Unsupervised.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervised Learning\n",
    "\n",
    "by Atmam Al Faruq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "Y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = 3\n",
    "kmeans = KMeans(n_clusters=K)\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.006     , 3.428     , 1.462     , 0.246     ],\n",
       "       [5.9016129 , 2.7483871 , 4.39354839, 1.43387097],\n",
       "       [6.85      , 3.07368421, 5.74210526, 2.07105263]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2,\n",
       "       2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2,\n",
       "       2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1], dtype=int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f68a4bc5668>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7vnfv3oD7QeTHH39MVVWVZyGj9evX89lnnwXUB1VV6d69u08+2mg0enaaKSkp4Y033qCgoACn04nL5WL//v38+c9/9gnw8fHxpKWlySB9kZCBWrqkderUybOam6IoGAwGTCYT119/fcDrddx6662eEWlj1157LWFhYZSXl/tNf1gsFkB/fz6HwxFwZSLAvffeS3h4uCe4WiwWoqOjmTFjBuB+aHrutwSXy0VxcTFHjx4NqA0pNMnUh3TJu+mmm0hPT2f79u0oikJGRobP7tfHjh1j6dKlnD59mu7duzNlyhTi4+MB92j21VdfZfny5WRmZhIeHs4NN9xA165dAXfVocVioaamxqfthi2pmqpMrKqqCmhkm5iYyMsvv8yWLVs4deoUqamppKene3LdDSNpPcXFxZ5trPbt28fy5cspLS2lf//+TJw4MeTW1Za8yUAtXRZSU1P9LqiUlZXFe++95xnZ5ufns2XLFp555hnatWsHuBfhnzJlClOmTPG5vmPHjrrLljakK8C9VOru3bt1X9OcIGm1Wj1TAs/VvXt3du/e7TNC1zTN895Xr17N559/7nlNQUEBmzZtanZlotS6ZOpDuqxpmuaZZtf4WG1tLV988UVA94iOjmb06NE+e/xZLBbGjx8PwPTp039QZWIgRo0aRVhYmNf9zGYz/fv3Jzk5Gbvd7hWk4Wxl4ooVK4LSB6llyEAtXdYqKip0p7cJITh48GDA97njjju47rrrsNlsqKpKjx49eO655zyj1JSUFJ566ilPZWKnTp247777Aq5MDITNZuPZZ59l5MiRREZGEh8fz9SpU/nJT34CQF5enm4u3el06o72pdAhUx/SZc1ms/k9FxEREfB9NmzY4DUCP3jwIH/5y1+8KhMbdoVpSdHR0dxzzz3cc889PuciIyP9TkkMdmWiFFxyRC1d1sxmM+np6T57IprNZiZPnhzQPVqjMjEY4uPj6dy5s0+qRVYmhj4ZqKXL3t13302/fv08Zdomk4kJEyY0OT+6saYqFzds2OD1u6ZplJeX/6A9E51OJ2VlZQEX7DT28MMP06VLF0wmE1arFbPZLCsTLwIy9SFd9sxmM4888gilpaWUlJSQnJzcZErkXP6mxIF3ZeKmTZuYN2+epxT8yiuvZMaMGc3aM3HRokWsWLECTdNQVZVp06YxYcKEgPsaERHBU7Iy8aIjR9SSVC8mJoYuXbo0K0gDTY6809PTAdi1axdz5syhsrLSqzJx7ty5AbezZMkSli9fTl1dHQ6Hg5qaGr744gs2bdrUrP6CrEy82MhALUk/UFhYGNdff73P8fDwcM9i+XqViXa7nfXr1wdUmSiEYNmyZbr3WLRo0Q/ovXQxkKkPqU0dKdrO2+seoqy2EINiZETazfy/EX/0nBdCsP3EYtYc+oAaRwXpKddzdc8fYzWFe15TWJHN0r3/ILs4i/bRPbim78OkxPYLaj+r7WWsOvBvvj/5DRGWWMb3up9BHSd6zt9www3079+fhQsXUllZSXp6OpMmTfI8pCwuLta9b6CViQ6Hw+/qeYEu19qgoTKxrKyMfv36MWnSJN0SeSl0yEAttZkjRdt5dXmjJUCFk43HPuN4yW5+fe0SAOZ9/zvWHp6N3eUuzz5VfoTN2Qt4dsoizKqV3NIDvLL8JhyuWjThIq/sIDtzV/DI2PfokzwmKP2scVTw8tKplNUU4NDcwfJo0Xam9HmIqQMe87yua9euPPbYY7r3aN++vd8tswKpCDSZTMTExFBSUuJz7txy+KasWbOG+fPne0bmp06dYtOmTfzv//6vDNYhTKY+pDbz9rqHdI+fLN1LUeUJSqrzWXPoQ0+QBnC4aimuOsmWHPec5Xnf/446ZxWacD+0E2jYXTXMyXw+aP1cf/hjymoLPUEawO6qYenef1BZ5xs49fhbe1pRlIA2fFUUhRkzZviMvE0mE7fccktAfbDb7V5BGlpmz0Qp+GSgllqU02Xn+xNLWX3wv+Sc8d6Fu6y20O91aw/P4WjRdlSD75c+u6uG3XmrAThclKl7fVHlcWodvhWHF2JX/mocLt/dv1WjmeziLJ0rfJ04cUL3uKZpAacuMjIymDlzJklJSZjNZjp16sSsWbPo2bNnQNfLysSLV0CpD0VRsoEKwAU4hRD+F+iVpHoF5Uf544pbcbhqcWkOFMVA7+QxPDTmLYwGFaNBxaXpzyfuGNObSGs8vsvkg0ExEhvWHoAwNRK7s1r3NSajJSjvI9raTve4U7MTZY0P6B4RERG6m+oKIQKeZVJeXs5nn31GWVkZQgjPNmNPPPGE3226GmuqMjE6OjqgPkhtozkj6quFEINlkJYC9fb6h6isK6bWWYlDq8PuqmH/qfV8e9hdxTeqy2261ykYGJ52I90ThxFujkE55z9To8HE2O53AZAYmaZ7j2hrEkad0fiFSI3rr3vcqTnoEN07oHtMmTLFJ22hqiqDBw8OKMgCfPTRRxQXF3um59XV1ZGbmxvw4lGyMvHiJVMf0g9WZS+j1lHpday46iSFlTmIc8bEdlcN6w7PAeDuYb8nNXaA13kFA7+c4J5bbFAMPDHhE5KjumE22rCqEdhMUdw/8g3aR/cAIK/sgG6fSmpO4XB5z5KotpdR49B/oNeUQ4Xf6R43G60cLd4e0D2GDx/OpEmTPBWBqqrSp08f3TU59LhcLnbt2uUzInY6nWzevDmge4B3ZaLFYvFUJvbt2zfge0itL9AhhwCWKYoigLeFEO+c+wJFUWYCMwG/6/5Kl5YTJXv4z3dPkl92GBD0SBrO/SNfJ9rWDpfmREH/IVnjdMdz1yziTGUu645+TIfongztfIPXaxMjUnnx+hWcKj9CraOKlNg+GA1nH8w1PET0JTzbT50qP8K/Nz3OidK9IARdEq7gvhF/JiEisP9OnZq/ec6K39SNzysVhRtuuIFJkyZx6tQpYmJiiI2NDehaQHevxAbNKSU3m80kJiZy7NgxNE3zrLInhbZAR9RjhBBDgGuBnymK4rNyuRDiHSFEhhAiI9AtjqSLV2XdGf608jZOlu7DJRy4hJODhd/xxxW3ogmNxIjORFp8A4DJYGFY5+lex+IiOnLjwF/6BOnGkqO6kRY/0CtIAwzuNAWj4j3eUFDomjAEs2ql1lHFq8tvIefMTlyau59Hirbx6vJbfEbc/nSM7qV7vM5ZRffE5mUCbTYbXbp0aVaQBneapFu3bj4zRAwGQ8B7PwL861//IjMzE5fLhcvlorS0lHfffZfs7Oxm9UdqXQEFaiFEbv0/C4EFwLCW7JQU+jYemYtL817jQhMuymuL2H9qA4qi8ODov2JRwzEZ3TlYixpOu6huTOrzk6D14+bBzxBta4dFDQPAbLQRZo7m3mGvApB5/CscWp1XCkYIjVpnFTtzA1ssf2fuKr/n9p3yvyBTsJ1vz8TzKSsrY9euXT4LQtntdpYuXRr0/krBc97Uh6Io4YBBCFFR/+fJwEst3jMppBVUHNWdsiaERnHVSQC6Jgzhd9PW8t2xzympzqN74lAGd5rsMypuihCC708uZc3B/7orE1OnMq7HvZ7KxChrAo9e9R8+yfw1uWUHiA/vxO1DXqBdlHs/w6LKE7qzQhyuOoqqzk6Zyy7eyTf7/klhxTG6Jw5jSp+fEhfuLiRpahrh8TN7GNioQrElJSUl8bvf/Y4tW7aQn59PamoqGRkZXg8p65zVrDn4AZnHF2E1hTOux70MSbkORVE4c+YMJpNJdxGpwkL/71Fqe4HkqNsBC+q/cqnAHCGE/Pi9zHVNTGfr8YXU6QTBxuXbUdYEJveZecHtzN/xMt8emo3d5W4nv/wwm7M/55nJX+lWJlbby3hzzT38bOz79E4eTee4AVjUMJ9+mowWUmPdszmyclfw7oaf4XS5R975ZYfYnP05z05ZRFJkGh2je3O4aItu//p3uPqC39uFsNlsfneFcbhqeWXZTRRWZns+RLOLszhyOpPb0l8gOTlZN0gbDAbPRr1SaDpv6kMIcVQIMaj+p58Q4uXW6JgU2oam3kCEJc5rdGwyWumWkE5a/MCgtFFSnc/qgx94gjS4g1FR5YnzVibOznwOgIEdJxIX1hHVcHbUqRostI/qTq92oxBCMHvrszhctZ70iEs4qXVW8UWWO31y17CXQefBaFJkl6C912DYmvMVpyuPe33Tsbtq+PbwbM5U5WGz2ZgwYYLP3o5ms5lrrrmmLbosBUhOz5MuiFm18uyUrxjd9XYiLfHE2tozpc9D/OyqfwWtjWBUJhoNKk9P+pz0lOswGS2YjFZGdLmZJyZ8gkExUF57mqq6Up/rhdA4UOhePrRDdE+emjiPGFsy4J5COKDDBF64rvU3hNWExt78taw++F8OFm72mg2yO2+114daA9Wgcvj0VsC9ye7tt99Ou3btCA8PZ9CgQTzzzDMkJCR4XXOiZA9rDn3A9yeW4nSdf3U/qWXJRZmkCxZhieOuoS9z19CW+ZLVVGViQ9AMN0Xr5qCNBtVTmTgn83m25JzdEmv9kY8xKio/Gvo7rKYI0G0FIizumRlCCDJzFlJlL8FktGJQjBwq3MzxMzvpmjDkh73JZiivLeJPK26ltKYATXNhMBhoH92Tx6+eg9UUTowtGYOiognf9EZkfQWloiiMGTOGMWP0F6xyaU7e2fAz9uavQQBGRcVktPLLiZ+RHNWtJd+e1AQ5opZCVpOViT3clYkTez+I2ehdgm0yWhnV9XaMBpUDBd95BekG3x7+kJMle7GoYQxJuRbV4F1ubjbamNz7pwDsylvFhqNzcbjqcLhqqXNWUeus5O9rH/CZ+dKSPtryDEWVx6lzVuHQaqlzVnOyZC9fZL0CwNged6Ge86BWQcFmiqJX0siA2lh/5GP25n+L3VWLw1VLrbOSyrpi3lr306C/HylwMlBLIcugGHhi/MckR3X1rkwc8QYdot0LEY3vdT9jut2ByWDBZopENVgY2GEit17hXj1v6d5/+L3/kr1/B+DuYX+gb/srPfcwGS2M73kfo7q6S9w3HPlEN6XgdNk5UrStWe9JExoVtcVNphNqHZXU2L3XBXFpTnblrcJ1zmjZqdnZXJ+vT47qxgOj/oLNFIVVjcBstNEuqqs7zWMIbLuvtYfneK1WCCAQFFWdoKjyeED3kIJPpj6kkJYY2ZkXrlvBqfLD1DmrSYnt6/UA06AYuD39Rab2f4xTFUdICO9EtO3sIkpNBURH/TmLGsbPxr5PSfUpzlTn0j6qO2Hms4sUNV7e1ItCwJWJAN8dnc/cHS9T66jAoBi4svtd3DL4Wc+aJMVVJ/n3psc5WuQuS+8U25f7R75OclR3hNDwl6JpPKof3Gkyr928nRP13xaSo7oHtIzq2Xvpvx8FBWcz3qsUXHJELYU8RVFoH92DtPhBfudgh1ti6JaQ7hWkAUZ19V8MclV373U2YsOS6ZaQ7hWkAYan3YTZGOZzvRAi4MrEXXmrmJ35HJV1xTg1O3ZXLesOz2He978D3B8ory6/hSNF23AJJy7hJOfMLl5dfgu1jkpUo5luCek+ZfkGRWVQR+8FlYwGE2nxg2gf3aNZQRpgWNqNuqsOhltiaBcpp/C1FRmopUuaJlw+OW5wj8RrnYEt0JSROo0eSUM91Y9GgwmT0cp9I/7sqbo8n0W73/RJKdhdNaw78jF2Zy0781ZS46g4Z+0SgcNlZ2vOVwDcM/xVwszRnpy8RQ0j2pbIjCueC6gPgZjQ6wGSo7pjUd0FRSaDBYsaxoOj/trsoC8Fj0x9BJmmufgu+3PWH/kYTQhGdZnB6G63NasarzUIIdias5C1hz/C4apjWNp0xnb/UcCBB9xzmtcensOW7C8wGa2M7X4XQzvf4PU/9OHTW1m2723OVOXRO3k0k3r/hGhbUlDfy4GC75iz9TmKq04QaU1gxhXPkZ7q3mz2dOVxBJrOVQavysScM7tYutddmdgjcSiT+zxEXHgHwD2D5NGr/sO+U+vYnbeacEssI7vcQnx4J6877s5bw8oD71NZd4ZBHSczodd92MzubbaKK/U3DlCAKnsJxZUndBd/sruqOV2ZDUC7yC68PG0dm7O/IL/8EKlxAxiaOg2z2rxd05tiUcN4ZvJCsnKXcaDgO+LCOnij18wAACAASURBVDCyyy1E2eT6PW1JBuogEkLw9oZH6p+au0dPuaX72H5yCbPGfRhSI5IPtzzN