MLPractical44(Unsupervised Machine Learning Clustering Algorithm).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input data and shape\n",
      "(3000, 2)\n",
      "          V1         V2\n",
      "0   2.072345  -3.241693\n",
      "1  17.936710  15.784810\n",
      "2   1.083576   7.319176\n",
      "3  11.120670  14.406780\n",
      "4  23.711550   2.557729\n"
     ]
    },
    {
     "data": {
      "image/png": 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WlpZw7Ngx1Go1331NpVJYXV2N1GGTn/2QwmTiRykAPg/g3zb/TwHYC+AsgMea3z0G4PGgekiuGRzIS+Ft2Y3U/KK2cLZyPalUyovJImK1ywG48vl8qIBhtpLJZELJIL0uYSUq27yCHNpZTMaKVb+6WDdyvHt5mzCyDck8gw90U5MHMA5gFc2FVdL31wDc2vz/VgDXguoikh8c6Ahap63r9GFZv7fFmp+enva206W16zcZ96vs2rXLm2A2BR1TY/XIpK1LbpLP543x+4PiFFFY4sGHjeTjkGveAeCHAC4yxr7GGHuCMbYHwAHO+fcBoPn353Q7M8YeZoxdZoxd/uEPfxhDcwhxQJVaxGIb1fddlgAEhExw/PhxvPDCC8Ztb9y4geeeew4AcPPmTU/WSKfTSKfTHZ+DSSYZdNxxxx24cuUKrly5gkcffRSu67aEQ3jyySdx//3349ixY1haWsL6+jqAxr16/vnnfVKYkHHE/dza2vJJPUF+86p8R372QwYT+4ctAA4D2ARwpPn5PwH4XwG8rmz3k6C6yJIfHEQZoqvWpFzUSJE610zVjXIY3B+71UZTaOCghCZBRY1BbxqZ2e4xuV4OLtBluWYKwLelz/8tgKdBcs3QI+qLrSYDMckCamjc/fv395WwB2khlS4csrgHURKL6wpFpxxd2Ei+Y7mGc/4DAN9hjL2r+dU9AP4GQA3Ax5rffQxAtdNjEboL1YOiWCyiUCh4XjFBKBaLePrpp3Hp0iWUy2U8+uij3jDfcRxv6X2xWITrugAa0sFPfvKTSO3ctWuX9vthlGdEmxljYIxBlSxTqZQXUuLUqVPaOoQ7ZSKR8EXvnJub88ks8/PzgeEmCCMIE/tHKQBmAVwG8HUAXwbwFgBvBfAsgG81/+4Lqocs+f5BJ8/E4VVRqVQ8aUPEqdet2MzlcnxmZoaXSiWfxeo4Dnddl5dKJe9313Vb4s2LbWwJsQelhLHIx8fHtYvP1Exa4+Pjvvyw6qK1MKMxkmKGH7BY8rEEKOOcX0FDm1dxTxz1E7oP00KZTqMXXrlyxQs8tr6+7oUjVoORbW5u4syZMy0ByLLZrHf8YrHohdGVsb29jZs3b+Lll1/G1taWZxUPamz5RCKBbDZrjT7505/+FJlMBgsLC1hYWPBCMb/zne/0hVmenJxEvV73rtn6+joOHjzo3adisai9ZyLwnBya+OLFixSGeBRhYv9+FLLk+4c4LXnZMlQnDPP5PJ+entZar8L9T/cbYJ/sVC3cfD7Py+Vy7Im34ypRQy6YRkHpdJqXSiXftkEp/9Q5EXlfco8cToBi1xDCQDdsb2fyVfWvFxOxjuNoPXDiKGoH4Lqu155eTawmEomuHksQsNpx6ryVbOkcVb93Xa5cwnDBRvIUu4bgoVgstkzM6b6zQedfLyZiC4WC588dN2RpxnEc3Lx5E/fffz8A4H3ve591X118nHYwNjaGAwcOtLUvYwylUgmVSkWbIlCk+qvVapifn/dNqBaLRV/4hpWVFV9c/PX1dV8oCtXv/dSpUyiXy55U024IAwp9MKAwsX8/Clnyww+bxKOLbhm2qOEJcrmcMXG3/F0qlQqUbAZlspYxxqvVqjaUsmijLKXJI6wgP/pKpdJyn3QjtE4kOgp90D+A5BpCL2GTeGwLp2SyK5VKvFKp8JmZGW/hjryNqL/fxBx32b17d+BCq5mZGS052zqrsFp7uyEMKPRBf0EkT+gbbBZjuVw2uj3KC3fURVapVIrn83mezWa7SriDYuHris5all0p5QBwuhFVuVw2ZpAiS374YCN51vh9MHD48GF++fLlfjeDEBPkBN/pdBozMzM4cOCA5w4otllYWMC1a9fw7W9/G1tbW0in0+CcY2Njw9PLt7e34TgO3vve9+LFF19Eu89tlATfe/fuxeuvv97WcXoB13Xx9NNPA/C7RNbrdS++jJqft1ar4dixY765kUwm43OdDJPXV4d29yN0DsbYCudc58ZOljyhezBlMEokEp6bn6rRB2V0Ul0l1X13795t/D2Xyw3kgqlkMqkNZxBURFwbk/urbgRl0u518gotkhoegOQaQq8RpL2LlaymTsCkS+/atUtL3tPT055UYdO0M5lM6LyyvSxCumpnP7UzdV3XKJ3orrlOXiH5ZbhgI3lyoSTEDiHTLC0tic67xS1QrNBUwxQDDWnm4MGDLd+n02l88IMfbPn++vXruHHjhlenbaXr2tpa3yQYk6umCN+rruQFoE3zp4YJVl0igdaVygLz8/Oeu6XjOHBdFydPnmyJT2RaAU1ukkMIE/v3o5AlPxrQJQCpVCotMoyQbNQJ2Ewmo13wI2QIBFi3juMMVGRJoLFASWdFJ5NJ7zqoMW0cx+GVSqVl5DE3N2ddtBZkhYfZtluxjAjdAUiuIfQSOq09kUi06M6qDhyGfEw6P/Cm100cq2rj7CQYY14ANZ2UpJNcRMdQLpdbvIiEP33QPTCFNZBhc31UNXlykxxc2Eg+lgBlBIKMYrGIxcVFnD59GlevXgXQkFDkMLq6DEMimNbp06dRq9Xgui4OHDiAiYkJTy4oFAr43Oc+51vRCTSkh0ceeQT1et0XwCsIsveOwNTUFLa2tlrC/kapU66PMYalpSXj7yIEs1rH1atXsbKy0iJ1cc5DBYt77rnnsLa2hueeew6Li4sAWr1tCoUCLl68iLW1tZZ7ogY3s21LGGCY2L8fhSz50YJpgU42mzVal5VKxbdtqVRqsegrlQqfnp7WjgzU/XO5nDG07y233MIrlUqLjBLV+6ZTq19nyavWey6X844TRiqJMhkbxYuGPG4GEyC5htAv6JJRqwGzZKjBtvbt22ckK92CH1W/FpKFiWATiYRP4uk0+1KYIgcyE0Hc1GTclUoltFukDqrcZZrjIIwGbCRP3jWEruLMmTP44he/6MWFB1oDZsk4dOiQ7/MHPvABz2MklUrhG9/4huf1sbGxgXvuuccLrgU04terkAN6qdje3sbKyoowMvDe9763xaNlamrK+58xpvV4sYEx5vOs2d7ebrx8iQRc18WnP/1pLC0tYWtrC67rYnFxEWfOnMHi4qIvcFgUFItFnDx5EjMzMzh58mTLNbh69SqOHz9OXjI7ASb270chS350IVuWwmtEt408aVoqlbzvdT73quygShSJRML7XV7ybyom/3rdAqwoibxN8fN1v4kQybbrJ2d/ihJorFptTaJOk6ejAZBcQxgEyNKNkCRskRRlwtO5ZdqITe1IbF45QHu6um31rSipVMonvagdhU5e0kHXflt8GpMnDLlBjiZsJE9yDaFnqNfr2NraAtBYYHP27FlcuHABDz74IE6fPm3dV/VASafTLdsIr55yuYxTp06hXq97coS8YEhGPp9HuVzG/v37I5/Pz372M+MCp8nJSbiui0uXLnnSi+u6nqdMIpHAY489hk9+8pPeuaTTaczPz2vr07V/Y2PDi0GjLnqSt0+lUl4sevkaUaq/HQIT+/ejkCU/2rClnROWt5BUHMexWqawWKLqiEFe4KNGsxSyh+qVoxaTPJNMJrXRMNVnOSjyY5gJVV375QnjUqnkq0eVuchyH12A5BrCoEAmO5XoZUJSPXBMCUd0C6pU7V23jc6bJZ/PGyUY4aWjS1Tium7LuUxPT1tXj8ZxDUXnpB7btniMNPjRBJE8YSAhW+46N78w5KwSpkpqyWRSS6q67WDQ5lVd3XEcj+hl332V/MVvtrmGTmGaa5ATqZMlP/roCckDSAL4GoCvND8fBPACgG8B+GMAqaA6iOR3HqLEXNHto/styItHt51Jpsnlct7xVKJW21CpVFo6CVlekaWpKMk4bFKOboSj+tnLshRhNGEj+TjDGvwGgG8CGG9+fhzA73DO/4gx9lkAvw7g92I8HmHA0E7SCLF0Xux78uRJra+7bh/Tb4uLi9Z2yMeq1+uYmJjAuXPnvOX6ruvilVdeQbFYxJkzZ1Cr1bC6uop0Oo319XU4joPZ2VmcOXPGV2+9XhcGD4A3I0iqIRg2NzexsLAQmKRDTrpy8eJF7USpfL5ywhA5iuTGxgYOHjxIk6w7FSb2j1IAvA3AswA+AOArABiAHwFwmr+/H8BXg+ohS7576PZy9CiRD2372twCbcduZyWobOGG8TfXyTS2bU2JUSBJNrbr1omerk4km0Y0hNEAui3XAHgKQB7A3U2S3w/gVen3twO4atj3YQCXAVyenp7uxfXYceiFb3RQNEPb8W0+7EHEZqtbR9xRvHTaaZvumEIbF7KQPKncyXWzgSZcdxZsJN+xnzxj7IMA/p5zLof+Y5pNueY7cM5/n3N+mHN+eHJystPmEDQwJYCIE2riCjlCYdDxVZ9u4TceJtKhLbnF8ePHceHCBd/yfZ2/ue2aqP75asIOFcViEefPn/dJI8ViEU8//TT+5E/+BK7r4p577tGeuy4KZLs+7YVCwed/TxEjdy7i0OT/OYAiY8wFsAsNTf4cgL2MMYdzvomGnPNfYzgWoQ30IkSsTQsPOr66L9AaEjfquenIX2j5i4uLWFhYwLPPPov19XWk02nfYiEZ9Xq95XgHDx70hT82tVGntavhf1VNXa3TNv8QBN6cHxB/CTsUJhO/nYKmXNP8/xKAjzT//yyAfxe0P2ny7SOMF0Y/Q8R28/gmiSRI6lAXF4XNdRq2bnWbqNKM6nkU5fqRXLOzgF75ySsk/w4Afw3g1Sbhp4P2J5JvDzslHklUoguzfRgyVOsJs49umyiTrHJI5XQ6be2ITOe+E54JQgM9I/lOC5F8MMJMJsa52GZQ0C3Saqfedi158X2YqJG2GPg6H31TOynBx84AkfyIwEYcchjeVCo1ci92OxZ3WLSzX5h9Ohl5yPdatuSDrHoi9p0JIvkRgY3ogkIC2DAMxBDGDz+KRd6LdQOd1q/T5G33mSSanQsi+RFBkE94Oy94nMTQT+IME3ZArkc+Z11kyE7b2S2y7dbiKcJwg0h+hGAjunZINi5i6LcVaZI3wgYxi7Pd3SbbsLo+WfI7B0TyBCNMrnsitO4wueyFkTTEdqbgZHG0OyzZxtVhd+JqSRgNEMlHwE58SVSSkIkvbMTETqzIuCc+w3q/iLj23fLaCRs9shPpjax3AudE8qHRrxdGJYQwBBG3Z4eAznUvrEtmpVLhMzMzkYJhRSW0crnMS6WSLw59nJZwr2Ab+UQZFQ3CCIrQfxDJh0Q/XhjdJGCnXiSddFbtkny7xwx7zeX61TKMxEaWPCFOEMmHRD9eGJXkZmZmrASm2z5oYlFYqWF026hyTVgd3LZ/mGtuigYZJQHHoKEbmjxhZ4JIPgK6+cLo6lbjfpdKpdCWvCg6S1DOlapmCdKFvFWPEWbiVU7fJ3u0yLHU41qZqTvvZDJJcdIJBE4kPxCQSUomp3as7mq1arX4q9WqR7giMYbOCo6it4t65XapCaTz+bz3XTvJP8IeP26/dgJh2EEkPwBQyVzIDO1qs7bfbMGudCQfxnKXj5dKpXg2m205H1tnMoy6OYEwLLCRfMdJQwh+1Go1nDhxwktSIVAoFLycn0Ajz6eIcW5KDGFLtmHbT010MTs7i5MnT/qODzQSdMzOzuKhhx7C0tISlpaW8OEPf9hru3wuCwsLvpyh169f9+pJJpM4deoU5ufn20r+Ybpmg4JBbx+BYEOcibx3PEyJl0XyiCNHjuAv//IvwTlHJpPBxMQETpw4gUKhgPPnz/vqEUkkRPJoXXYfU0IJNdGF+Ly1teV9NzMz47VNTjQtkkwD8M7lc5/7HLa3t7XnPDMz4yW0lpNji7aKuuTzkpNohElWHRbtJBIPU2dc7SMQ+gKTid+PMuxyTVAMcUjShmmCVZVFxMRmlMiSch26hNKqq6ZaRNwX3W9AaxgAk3Qkf59IJDhjzNcm3TULG0bXds5xekaRH/pggzyLGgDJNb2BLl+nLLkIbG5u4pVXXtFKMfL2Gxsb2Nzc9P4Pm5u1WCx68szm5ibOnTsHAD55p16vt7QLABhjmJ2dxerqqpfPNJHwPyYPPPCAVw8AnD59OvBctre3G5NAzfN//PHHUavVWvK7Pvvssy15WcNAlbYWFhZikVhsOVgJ/YUpjy9BgYn9+1GG3ZLnXO8tVxUAACAASURBVL96VefyaFr0pFq/iUTCN7EZdpWryW9dXjUKjZWeSCR8senFd/LnbDbLXdfVjgZM56Iraps6DZcsj4Di9Owha3EwQaOsNwGLJc9407oaBBw+fJhfvny5382IHbLGfuXKFQDA/Pw8AH3C6tOnT+Ps2bOeFS8jk8l4FrSos16vY2JiAufOncPa2hoSiUSLhl6pVHDkyBEcO3YM6+vrSCaTPo0+CLo61e/GxsZw33334cCBA9451Wo1fOpTn8LKyopv31QqhUceecTT78W2Qv8W5xlF/xbXeXV1FUtLS9735XLZN+cRtb44NX5CfOj0eRklMMZWOOeHtT+a2L8fZRQseRs6Xd0pis0l0lR0lrKuyFawXMbHx1vcJqGx8kVRLfp8Ps/379/v6fmO4/jCAQvf9zh84OPQ5ylcwHCARlkNwGLJk3dNlyHcDwVU7VpneUxMTMBxHK0lDwA3b97U6uk2TExM4Nq1a4HbPfLIIwCAz3zmM/iHf/gH7/uf/vSnLaOEVCqFt7/97Xjttdda6llbW8Pp06fxwgsveCMMefQgn9va2po3conDIhPupZ1Y4Tr31Z1qJQ4yTB5mBAkm9u9HGTVLXg4vgKbVqyazUC0ROVRAMpnkc3NzfGxsrEUTV+OgBxWTxa2WfD7vecHY6srn81rtXy1qO3VhENSVs7p4PP24d2TJE4YFIE2+Pzhx4gQuXLjg+y6fz+Ouu+7yvDRkTfHkyZN4/PHHfVq5TguXkUgkvJvZDmTr2nEcbG1thapL9o8XOr8AY8xXh/gszlH2oxfzCsLaF4hi0XdLOydNnjAsIE2+T9DFd5FjxQRFoAxb5JgxUYpqses0d1sRMXjUIGvQWPDJZJKXSiVr1MWgCJyma0wWN2GnA930k2eMvZ0x9heMsW8yxr7BGPuN5vf7GGN/xhj7VvPvWzo91iAhzFL3YrGIRx991PMzT6VSnlcN0PDBFkv/0+k0isWi55OdSCRa/NMdx0EqlfJ9l0qlcPToURw9ehSTk5PWNicSCd82nHPPFz6TyeBXf/VXvc8m7Nu3z/t/a2sLZ8+e9TyG5HaeOnUKMzMzvm2//OUv48KFC3jooYdw+PBh3H///d71KxaLOHPmjM8nXawItl1jW+gHAoGAzi15ALcCeF/z//8GwN8CeA+AswAea37/GIDHg+oaFks+qvVo8gBQo0XKGr2qdYtVssJTRWjsiUTCp3PbtHqxv7ryVfXrn5yc1FrlYnt1BKB6+5RKpZbrZBppqOGORXapoJDL7d4LAmEUgW5a8pzz73POX2z+/zMA3wRwG4APAfh8c7PPA/iXnR5rUBDVeiwWi97qV9kqXV5e9uLGbGxs4PTp0wCA8+fP+4J9AQ1vlKeeegpPPvkkrly54un029vbnqfK+vo6CoUCXNcFY6ylHcJ33HVd7Nu3D67r4siRIy1tfeKJJ3wW9alTp3wrXLmi2QutXYwClpaWvPO8++67kc/nMTs72zIKEW0W169Wq+HcuXO4evUqvvSlL/mu8ac+9SmtVW8L1DZMiDsI2rAHVRv29g8UTOzfTgFwO4AbAMYBvK789hPDPg8DuAzg8vT0dFd7u7jQjiWv216nZTPG+NzcHHddl+fz+RbPmn379lk18lKpxMfHx636vfxZWOmMMc/St/mrm+YN1Hp1vvzpdJrn83nfaEMkNXFdl09PT/u213kEjaK1HvdoZNhHN8Pe/n4AvYgnD2AMwAqAB5ufQ5G8XPoh17S7mCLKfqbl12EWJ6nSyNzcXOSFUKKkUil+yy23WLeR3TflrEvifA8dOqTdL5vN+l5MlfRFEYm+Rfz6SqWiXXxlq2PUlq/HvTx/2Jf7D3v7+4GukzyAWwB8FcD/In13DcCt/E3d/lpQPb0m+W5YUCbtXXecMCSvIzjZ2latX1sJ6ysvb2+KUaMWMQoQIxDb3IB8DXSre4WfvCnuj8k7Z1hXPo6aJR/1XtjiPZElHw5dJXkADMB/BnBO+f634Z94PRtUV69JvpOE1yqCHkxdvfLCpzDkLIcbFnXkcrnIHYVc9uzZE7hN0OIoce0qlUpoV045kJq6j24EYQt3MAqkEHcn1a9OT14AaMohrG5vCtQ3rJ12P9Btkv8XzZfz6wCuNIsL4K0AngXwrebffUF19duSly1WeUVmGOIIO8SUj5lOp0NZ14IExUsTFN0xSum0kxDltttu03YGOtIX8w7iN/UaqPlqg152Gt4PDtTRaVAOYbp38cBG8nF41/wXzjnjnP8zzvlssyxxzv9fzvk9nPN3Nv/+uNNjxQ3VM0OOsb6+vu55vug8aGq1Gu6//37P11uOO+44Dm7evKn1DpA9c9bX162rWQXEilThiSKn4usUurgzQbjllltavvve974nOn0PiUQCDzzwQItXDecczz//vHde29vbXmpC2T/+9OnT1njhwgNjYmKCYr73EZ14wlC8/h7AxP79KP32k1etbJMlL/u3i22r1apWrpCzIKnH0Ek1tolREcsmqrauK0ETsEHFNAGrlrGxMV4qlbjrusYVtWKSV8gyJt961cK3+fur9zWuCJcEP3T3wbT2w7bWoZPfw7ZzlO89euFdE0cZBO+aMJq8brIwKJRvNpv1yF7Ua/IeiVJkiWRycpLv3r274zrDFps7p1pE+ANdwDKRntC0CExH5GETjESZvCVER5h5rU7mS+KYbxmFOZsgEMkb0O7NV/3bxWrUMBOUYjUo5+1519hKIpHgU1NTPSP5MB2PfI1kn/zx8XEvkqVM/uoISmfhqyt7bRN8ptj8O137tVm2cTocdKq5x6HZ7wTdn0jegHZvvrpfkMugzqrlvPGCRA0ZPAylVCqFPq9SqaQN4qaOpmwdopjc05HTTrTkw8gfJmJux/AJ6jA6saJlA4AseTOI5A1o9+ar++kIyOZK6DiO0Vd+cnIy1IhgUIuINZ/P53kulwske1Xyka+NfJ3ljFW6+ZIg4lI1+VHVaMPMVdiMmyDDp53r1u6ciHwu6uK8qBjV+y1AJG9B1JtvIgzxMDLG+OTkZKDeLiexDrNStFelHU3fJBElEgk+Nzdn3TeXy/mSh8ijHDWEgs7CF/ctCjkNq2XXjjup6GQdx/HmhXTnLz/XcVr5ney7E2SWuEAkHwHtDj3FZKBsbZq8YFQNWRyzVCpF9pzJ5XK+qJTtErIo7XjdMMZ83kYq0edyOZ7NZo0++el0mruuq7XgRQkihigSRNhJ20FCWJJUrV/d9ZYnunWdnsni7oR029l3WDvjfoBIPiQ6nUQyTfKpRV0gIjqIdlwjy+Vy6ON2s+RyOe3qVZns5Q5GlaRsksH09DTP5/NeiAUR90a9P2G9oeTgacNCHlFIUmeVy2VmZqatuk3vR5gRRidODiLeEcGMHUHycWhuYYb8QaELZNlGR3Y2KzNqYYwZMzP1o9x2221t7yu/xOr5mDo/OcxD0LNhkieGgeA572z+SJUAhVdTO3Wr161aDR/GoB1pdNg6435h5Ek+rodBJRed9RAk55ikExHsS3252k35J3cacYUmMJVdu3a1tZ+afMRWZP/qKK6lYSWDYSN1HVSJJcr5VCoVXwJ40+R01OsTNYxBFOhGYMN+D7uFkSf5uCZoOvEsCGORm1Zshik2j5s4VsB2owRNIot22+IG2c5NteRHgcjDoF2jRkfIUXzldd91k+Tl85SfCVMY7FG/7zaMPMnHZcmHnbzTuXPpdHHZ7U9d2KNa8EFWb5RE3Z3IJnEWXeIQtUxNTXkau46A5GXyiUTCC5GgGxXJhKDT7EcFUY0RMcKUDQXHcXzPZpB8Y9Pj1TAGYRFWyzethLa1a6dh5Eme8/h687CTd7I/t9hPRzJiUtW2YMrkaz9sRT4/0RHK8XwcxzF2Zo7j+Cz7qBa6rpMd1Zc+iieR3EnKRY0jJGL4c67vRGwdSzvvnvxcBN0nITXpJuvJzbKBHUHy3Ua1WrUGzBLb6Ia4usxH8ssmFg/1avWr4zgd6fimdqovYSqVaum8giQcNQ1hO5N0w/zShz3vsMaIbs5HdMAmN1VdJyKH7ei086xWq9bw0jJKpZJvO9UQCDOPthNAJB8T2llibbPQVVJ0HIfn83mjz3lcRbgiRt1PRMGMso882ZdKpXilUuG5XM46x8AYM0oJnJsJToyaouQBiBudjCijWLe249sseXXBmZpZTF6kJ85DfVbk+EtBbdFdC/WdSCQSWt/8arXa8pxMT09HWgS3U0AkHyPCTkgJmEjeRHJRPFLaKYI82pGHUqlUR+1LJpOeJSZWYIYZUZgmrE1E2K+JuE704Wq1NY5Ru4Sl0+RN8xjyKNOkqaujAUG0UbN1iXapo7mpqakW46la1XueqZY6afINEMl3EfJDJuQJ9UXqtmU+Pj7Ox8fHA1er7tq1y5NCTLHdRfz3bnc2ooTxDJJf7HZXq/aC+ONcEZpMJiNJVbZzM5G+eo9zuVzLdmIltrydLlx0qVSyWtjyJHwikfDuu9zxy+3QSW9zc3ORz39YPG86bSeRfAcIuvhBE37yxKtNmzeVMDlY5TC+YUrQto7j9C1Imu64Ok8b0U5xnW0rI3tl7bVzHJlI5TgzJm1ZR9hBC/TEc5dMJvn+/fsDPbVUl9Yoz5ZO1xcypG77MJ18u3MBw2Llx9FOIvk2EVYaME34qRqr8BII87CLz2rExXbi23Rauj0SsZVEIuEjP/W3MJNvvYxVE8Ui0z07tmiLuucxaPTQbsA72yK9IANAtEF+/lOplDXkhe3+R7134roMS4yiOOYV0M0cr6MMOR+rLs8r0MgTe/LkSS9HKQCkUilMTEzg7NmzXh7TtbU11Ot13HHHHS11ZDIZPPbYY8jn8149Ivfr+vo6br31VuTzedx99934hV/4BSQSnd02sb/cZhtErtu4kMvlkE6nQ227vb2Nzc1NAMDm5iYYY77flpeXtblf5f+fffZZ73MqlepqHtFisYjz58+jWCwGbis/XwJbW1uo1+uB24vn0ZYjtVar4Wtf+1rkc8hkMigWi76cxTJ+/ud/Hvv27cOhQ4danqF0Ou214cqVK97zv7Gxgdtvv913/4DG/bA9z5xz71lRz0+HWq3m5QV+5plnfPuK3MHt5KLtJrqd59YJ3mTnoVarYXl52UsQvba2Zr349Xrde5gB4N5770W9XvfICWgQqth/eXnZ+40xhj179gAA7rrrLqysrLTUf/36dVy/fh0A8Kd/+qeNIVgHeNvb3obJyUm89NJLHdVjw+7du/GP//iP2t+++93v4uMf/ziuXLniuxZByGQymJ2dxfPPP+99NzExgWKxiKtXr3rfyQS7vLyM9fV17/O9995rJGBx3wuFQiiS7nTfQqGAixcv+ohePGe6+uTtxXYiGb3u2MvLy8ZE8Xv27MEbb7zR8j1jDO9+97tx5MgRX71PPvkknnrqKQCNpO0A8OMf/xilUsnrcDY3N7G1tYWFhQW88MILvs7VcRxcv369YVkmEnjwwQdx4MABrK6uYmlpydtubGwMb7zxhveMc85xzz33GK+h/K7W63Wsrq5613NjYwPT09OYmZnB7Owszp07h7W1NVy8eBGLi4uR73G3YLuHscBk4vejDIJco3NBayfCnqqFiompSqXS4rYmSlT3xE5KLzNSiXDD8nczMzOhPXzkxWWmoa1Jkw+rd3bqGdPJvqqnivzsiKBfuu1s9QkZyyStpNNpXiqV+NjYmPZ3dV7JJN/MzMyEioKqTvKK+ymH5xbvWz6fb5E5TattVblLXmUun0tU6WZYJmwF0E9NHsB9AK4BeBXAY7Ztu0nyYW9aO/qYzkVN9qpxHCeUrh0lMXZY90NbCRMqQX1h2kkqIq6L+kLK8w26whizhi+IOrlp2zZOz5hOtV+VkHK5nHevbJEe5WsTdG2BxsK0/fv3G38XnabNIFCTkIR9jvL5fItXmkzmsqea6fqaOhfXdVs6JXmxYZDxNiwTtjLQL5IHkATwGoB3AEgBeAnAe0zbdxK7Jsi6CXvTbNvqjmPaPox1o5ZDhw6F2k4cp5MIlul02ovNrnuJhSul/JuIHRPlOEExe0TnqIvxE9YlsN1t2n1G4txX1+agydJsNqs9RtRnTrXyGWORRnhTU1PesW2jBrUkk0ltrCLbwqywljzQ2vHI1r0YVdvu1zAusOonyb8fwFelz58A8AnT9u2QfJgXLOpN64TMdQ9mkFUlVpLavA+y2awXnEtkWlLlnampKT49Pe2Rt+1Fk691teoPYCUWxejqcF03tKyUz+d9bo+pVMrXcagvbFzD43ZJt5M2tNOpyNurMX6idJ460rNlJjMVYTmHNR7Gx8e9zjvKuorx8XHfCCGdTrecs7wwSzdSFtCt3FZX7arPsXp+uvAkZMmHJ/kSgCekzx8FcF7Z5mEAlwFcnp6ejnxyYQg8jpsWhszVlX1yFiPZmtVlUNK92OoQvVptXRU5NzfnDatl4lAt7r1797a80KL9shYs6746i9JxnJaXZnx8nE9OTraQii66pLwIRqefx0H0UTr1fmiv6jMjx4XRFVPnL3eWpVKJz8zM+KJ0BnXG8v0S8yYiAXuUzqGdIlvWuudMfj5NVryOwNXAgbo6gvLYhpn7GDT0k+Qf0pD875q275YlL7br5KaFlXHk7YIiK6oWhXiZdQ++LuIe8Gb0QFXyUC2rycnJlpCw6pDWdV1eKpV8vs26zke3sEtnNaZSKav0EDZcQbekl35ZbEGySiKRaJGvxII6Oaa6aX9xz3TbqBOa3SL0PXv28F27dvGpqanAY6iJduR5B5XERSpIcX3U59MUV0edmI8yWg+CzlDqdefQT5LvulzDee+ssXYm8EykJuozefLoFvhUq63R+6amprRSkEryQjoRL4lOHze9gOp3Ogtd98Lp9jcFeIsyUgpzn0zDe9u96pX2Wq2aI5OKlI7qs6aODmXCDzsKEAHiZELqpZeVXHSL/RhjfHJy0iPiSqXSluQk33udAWR6Jtp1utBp/72WefpJ8g6AvwNwEG9OvN5h2n4QXCg7hWkySDeMFNvrXmadJc95uBWMosOQV4q2s1JWpCxUvxckJBOEWLqey+V8o5ew2YiiznmEuf6dTLJ3G6ZOMigsgzw6FK6ltslwU5IVzttzDoijCKlON9mqe37DdhjqyNPk1WZ6htp5HqIYdd1E30i+cWy4AP4WDS+b07ZtR4XkxeSoTX827avrIGR5xTaBK4hWvNDCWpclmChFJHzWRUeUrVE59IBKWFGkk6CMT2FePF2nYBuB9Wt4rZPKTO2zjbgEkZj0d9s7ZXreul1EPgOb0RHFc2zXrl3G0aWuAwnq+NuVBnekJR+1DDvJ616aoFgk8gNlsgrEdQl6iMfHx32LaGQ//bAvjOphIzwn1CBRYaxA1cuhXW08yosXZZKt3whzXWxB7eQUeKZ7HPT8ua7Lp6am2ibsbmn6uVyuxaBJp9NGLx7h3itfL9mSt3Wkcd7HHafJRy3DTvIm4tMN23QJIkwv6+TkpG8VYDtFWE2MMZ7P57XWvbC+hfarSkPCsue81V1P1+6wWXri1sblF62XunvcL3hQpz45OclLpZJxBbXuXsjafhyB57rpiSNHQxWJRWwdn9hG1eSH0VsmKojkewTT8FfnKmhKENHOZJOuqJa8eFmE54Jt1KBLDQc0htCi/eqLoyOksITaTW28V7p7J8cxeXqEIe+wRaSZlEkzjnrbWQHdbhH8IJ43nVVvcqFWw0TYrv0wgki+h9BpqOqDp/MdV0MiuK5rDEscVMQQXdSjy01r02NNQ3+bxq7OF5gmmm3XrVsvWy9e5KARg6kNuuvZDa28X/kB2n1+5b/y96p1rnOhVK+1bvLfdO1tGOQOgUi+x7A9PCoZypOWum3VsAJCulGTTKi6qim+h5xxqFqtGr115NWXYrWtbvGJTGbyKMSUSm6YETSBa7vnpt90nUO7Xi9xEHmvMoLZytjYmJfcXj0n2R1ShOZQJSM1+YqJ5KMumhvUuR3OieT7AhMhqA+WLl6HsMDloEqC5MVDZlpJKh5+nXZuctETyUzkRTRyOrewHgTquYmFWt2+pr1AmJc87D03LQJLJBJGuUxNHjM3N8fHxsZ88osaa6idTmJubs4o5bTbiYTJbmYqalvGx8d9n3O5XGBgP/mcxIpt4VQgb2ebQ+rXmoqwIJIfIKhkofo4l0qlUBNics5M9cUWlop8zKDFQcKqVyeDOdd3TLq6dDJDXFZPvy2pTl7yoLar8zDCQlXnPWSZQp70NkVrlIsYAQa5Lo5ySSaTvlGzaS1KO/ew3yCSHzDIL6uq34fxD9bFyAlyQTStrFV/1z30Ucg7zJxEO1AJTO3Iuo2gaxhmf104ap0EJq6ZaUJW98zoRly6oF/tpgNMJBIdeXe1WxKJRGgvoKidl7oamDR5IvlYYSJOm3eNbLXp6jM9gDpdXiZqnQWoSjtRyDsOq0c9H9lDIuxLGTfCdqqmfU3++ypBCUs+yoSs2EaO0VKtVlsm7zvR7fuR61fIVzoHAltxHKdle1nyUn/rtdEQN4jkBxA2/VomerE6MJvNhvY7V6Hq8ipRq7/bgqpFCRnQrtVjOs6gJGZuR7rR3W+VmHK5nFF60SXCULVodVJSjB56TcxxFdUhQedSqtPk9+3b54v/o8aEEqvAdZ2obuQ0qNa7DCL5AUQQYYoXVCblTqzXICvUJB2EdQWMA0FtGBRdtJ126Cx51aoW6xDU7XXSi241ryrFiA5DDusbRdLolnYfNC8gOjv5O+HhpbbPtDAqjGwpwjObXIMH5XkLAyL5AUUQYepklKjWa1TrpF8PdlgPnkGxrNpph2pZqvFm1JGasDpVF8GxsTFtyFxd2kDhpSVHrQxbDh06FOitE7XjEMHyVC8ZtXPSZSwTSWdEJNWg+QXVvVe1+OWRgs6wGHSPGhlE8gOKIKLoVIcOM1owLdDpNZHq5IlBIPO4YbMmZcgLfXQ6uul+2mLYxGmN6+q3hTiQg/XZ6jHlMFCJtlq1B+uTO01dlE61HbILsS7fAlnyRPKREfYB0nllhEVY/+yg4/diJeowvVCdIIx1WK1WQ5GyzlW23VXScZROAp2Nj4/z6enpwFg4IilImLkGU2RKXf5iuajrTIbB2CCSH0D0YihoI86wZNPLmDLD8kJ1gjDX1Obvblv0ZotYOUpF1fQdx2mRf2yul1NTU4FhIwZZmtGBSH4A0SvL1SbJRCUbk+92OxgmvdOGdkZatmtYqVT4/v37jeSjkpk4thqOQFirruvysbGxvhNzOyWsu6c6JyBWDofdXj3OMMaxIZIfUPT7QQnS5FXvjThjs4+CPNPpnImoQ3bvi0J8Ol9wuQhLP0y9/Symidt8Ph85lo5wRZbnNFKpVEvnqAt+JofyUO+Nes86WRjXDRDJE0LD9gDHbX33u5PrFJ16P6nXWqenyxORKjGFIUDXdQNJvh8RKuUkNCJ3gdwOk3ukLgeCbcJUkLfqiqxbbR52zkq976YAg70EkTwhNOKYrO02BqVz6NSSV6+1KUmLaWQVJkTB/v37W+QamUzF6too6fbiKPLCr2rVnuIQ8IdukN1QK5VK4AIwtfOU49KbnmfbehG1EzFt10sQyRNCo123y0FpXz/a0473k9hPjiwpZAbVpVINVSB7JHUadVL2tzfJJslkkk9NTfG9e/fGQvByOOqwAdNMq73VjjKXy/k6MV28HtkrSfc8h+m8dR2vKcprL0AkT4iEfhO5DaMwYSt3VLZ4RJxzazjcOLKIiXeuV7p9u9KQaknrXG91HZ64tnKu16Dn2hYMT9ep61J59hpE8oSRwaBZ8u3AtvBL7WB1ESc559qwCLKVHLZMT08b6xqkIvzjTbmRy+WyNraNbmVwkBFjesZMFv4gGB5E8oSRwiCPNMJAteRlK1NYo0Jf1uUb0C2WEgna1UQzQXJOmNAFtiInf4/ithi1CLdItR65g9SNRoKC7Oni9Zs6AtNE+yAYHl0jeQC/DeAVAF8H8CUAe6XfPgHgVQDXABwNUx+RPGGnQJCIbeWmIDCVVHRkYwvWFXeZmpryJAuVJHVtEOn8utEWNdWfLkaNLLeYPGPUzFs6orZp9f02PLpJ8gUATvP/xwE83vz/PQBeApAGcBDAawCSQfUNA8n3+2YSRguyFahaqSKWuvydGjGxH0UkaddZsDrpR7Q5aMTQqbUvJ/qWV7uaVgbbjmfLldBumJFuomsk76sIeADAH/I3rfhPSL99FcD7g+oYdJIfhGEZYTQgk4XsDigToZiU1ZGPOunqOE5PwxroRhTZbLalvfI7Xa02UkyOj49rCTafz/NyudwSv2ZycjJ0BE1xfWzhqkVdcrarMJb8IMNG8g7iw78B8MfN/28D8FfSb99tftcCxtjDAB4GgOnp6RibEz+Wl5extrYGAFhbW8Py8jKKxWKfW0XoBWq1GpaXl1EoFDq+57VaDceOHcP6+joA4JlnnsGlS5dQLBZx5coVLC0tAQDW19dx7do1b79MJoNCoQAAqNfr2N7e9n4rFAqYn5/H8vIyJiYmcOXKFdy8eROvv/46XnvttY7aq8PExASOHDmCJ554wjuP69ev48aNG77tjh496p3zwsICXnrpJWxubgIAGGNgjGF7exuMMe88CoUCPvzhD3vb1et1FItF/Pmf/zl+/OMft7SFMQbOOVKpFFZXV1Gr1TA7O+tdR9FeoPEOb2xsAIB3/ZLJJD7+8Y/jyJEjsd3jgYKJ/fmbVvgzAK5qyoekbU6jocmz5ucLAH5N+v0PAHw46Fg71ZInCag/CHvd477vtpWyNpdA4WESpk2qH37cRbQ3SGtXXRjVIiePB95cjaqbWLYdQ44/bxsBmaSuYXTFlYFuyjUAPgbgLwFkpO9GUq7hPH5CJgmoP5ClkaDrHqeLnJAsZLlC9d0Wz5i6UlPOHCVvpyP4KJo9Y0wbelck6VBXzArt2+SnyPOcvAAADKtJREFULxNyMpk0hl9IpVLaieco4ZKF143a2cgSj87DRk6VOArvXddIHsB9AP4GwKTy/R3wT7z+HUZk4jVuDIKP7U6DzgXRdt3j6ohVi1a4PZrq07lPhkGUvK65XM43KSkySckhB1Qiz+VyRoIXOYltx7zlllu8sAbqPITaSUTpqNTvghaajdIIupsk/yqA7wC40iyflX47jYZXzTUAvxKmvp1I8mTJ9x46N7owkk2nhBA1oJnJALC1RZU6gjxWbHVWq+YkJCZPGVOiDlNJp9OhvW4SiYTPZTJM/TvFaOoaycdddiLJcz5aFsUwQCbCZDLZs3gjNtc+0/a6xCpqQC6xreu6WjdMk3STSCR4pVLRShcmn3cd+ar16az/dksymfQRuxhlVCoV37VMJBItx9xJRhORPIHQhDqx2euAUlH9rFUDQLWSRT06Ipf92cvlcov8c+jQIW08+rBx3Ofm5rjrujyXy7WEGtBNnposeUHWuu110o/cEcm/i2BkstwU9foOK4jkCYQmhn0OREfyphWwagdmSysYVKanp40TwaYMYq7rtuyTzWZbiFgQrS72jOu6Rm8Y2wKrsNmdRkUqtZF8AgTCDkKhUEAmkwHg9zsfNNRqNZw4cQK1Ws33/fz8PFKpFAAglUphfn4ehUIByWTSt919992HM2fO+L6Tz13d3gbGGH73d38Xv/RLv+T7XviSq9d0YmICx48fx9LSEr73ve/59nEcB1evXsXKygqWl5fxwgsvoFgs4vz58/i1X/u1lm3n5+exuLgI13WRTqe9YxQKBSwvL3u+9CrEOhYbdOte+gHTvY4NJvbvRyFLntALDPoQPYwPvNr+Uqnky7YU5FGiuhzmcjmjdi9kLVmjZ4xp3RKFd46uHl1JJBItYXunp6e1XkfqecvXSfWuEW6eYa+zcOfs9TMR12gCJNcQCMODIEnJRnZhJ5JlqUOQuCBpVbtHU5pRv5ucnNS2Qe0gwoRb0Pmyh12o1knyjn77zMclHxLJEwhDBJt1p/utHaKQvWccx/EtHFJ1f9FxmHzRdW0QRVjkYVbf6lalRiFdXVatMOjnPE0vLHnS5AmEGBCnrlosFrG4uIhyuYzFxUVfHBVVR15YWMDq6mqLXh2E5eVlL+bM5uamF89F1C00dsdx8Oijj+LIkSNaHX99fd2L9yL2kfHyyy8DAJ5++mlcunQJ2WxW2x653bpzDEKtVsO5c+dw9epVnDt3LtJ96Oc8je1exwYT+/ejkCVPGEb00ktDPpaacCSKpmySV0Rib9XN02Spi+2FZKLz1VdT96m/C+tbjktvW0+gk3I6tcYHfZ4mCCC5hkDoHuIa7tti0agavC7hSNjjyjq2qrXLi6fksADqJKW8KEnu4HRSj3o+atwgOW6O6CTDBBiT5x9GyR2yHRDJEwhdRBwEY6ojqj4f9TilUsm3kEkXkkBefBRk4ct+7bYJULmj0cURkr+XRym2kBTDbo13AiJ5AqHL6JRgTKOBqJ42QW0zpb8ThGyScUy5TE3fhb0WuvbIowM5Xo3IyRoluFyU6zTMIJInEAYcujR/nHc+SlD3lwnURJaqN0yQF09Ud0fTaETIL7aVuWoGrZ22stUEInkCYcBhs9g7sUJ1coqoT6eFy1BdEttZpCX/ZpOdZBlI1f91PuydjBaGLZRFGBDJEwgDjm5ZmyZPlaAFVKbVoLbJYVv7gzoxm+TTqdRClvwAkLsoRPKEnYxu6cY6TxWdhS9DJ5nYCDLM3IGJaHthaZMmPyCFSJ5AiB8mS1kON6BLQRiUC1W1toOs5XZHAYRgEMkTCDscOoIN8rOvVv35aINcO8OGFTBNwI6ypd1tEMkTCIQWhJlINfm82/zjg6JgxmW1U8fwJmwkT7FrCIQRQ9g4OkFxU+QYMpubm6jX695varwXAKFis8cVw71Wq+H48eO4cOECjh8/3r1Y7KMAE/v3o5AlTyB0hrgt5bAuk2GPG1f7doJbZBSA5BoCYWcgyGsmKqJIIp0simqnXTRZ+yZsJM8avw8GDh8+zC9fvtzvZhAIQ4tarYaHHnrICx2cTqfxhS98oTshbPuMWq3mhTkexfOLAsbYCuf8sO430uQJhBFCsVjEvffe630W8d6D0PU8o11AsVj0cr0OU7t7DpOJH6UAeASN4eH+5mcG4DMAXgXwdQDvC1MPyTUEQueIKmUMq/QxrO3uBtBN7xrG2NsB/PcAbkhf/wqAdzbLwwB+r9PjEAiEcIiabSguj5deY1jb3WvEIdf8DoBTaFjyAh8C8J+bncxfAdjLGLs1hmMRCIQQKBaLOH/+fCitup/p7zrBsLa713A62ZkxVgTwPc75S4wx+afbAHxH+vzd5nff19TxMBrWPqanpztpDoFAaAPC8h+2ScxhbXevEehdwxh7BsCU5qfTACoACpzzOmPs2wAOc85/xBh7GsD/xjn/L806ngVwinO+YjsWedcQCARCdNi8awItec75vbrvGWO/COAgAGHFvw3Ai4yxX0bDcn+7tPnbAPzXiO0mEAgEQodoW5PnnL/MOf85zvntnPPb0SD293HOfwCgBuBfsQbuAlDnnLdINQQCgUDoLjrS5C1YAuCi4UK5BuBfd+k4BAKBQLAgNpJvWvPifw6gHFfdBAKBQGgPtOKVQCAQRhhE8gQCgTDCGKgAZYyxHwK4HnO1+wH8KOY6ewlqf39B7e8vqP3hkOWcT+p+GCiS7wYYY5dN/qPDAGp/f0Ht7y+o/Z2D5BoCgUAYYRDJEwgEwghjJ5D87/e7AR2C2t9fUPv7C2p/hxh5TZ5AIBB2MnaCJU8gEAg7FkTyBAKBMMIYWZJnjP02Y+wVxtjXGWNfYoztlX77BGPsVcbYNcbY0X620wbG2H3NNr7KGHus3+0JAmPs7Yyxv2CMfZMx9g3G2G80v9/HGPszxti3mn/f0u+22sAYSzLGvsYY+0rz80HG2AvN9v8xYyzV7zaawBjbyxh7qvnsf5Mx9v5huv6MsX/ffHauMsYWGWO7Bvn6M8b+D8bY3zPGrkrfaa93M2DjZ5rv89cZY+/rRRtHluQB/BmAGc75PwPwtwA+AQCMsfcA+AiAOwDcB+B/Z4wl+9ZKA5ptuoBGKsX3ADjebPsgYxPAxznn7wZwF4Bys82PAXiWc/5OAM82Pw8yfgPAN6XPjwP4nWb7fwLg1/vSqnD4TwD+L875IQDvReM8huL6M8ZuA/A/o5GXYgZAEo13dZCv//+JBo/IMF3vvqRFHVmS55wvc843mx//Co2Y9kAjNeEfcc7XOeeraETK/OV+tDEAvwzgVc7533HONwD8ERptH1hwzr/POX+x+f/P0CCY29Bo9+ebm30ewL/sTwuDwRh7G4D7ATzR/MwAfADAU81NBrb9jLFxAP8dgD8AAM75Buf8dQzR9UcjaOJuxpgDIINGNrmBvf6c8/8bwI+Vr03Xuy9pUUeW5BX8GwB/2vzflJpw0DAs7dSCMXY7gDsBvADggMgn0Pz7c/1rWSDOoZGzeLv5+a0AXpcMhkG+D+8A8EMAF5ty0xOMsT0YkuvPOf8egE8DuIEGudcBrGB4rr+A6Xr35Z0eapJnjD3T1O7U8iFpm9NoyAh/KL7SVDWIfqTD0s4WMMbGAPwJgJOc85/2uz1hwRj7IIC/V9JUDtN9cAC8D8Dvcc7vBPAGBlSa0aGpXX8IjYxzPw9gDxoSh4pBvf5B6Muz1K2kIT2BKTWhAGPsYwA+COAe/uaCgGFJTTgs7fSBMXYLGgT/h5zzLza/vskYu5Vz/v3m8PTv+9dCK/45gCJjzAWwC8A4Gpb9XsaY07QmB/k+fBfAdznnLzQ/P4UGyQ/L9b8XwCrn/IcAwBj7IoA5DM/1FzBd776800NtydvAGLsPwKMAipzzNemnGoCPMMbSjLGDaEyC/HU/2hiA/wfAO5ueBSk0JqBqfW6TFU39+g8AfJNz/h+ln2oAPtb8/2MAqr1uWxhwzj/BOX9bMwHORwD8Oef8VwH8BYBSc7NBbv8PAHyHMfau5lf3APgbDMn1R0OmuYsxlmk+S6L9Q3H9JZiud3/SonLOR7KgMaH6HQBXmuWz0m+nAbwG4BqAX+l3Wy3n4KLhGfQagNP9bk+I9v4LNIafX5euu4uGrv0sgG81/+7rd1tDnMvdAL7S/P8daBgCrwK4BCDd7/ZZ2j0L4HLzHnwZwFuG6foD+A8AXgFwFcCTANKDfP0BLKIxf/D/oWGp/7rpeqMh11xovs8vo+FF1PU2UlgDAoFAGGGMrFxDIBAIBCJ5AoFAGGkQyRMIBMIIg0ieQCAQRhhE8gQCgTDCIJInEAiEEQaRPIFAIIww/n/3/Q4J6HNefQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  2.072345  -3.241693]\n",
      " [ 17.93671   15.78481 ]\n",
      " [  1.083576   7.319176]\n",
      " ...\n",
      " [ 64.46532  -10.50136 ]\n",
      " [ 90.72282  -12.25584 ]\n",
      " [ 64.87976  -24.87731 ]]\n",
      "C_x =  [44 66 28]\n",
      "C_y =  [95 89 21]\n",
      "Initial Centroids (random centroid) : \n",
      "[[44. 95.]\n",
      " [66. 89.]\n",
      " [28. 21.]]\n",
      "(3, 2)\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C = \n",
      " [[44. 95.]\n",
      " [66. 89.]\n",
      " [28. 21.]]\n",
      "C_old = \n",
      " [[0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]]\n",
      "len(X) =  3000\n",
      "clusters =  [0. 0. 0. ... 0. 0. 0.]\n",
      "Error before loop [104.69479452 110.80162454  35.        ]\n",
      "Error in loop [29.04983  29.571812 16.671793]\n",
      "Error in loop [5.917121 5.662632 9.349691]\n",
      "Error in loop [3.0297205 1.0410198 1.0940428]\n",
      "Error in loop [3.0802405 1.5698444 0.5180989]\n",
      "Error in loop [4.9042006 1.3662412 1.1576805]\n",
      "Error in loop [9.277265  2.102805  2.1496415]\n",
      "Error in loop [19.361467   2.4246607  8.135959 ]\n",
      "Error in loop [16.660158   2.6865053 16.551924 ]\n",
      "Error in loop [1.7843636  0.63587004 1.8581115 ]\n",
      "Error in loop [0.03402718 0.02599239 0.        ]\n",
      "Final Centroid  [[  9.478045  10.686052]\n",
      " [ 40.683628  59.715893]\n",
      " [ 69.92419  -10.119641]]\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": [
    "from copy import deepcopy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\")\n",
    "\n",
    "# plt.style.use('ggplot')\n",
    "# plt.rcParams['figure.figsize'] = (8,6)\n",
    "# Importing the dataset\n",
    "\n",
    "data = pd.read_csv('/home/diwakar/Downloads/IITK ML Data/KMeansData.csv')\n",
    "print(\"input data and shape\")\n",
    "print(data.shape)\n",
    "print(data.head())\n",
    "\n",
    "\n",
    "# getting the values and plotting\n",
    "f1 = data[\"V1\"].values\n",
    "f2 = data[\"V2\"].values\n",
    "\n",
    "plt.scatter(f1, f2, c=\"black\", s=10)\n",
    "plt.show()\n",
    "\n",
    "X = np.array(list(zip(f1, f2)))\n",
    "\n",
    "print(X)\n",
    "\n",
    "# Euclidean Distance Calculation\n",
    "def eu_dist(a, b, ax=1):\n",
    "    return np.linalg.norm(a-b, axis=ax)\n",
    "\n",
    "# Number of clusters\n",
    "k = 3\n",
    "# X coordinate of random centroid\n",
