15917263  0.32841405]\n"," [ 1.03800476  0.55861082]\n"," [ 1.03800476 -0.13197948]\n"," [ 0.55333328 -1.28296331]\n"," [ 0.79566902 -0.13197948]\n"," [ 0.4321654   0.78880759]\n"," [ 0.06866179 -0.13197948]]\n"]}],"source":["X = df.iloc[:, [0,1]].values\n","\n","from sklearn.preprocessing import StandardScaler\n","# Scale the data\n","scaler = StandardScaler()\n","X = scaler.fit_transform(X)\n","print(X)\n","# number of clusters\n","k = 3"]},{"cell_type":"code","execution_count":null,"id":"496cdf7a","metadata":{"id":"496cdf7a"},"outputs":[],"source":["centroids = np.random.rand(k, X.shape[1])\n","\n","# initialize weights as an empty array\n","weights = np.zeros((X.shape[0], k))"]},{"cell_type":"code","execution_count":null,"id":"45d475db","metadata":{"id":"45d475db"},"outputs":[],"source":["for i in range(20):\n","    # calculate the distance between each data point and the centroids\n","    distances = np.linalg.norm(X[:, np.newaxis] - centroids, axis=2)\n","\n","    # assign each data point to the closest centroid\n","    clusters = np.argmin(distances, axis=1)\n","\n","    # calculate the reciprocal of the distance between each data point and the centroid of the cluster\n","    for j in range(k):\n","        weights[clusters == j, j] = 1 / distances[clusters == j, j]\n","\n","    # calculate the weighted average of the data points in each cluster to update the centroids\n","    for j in range(k):\n","        if np.sum(weights[clusters == j, j]) != 0:\n","            centroids[j] = np.average(X[clusters == j], axis=0, weights=weights[clusters == j, j])"]},{"cell_type":"code","execution_count":null,"id":"7c0f7e8a","metadata":{"id":"7c0f7e8a","outputId":"055e24b3-e43a-4b9a-d0fc-6657fa1cb127"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# Plot the results\n","plt.scatter(X[:,0], X[:,1], c=clusters)\n","plt.scatter(centroids[:, 0], centroids[:, 1], c='red', marker='x')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"id":"49471dfc","metadata":{"id":"49471dfc"},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.9"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}