1pyFZ/tZdoCtOQt5auK8gNZxdmlOXlt5OydL93sKLHLO7GR/wQbuHe4uFNmc/QUfbfkfTxt55QfZdGwez1+zhNiw5KC8jx0nl/HPdWfXDjlTncs7Gx7hlqrnmNxnJqlx/bGo4dQ5vXd7aVyZuDN3Je9seKRRZeJBvstewLNTviKpfr1rg2KgX/ur6NdevypwyZ6/s3jPXz3vNb/sEJuOzeX5a5dgM0WSGjeAPflrfK4zGsxEWRNIjeuPajD55Igtajid484W1djMUYzreW+z/56aw2hQGZJyHUNSrmvRdqTAydRHEGUX7/AK0uD+enu0aBsHCja2Yc+8nSo/zJacL332IswrO0BWbmB752XlLiOv7KBPFdyWnC84VX4El+bgk8xfe7Xh0hzU2MtZsuevQXsvH27+le7xhqrCQR0nERfWwacyMTmqW6PKxGd0KhMr+XLnH3Xvfa4qexlf7/FObTi0OspqT7PukHvt7ekDn/KZRmg22rhx4C8xGkz0TBpJh+ieXtMEjQYTsWHJDO40OaB+SJcuGaiD6GDhZt2n5nXOag76WXy+LRwq3Kq7VnSds5p9+es8vwshOFCwkTWHPuBAwUavKri9+et01/lQMHCocAuFFdk+U8nAHQT3nFrbvA6fuxZzo98r7fqby7qEg7KaQk9l4tjudxFpSSDKmsj4nj/myQmfeioTK/1UJu4/58O1oOIY3x76iK05X2F3ng3KOcU7MRrM594Ch8udewb3LjG/nDiXPu3GEGaKpkN0L3484jWu7vn/APcD08fHf8yV3e7EpkZiUcMZ3vkmnp604Jz1tzX25q9jzaEPOFS4pcl1qqVLh0x9BFGkNR7VaMZ1zsI3JqOVSGuCn6taX6Q1DoPiO69WNZg8+eMqexmvrbyNosoT9VVwRhIjUnlywqeEmaOJtiWhGsw+eVWDYiTSGk+4JdZvMUiUzjrVfr34IpSWwuuvg6K4g/Tjj0NMDLz4IgoGPzlosJnc+WGbOYrb01/k9vQXfV7TZGWi+Wxl4mfbf8O6I3NQUDAoRmYrBn4x7gO6Jgwh0hqHpvku3q+gENMoxdM5bgCPjZ/t963uzF3B+qPu0nYhBFtyviQtfiBX9XDPTqmoLeaPK2a4KxOFC4NioEN0Tx6rr0yULl1yRB1EQ1Ku9TvDoKlF8Vtb//ZXoxp9H24aFJVRXW8F4LNtv6Gg/EijKrgq8ssO89l29wq3o7reqh/sjSb6tx9HlDWBXu1G+lTKmY02Jvd5KLCOCuEO0m++6Q7ODUH6zTfdx4VgQMfxupe2j+qJWT3/g1GLGsYVfioTJ/VxV+PtzlvF+iOf4nDVYXfVUuusosZR4alM7BTTl/jwjj5/HyajlQk97wvorZbXFvHfzb+sr3ysxu6qwanVMff731FYkQ3Ah1v+52xlYv3rTpTsDThFI128ZKAOIqspgsfHzyGmfiF7ixpOpDWBn1/1X8+6EaFANZp5YvynxIenYFHDsKoRhJtjeOjKtz1rMG87vsinwMElHGQeXwRAfHgnfjrmLcLNMVjVCCxqGPHhKTw54TNUozsN8OCov9I1IQOTsaHiz8q1/R7lipQAV2pTFPdIetYsd3A2GNz/nDXLM8L+6Zi3fTaYjbEl86tJ8wL++7hn2B/okzwaVTFjUcNRDWau7vljRtdXJq5vojLxaNF2FEVh1tUf0jGmN6rBgkUNx2y0cUf6S3RJuCKgPuw4+Y3uh7ymOdma81WTlYnfZX8e8HuVLk4y9RFkafGD+L8bvyO3dB9CaHSK7YdBCb3Pw44xvXh52jryyg7gcNWREtvPa7aHy89ehFqjdEb/DuP4403bOFGyB5PRQofoXl4zW8LM0Tw54ROKKk9QVltIx+he9amGZmgI1m++efZYQxoE98pwL1z3DQXlxzhQsIG0uMGkxuvvGu6PyWChfVQP9hdsxOmqw2aOpH2jir6mKhMbUj82UyTJkV3JKz2Iw1VHXHgHEiJSAu6DU7PXVx9604SGU6sLuDJRujSFXgS5BBgUAymx/UiNGxCSQbqBoih0jOldX/Hn/Zndr/1Yn6/yBsVI33OmpxkNKmnxg+gY09vv9MOEiBS6JaQ3P0jD2XRHYw1pkEbaRXVhbI+7mx2kAT7P+j/WHPoAh6sWl3BSWVfCnMznyarfqmtY5+mYVf3KxG4J7srEf6z7Cd+f/AaXcKAJJ0WVx/nbt/dxqvxwQH0Y2GGC3pLXmIwWrug0pYnKRCODWmmHGanthG4UkdrUnRm/Jdwci6W+dNpiDCPcHMuPMn7bep1onJOeNQs07WwaRCdYXwiHq441hz7UrRr8etcbAAztfAPdEzJ8KhN/POI1zKqVgvKjHC3a7vNg1anZWb7//YD6kRCRynX9foHJaEXBCCiYjTau7HYnqXEDALhn2Cu+lYnWpKBWJkqhSaY+JF3x4Z343bRv2ZL9JSdL99Eppg/D0qZ7zS5wuGpZd/hjtuScrUzMSJ3mNbJee+gjFu1+k2pHOR2je3LvsD/SMba3XpO+FAURHU3RA9P5aFohNctuJGPmdYzXHkGNifGkP36Iarvv1LwGxdW5gPtbw8/H/Ze9+WvZlbeKSEscI7rc4kltFFedRDWYfPaQ1ISLggBH1ADX9XuUAR3GsyXnSzTNSUbqVK8cd7uorrw8bR3fZX9BftlBOscNYGjnG4JamSiFJhmoJb+spgjPus/nclcm3kFu6X7PaDS7eCf7Tm3g3uHu9ZHnbH2ebw9/6Lkm+8xOXlp6Dc9NXhRwiuLz6RbWHNyLvdAdBPPKDrBpSirPXvMawSjKj7DEYzKYdTfqbbz3o0Ex0L/DOPp3GOfzug7RvXC4fPPYqsHsSY0EKiW2Lymxff2et5mjuLqFKxOl0CNTH9IFcVcmHjinCrOaLTlfUFB+FLuz2itInyX493eP6xz3VVpdwKqD/8GunQ2iDlctRVUn2JLtuxnAhTAaVKYP+pVu1eD0gfpVj+eKCWvHiC63eN1DUQyYVRvjewU2PU+SmiJH1NIF8V+ZqHCwcDPRTSzic6riqNfv+WWHOFi4mUhrPAM6jPcss3mkKBPVYMJ5zqwLu6uGXXmrGN3t9iC8E7iqxz1EWOJYtPtNSqrzSY3rz82D/qdZG9feNfT3tI/qwaqD/6bGUU7f5Cu5adDTQV+ASro8yUAtXZBoWxIGjGh4T+MTCCKtcSREpPq9tqEIRhMaH2x+iszji1BwF9wYDSaenPAJHWN6E2GJQ29KWuM9E4MlPfV6z4p7F8KgGJjY+wEm9n4giL2SJDeZ+pAuSFr8YJ8gDe7URLfEoXSI7okB/e2fusS7N4TdmvMl245/jcNVW1/xV0mVvYS/r30QIQQ9koZjM0f7TElrvGeiJF0OZKCWmuR02ikoP4bT6T31zL0aoO9/PiajjX35azlTlet3n76KuiIA1unszwfuNS3yyg549kxMijy7Z6LVFMGPR/zZs2diA01zUVlX0mTxR62jklpHld/zkhSqAk59KIpiBDKBXCHE1JbrkhQKNE3j72sfYHf+Ks+xAR0m8MiV72EwGOpnOeilJRQcmh2n5tBdCwTO7sunN1OiQcN+hkmRafzm+pXklx+i1lFJamx/T4l6g28PfciXO/9EnbMao8HEpN4/4fr+szzFRgUVx/jPpifIObMTgG6J6fx4xJ99Fv6XpFDVnBH1LGBfS3VECi3vbnzUK0gD7MpbyXsbfw64F6A6d6YEgEtz0b/9OBIjOtfnmL2ZjBbPAlU9kobrtu3SHF5T1BRFoUN0T7omDPEJ0luyv2Te9y9TZS/Fqdmpc1axbN/bLNnzNwBqHVW8uvwmjhV/79mL8PDpTF5ZfnOTG99KUigJKFAritIJuB54r2W7I4WK708s0T2+/cRiAHomjSCj89T60mr30p8mo5VbBj9DtC0JRVF4cNRfsKhhmOpXprOoYSRFdmFS75mAu1BEl+JeTS4Qi3a/oVtVuGz/22hCY9vxRTjqd25poAkXdY6qgDdJkKS2Fmjq4w3gV0CkvxcoijITmAmQmur/ib8UHAcKNrJi/3uU1RbSv/14JvS6n3BLTNDu72+N54bjiqJwZ/pLWNUIMo8vxGgwM77nfVzd88ee13ZLzOC3U79l07H5FFedpGfSCIakXONZCL9h+c5zmYxWSqrzAtquq6Q6X/e43VmL3VnD6crjutMI7fXzsSXpYnDeQK0oylSgUAixTVGUcf5eJ4R4B3gHICMjQ2470YLWHPyA+Tt+32hfxoNsPPoZv752adCCtVFRdXdoMSruIOvSnLy+6kecLN3n6cdXu1+noOIo9wz7g+f10bYkrun7sG4bqbH9OFm61+e43VlDu6huAfWzY0wvjhXv8DkeYYnDooY1uWdiSkw/n+skKRQFkvoYDdygKEo28AkwXlGUj1q0V5JfdmeNV5AGcGp1VNQVs/JAYAsABeKavo/oHr+2/nhW7nJyy/Z7VyY6q9mcvYCC8qO6155Lb0U6AASYdLa20nPz4Gd9dk43G63cMvhZFEVhUMeJxIa199rOSjVYaBfZld7JowNqQ5La2nkDtRDiGSFEJyFEGnAHsEoIcXeL90zSdaJ0r+60N6dmZ1feKp0rLswNA59k+oBfeXY+UQ0Wpg/4FdMGPgHA3vy1fisTD53eHFAbR4oydY+bjBZyS/cHdI+eScOZNe5DuiWkYzNF0immDw+O+hsjutwEuOdcPz3pc8Z2+xERljgiLQmM63mvZ89ESboYyMrEi0ykJd7/XoRW/2XbzVVtL2Pbya8xGowoihWDYmDbya8Z1/MebOYoom1JGA1mXDp7JurN9tATbU3iBHt8jmu4Ar4HQI+kYfxqkv9dTsLM0dyR8RJ3ZLwU8D0lKZQ0a0ghhFgj51C3raTINDpG98KgeH/Gmo02JvZ+MGjtfLb9JfLKDlLnrPbsz5dXdtBrz0SjzohUNZro135cQG1M7P2gzxQ/g6LSKaY3iZGdf/B7kKRLhfzudxF6ZOx7pMb2w2S0YjVFYjbauGnQ0/RJHhO0NjJzvvIUpjRwaQ62Hv8KcK9XPXPMPwkzRTfaM7ETT4z/xLOo0vn0SR7D9IHuleus9Xsqpsb245Er5SxQSWpMEUHYJeNcGRkZIjNTP/8oBU9BxTEqa4vpFNvXs/tIsDz0cRfdKXoKRt668+zDQpfm4PiZ3ahGC51i+vjdjqsp7t209xBpTaBdZJcf1G9JulgpirJNCKG7gLnMUV/E2kV2abHAlhzVjfzyQz7H20d7T5szGkwB77Ttj0UNo3vi0B90D0m6lMnUh6Srou6M/vHa4lbuiSRJckQt6ar0E6gr60o8f3a46lh/5NP6PRMtjO1+F+kp119Q+kOSJP9koJZ0tYtMo6DimM/xpMg0wF2Z+OdVd3Cy5Gxl4rGiHew/tYG7h/1fa3ZVki55MvUh6brliud9Kv5MRiszrngeqK9MLN3vs2fid8fmB1yZKElSYGSgbgOa0Nh3aj1rD8/mWNH3tMTMmx9qUMeJPHLlu3SOHYDVFEnn2IE8cuW7DOw4AWiiMlExeFUmujQnu3JXsvbwnICrDSVJ8iZTH62svLaI11bcRknNKYRwAQZS4/oza9wHmFXf9Z3bUt/2Y+nbfqzuuShrwnkrE09X5PDHlbdS56hEEy4EMKDDeH4y6m9+d3+RJMmXHFG3sg+3PM3pyhzqnFXYXbXYXdVkF2fx1a7X27przTKq6226a2UYDWcrE99a/xDlNaeprX+vDlctu/NWs+7InFburSRd3GSgbkUOVx178tb4LB/q1OrYeGxu23TqAiVEpPDT0f/AZoryVCbGhXXgifEfYzJaOFOVS0HFEZ+iGburhm8PycUXJak5ZOqjFQkhvHYaaezccu2LwYCOE3jt5u3knNmFyWihU0xfz9Q8h2ZH8TMOcGj+90qUJMmXHFG3IrNqpXPcQMB7nrFBURnUcVLbdOoHMhpMdE0YQkpsP6/500kRaYRbYn1erxosDOt8Y2t2UZIuejJQt7J7h79KWP1CSgBmYxjR1kRuHvxMG/csuBrvmah67ZmYxqTeP2nj3knSxUUuyhRkmtDYkv0FG45+iiY0RnWZwYgut2A0nM0yHTmdyafbfkNx9Uk6xvTizvTf0j66Rxv2uuWU1RSw4ehczlTl1u+ZeK3PTuKSJDW9KJMM1EH2zvqH2ZW3BrvLPcfYbLTRM2kEj171bxRF4UDBJv727X04NTuacGFUTJiMFv5n8heXbLCWJOn8mgrUMvURRNnFWezKW+0J0uCe5XDo9GYOFn4HwOytz2J31aAJFwAu4aDWWcW8719ukz5LkhT6ZKAOogOFm3Rnb9Q5qzlQsJE6ZzWnK3N0rhQcOr2l5TsoSdJFSQbqIIqwxOnmX01GKxHWOFSD2WcLrQY2U2RLd0+SpIuUDNRBNCTlWt25wwbFwLDON2I0qIzocjMmg/dWVWajjQm9HmitbkqSdJGRgTqIbKZIZl39IVHWRCxqOFY1nAhLHD8b+y/P+he3D3mRvu2vwmSwYDNFohosjOhyS1A3ppUk6dIiZ320AE1oHD+zCyE0OscN1F2A6ExVHkVVJ0iO6kaUNaENeilJUiiReya2MoNiIC1+UJOviQvvQFx4h1bqkSRJFzOZ+pAkSQpxckQdguzOGtYemcO2nEXYzFFc3eNeBtQv2C9J0uXnvIFaURQrsBaw1L9+nhDihZbu2OXK4arlleU3UVBxDIerFoBDhZuZ0OsBpg96qo17J0lSWwgk9VEHjBdCDAIGA9coijKiZbt1+dqSs5DCimxPkAZ3dePy/e9SVlPQhj2TJKmtnDdQC7fK+l9N9T+ht8nfJWJn7gqvDWMbqAYTh09fvjNpJOlyFtDDREVRjIqi7AAKgeVCiM06r5mpKEqmoiiZp0+fDnY/LxvR1iQU9PcTbJiLLUnS5SWgQC2EcAkhBgOdgGGKovTXec07QogMIURGYmJisPt52biqx92oRpPXMQUFmymSHknD26hXkiS1pWZNzxNClAKrgWtapjtSx5je3DPs1frKxgjMahgJEak8Pn6O7maykiRd+gKZ9ZEIOIQQpYqi2IBJwCst3rPL2PC0GxmSMoWcM7uwqOF0iunjtc2VJEmXl0DmUbcH/qsoihH3CPwzIcSilu2WZDJa6Z44tK27IUlSCDhvoBZC7ASuaIW+SJIkSTpk0lOSJCnEyUAtSZIU4mSgliRJCnEyUEuSJIU4GaglSZJCnAzUkiRJIU4GakmSpBAnA7UkSVKIk4FakiQpxMlALUmSFOJkoJYkSQpxMlBLkiSFOBmoJUmSQpwM1JIkSSFOBmpJkqQQJwO1JElSiJOBWpIkKcTJQC1JkhTiZKCWJEkKcTJQS5IkhTgZqCVJkkKcDNSSJEkhTgZqSZKkECcDtSRJUog7b6BWFCVFUZTViqLsVRRlj6Ios1qjY5IkSZKbGsBrnMCTQojtiqJEAtsURVkuhNjbwn2TJEmSCGBELYTIF0Jsr/9zBbAP6NjSHQtlQgiqHXY0obVoOzVOBw7N1aJtSJIU+gIZUXsoipIGXAFs1jk3E5gJkJqaGoSuhaaFx7J4acvXnK6pxKaamNlvDI8NnoBBCV66f3dxLk9tmM+eM/kYFQPXd+7P70fdRJTZGrQ2JEm6eAQcXRRFiQDmA48JIcrPPS+EeEcIkSGEyEhMTAxmH0PG6pMHeHLdPE5Vl+MSGpWOOv65ey1/3L4saG3kV5UxY8k77CrOQxMCh+bi65zd3L3s/aC1IUnSxSWgQK0oigl3kJ4thPi8ZbsUul77fjk1LofXsRqng/f3bsTucgaljQ/3f+dzL4fmYn/JKXYX5walDUmSLi6BzPpQgPeBfUKIP7d8l0JXTsUZ3eOa0Cipqw5KG/tLT2HXyUsbFAPHyouD0oYkSReXQEbUo4F7gPGKouyo/7muhfsVkvrEJuseNxtV4q3hQWljSGJnrEbfRwdOTfPbviRJl7ZAZn2sF0IoQoiBQojB9T+LW6Nzoebp9ClYjSavYzbVxBODJ6IajEFp466eQ7GpZgwonmNWo8qYDt3pHpMUlDYkSbq4yMrEZkhP6szsyfczJDEFm2oiLTKe/xs5nQf7jQlaG7HWcBZPe5RxnXpiNqhEmizc32c071x9V9DaaFBUU8lvtnzNMxsXcKi0IOj3B3BpGmtzDzH30DaOlJ1ukTYk6VKnCCGCftOMjAyRmZkZ9PteLv6atZo3slaiKgYURUFB4aPJ95Ge1Dlobfxj1xp+n7nU69i0tAH8M4gfCCcqznDLkncot9egCYFLaFzXuT9vjr0tqNMZJelSoCjKNiFEht45+X9LiMksyOEvWauoczmpctqpdNRR4ajl3uX/CdrMklPV5T5BGuCr7F18k7MnKG0AzFw9m1PVZVQ66qh22qlzOVl6fA9zDmwNWhuSdDmQgVqHU3NR63T4Pa9pGmdqK3FqFx447S47uZUluFzeMzzmHNxCrU5AdgmNDflHLri9xv62c7Xfc39t4lxz5FWWcrC0AO2cb2w1Tgcf7P8uKG1I0uWiWZWJl7pqh51fb17IgiM7cAoXvWOT+cOomxiSeLbS8jebv+L9fRs9AWhkclc+nnI/qiGwv0qXy8XVX/yZo42m2o1s15W5181098FpR+CbjhJCUOvy/+HRHNUOu99zNU18QDVHrcuBUVF0z507F12SpKbJEXUjD6z8gC+O7MCuOdGEYO+ZfO5Y+h45Fe6g+pesVby7d4PXKHHTqaPcuuTdgNsYt8A7SANsKjjK/Ss+AGBMh+6611U77YxK7tbct6Tr//Ue6ffcjO5DgtJGWlQ8UWabz3GLQeWGLgOD0oYkXS5koK53pOw0WwtzqDsnneFwOXl/zwbAf8pga2EOlfba87Zhd9k5VqFftLLshHsxwtzKEvTGoarByIlK/YKb5hqU2IlxHXv6HO8YHsNP+10ZlDYMioG/jL2dMNWEuX7qYphqJiUylp/2HxuUNiTpcnFZpT6qHHXMObCFb47vJc4azv19RzEiuSsA2eXFmAxGn/SCQ2gcKHFPXWsqLZBdcYb+8R1wak5e3rqUeUe2I4RgWpeB/GbYNMyqyonKsvP28UBpgU7iAyxGlWPlxfSPdy9cOPvAZt7MWkWFvZaMpM68Ovpm2ofHeF5fUlvFf/ZtYl3+YVIiYvlJvzGeawE+mPhj7l/1AatPHkAT0C++PfOumYnBELzP7lHtu/HWuLv4/bYlnK6pJCOxMy+PvFEuLiVJzXTZjKirHHVc/9XfeGX7Mr4rOMbinN3cs+zf/Guve7TcK7Yddp2Hg2aDyhVJKQBEmvwHmO7RCQBcOf9PvLt3PSV11ZTaa/jwwGZGzPsDmqaRFhF39oJzHrIp9b/3itGvPqx01HkqE59YN5enNy4gr6qMCkcdq3MPMmLuq5yoKAHgdE0F4794g7/tWsOWgmwWHNnBTV+/xeLsXZ77TVn4F1ac2I9LCASC3cV5DPvsD9idwZlZArA4exc/XT2bgyWFFNdWsSb3INd99TdO11QErQ1JuhxcNoH644NbOVlZ6jVirnE5+H3mUirstXSKiOWa1H5elYcKClZV5b4+owB4bui1uveektoXq2pmweHvOVFZ6nO+sKaS/+7/DqPRiEUx8sQX63nx45Vng7UQvDpvPbz4IhtP+Z/ZcbqmnJLaaj47vM3nnEtoPLl+LuCeh11aV0Vd/ewRDUGNy8HTGxfg0jQ25B1mX8kpn3uUO2p5M2ul3/abw6VpPL1xATUuB1r9d4Q6zUlJXRV/27kmKG1I0uXiogrUpXXVfHk0i0XHdlLpqGvWtUuP79GdNWEyGNlRdAKAN8bexs8GXkWiNQKbamJiSm8WTf0Z7cKiALir13B+O/wGzPUzPAzAHd3SeX/CvQAsOLbDb/uLju2k2mHHKTSiqmt5cMU2T7B+8eOV3LlkI5SWsvu0/xXy3tq9niU5u/2e337a/T5WntyPQ/Pd1KDO5SS7ophPdQJ9g8WN5lELIfj+9AnmHtrGjtMnaE5x1LHyIs8HRWMOTWPFiX0B30eSpIsoRz330Dae2bQAVTGC4h5B/nPcj5iY0ieg62PNYbrH61wOYi3ucyaDkccHT+TxwRN1X1vttPOXrFWeFIkGzD+2g/v7jaFvfPsmF2aKs4VjMhoxKAov3jkBgAdXbOPBFe6gOeeakfzo9dexzPkNdXb9XV3ah0XRMTzabxs21f1tIMYSprvSn0toRJmtJFoj/N6j4e+i0lHHXd+87zXy7hObzJwpDxBusvi9vkGU2YZL58OicRuSJAXmohhR51QU88ymBdS6nFQ666h01FHjdPDQ6jmU1FYFdI8oi+9UMQC75qJXgIsdPbjyQ07XVnodc2gublv6DgDTuwz2e+31nftjMhgxGozQKFg3eOPHU0FRmpyC9+SgiYzVma3R4Mr6qX0/6TfGE7QbqIqBjKTOJNoi+cWgq/3e45dDJgHwmy2L2FWcR7XT7vnZVZzHb7Z87ffaxpLCIklPSkU9p1TcvStOcGaWSNLl4qII1F8ezcKlsz+hosCS494lz5rQqHE6fL6mrzqx3+/95x3xTlm4NE33a/uG/MO615faa8ivKm0ypbDgaBaV9lp3+qU+3dHYzPc+ByE4oJM7bjDn8FYOlBZg8VNcc7rG/SFyY5dB3N9nFBajSqTJis1oYkB8R/457kcARFvCePPK21DOmQj4swFXMaq9+4NiQf188sbsmpMFR773279z/fPqH9E/vgM2o4lIkxWLQeWBPqOZJudRS1KzXBSpjxqnXTfn6nS6PFPmNKHx+o6VvLdnPVVOO+3Donlx2FSuTesPoBt4G5TX1QDuir3/3byQz+srE/vEJvOHUTdzRWJKfRv+c7Tu9Sz8582rHXXuwFcfpB9csY33Jqbz4p0TKj35GAAADCBJREFUPL/z+OPUjvA/uq+o/yZhMhp95nuDOzUDoCgKz2Rcy0P9x7L7TB7twqLoGdPO67VXd+rFlJQ+LDuxD4FgSGIqt/U4ux6Mw095vN6mBv7EWyNYNO1RDpYWUFBdTv+4DsQGad1uSbqcXBQj6ozENN3jDjRG16cK/rDtG97atZYKRx2aEORWlfLztZ+yPs89Cp7URC67IUA9sOoDFhw9W5m450w+ty99l+P1+d7u0fp7QZoMRnrEtOOeXiP8tnFHz6HEWSNQDUbKw6yeIN2QBvnomhEQE8PUroP83uP/9R5J//gOGHRKs61GEzd28b421hrOlR16+ARpTWjcsvhtVuYeQKsvWN9++gQ3LPoHpfU71XSN0n+v3aIS/PbPn54x7biyQw8ZpCXpAl0UgXpRzi6/5746lkWt08G/9m70WUOi1uXgT98vB+C3I28kXPV9CHZ/n1HEWsM4WnaarQXZPiNvh8vJ+/Vzrd8Zf7fu+hWvjLoJgAkpvRkc38nnfM/oJG7pPgRNaJgMBv48fYwnSAOgKPz9vhvhxRd5ZsgU4nQetk3vOoiUyFhMBiNvXnkbNqMJU33+N0w10yMmiXt6+/+gaGxT/lHyqkpxNBodCwR2l5N5h7cD+N1arMQenC3HJEkK3EWR+mhqUfu9Jac4U1ulW3YN7opDgCizlaw7n+OVbcv45vgeYixhPHnFJCak9AbgWHkxJoPqs3KdQ2jsP+POG/eIace225/jhc1fsb3wOCmRsfzvsKn0j+/gef2iGx7lg32b+Pe+TWhC4+5ew3mg72gAqp2OsymccwJ+cX1gNKsqmbc9y+tZK1l4LItw1cJjgydwXX0KB2BSal+WT3+M2Qe2UFhTzriOvbg+rT9mnS289BwrL9ZN49S4HBys/7suPuehaYOimsAe3gaqwl7LRwc2s/LEfpLDori/32ivRbAkSbpIAvWghE7sKDqpey4jqTMJtgiMBgPopE/7xLX3/Nmqmnlh+FReGD7V53X+KxONDK7PUQMk2CL4+7g7m+zvvX1Gcm8f34WPwlUzYSYz5TrrgnSKiD3bpqrydPoUnk6f4reNtKh4vwU459MnLhlF55tBmGpmUIL7G0FqZJzuFL/UyFifYxeqrK6Gaxb+hdM1ldS6HCgofHN8L78bcSO399RdP12SLksXRerj6fRrMOrsCGIxqPy0/xjMRpVfDBzvMyXNZjTxVP10s/PpFBHrrjA8tzLRaOL+vqN+2BtouJ+i+Oy52CAygLnJwTIkMZW+ce2xNBqBGxWFSLOV6V3dUwyfy7jWp69Wo4nnMoK3r/G/9m6gsKbCU4gk6iso/3fzwibXA5eky01IBeo9xXnMO7yNbYU5XtProsxWlt84i86RZ9fK6BmTxPoZv/SsA/3wgLG8NHwaKRGxWIwqVySkMHvKA836Gv3m2Nt5ZMBYEqwR2IwmJnTqxaJpZysTf6hqh51iP/O+9zcxLS/YFEVhzuQHuLvXcKLNNsJVM1PTBvL1tEc9xSzXpQ3gH+PupGdMEhajSs//3979x0hxl3Ecf39u77gfUO5ojxY4KFwRaEXBYqTUIlBoqbUXMBYjxMaCxdqKWjXG2P4hxviXaYxRYxuEWm0pxV6kwYYKGo1Nk0o8oFakJAWBAwrl+CFoJcjB4x87R5e53du9sLfz3bvnlVxud2Zu9pMnm2dnvzffmYZr+dmcJZcNwVypzQd3ZT0bRxK7Th0p2us4V+6CuGfi2c7zLPvD02zraKcCYcD4+kaev+sL1OeYqFKOzl+8wKRnVmYdYhlRN5S2zzyWQKrkfHbzGv789lvdltemqti88BFuqO/9GSbOlavg75n4+PYt/PXYAc52nufdaBbc7lPv8NhrLyYdraiqKlJ88oaplw05QLoxLb2pOMMr5eSBLDMoU6pgfP1wb9LOZQiiUa9/q637aXEXL7DpwE46ezHBolDnLnRyMctMx1L4/oyFzBjRTE2qkqFVNVSnKmlp/iAPD8CL6c8dPYlHpsyNZlBWU1c5iPfVD+epO+5POppzQcl71oekp4AW4JiZFW+AMkO2WXaQvohQT7MBe2vbsXYefW0Du08dpaoixaLx0/juLS3UVg4q2mvkU1c1iLXzH2DfmeO0//skExuuY2QPF1rq77489Xbuu/EW3jh+mMbawdw0bGTWM1KcG8gKOaJ+Gvh4X4a4vWkSFbEzoQXc3Dim4HOD89l35jhLNq9m18kjXDTj3IVOWvdu56E/rS3K/nureWgjs5smDugm3aWhuo5ZTRN4/9WjvEk7l0XeRm1mrwDFuVlfDiuntzCspo7a6HSwmlQlQ6pq+MFt9xbtNX6+89VuwyvnLnTy6pG9l25e65xzISrahBdJDwIPAlx/fe9mljUNaeCVT32TF/ZsY0fHQW68egRLJnyExtrc103urd2njma9Al91RSX7z5xg7FXXFO21nHOumIrWqM1sFbAK0qfn9fbv66trWT55ZrHidPOh4WPYcfzgZde3gPT4+ITYRYuccy4kQZz1UQrLJ8+kJlV52Uh4TaqKe8Z+gFE+TuycC9iAadSjBtezsWUFs5smUpOqorFmCCumzOaHH/t00tGcc65HhZyetw6YAzRKOgSsNLM1fR2sL0xouJZn538+6RjOOdcreRu1mfV8qTjnnHN9asAMfTjnXLnyRu2cc4HzRu2cc4HzRu2cc4HzRu2cc4HrkxsHSOoADhR9x4VrBI4n+PqF8pzFVy5ZPWdx9YecY81seLYVfdKokyapLdedEkLiOYuvXLJ6zuLq7zl96MM55wLnjdo55wLXXxv1qqQDFMhzFl+5ZPWcxdWvc/bLMWrnnOtP+usRtXPO9RveqJ1zLnBl36glpSTtkPRSlnVLJXVIej36WZ5Qxv2S/h5laMuyXpJ+LGmPpDckTQs05xxJpzPq+Z2EcjZIapW0W9Kbkm6NrQ+lnvlyhlLPSRkZXpd0RtLXYtskXtMCc4ZS069L+oeknZLWSaqJra+WtD6q51ZJ43rcoZmV9Q/wDeA54KUs65YCPw0g436gsYf1nwBeJn3z9RnA1kBzzslW5wRy/hJYHj0eBDQEWs98OYOoZyxTCjhKevJFcDUtIGfiNQWagH1AbfT818DS2DZfAp6MHi8G1ve0z7I+opY0GrgHWJ10liu0EPiVpf0FaJA0MulQIZJUD8wC1gCY2f/M7F+xzRKvZ4E5QzQP2Gtm8ZnFidc0JlfOUFQCtZIqgTrg7dj6haQ/yAFagXmSRA5l3aiBHwHfArrfXvw990Zf1VoljSlRrjgDtkjaFt2tPa4JOJjx/FC0rNTy5QS4VdLfJL0saXIpw0WagQ7gF9GQ12pJg2PbhFDPQnJC8vWMWwysy7I8hJpmypUTEq6pmR0GHgfagSPAaTPbEtvsUj3NrBM4DVyTa59l26gltQDHzGxbD5v9FhhnZlOA3/PeJ1ipzTSzacDdwApJsxLKkU++nNtJf9WcCvwEeLHUAUkfqUwDnjCzm4F3gW8nkCOfQnKGUM9LJA0CFgAvJJkjnzw5E6+ppGGkj5ibgVHAYEn3Xck+y7ZRA7cBCyTtB54H5kp6NnMDMzthZueip6uBD5c24qUch6Pfx4ANwPTYJoeBzKP90dGyksqX08zOmNl/osebgCpJjSWOeQg4ZGZbo+etpBtiphDqmTdnIPXMdDew3czeybIuhJp2yZkzkJreAewzsw4zOw/8BvhobJtL9YyGR+qBE7l2WLaN2sweNbPRZjaO9NegP5rZZZ9asTG0BcCbJYzYlWGwpKu6HgPzgZ2xzTYCn4v+sz6D9FelI6HllDSiaxxN0nTS75+cb66+YGZHgYOSJkWL5gG7YpslXs9CcoZQz5gl5B5OSLymGXLmDKSm7cAMSXVRlnl07z0bgfujx4tI96+csw/z3ty23Ej6HtBmZhuBr0paAHQCJ0mfBVJq1wEbovdOJfCcmf1O0kMAZvYksIn0f9X3AP8FlgWacxHwsKRO4CywuKc3Vx/6CrA2+gr8T2BZgPUsJGco9ez6cL4T+GLGsuBqWkDOxGtqZlsltZIehukEdgCrYr1pDfCMpD2ke9PinvbpU8idcy5wZTv04ZxzA4U3auecC5w3auecC5w3auecC5w3auecC5w3auecC5w3auecC9z/AaUCVGdBCV9jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "centroid = kmeans.cluster_centers_\n",