    "C_x = np.random.randint(0, np.max(X), size=k)\n",
    "print(\"C_x = \", C_x)\n",
    "# Y coordinate of random centroid\n",
    "C_y = np.random.randint(0, np.max(X), size=k)\n",
    "print(\"C_y = \", C_y)\n",
    "\n",
    "C = np.array(list(zip(C_x, C_y)), dtype=np.float32)\n",
    "print(\"Initial Centroids (random centroid) : \")\n",
    "print(C)\n",
    "print(C.shape)\n",
    "\n",
    "plt.scatter(f1, f2, s=10, c=\"k\")\n",
    "plt.scatter(C_x, C_y, marker=\"*\", s=300, c=\"r\")\n",
    "plt.show()\n",
    "\n",
    "# To store the value of centroids when it updates\n",
    "C_old = np.zeros(C.shape)\n",
    "print(\"C = \\n\", C)\n",
    "print(\"C_old = \\n\", C_old)\n",
    "print(\"len(X) = \", len(X))\n",
    "\n",
    "# Cluster Labels (0, 1, 2)\n",
    "clusters = np.zeros(len(X))\n",
    "\n",
    "print(\"clusters = \", clusters)\n",
    "# Error function - Distamce between two centroids(new and old)\n",
    "error = eu_dist(C,C_old)\n",
    "print(\"Error before loop\", error)\n",
    "# Loop will run till the error becomes zero\n",
    "while error.all():   # error! = 0\n",
    "    # Assigning each value to its closest cluster\n",
    "    for i in range(len(X)):\n",
    "        distances = eu_dist(X[i], C)\n",
    "        cluster = np.argmin(distances)  # cluster 2\n",
    "        clusters[i] = cluster\n",
    "    # Storing the old centroid values\n",
    "    C_old = deepcopy(C)\n",
    "    # Finding the new centroids by taking the average value\n",
    "    for s in range(k):\n",
    "        points = [X[j] for j in range(len(X)) if clusters[j] == s]\n",
    "        C[s] = np.mean(points, axis=0)\n",
    "    error = eu_dist(C, C_old)\n",
    "    print(\"Error in loop\", error)\n",
    "\n",
    "colors = ['b', 'c', 'r']\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(k):\n",
    "    points = np.array([ X[j] for j in range(len(X)) if clusters[j]==i])\n",
    "    ax.scatter(points[:, 0], points[:, 1], s=25, c=colors[i])\n",
    "ax.scatter(C[:, 0], C[:, 1], marker=\"*\", s=100, c='y')\n",
    "\n",
    "print(\"Final Centroid \", C)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kmeans of sklearn\n",
      "labels :  [2 2 2 ... 0 0 0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Best place for new shop\n",
      " [[ 69.92418447 -10.11964119]\n",
      " [ 40.68362784  59.71589274]\n",
      " [  9.4780459   10.686052  ]]\n"
     ]
    }
   ],
   "source": [
    "# Unsupervised Machine Learning K-Means Clustering Algorithm\n",
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# Importing the dataset\n",
    "filename = '/home/diwakar/Downloads/IITK ML Data/KMeansData.csv'\n",
    "data = pd.read_csv(filename)\n",
    "print(\"Kmeans of sklearn\")\n",
    "# Number of clusters\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "\n",
    "\n",
    "# getting the values and plotting\n",
    "f1 = data[\"V1\"].values\n",
    "f2 = data[\"V2\"].values\n",
    "\n",
    "X = np.array(list(zip(f1, f2)))\n",
    "# fitting the input data\n",
    "kmeans = kmeans.fit(X) # calculation of centroids\n",
    "# Getting the cluster labels\n",
    "labels = kmeans.predict(X)\n",
    "print(\"labels : \", labels)\n",
    "print(list(zip(X, labels)))\n",
    "\n",
    "# Centroid values\n",
    "centroids = kmeans.cluster_centers_\n",
    "print(\"Best place for new shop\\n\", centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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