    "\n",
    "plt.scatter(X[:,0],X[:,2],c=kmeans.labels_,cmap='Dark2')\n",
    "plt.scatter(centroid[:,0],centroid[:,2],marker='x',color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[50  0  0]\n",
      " [ 0 48  2]\n",
      " [ 0 14 36]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        50\n",
      "           1       0.77      0.96      0.86        50\n",
      "           2       0.95      0.72      0.82        50\n",
      "\n",
      "    accuracy                           0.89       150\n",
      "   macro avg       0.91      0.89      0.89       150\n",
      "weighted avg       0.91      0.89      0.89       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "\n",
    "print(confusion_matrix(Y,kmeans.labels_))\n",
    "print(classification_report(Y,kmeans.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f68a1e30668>"
      ]
     },
     "execution_count": 67,
     "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": [
    "K = 4\n",
    "kmeans_v2 = KMeans(n_clusters=K)\n",
    "kmeans_v2.fit(X)\n",
    "\n",
    "plt.scatter(X[:,0],Y,c=kmeans_v2.labels_,cmap='Dark2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f68a1d9e1d0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/ckPYl1V6RgK5eWkqSF8mVKBWrmg9e/Zk9uzZOBwOhBBomobdbufGG28k2i6PN90ylMQkR9jxmXP6kZDgoLbWG7GPRWuV4EfvHwzfR9BnsmnjSctGSla+cNcI3An24DUdDp2UFCc3fi6w7G3DuhMY55V3m6akqqqRE8dVMfHlTKU+lCverbfeytixY9m2bRtCCAoKCujRo0fIa06eqGLlx0coL28gv08GM2f1JT0j0AzfZtP4/g/nsHrlMXZuP01CgoN51w8kr2VbqOrKJhwOHY8nvHKwdUuqtioTGxq9pF2gMhEgs0si//pvs9mx7TSlJfX06JnKiFE5wV1KysoaLFd1iJb75/cO/HzoQBmrVx6lpsbDoMFdmTajT9z11VZCqUCtXBXy8vIibmixd08xi1t6LAOUltSzY+tpvv7EFLKykgDQNI0Zs/oyY1b46ojsnOSQbbpataYrAHrmpnJgX3irUZtNI7kdQdLlsjFhUi/Lc/m909lfWBLWU8QwJT16BJpJfbLmGO8s3R+cxZeVNbBl86l2VyYql5ZKfShXNdOUvLFkd0hwM01Jc7OfZe9Et7QuJdVFwTW5YXv8ORw6k6cFprHX32hRmejQue7GQVG3Ob2QceNzcSc4QtIwdrvG4MFd6dotCV9rZeI5qRbD31KZuDJ2lYlK7KlArVzVGuqbaWwMX8YmJRw9XBH1dW65bRhzru2L0yXQdcjvk8rj354anC1375HK174+iQGDskhIsJPTPYW77hnFxMn5sfoouFx2Hn9iCgXjc0lKcpCe4WbuvAHcfX9gn4/itioT94X3IVHih0p9KFc1pyty+0+rB4iRbNywl3eXHiKQERYcPVzJyy+9x3efvjVYmRjYFebiKhOjlZzi4vY7RsAdI8LOJSU5MCxSNBD7ykQlttSMWrmqORw6w0fmhO2JGKjW6xPVNfx+g9eXHEQIO0LYEEJHCJ2KMp13l2688AUukfSMBHrmWVQm2nWmz4jusyqdQwVq5ap32x0jGDAoK1Ct57Jhs2lMndabgmsir48+14b1e0GG/6ckhI1NYZWJJrW1tfh84emWaPn9JnW14XsmRuNLDxaQ1ys9+FkDlYmD6D9QbUgdz1TqQ7nqORw6Dzw8jpoaDzXVTWR1TWrXjih+n4FlxSCEdLxbv349r732Gs3NgUb/U6dO5fbbb7csyIl0rQ+XHWTNqqOYUmKzacydN4Cp06OfDScmOfjq1z+tTOyWnRxcl63ELzWjVpQWqaku8nqlt3vbqvEThxDITYeS0s+Ikd0A2L17N4sXL6a+vh6fz4fP52Pt2rUsWbIk6vus+OgQq1cexes18PtMPE2BlSlbNhVd+M3naa1MVEH68qACtaJ8Rm63k5lzc5DSaPkjkdKPy+3lls9PBmDp0qV4vaEN/r1eL2vXrg07bkVKyaoVRy2qGw0++uBQ7D6MEpdU6kPpVEeOHOHll1+mpqYGTdOYMGECX/rSl4LnpZRs27aNlStX0tTUxNixY5k5cyYu16erFEpLS1m2bBnHjx8nJyeH6667jtzc6PLL0WpsbGT58uVs376dpKQkZs2aFdIr5IYbxzNkyFnee3cbjQ1eRozKZeas0dhsgRlrRYX1Uj8hBA0NDTgcba8w8ftMmiPsmVhX62nXZzl0sJzVK45QW+th4KCuTJvZJ9i0SYlPKlArnebIkSM899xzwZ9N0+STTz7h5MmTfP/73wfgtddeY/Xq1cFZZ3FxMRs3buSZZ57B4XBw+vRpfvrTn+Lz+TBNkzNnzrBr1y6+9rWvMXhwbHYsaWpq4sc//jE1NTXBh4BHjx5l3rx5zJ8/P/i6/N45fPXRGy2vkZOTQ11d+D6CQFS7zdjsGikpLmpqwoNyt+zkaD4GAJ+sPc47b+0LzsxLSxvYuvkU3/ruNBWs45hKfSid5uWXX7Y8furUKcrLy6mqqmLlypUhqQGfz0dFRQWbNm0CCD6ca+05LaXE6/WyePHimI1z7dq1IUEaAmmLZcuWUV8fvqGAFbvdOu8thIhqw1chBPNvGRJS/Ri4rhZsynQhPq/Bu2/vC90z0W/S2BTbPROV2FOBWulQfr+f7du3s2LFCk6cOBFyrq0dVlavXs3Ro0ex2cJ/6fN6vezZsweAw4cPW76/vLwcj6d9KYFIdu/ebbmczmazcfz48aiuUVRk/cDPNM2od5oZOao79z4whswuDux2yM5x88hXxtO3X2ZU749YmehXlYnxLqrUhxDiOFAHGIBfSlnQkYNSrgwlJSX87Gc/w+fzYRgGQggGDRrEV77yFXRdR9f1iM37e/ToQXJysmXTf03TSE8PdK5LSEiwfBjX2s40FlJTUy2P+/3+qDfJTUpKora2Nuy4lBK3+8Kd8wBqa2tZ/JcXqampwTRN6k5p/OWvG3niiSdCcvaRx9BGZWKKSnvEs/bMqGdKKUepIK1E6+WXX6a+vh6PxxPcs3D//v2sWrUKgEmTrDc/FUIwfvx4+vXrR2JiYlhqQNd1pk2bBhCxp3RqamrU65MvJFLXPb/fT/fukTcVONe8efPCHhjabDZGjRoVVZAFePXVV6moqKC5uRmfz0dzczOnT5/mzTffjOr9rZWJmkVl4jS1Z2JcU6kP5TNraGgISzNUVFRQWloaNiP2er2sWbMGgHvvvTcsCAohePLJJ4HArPiJJ54gOzsbh8OBy+XC7Xbz0EMPkZOTA8CZM2csx1RVVRWWrmhsbKSpybovdFsOHbJe/uZwODh6NLrc7vjx45k7dy52ux2Xy4XNZmPw4MHcd999Ub3fMAx2794d9huI3+9n48boy9QDlYlpwc1yHQ6dG24axABVmRjXol31IYEPhBASeFlKufD8FwghFgALIPIMRLmyFBUV8Yc//IGzZ88C0L9/fx566CFSU1ODqQ4r5wab733ve1RWVrJmzRq6d+/OuHHjQl6blZXFD37wA4qLi/F4POTm5obMlCNtXAsEvySKi4v5/e9/H8wT9+7dmwcffJAuXaLb0NXvt14Wd/5naYsQgs997nPMnTuX4uJi0tLSgumbaFilgNo7BgC7AxKSD1HbuAchHCQkQnJqjwu/UelU0c6op0gpxwDXA48KIaad/wIp5UIpZYGUsiDaLY6Uy1d9fT3PP/88p06dwjAMDMPg4MGD/OxnP8M0TbKyskhODl82Zrfbueaaa0KOZWRkcPPNN4cF6XNlZ2eTn58fls6w2hNRCEGfPn1wOBx4PB6ee+45Tpw4ERxn67LAaPttnL8bTKvm5mb69esX1TVaud1uevfu3a4gDYE0Sd++fcO+/DRNi3rvR4Df/e53bNmyBZ+/Dq+vgurqCl555ZWoH4oqnSOqQC2lPN3yv6XAG8A1bb9DudJ98sknYTO51oZD+/fvRwjBI488gtPpDD7UczqddOvWjblz58ZsHJ///OdJTU3F6Qw8DHM4HCQkJHD//fcDBIKSzxcyI5VS4vF42LVrV1T3aOt1+/bt+wyjb5/777+fxMTEYK7b6XSSmprK7bffHtX7a2pqLFewtC41VOLXBVMfQohEQJNS1rX887XAv3f4yJS4VlJSYjkjlVIGq/D69OnDj370IzZs2EBVVRX9+vWLuCt4JFJKtm/fHlKZOGPGjOADuJSUFB577DH++te/cvr0aTIzM7nzzjvp1i3QY6O8vNxyVYjP56O8vDz48/Hjx3n//fcpLS2lX79+zJs3j4yMwDZabS2fO3nyJCNGhPd+7ghdu3blRz/6EZs2beLs2bPk5eVRUFAQ8pCyubmZlStXsmXLFlwuFzNmzGDMmDEIIaisrMRut1umckpL1fK8eBZNjrob8EbLr1w2YLGUUn39XuX69OnD5s2bg53gznVu+XZKSgrXXnvtRd/nH//4B6tWrQoG27Nnz7Jx40aefvppy8rExsZGXnjhBR599FEGDRpEr169cDqdYeO02+3BZyk7d+7klVdewe/3I6UM3uOZZ56ha9eu9OjRI+J67WHDhl30Z7sYbreb6dOnW57z+Xz89Kc/pbS0NPglevz4cY4cOcIdd9xBdna2ZZDWNI0+fVQ/6nh2wdSHlPKolHJky5+hUsofX4qBKfFt3LhxJCUlhcyO7XY7ffv2JT8/Pyb3qKqqYsWKFWGVieXl5ResTFy0aBEAI0aMICMjI6RwxmazkZOTw8CBA5FSsmjRopD0iGEYeDye4LK3e+65x3J8Xbt2jdlnjYXNmzdTVlYWVkG5atUqKisrcbvdzJ49O2QGLoTA4XBw3XXXdcaQlSip5XnKRXE4HDzzzDNMnjyZ5ORk0tPTmTdvHo8++mjM7hGLykRd13nqqacYO3Ysdrsdu93OhAkTeOKJJ9A0jdraWhoaGsLeL6XkwIHA5rbdu3fnO9/5DmlpaUAguA0fPpxnn302Vh81aqZpUlhYyIoVKzh48GBI7n3Pnj2WaR6bzRb8e7rllluCqaHExERGjhzJ008/HbYCpqioiJUrV7J9+/Y2V70ol4ZqyqRctKSkJO65556IM87Pqq3KxNagmZiYaBmcdF0PPsRcvHhxcAYOgd4duq5z9913t1lskpSUBASC9pYtW2hoaMBut6NpGocOHeLkyZOXNGVQW1vL888/T3V1NaZpomkaOTk5fOtb38LlcpGWloamaZZLFltX4AghmDJlClOmTLG8h2EYLFy4kMLCQqSUwb/HJ598kuzs7A79fEpkakatxK1oKhPnzJkTVvFnt9uZNGkSuq5z4MCBkCDdatWqVZw6dQqn08mYMWPCZu4OhyOYW9+9ezfr1q0LNvxvbm7G4/Hwy1/+sl1rmD+rV199lfLy8pDKxFOnTgVTNNOmTQv7HEII3G43AwcOjOoea9eupbCwEK/Xi8/nw+PxUF9fz69//euYfx4leipQK3GrrcrE1tLtWbNmMWXKFOx2O263G5vNxogRI/jCF74A0Oays/feew8IVEgOGTIkeA273c6sWbOCJe7r1q2znLX7/X6OHDnSrs9kmiZ1dXVtphM8Hk9YBWU0lYnZ2dk8/PDDuN1uXC4XDoeDbt26BdM80Ti3pWwrKSXl5eUhq2SUS0ulPpS4lpWVxbPPPktxcTHNzc1hlYmapnHnnXcyf/58iouL6dKlS0gTpbYCYutDN6fTyaOPPkpVVRWVlZXk5OSQkJAQ9jor7ZlRb9iwgSVLluDxeNA0jalTp3LbbbcFP09FRQW///3vg2XpPXv25KGHHiI7OzvqysRRo0bx3//93xQVFeF0OsnOzo6qjeqFPo8QQuWqO5GaUStxTwhBTk6OZWViq8TERPr27RvW6S5S4ycgbJlbeno6ffv2DQnSEOjTYbUDi5Qy6srE3bt3s2jRIurr6/H7/cGeJ6+99hoQ+EJ57rnnOHLkSLCC8sSJEzz33HN4PJ52VSbquk5+fj45OTntCtIA11xzjWXXwcTExODadOXSU4FauaKZpmkZrDRNi7pfdUFBAf379w9WP7Y+YHvwwQejbqUaac/ENWvW4PV62bVrF01NTWEPAn0+H5s3bwbgvvvuIyEh4aIrE6Mxe/ZssrOzg5/VbrfjdDp55JFH2h30ldhRqY8Yk9Kg6cSfaTz2W5Am7l5fIqH3gwgtNr2RY0VKiaforzQcfRkMD668u0nsswChR9dyE0AaHhqOLsRzcjHobhL7LMCVe1fIf9De8nXUH3geo/Ekjm6zSRrwBLortqsHDhw4wOLFi6moqCA5OZnbb7+dsWPHAlBWVhYxbXBuzvXEiRMsW7aM0tJS+vfvz7XXXhusTNR1nccee4x9+/axZ88eEhMTmThxIpmZoQ379+zZw8cff0x9fT0jR45k9uzZwV7TF9ozsaKiwjK14PV6KSsrA6Bbt278+Mc/ZuPGjcHKxHHjxl1wv8X2cDqdPP300+zcuZMDBw6QkZHBxIkTo+67rXQMFahjSEpJ1fo7aS75AIzA2lxfzS48Z14nY8p7cTUjqdm6gKaiv306zto9eIr+SuaM1Qjtwv9aSNNPxapZ+Gp2gRF48FVdtRV36XLSCl4BoPHkYmq2fhmMRgD8dYU0Hf8jWXO3obtj07Ftx44dvPTSS8GfKysrWbhwIbfddhvXXnsteXl5F6xM3LVrFwsXLgypTNywYUOwMhECM/ChQ4cydOhQy4C+bnwAACAASURBVHG89957vPvuuyEVlOvXr+ff/u3fcLvd5OXlsXfv3rD36bpOSkoKeXl52Gy2sByx0+mkV69ewZ/dbjczZsxo/19UO+i6zpgxYxgzZkyH3keJnkp9xJCvchPekveDwQ8AoxFfxXq8ZSs6b2Dn8dfup6noL+eNswl/zV48Z9+K6hqeM//EX7M3GKQD12ig6eRi/HUHkKaP2u1fDwZpAEwv0ldN/b7/jNEngT//+c+Wx1uXrI0cOdKyMjE7O/uClYn//Oc/oxpDQ0MD77zzTlgFZU1NTbD39i233BI283U4HNx8883ous6AAQPo3r17yDh1XSc9PZ1Ro0ZFNQ7lyqUCdQx5y1cjTYtGRf56mstWdcKIrHnL1wDhs3tp1NNc8uGnP0tJc+kKGo68RHPpipAUQnPJh0jDYmNXoeEtW42//jBIi1UC0h/4jSNGIm0uaxgGNTU1wcrEadOmkZycTEpKCrNmzeLb3/52sDLR6hpSSvbv3x9yrKSkhFWrVrF58+aQoHzixAnLh5w+ny/YeS8vL48nn3ySwYMHk5CQQPfu3XnggQeYOXMmEEiBfOtb32Lq1Km43W6cTifjx4/nqaeeCuu/XVhYyMqVKzl06FCbq0GUK4dKfcSQ5swCzQHGecFac6M74+eJuebMQgidsP/ENQe6K7BziumtomLVLIyGo0jTj9Bs6Il9yZy+HM2RFnid5gAz9AGZEDqaqyuaI9PySytw/64x+yxCiIjBqjU/7Ha7ufPOO7nzzjvDXhNtZeLf//531qxZgxACTdNYtGgR3/jGN+jTpw/JycmW1YBCiGAFJUCvXr345je/GfF+u3btYu3atWiahpSSTZs2kZ+fH1ydUldXx89+9rOQysTu3bvzzW9+M+rtvJTLk5pRx5Crx20IET6zEpqOOzc8SHQWZ84NoIVvZiqEjYT8BwCo2fkE/rr9SH89mB6kvx5/7T5qdz4BgDv/ARAW3/OaE2f29eiurjiyZgSC+bn30BNIGvhkzD7L8OHDLY/n5ORE9ZDN6XQyevRoy8rE1r7Ze/bsYe3atcF9H1sLUlorE3v27ElmZmZYUYndbmf27NlRfY7a2lr++Mc/BisOvV4vfr+fJUuWBFuQ/vnPfw6rTCwqKoo6RaNcvlSgjiHNnkzGtA/QXN0RehLCloTm7EbG5KVozswLX+ASEZqDzOkfoyfkI/REhC0Z4cggfeJr6AmBB2yeor+HzZaRXpqK/g6ALbEX6ROXIBwZgffriegJvcmcvhzREpzTxy/GkTkJNBfClgKam6RBz+DqcWvMPsuXv/zlsA1m09LS+O53vxv1Ne677z4GDx6MzWbD6XRis9mYOXMmkydPBgJl1ZEqE48ePYoQgscff5wePXoEr+FwOLjrrrvo3bt3VGPYsWOH5cNm0zTZvHlzm5WJGzZsiPqzKpcnlfqIMUfGOLreeAJ/zS6QJra0UQgRf9+H9tRhZF1/GH/tHqThwZ42OnS1h1V++bzjruzr6Db/LL7q7QjdhS1lWEiw0RxpZE7/GH/DcUzPWWwpw9Ds4dtzfRY2m41nn32WkpISDhw4QH5+frv37LTb7eTk5LB//378fj9utzukWKStysTWJXVut5vs7GzOnDmDz+cjIyMj6j0ZW69jlcIxTTPiuVaXst+I0jniL4JcAYTQsKeNwp4+Ji6DdCshBPbU4TgyxoUtyXN2uxbOT+MIHWf2vNBDmg1HxjjsqcMjLj+0JebjyJwY8yB9rm7dujFt2rSL2lj59ddfZ+XKlfh8PgzDoL6+nsWLF7Nz504gUK0XqTKxb9++APzqV79i+/btGIaBaZqUl5fz4osvUlxcHNUYIu0SY7fbg6mZWOyZqFye4jeKKJ0qZfSLaI5M0BMDB/RENEcmqaP/r3MHFmM+n4+VK1daVg2+8847QGCThH79+oVVJj7wwAM4HA5KSko4evRoWMGK3+/nww8/JBpdunThhhtuwG63B4Oxw+Fg6tSpwS+fS1GZqMQnlfpQLNkSe5F13UGaTv4Ff80ubKkjcPe6G82WFHxNoDLxFTxFf0FoLhL6LsDV886QWV/DkZepL/wPTH81tpRhpBX8Bntq9NtXSSlpPvNPGo78CumrwdXzDhL6fgXNlhiTz9nY2BjxXGs1oa7rfP3rX6ewsJDdu3eTnJzMhAkTgqmNiooKbDZbWIrENE1KSkqiHssNN9zA8OHD2bRpE6ZpUlBQEJLjbq1M3LBhA2fPnqVXr14xr0xU4pMK1EpEmj2ZxL4LLM8FKhNnt1QmBoKdt2oL7pLlpBUsBKB626M0Hf20j7G/ajPlH44ic/YmHOnRVb3V7f5XGo68dE4F5V6aTvyJLrM3tqvcPZKkpCTsdrtlHvrcvR81TWPYsGGWeyR2797d8v2t6Yr2yM3NDbnv+dxud3DttXL1UKkP5aIEKhP3hFYeBisTD2L6G2k6+rLFOyXVmx+I6h5G0xkaDr8YVkFpNByj6eTizzT+VrquR6wavOWWW6K6RlpaGhMmTLDci3DWrFkxGadydVMzauWiRK5MFHjLViFcORBeUgOAUXcg5Gdf7T68ZavQXF1xZd+I0AO5YG/FOoRmR5qhXe6k0YCn+D0Sej8Uk88yffp0kpKSWLp0KVVVVeTl5fH5z3++XRvX3nPPPeTk5LB8+XKampoYMmQIt956a1jbVUW5GCpQKxclUMGoA+ctDZMSzZmFntjGXoLC2fJSk5otj9B06u+AQAidmpY13vbU4WiOrEh3R3d1j3Du4owdOzbYce9iaJrGnDlzmDNnTgxHpSgBKvWhXBR7xjWEBWkAswl7lynYU4cQCOQW782cAICn6C80nXot0NjJaET665DeCqrW3YKUEkfWNIQ9jbC+JLqDhD7WuXNFuRKpQK20yTS9+OsOYZ5XpRjoBmjxr4+egLfkA4zGkxChXapsDqyEaDj6Smj+ufWezWX4a/cghEbm9I/QkweAnoiwpSBsKaQV/AF7ami7USkNzOYKpBl5uyjTV4fpt27ipCjxLOrUhwg0sdgCnJZSzu+4ISnxwDRNqj65BW/xO8Fjjuz5pE96I9A0yPBglYMWaEizGWl6rRs/AbI16BvWO6xIBNII9I+2JfUj69q9+GsLkf66QBHRef1DGo78mrq930f6GxCancT+T5A05PvBYiN/3SGqNz+Ar2pr4HNkTiJ13O+xJfZCUS4H7ZlRPw7s66iBKPGleuPdIUEawFu8lOpN9wDg6vF5hJ4Q9j4p/YGmTIl90ZwWJdSaC3feFwGwd5lmfXOzGXvapz2YAxWUQ3FkTggL0o0n/0rdru8gvZVgNiP99dQffJ76/f8VuJS/nvIVU/BVbgTpA+nDW7GWihVTPv3CUJQ4F1WgFkL0BG4EftOxw1HiRfPpf1gfPxXYjNWRNR1X7h0IPZFADlkH3U3K8J+guwI7X6ddswhhSwItsN5Z2JKwJQ8gcUCgA5/ZeCLi/c3m6ApF6gt/iDTOK1oxGmk48DxSmniK/o40mgiZ/UsD6a/Dcya6TRIUpbNFm/r4OfBdIGKzBiHEAmABcFH9FpT2aS5dQcOhn2M2ncWZcwOJ/b+B5siI4R3C+yufe1wIQcqo/wNbMp6ivyE0B4n9vk5Cv8eCr3R0mUTWdQdoOv4njMYTOLKmBWbiLftH+usPWd5B6G6MxqKotusym05ZHpdGI9JoxN9wzDIPLo0mjIbjF7y+osSDCwZqIcR8oFRKuVUIMSPS66SUC4GFAAUFBWrbiQ7UcPhX1O5+Klhs4qvdQ+Px35M1d3vsgrWwB1IFVscJVCZWrp4TrEyUQF3hD/HXHyRt7KeFLrorm6RB1i1H7Wmj8dfsDDsujUZsyQOjGqYtdRi+yk1hxzVnF4SeiD19NMKWFOirfe7H0F3Y01QzI+XyEE3qYzLwOSHEceCvwCwhxKsdOiolIulvpG73v563F2EzZnMZDYdeiNl9Egc+ZXk8adC/AuA5+5Z1ZeKJRfjrDkZ1D6FH6NchQVhsbGAlefhP4fxcuZ5A8vDnEELgyrkJzd0TxDm5bc2JnjQQR9fomvorSme7YKCWUj4tpewppcwH7gKWSynv7fCRKZZ8NTvD248CmM14zr4TfvwipQz7IUlDfxzML6O5SBr6Y5KH/gCA5uIPIlcmlq+O6h7eynXWJ3Q3vprdUV3DmTWNzCnvYs+chLCnYksdSfr4RST0Cjz0FJqdLjPXkdBnAZqjC5qzKwl9HyVzxvK4bkGrKOdSlYmXGc2ZhbRKSRBIM8SK6a3Gc3oJQrMhhRshdDynl5DY76to9tS290x0RNcwX3fl4GeHxRm/9YqRCBxZU+kyc03E85ojjdTRL5A6Ona/cSjKpdSuKYWUcqVaQ925bEn9sKcMC9+vUE8gsf+3Ynaf2p1P4K8pDOR2jabAnok1hdTuiG7PxGgk9v9m+BI/YcOeOgJbUvu6zinKlUz97ncZSp/8Jva00aC7A9V6egIpw/4LZ7fY5Vybiv4G0mrPxL8BLXsmTvgbwp5+zp6J+WRO/yjYVOlCnN3mkDTsxwg9IbCnou7Gnjaa9ElvxOxzKMqVQLS1F9vFKigokFu2bIn5dZVQ/rpDmM1l2NJGxqyRfquzrzmw7OWBTs7tnwZwafrwVW0L7JmYOiLidlxtMf0N+Kt3oDm7Ykvuf/GDVpTLmBBiq5SywOqcylFfxmzJ/aGDApuePBCjrtDi+KCQn4Vmx5E5/jPdS7Ml4ugy+TNdQ1GuZCr1oViS3rJ2HVcUpeOoGbViyWwuv+BxaTTTeOy3NBUtDuyZ2GcBrp5fuKj0h6IokalArVjSk/pj1IcXruhJ/YBz90zc+WmFZOUmmkuXkzb212HvUxTl4qnUh2IpZcRzoLtDD+puUkb8DGitTNwdUpkojQaaTvw56spERVGiowJ1J5DSpLnkYxqOvoy3YiMdsfLms3J1v4mMia9jSxuLsKVgSxtLxsTXcXUPLKOPXJmohVQmStOP5+w7NB5dGHW1oaIooVTq4xIzPKVUrJqJ2XQKKU0EAlv6aDKnvIewhfd37kzO7GvJyr7W8pzuyr5gZaK//ggVK2cEttiSgZ1XXNk3kjZhMcKqDF5RFEtqRn2J1WxdgFF/uKXirxFpNOCr3EJd4Q87e2jtEqhMtAi2miNYmVi1/guYnmKkv65lX8QmPMXv0nj0lUs7WEW5zKlAfQlJo5nm4mUgz9vXz/TQePwPnTKmi2VLzG+pTEwLVCbaktDceWROC1QmGo0n8dcdIKyvtdFI4xH1sFFR2kOlPi4pk4gN+S/DbaFcOTfS7aZifFVbWyoTRwaX5kmjGSG0CHsmWu+VqCiKNTWjvoSE7saeXkBg66pzT9hwdr+5U8b0WQUqEydgTxsVsn5aT+qHcGSGv0Fz4c67+xKOUFEufypQX2KpBb9B2FM/bXavJ6K5skkZ8ZPOHViMCSFIH/9qYHOA1p7WehK2pH4x7fKnKFcDlfqIMSlNmk4upunY75AYJPT6Eu5e9yO0wF+1PWUIGZPfpmbntzAajmNPHU7K6P+LaS/peOHoMoWs6w7QePwPGI0ncGZNx9XztrCdxBVFaZvqnhdjVevvpLn4PWTrhqp6Is6saaRPfhshBM2lK6lcdxOYzSANEHaE7iZz1ifYUwZ37uAVRek0bXXPU6mPGPJWbqa5+N1PgzSA0YC3bDXeslUA1Gz/WqCaT7a0EJU+pL+Oul3f6YQRK4pyOVCBOoa8ZauQZvg2WdJooLlsBaa/AaP+iMU7Jd7yyFtJKYpydVOBOoY0Z2agWi/shBvdmRXYWdtq+yoIPGBUFEWxoAJ1DLl63Ga5s7UQGu7cuxCaDXevez9dBdFKTyCx3+OXaJSKolxuVKCOIc2eQsbUZWjObghbUqBiz9GF9MlvBXfVTh31c5zZ80BzBWbRmouEXveROEAtWVMUxZpanhdjjswJdJ1/Cl/VVpAm9oyCkAZEQneTMel1jMYi/A3HsCUPQnd17cQRK4oS71Sg7gBCaDgyxrX5Gj0hFz0h9xKNSFGUy5lKfSiKosQ5NaOOQ9LfSMOxV/AULUFzpJLQ92u4cm7s7GEpitJJLhiohRAuYDXgbHn9a1LKZzt6YFcraXgoXzEFf/3BQA9nwFu2hoT+3yBl2I86eXSKonSGaFIfzcAsKeVIYBRwnRBiQscO6+rVVPQXjPrDwSANgYKZhoP/g9F0thNHpihKZ7lgoJYBrZvj2Vv+xN8mf1cIz5mloSXoLYTmwFuxrhNGpChKZ4vqYaIQQhdC7ABKgQ+llBstXrNACLFFCLGlrKws1uO8aujuHMB6P8HWvQgVRbm6RBWopZSGlHIU0BO4RggxzOI1C6WUBVLKgqysrFiP86qR0OfLoJ9fhi4Q9lQcWdM6ZUyKonSudi3Pk1JWAyuA6zpmOIo9dTipY19pqWxMQeiJ6Il9yJz2oWV5uqIoV75oVn1kAT4pZbUQwg3MBX7a4SO7iiXkfRF3j1vxVW1B2JKxpY4I2eZKUZSrSzTrqHOAP4pAHbQG/F1KubRjh6UI3YWjy5TOHoaiKHHggoFaSrkLGH0JxqIoiqJYUElPRVGUOKcCtaIoSpxTgVpRFCXOqUCtKIoS51SgVhRFiXMqUCuKosQ5FagVRVHinArUiqIocU4FakVRlDinArWiKEqcU4FaURQlzqlArSiKEudUoFYURYlzKlAriqLEORWoFUVR4pwK1IqiKHFOBWpFUZQ4pwK1oihKnFOBWlEUJc6pQK0oihLnVKBWFEWJcypQK4qixDkVqBVFUeKcCtSKoihx7oKBWgiRK4RYIYQoFELsFUI8fikGpiiKogTYoniNH/i2lHKbECIZ2CqE+FBKWdjBY1MURVGIYkYtpTwrpdzW8s91wD6gR0cPLJ5JKWn0eTGl2aH3afL78JlGh95DUZT4F82MOkgIkQ+MBjZanFsALADIy8uLwdDi01vHdvLvm96hrKket83OgqFT+Oao2Wgidun+PRWn+c66f7C38iy60Lix1zD+c9KtpDhcMbuHoiiXj6ijixAiCfgH8E0pZe3556WUC6WUBVLKgqysrFiOMW6sOHWAb695jeLGWgxpUu9r5qU9q/nZtg9ido+zDTXc/t5CdlecwZQSn2nwzok93PvBb2N2D0VRLi9RBWohhJ1AkF4kpXy9Y4cUv/57+4c0Gb6QY01+H78t/ASv4Y/JPf68f0PYtXymwf6qYvZUnI7JPRRFubxEs+pDAL8F9kkp/6fjhxS/TtRVWh43pUlVc2NM7rG/uhivRV5aExrHaiticg9FUS4v0cyoJwP3AbOEEDta/tzQweOKS4PTsy2PO3Qbma7EmNxjTFYvXHr4owO/aUa8v6IoV7ZoVn2slVIKKeUIKeWolj/vXorBxZunxs7DpdtDjrltdp4YNQebpsfkHvcMGIfb5kBDBI+5dBtTuvejX1rXmNxDUZTLi6pMbIexXXux6NqHGJOVi9tmJz85k/+aeAuPDJ0Ss3ukuxJ596bHmNFzAA7NRrLdyUODJ7Nw5j0xu0er8qZ6frjpHZ7+5A0OVZfE/PoAhmmy+vQhlhzaypGasg65h6Jc6YSUMuYXLSgokFu2bIn5da8W/7dzBT/f+TE2oSGEQCB49doHGdu1V8zu8avdK/nPLctCjt2UP5yXYviFUFRXyW3vLaTW24QpJYY0uaHXMF6YdkdMlzMqypVACLFVSllgdU791xJntpSc4Bc7l9Ns+Gnwe6n3NVPn83D/h3+I2cqS4sbasCAN8Pbx3bx/Ym9M7gGwYMUiihtrqPc10+j30mz4WXZyL4sPbI7ZPRTlaqACtQW/aeDx+yKeN02TSk89fvPiA6fX8HK6vgrDCF3hsfjgJjwWAdmQJuvOHrno+53rxV0rIp77vzbOtceZ+moOVpdgnvcbW5Pfx5/2b4jJPRTlatGuysQrXaPPy/c3vsUbR3bglwaD0rP5yaRbGZP1aaXlDze+zW/3fRIMQBOz+/CXeQ9h06L7qzQMg5lv/g9Hz1lqN7FbH5bcsCAwBr8XSXg6SkqJx4j85dEejT5vxHNNbXxBtYfH8KELYXnu/LXoiqK0Tc2oz/Hwx3/izSM78Jp+TCkprDzLXct+w4m6QFD9xc7lvFK4LmSWuL74KF9475Wo7zHjjdAgDbC+5CgPffQnAKZ072f5vka/l0nZfdv7kSx9adDEiOdu7zcmJvfIT8kkxeEOO+7UbHyu94iY3ENRrhYqULc4UlPG5tITNJ+XzvAZfn67dx0QOWWwufQE9V7PBe/hNbwcq7MuWvmgKNCM8HR9FVbzUJumU1RvXXDTXiOzejKjx4Cw4z0S0/jy0KkxuYcmNH4x7U4SbHYcLUsXE2wOcpPT+fKwaTG5h6JcLa6q1EeDr5nFBzbx/slCMlyJPDRkEhOy+wBwvLYCu6aHpRd80uRAVWDpWltpgeN1lQzL7I7f9PPjzct47cg2pJTc1HsEP7zmJhw2G0X1NRcc44HqEovEBzh1G8dqKxiWGWhcuOjARl7YuZw6r4eCrr14bvLnyUlMC76+ytPAH/atZ83Zw+QmpfMvQ6cE3wvwpzkP8NDyP7Hi1AFMCUMzc3jtugVoWuy+uyfl9OXXM+7hP7e+R1lTPQVZvfjxxJtVcylFaaerZkbd4Gvmxrdf5KfbPmBDyTHePbGH+z74Pb8rDMyWB6Z3w2vxcNCh2RjdNReAZHvkANMvtQsAU//xPK8UrqWquZFqbxN/PrCRCa/9BNM0yU/KiPj+1ln0wDTr6sN6X3OwMvGJNUt46pM3ONNQQ52vmRWnDzJhyXMU1VUBUNZUx6w3f86Lu1eyqeQ4bxzZwa3v/Jp3j+8OXm/eW7/go6L9GFIikeypOMM1f/8JXn9sVpYAvHt8N19esYiDVaVUeBpYefogN7z9ImVNdTG7h6JcDa6aQP2Xg5s5VV8dMmNuMnz855Zl1Hk99ExK57q8oSGVhwKBy2bjwcGTAPjeuOstrz0vbwgum4M3Dm+nqL467HxpUz1/3L8BXddxCusKxtbZ8CfFkVd2lDXVUuVp5O+Ht4adM6TJt9cuAQLrsKubG2huWT1iImkyfDz1yRsYpsm6M4fZV1Ucdo1an4cXdn4c8f7tYZgmT33yBk2GD7Pld4Rm009VcwMv7loZk3soytXisgrU1c2N/PPoTpYe20W9r7ld7112cq/lqgm7prOjvAiAn0+7g0dHTCfLlYTbZmdO7iCWzn+UbgkpANwzcDz/Mf5zOFpWeGjAXX3H8tvZ9wPwxrEdEe+/9NguGn1e/JaJDahpaeq0p/xMxGv8es9a3juxJ+L5bWWBz/Hxqf34zPBNDZoNP8frKvibRaBv9e4566illGwvK2LJoa3sKCuiPcVRx2rLg18U5/KZJh8V7Yv6OoqiXEY56iWHtvL0+jewCR1EYAb50oy7mZM7OKr3pzsSLI83Gz7SnYFzdk3nW6Pm8K1Rcyxf2+j38oudy4MpEhP4x7EdPDR0CkMyc9pszJThTsSu62iA1Z4tiTYHAE6bjWavdfohJyGFHompEe/htgV+G0hzJlh2+jOkSYrDRZYrKeI1Wv8u6n3N3PP+b0Nm3oPTs1k872ES7c6I72+V4nBjWHxZnHsPRVGic1nMqE/UVfD0+jfwGH7q/c3U+5pp8vv4yorFVHkaorpGijN8qRiA1zQYGGWzo0c+/jNlnvqQYz7T4I5lCwG4pfeoiO+9sdcw7JqOHqF5k00PHG9rCd63R85hmsVqjVZTW5b2/cvQKcGgHby+0Cjo2ossdzLfGDkz4jWeHDMXgB9uWsruijM0+r3BP7srzvDDTe9EfO+5uiYkM7ZrHrbzSsUDu+LEZmWJolwtLotA/c+jOzEs9icUAt47GVrybEqTJr8v7Nf05UX7I17/tSOhKQvDNC1/bV939rDl+6u9TZxtqG4zpfDG0Z3Uez0Ri1bONgRWhBywyB23Wnx4MweqS3BGKK4pawp8idzceyQPDZ6EU7eRbHfh1u0Mz+zBSzPuBiDVmcALU+9AnLcQ8NHh05mUE/iieKNlPfm5vKafN45sjzi+8700826GZXbHrdtJtrtwajYeHjyZm9Q6akVpl8si9dHk91rmXP1+I7hkzpQm/7vjY36zdy0Nfi85Can84Jr5XJ8/DMAy8LaqbW4CAhV7/2/jW7zeUpk4OD2bn0z6PKOzclvuETlHG+hnETlv3uhrtlxV0qr1ylbl463qWn6TsOt62HpvCKRmAIQQPF1wPV8ZNo09lWfolpDCgLRuIa+d2XMg83IH80HRPiSSMVl53NH/034wvghjtdrUIJJMVxJLb3qMg9UllDTWMiyjO+kx6tutKFeTy2JGXZCVb3nch8nkllTBT7a+z693r6bO14wpJacbqvn66r+x9kxgFjy3jVx2a4B6ePmfeOPop5WJeyvPcueyVzjZku/tl2q9F6Rd0+mf1o37Bk6IeI+7Bowjw5UUlgpoleEMBLD5bcw2vzRoIsMyu6NZlGa7dDs39x4ZcizdlcjU7v3DgrQpTW5792U+Pn0As6VgfVtZEZ9b+iuqWx5q9kmx/qx9U7pEHF8kA9K6MbV7fxWkFeUiXRaBeumJ3RHPvX1sJx6/j98VfhLWQ8Jj+Hh++4cA/MfEm0m0hT8Ee2jwJNJdCRytKWNzyfGwmbfP8PPblrXWC2fda9m/4qeTbgVgdu4gRmX2DDs/ILUrt/UbgylN7BEKShLtgYeJT4+ZR4bFw7Zb+owkNzkdu6bzwtQ7cOt27C1BP8HmoH9aV+4bFPmL4lzrzx7lTEM1vnNmxxKJ1/Dz2uFtABG3FqvyxmbLMUVRondZpD7aampfWFVMpafBsuwaAhWHACkOFzu/+D1+uvUD3j+5lzRnAt8ePZfZuYMAOFZbgV2zhaUefNJkf2Ugb9w/rRtb7/wez258m22lJ8lNTuf/XTOfYZndg69f+rnH+NO+9fx+33pMaXLvwPE8PGQyAI1+3K9HSgAACaRJREFUn2UKB6Ci5aGow2Zjyx3P8L87P+atYztJtDn55qjZ3NCSwgGYmzeED2/5JosObKK0qZYZPQZyY/4wHBZbeFk5VlthmcZpMnwcbPm7rjjvoWmr8qboHt5Gq87r4dUDG/m4aD/ZCSk8NHRySBMsRVEuk0A9sktPdpSfsjxX0LUXXdxJ6Jpmue5tcEZO8J9dNgfPjp/Ps+Pnh70ucmWizqiWHDVAF3cSv5zxxTbHe//gidw/OLzxUaLNQYLdQa1FX5CeSemf3tNm46mx83hq7LyI98hPyYxYgHMhgzOyERa/GSTYHIzsEviNIC85w3KJX15yetixi1XT3MR1b/2CsqZ6PIYPgeD9k4X8aMLN3DnAsn+6olyVLovUx1Njr0O3yO06NRtfHjYFh27jGyNmhS1Jc+t2vtOy3OxCeialByoMz69M1O08NGTSZ/sArdcTImzPxVbJUaxNjpUxWXkMycjBec4MXBeCZIeLW/oElhh+r+D6sLG6dDvfK4jdvsa/K1xHaVNdcCWMbKmg/H8b32qzH7iiXG3iKlDvrTjDa4e3srX0RMjyuhSHiw9vfpxeyZ/2yhiQ1pW1tz8Z7AP91eHT+PfxN5GblI5TtzG6Sy6L5j3crl+jX5h2J18bPo0uriTcup3ZPQey9KZPKxM/q0afN5jiON/+NpblxZoQgsXXPsy9A8eT6nCTaHMwP38E79z0WLCY5Yb84fxqxhcZkNYVp25jQFpXfjXjiyEpmM/q/aJCy9U4QggKq87G7D6KcrmLiz0Tm/w+HvzoD2wtO4mGQAJ9U7vw13n/QmqEQpXLkc80GPjnZy1TLNkJKWy585lOGFXnuef937LqzKGw427dzvs3P06f1PavMFGUy1Xc75n4/LYP2Fx6gia/j4aWKrj9VSU8s/7Nzh5aTNk1nVv6jAxJOUAgMD0wODbplcvJwxYVlLrQ6JuapYK0opwjLgL13w5tCV8WZxq8e2IP/nYUWESr2fBjWlQ6Xgo/mnAzE7J749JtpNhdOHUb83sP56tXYTP9WT0H8viIWS0VlE4SbA76pWbxuzlf6uyhKUpcueCqDyHE74D5QKmUMnYJynNYVdlBoIlQW9WA7bW19CRPr3+D/VXF2DWd2/uO4Qfj5+NuaYh0KSTYHSy69mGO1ZZzsq6SAWndyGmj0dKV7rGRM7l30Hh2lZ+mizuRwek5litSFOVqFs2M+g/AdR05iJk9BqKdtxJaAKO75Ea9NvhCjtWW88X3f0Nh5VlMKWk2/Lx2ZBtfWbEoJtdvr94pXZjeY8BVHaRbpTkTmNajP0MyuqsgrSgWLhiopZSrgdhs1hfBs9fMJ92VgLtlOZhLt5Fkd/Hc5Ntido9X9qwNS680G37Wnj0S3LxWURQlHsWs4EUIsQBYAJCX177Ksh5Jaaz+/JMsObyV7WVFDMrI5ov9x9HFHblvcnvtryq27MDn1Gwcr62gV3JmzO6lKIoSSzEL1FLKhcBCCCzPa+/7U51uHvn/7d1vaFV1HMfx94dNm65w0ezPVNQH5gPDykBmLZVWginzQQYLomYM+iNF9SDqSUGPI6KCRJSozGWNjCUaBj1uMJ2VZQ8sTWea00jpD9Xg24NzNq9n91947z2/c/2+YOzc8/tx9uHL5XvP+e3eexZ1VCrOJLfMnMPwmeMXfb8FROvjCxJfWuSccyEJ4l0ftdC7qIOmhsaLVsKbGqawZu5NtPk6sXMuYJdNo25rnsHA2o2smHUjTQ1TaG26ko2LV/DqnfenHc0554oq5+15fcBKoFXSCPCSmW2tdrBqWNByLdtWPZJ2DOec+19KNmozK/5Vcc4556rqsln6cM65rPJG7ZxzgfNG7ZxzgfNG7ZxzgfNG7ZxzgavKjQMkjQI/VfzA5WsFzqT498vlOSsvK1k9Z2XVQ865ZjYz30BVGnXaJA0VulNCSDxn5WUlq+esrHrP6UsfzjkXOG/UzjkXuHpt1JvTDlAmz1l5WcnqOSurrnPW5Rq1c87Vk3o9o3bOubrhjdo55wKX+UYtqUHSsKRdecZ6JI1KOhD/9KaU8aikb+IMQ3nGJel1SYclfS1pSaA5V0o6l1PPF1PK2SKpX9L3kg5JWpYYD6WepXKGUs+FORkOSDov6enEnNRrWmbOUGr6jKRvJR2U1CepKTF+haQdcT0HJc0rekAzy/QP8CywHdiVZ6wHeDOAjEeB1iLj9wJ7iG6+3g4MBppzZb46p5DzHaA33p4KtARaz1I5g6hnIlMDcIrowxfB1bSMnKnXFJgFHAGmxY8/BHoSc54ANsXb3cCOYsfM9Bm1pNnAGmBL2lku0TrgXYt8CbRIuiHtUCGSNANYDmwFMLN/zOy3xLTU61lmzhB1Aj+YWfKTxanXNKFQzlA0AtMkNQLTgZ8T4+uIXsgB+oFOSaKATDdq4DXgOWDy7cUvuC++VOuXNKdGuZIM2CtpX3y39qRZwPGcxyPxvlorlRNgmaSvJO2RtKiW4WLzgVHg7XjJa4uk5sScEOpZTk5Iv55J3UBfnv0h1DRXoZyQck3N7ATwCnAMOAmcM7O9iWkT9TSzMeAccE2hY2a2UUtaC5w2s31Fpn0KzDOzxcDnXHgFq7UOM1sCrAY2SlqeUo5SSuXcT3SpeTPwBvBJrQMSnaksAd4ys1uBP4DnU8hRSjk5Q6jnBElTgS7gozRzlFIiZ+o1lXQ10RnzfKANaJb04KUcM7ONGrgD6JJ0FPgAuEvSttwJZnbWzP6OH24BbqttxIkcJ+Lfp4GdwNLElBNA7tn+7HhfTZXKaWbnzez3eHs3MEVSa41jjgAjZjYYP+4naoi5QqhnyZyB1DPXamC/mf2SZyyEmo4rmDOQmt4NHDGzUTP7F/gYuD0xZ6Ke8fLIDOBsoQNmtlGb2QtmNtvM5hFdBn1hZhe9aiXW0LqAQzWMOJ6hWdJV49vAKuBgYtoA8FD8n/V2okulk6HllHT9+DqapKVEz5+CT65qMLNTwHFJC+NdncB3iWmp17OcnCHUM+EBCi8npF7THAVzBlLTY0C7pOlxlk4m954B4OF4ez1R/yr46cOSN7fNGkkvA0NmNgA8JakLGAN+JXoXSK1dB+yMnzuNwHYz+0zSYwBmtgnYTfRf9cPAn8CGQHOuBx6XNAb8BXQXe3JV0ZPA+/El8I/AhgDrWU7OUOo5/uJ8D/Bozr7galpGztRramaDkvqJlmHGgGFgc6I3bQXek3SYqDd1Fzumf4TcOecCl9mlD+ecu1x4o3bOucB5o3bOucB5o3bOucB5o3bOucB5o3bOucB5o3bOucD9ByNlVGXSX3meAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,2],c=kmeans_v2.labels_,cmap='Dark2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f68a1dae8d0>"
      ]
     },
     "execution_count": 73,
     "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": [
    "three_classes = []\n",
    "\n",
    "for i in kmeans_v2.labels_:\n",
    "    if i == 3:\n",
    "        three_classes.append(2)\n",
    "    else:\n",
    "        three_classes.append(i)\n",
    "        \n",
    "plt.scatter(X[:,0],Y,c=three_classes,cmap='Dark2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f68a1cfe278>"
      ]
     },
     "execution_count": 76,
     "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.scatter(X[:,0],X[:,2],c=three_classes,cmap='Dark2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[50  0  0]\n",
      " [ 0 32 18]\n",
      " [ 0  0 50]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        50\n",
      "           1       1.00      0.64      0.78        50\n",
      "           2       0.74      1.00      0.85        50\n",
      "\n",
      "    accuracy                           0.88       150\n",
      "   macro avg       0.91      0.88      0.88       150\n",
      "weighted avg       0.91      0.88      0.88       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "Y_new = np.concatenate([np.repeat(0,50),np.repeat(2,50),np.repeat(1,50)])\n",
    "\n",
    "print(confusion_matrix(Y_new,three_classes))\n",
    "print(classification_report(Y_new,three_classes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hierarchical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "\n",
    "species = []\n",
    "\n",
    "for i in iris.target:\n",
    "    if i == 0:\n",
    "        species.append('Setosa')\n",
    "    elif i == 1:\n",
    "        species.append('Versicolor')\n",
    "    elif i == 2:\n",
    "        species.append('Virginica')\n",
    "\n",
    "merge = linkage(X, method='centroid')\n",
    "\n",
    "plt.subplots(figsize=(20,15))\n",
    "dendrogram(merge,labels=species,leaf_rotation=90,leaf_font_size=8,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TSne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "model_tsne = TSNE(learning_rate=100)\n",
    "\n",
    "fit_model = model_tsne.fit_transform(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f68a3b89710>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cIp6/+g12rq+djZwjOtgphEgDegGLQpy7RQixVAixNCMj/ECEoihKZQ0dNZjkFkkBCbVsUVauefISYhNjQt7TpE0KL/7xJC06NAs5l1zX9JBJtyw2M3s3H17NGZMQzdi/RhMdH4XBaAi4zmQyUvaLgtft45f3p1fxDSsnYtMPhRDRwBzgOSnlD+Vdq6YfKooSKcUFTn59/w/+/n4RcckxXHT3MPqc05O8zHymjp/FjrW76NS3PUOuGxgwW0RKyXlRVwfkMi+PxWbmy23vktg0IeD4/u0H+fypyaz4cw1xyTH0OKMbf3z+V1A+dIBTLuzDmJ8eqva7hpt+GJFZK0IIM/A98HVFQVxRFCUSigucfPjAF8ya8Dc+r0afc3ty51s3kto6hR3rdnPvaU/g9fjwOD3M/3Ex3zz/A+8sfpHUVsmAv3ukRbum7FhXcXeH1W7htBH9goI4QLO2TXjki7tKf87cm8VvH80MLsNhpfc5PWrwxuHVuGtF+Of3jAc2SClfq3mVFEVRyiel5MGzxjDji9m4itz4PD4WTVnGHX0fpbjAyWs3f0BRXnHpYhxXsZu8zAI+fOCLgHJGvXB1hYm07DE2Lrp7GA98cnul6hZu44mUlomcfd3Aqr1oJUWij/xU4BpgsBBiZcmfYREoV1EUJaR1Czaxa/2egGRWui5xFrr447O/2LAweK9NXdOZ/9Nidm3cW3qs//kn8diEe8OmuLXH2Hhy0n3c9MLVYQdIQ7l17HU88MkddD+tM22Pb82Vj43gncUvYqtEKt3qqHHXipRyHiGHDBRFUWrHznW7Q047dBe7WTZjVdj7NK/G7b0f4uEv7ub0Ef0Af7/1yRf2Zs6k+UEDlEiIio+qcv2EEJxx6cmccenJVb63OtQSfUVRGpxWnVuEXFlpdVjJOZBb7r3uYg9jR72H74gNlIfffk7QVnJCQGxSDF36dahUnTYt2cKkl39m6vg/KcorqtQ9kaKW6CuK0uAcf3oXmrdvys71e0pXaQqD8Oc1ibFXeL/UJVtWbKdzX3+Q7n5aF258/irGP/I1JosJqUtik2J48Y8nyk3ABaBpGs9f9SaLf1+O1+3DbDXxwX2f88Sk/7Lm7w0s+m05CU3iGPnf84Pyo0eKyn6oKEqDVJhbxLv3fMLsSQvQNZ1eg7tz93s3s3jqCj5+5KvQucBL2BxW3ln8Am26tgoqc/0//xKdEEWXfh3KDeJejxchBLMnLeDN28bhKgpcRCQMApPZWNqPb4uycu3oy7j0/gur/c7hph+qQB5BUkqk80co+gj0bLD0QcTcjzC1re+qKUqjdSiGHQq6ziIXd/Z9hIM7MnA7g4O5EIIWHZrxyYY3Kmxth7J/20Feu/l9Vs/dgBCCqDg7+VmFlbrXarcw+cDHlfrWEEqtziNX/GThW1D0CeD0H3DPRHoWQNLPCFOrcu9VFKV6ygZje5SNdxa9wG/jZvL39wvJOZhLxu6skl1+BFGxdp759ZFqBXFnoZO7T36MvKyC0tWflQ3i4M/BsnXlDo4/vUuVn10eFcgjROqFUPQxcOTXKx2kE1n0ITiuAuFAmNLqqYaKcuywR9u55L4LuOS+CwBI353JuvmbiEuJpcfArhiN1ctEOHvSAlzF7pBL+CtD82nEp8ZW697yqEAeKdp2EGaQ7rInwPkt0vkD4PMnSjOfiIh7FmEKndRHUZTISm2VTOoVyTUuZ/emvUF94eD/VmA0G/B5NX++cSmRELD832gyktatFa06RT6noArkkWJoCjLc4IrEn+23hHc5MusySJ6KMDapi9oF18i7AVk8EfQMhHUw2C9AiNpZrKAojUX7nm2xR9twFgbmUbFFWblm9KUU5zuJS45l0BWnMv+nxbx/3+cYDAKfx0e7nmmM+bH6eVbKowY7I0jPuRPccwjsXgnHBFGjMMTcX9vVCqIX/wz5TwIeQAdhB2MbRNIkhKjeIIyiHAs8bi+jutxLxp4sNJ8/n7nJYqJN15a8t/SloLntHpeH7Wt3E5sUTbO2NW+0qT0764CIfwVsQwELYC35E44PvGtLf5K+7cji75Cuv5CyctnYqkNKFxT8D3ABeslBJ/h2IIu/rbXnKkpDs2XFdp678nVu7fUAb93xEQd2pGOxmnlr4fMMvuo0HDF2ouOjGDpqMGNnjwm5QMlis9Cpd7uIBPHyqBZ5LZDSCXoh0jUXCkYTuoVuAsd1iJiHkPlPgPMXwADCAMKGSPyyVvrQpWcJMudWkCFG2s0nYUj6JuLPVJSGZskfKxkz8lU8Lg9SlxhNRqwOC28vfIHWnetv3xzVIq9DQtgRxhSE43wwtSZkKhphRURdC64p4JyCP9g7QRaBno3MuS3snoA1q1wUpS3xoHORH01XlIZGSslbt32E+4jZKZpPw1ng4uNHvqrn2oWmAnktEsKKSJwMUXeCiMcf0AWYeiISv0YYmyGLJ1A677yUBC0dtK2Rr5SpCxiSCfpwEXZE1NWRf56iNAB7/t3HqjnrKMwtoiCnkMx92UHXSClZ8ecaRo94mTv6PMwnj08gNyOvHmobTM1aqWXCEIWIuQti7ippYUuEOOLzM2i6YumN4c/VpD5CQMJHyOzrQBaU1MELjlEI64CIP09RjmZ5mfk8eeFLbFu1A5PFhNft5dIHLgy7WMhV7Gb+T0sA2LpqJ1PHz2LcqldJaBJfl9UOolrkdUgIERjEAWznAbYQV5vB1Kl26mFqi0iZjUj4EBH3IiJlNoaYu2vlWYpyNHv28tf5d9lW3E6PfyMKl5fvX59Ct1M7YbGZg284ordT82nkpucx7qEv667CYagWeT0TUVcjXb+BbxtQDJgBEyL+VYSovf97hDCApW+tla8oR7us/TmsW7AJzasFHHcVuXEVuelzbi+WTFuB2WrG7fSgazq6Fjy+9Nc387BH2Zj34yIsNgsj7j2Pi+8eVq0UANWlAnk9E8IGSZPANR3pmQeGJgj7JQhTy/qumqI0aoU5hSXZCYOn+xZkF/L2P8+TuTeL9N1Z2GNs3HJC6DUfmk/n1w+ml/78/n8/Y/7Pixk7a0yt1b0sFciPAkKYwX4ewn5ehdd6NI0Ct5sEux1DHX7iK0pDsXT6Kia9/BOZe7M58czjufLRi0lukRR0XYsOzTCagnOumMxG+g7z5w1PbpFUeq/Vbik3Ne6RVs9ez7/LttLxpHY1eJPKU33ktUwvfA/9YG/0A13RMy5A96yrVjmarvPivLn0+vAdTv10HH0+eo/J69ZEuLaK0rBN+XA6o0e8wspZa9mzaR+/jZvJLT0eIGNPVtC1JrOJu94dhdVhKe0GMVvNxCRGc+UjFwddf+n94QdBQ5n68azqv0gVqUBei/ScO6HwDZD5gA+0TZA9At2zvsplvbzgb75cvQKnz4dH08hxuRg9ZxYztm6JfMUVpQHyuDyMe/BL3MWHZ3tpPo3ifCffvPBDyHsGX3k6r/w5mgGX9qdzvw5c9tBwPlrzWshZKFc+ejEde1e+hR2XElP1l6imiHStCCE+Ac4H0qWU3SNRZkOn6zngnh7ijIT8RyH550qX5dE0vlq9EqfPF3Dc5fPx5qIFnN1OZVFUlL2b94dce6f5NFb8Gf7ba5d+HXhi4n0Vlm+xWeh+Wie2rNhemmclLAGXPHB4J6Cl01fx2ZMT2bf1AK27tODG567ihAFdK3xmZUWqRf4ZcG6Eymoc3H+HP+erWis6z+1CD7PKc19hQZXKUpTGKi4lFp8ndIBNap5Y+t+6rrPyr7VM+XAGa+dvrNIK6nk/LK4wiAsheOizO4mOdQAw/6fFjB7xMpuWbKEgu5B18zfx2NDnWF7Oh0tVRaRFLqWcK4RIi0RZjYYxLfw5EVWlohJtduxmM24t+C9QqiMaKWWdTnVSlKNRYtMEeg7uzspZa0r3yQSwOqxc/tBwAPKzCrhv4FOk78xA13WEEKR1b83LM57EHl1x5k+rI3wiPIPJwJlXnc69H9ziz0le4oP7Pw8aJHU7PYx74As+WPFKVV8z9LMjUkolCCFuEUIsFUIszcjIqKvH1huD5QQQcaFPRt1cpbKMBgMPnXI6thC7muzMy+WhmdOqU0VFaXQen3APPQd1x2Iz44ixY4u2cfNLV5fuXv/WHR+z9999OAtduIs9uIrcbF25g48fnVCp8i+84xxsYYK57tOZ8+0/5GUe/pas+TQO7kgPef3O9bur+Hbh1Vkgl1KOk1L2llL2TklJqavH1q+kn0pyrBzBegGG6KoFcoArup/Arb37BXUBujUfv23+lw2Zjf/DUVEqEhUXxfO/P84XW9/ltblP8336eIbfMRQATdOY9+MifGUWAHndXmZ+OadSXSzn33o2p1zUF6M59FZxUteZPXF+6c8Go4HohOiQ1yY0Tajsa1VIzVqpRQZTCwxNFkPiTxD3BqQux5AwttrleTQfof6qabrOgt27ql9RRWlkkpol0K5HWkAXB5Kwe20W5zu5pt0dLJuxqtxyjUYjj351N5fedwFGY3D49Hk0ivKLS38WQnD5wxcFteJtDiv/9+TIKrxR+VQgrwMGS1cM9mEYDKE/mSsrwWbHGqJ7xWw0Em8Lla9FUZRDjCYjxw/oEnY86eCODJ66+GW2r9lZYVln/t8AjJbgIUarw0LfYScGHLvsgQu5/OHh2GNsWGxmouIcXDvmMoaOOrN6LxJCRAK5EOIb4B+gkxBijxBiVCTKVQJd2KlzyL+EAjinXYe6r5CiNDD3fnAr0YlRWOwhEmIBXrePb8f+WmE5ad1aMeymM7FFHW5p26KsnDaiH136Bf5bFELwf09eyg+Zn/LV9vf4PuOTKi8uqojaIaiBmbtzB3dNnYIs2aXbajTywfnD6d28/nYtUZSGpDC3iM+enMhvH80IOV2xc78OvP3P8xWWcyg/+fQv5qD5NM66+nT6DjuxVmeQhdshSAXyBsijaaw8sB+DEPRs2gxTiL0CFUUJLzcjj6va3IbXFZgwy2QxcdFdQ7n1lWvrqWblU1u9NSIWo5G+LVrSu3kLFcQVpRriU+I47+azAuaFGwwCm8PKyHsrTl53tFHZD+uI1IvANRWp7UaYu4F1cK3mG1cUpXy3vX49LTs154fXp1CYW0TPwccz6vmrQmZKPETzaSz4eQnLZqwmqUUC51w/iNRWyXVY69BU10odkL7tyKzLS7Zuc4JwgKE5ImkSwlB3iXUURak+j8vD/YNGs2PtblxFLswWEwaTgdE/PETvIT3qpA6qa6UeybwHQeZRusmyLAZtJ7LwrXqtV2UVejykFxVWKSeFojQ2v42bwfY1O3EVuQDweny4iz28cPWbaCHSZ9Ql9d2+lkm9ALzrIWgpjxdcUyD28Vqvw668XPbm59MxKZkkh6PS9xV6PDwy8w9mbtuKEJBgt/P84CEMTGtbi7VVlKPTzK/+DrmxhNfjY8uKHXSqQorbSFOBvNaVNxWpdr8QFXo83Pbbzyzdtw+L0YhH83HV8T144vSBlZoiddtvP7Nk3148ur+1caCwkDt+/4XvLr2SLimptVp3RalPmk9j66odWGwW2nRtiRAi9GbM+JflhztXV1TXSi0Thmgw9yT4V20B+0W1+uxHZv7Bkn17cWs+Cjxu3JrGxLWr+Wbt6grv3ZWXy9J9+/CU+cro1jTGLT92xjeUY8+SP1ZyWbObeWDwaO7q/yjXd7ybnet3c8F/hgQsADokoUk8ad1a1UNND1OBvA6IuJfBkFSSvtbkH+w0dURE3VFrzyzyeJi5bWtQIHb6fIxfsSzsfbN3bOesLz9l4Ofj8Wi+oPO6lOzIzYl4fRXlaHBwZwZjRr5CflYBzgIXriI3+7cd4IHBozltZD/OuPRkLHYLVocVe4yduOQYnv754XpPI626VuqAMLWElL/A/Sdoe8HUBSwnI0TtfY4We72E+7uV53aFPL5g9y5u//0XXCU7EYUa2jQbDPRr0TJCtVSUo8u0T2eh+fSAY1KCx+ll2fTVPPDJHVz20EWsmbue+NQ4+g7rhdlSv90qoAJ5nRHCArahdfa8ZIeDRLuD/WV2EDIgOLVVGzZlZfLJimXsyM2hX4uWXNvjRF77Z15pEA/FgMBhtnBjr5Nqu/qKUi+y9uXg84T4Jqrr5BzMA6B15xa07nx0pcRQgb+Xy90AACAASURBVLyREkLwwplD+M9vP+PRNHQpMRsMOMwWBrROY8Skr/FoGpqUrDp4gAlrVuPV9dBlAfE2G6e3TuP+k08jNapmWRwV5Wh10tk9+GvifFyFgd9adV1ywoAu9VSriqlAXsuklKzNSGdbTjYdk5Lpklx3m2oMaJPG95ddxcfLl7I9J5u+LVpyQ88TuXjyhICNnD2aRp7uCpsK12G2sHDUfzCHSKGrKI3JqRf14dtXW7Bj7W7cTv9UQ1uUlYGXn0rLjs3ruXbhqUBeiwrcbq7/+Xs2ZmZiEKBJyYlNm/PxhRdhM9VNv1qX5BTGDjncpXOgsIAcpzPoOk1KfLrEZjIFdK/YTSZu791XBXHlmGAymxg7ewxTPpjOnxPmYbVbuOA/Qxh05Wn1XbVyqUBei56eM4t1GekBM0eW7d/Lqwvm8cSAQUHX18UmylFmC2E2SSHZ4eCx08/g+b/nsC0nmySHg9t79+O6Hr1qtU6KcjSx2q2M/O8FjPzvBfVdlUpTgbyapJTgWQi+TWBsA9YBCGEMOP/r5k0h52F/u35dQCBfffAAT83+k9UHD+Awm7mi+wk8eMrpWGqhFRxjtXJGWhpzdmwP6BO3m0zc2PNEBqUdx6C04yL+XEVRao8K5NUg9UJk9v+BtgOkD4TZP088cSLC6M+EpkuJL8zg4aH52VLLICf7M/bunsVJcSlsz+5Mvhe+XrOKA4UFvD20dloEr559Ljf/+hNr0g9iNhjwaBqXdu3OFd1PqJXnKYpSu1QgrwZZMBZ8m4GSpPTSA5obmf8EIuEDAIwGAyc1a87SfXsD5mMbhOC01mlI7wZk9tVEaS7OaeljQNMd3NJ5JRfPHMH+4hhmbtvK/oICmsVUPTtiocfDC/Pm8NPGDXh1jdNatWH0wMG0josHINZqY9IlV7AtJ5u9+fl0TkkhxREVVE6uy8m6jHSaREXTPjF8ak9FUeqXWtlZHa5fKA3ipXzgnoOUhwcKnxl0FtEWa+mGyTaTiVirlScHDETmPQGyELPBf73dpJFgcfHICQsB/+YR1VlBKaXkup++4/v163D6vPh0nbm7dnDxpAnkl1kIdFxCIqe3SQsK4lJKXvtnPieP/5Dbf/uF4RO/4qJJX5NVXIyiKEcfFcirJXSXiX8t5OFzHZOS+fPaG7mjT3+Gtu/IPf1OYda1N9IyRoBvbdDdRgMMbLYL8Pelt01IqHLNVh88wMbMzNJEV+Dv5nH6vHy7PviZoUzd8i/jVyzDrWkUeDw4fT7WZ6Rz19QpVa6Poii1T3WtVIf1LHD9Bhy5AswAlj7+FZxHSHY4uLNv/9KfpXcDMv0aQi+AB5dmwmY0MaRde5pGV71bZXN2VuhyfT7WZaRXqoyPly/D6Qv8xuHTdVYc2Ed6UaFaEKQoR5mItMiFEOcKITYJIbYIIR6JRJlHMxHzCBhS/cmvAHCAiEfEPlfufVJKZM5tQH7I806fkZ92dufGXifyytnnVqtu7RISQ+ZYsZlMdE2uXOrZHFfwPHPw9/vnudzVqpeiKLWnxi1y4Z9z9y5wNrAHWCKE+EVKub6mZdcWqe1H5r8Injn408mORMTcgxChVzaWJYxJkPIHuKYhvesRpuPAdj7CEDxgGMC3CWRuuFKxO07hpgHvB7Xqq6Jn02Z0SExiQ2ZG6dRHgxDYjCYu6dot4NpsZzF/bt+GpusMSjuOJtHRLN67B68eercTi8FYre4eRVFqVyS6VvoCW6SU2wCEEBOB4cBRGcilXoDMGgF6Dv7+7GIo/grpW4dI/KLS5QhhBftwhH14FZ7uI+xGE8b2GBLHV6GscPUSfHHRJTwz9y9+/XcjXl3n+NQmPDVgMPE2e+l1U/7dxIMzpmE0CKSUjJkziws6dmbK5k1BibMEYDWZeHbwWZgMalhFUY42kfhX2QLYfcTPe0qOBRBC3CKEWCqEWJqRkRGBx1aPdP4AejGBA5Zu8KxCetfV7sNNXYDgxPRgA8cVEXtMjNXKC2cO4ZKu3TEKwdacbK78YRIPzpiGV9PILC7mwRlTcWs+ir1enD4fbk3juw3rQmY/bBodzaRLrmBYh05hn6n281SU+lNng51SynHAOIDevXvX37967xpKN0E+khD+rg9zt+BzESKEEeJfR+b+B6QGeAAHmLsgHJdH9FkfLFvMDxvW4dV1vB5/8p/fNm8ixRFFq7g4DFVIBeDR/K36soq9Xp77ezY/blyPR9Po16IlTw88k3Zqzrmi1KlItMj3Akfuc9Sy5NjRydSB0K1iwJhW648X1pMRyTMg+h5wXIeIfx2R+FWN+sVD+XTl8oAMh+CfufLl6hV4NY2qNKBTwmzYfOPP3/N9SStel5KFe3Yz8ttvyFTzzRWlTkUikC8BOggh2gp/NLoC+CUC5dYK4bgUhIXAvmqzP1+KuW6SQwljKobomzHEPo6wDQrI0RIp+e7Qs0uKvV4GtmmLDDH90SgE5jJ94HaTidv69Au6dn1GOmvSDwbkkpGA2+dj4tpVNau8oihVUuNALv1LGe8E/gA2AJOllLXc2Vx9wpCISJx4xIbIZrCejUj8ImKZB6Weg174Pnr2LegFLyO1uv+C0qNJ05DHOyUl0zo+nnv7n4LNZMIoBAJ/wL76+B4Madcei9FItNmC3WTijj79uaBj56BytuZkYwixVZ1b01hfj2MginIsikgfuZTyd+D3SJRVW6T0IAvfhuJJIF1gPRmSpiBMaQgRuaECqe1DZl4Mshj/IOp8ZPEESPgczCeA+w9k0aegZ4N1ICLq1tJEW5H05IBBXPn9pNJdgAxCYDEaGT3wTABuPakvZ7Rpy6//bsSn6Qzr0JEeTZsB/mmJB4uKSIuLx+nz8sD0qfyxdTNCCM7r0IlHTxtA+4REdBm8wtVqNNItpXLz1RVFiQxRH7MNevfuLZcuXVqnz9Rz/gPuBcChfCMGEDGI5Gn+eeGRek7ufeD6naBl/KYOYD0HisZzeLDVDIZ4RPIUhCHy87O3ZGfx/tLFrEs/SKfkFG7r3ZfOVdihyKNpDPnqU/YXFJSmvDUbDKTFJ/D7Vdfyfz9+x4oD+0q7VwQQa7Uy85obSQrTr64oSvUJIZZJKXuXPX5MLNGXvm3gng8c2W+sg3Qhnd8gou+M3MPccwmZi8W3HXzj8M9UOcQLeh6y6CtEzF2Rq0OJ9olJAbsDVdWMrVvIKi4OyFvu1XX2FeQzd9cOPrnwYl6cP5fvN6zDo2n0b9mK0WcMVkFcUepYow/kUkqkZ60/Z7gsOwDoBs/qyD5Q2EGGXoIPFgIDOf6fPX8DkQ/kNSGlZP7unRR5y2Z59A+Yrj5wgEFpxzFm4JmMKemuURSlfjTaZXpSSvSiL5DpfSH/IZCFIa6ygDn8IpdqcVwFlF3qbwZLfwKTbB0iwBi0fqrUwcJCJq5dzeR1a8h21s20voOFhQyb8AXfbwi9OFcCn65azoHCgjqpj6Io5Wu0LXJZ/A0UjgUZOgEUAMKMcFwd0eeKqJuQ3g3g/qvkW4DPv3uQXgzCCtILHJnLxIqIuiFkWZ+vWs6L8+ZiEAKB4KnZf/Ly2eeGnEVSGV5NY9rWzfy8cT178gtoHhPNFd1P4Ozj2gfM2Ln1t5/Zkp2FVs74SZHHwwvz5nJR5y48O3c2u/LyiLfZuP/kU7iie49q1U9RlOpptIOdevopoGeGOWsAc3dE7NMIc9daeb707UR610Dhm6AdJGCQFeEP6pghdjQG+3lB92/Lyeb8b74MWjJvNRqZd8MtVe6Hzne7GDn5G3bk5gQEaJvJxPkdOvFySbbF3Xl5nPP1ZyGX6pdlNZrQpB60pd21J/QsnR2jKErkhBvsbJRdK1LKcoO4aLIaQ9J3tRbEAYSpDeiFoKVzOIiDfyDUCAnjEakLQgZxwJ/wSgvOQmgQghnbtlS5Pm8u/IedeblBrWyXz8eUfzeyviRXeZ7bhSnE/PBQvLoWcl/SL1avJKeOuoEURWmkgVwIAcaWoU8a25Quh5d6MXrhe+gZw9AzL0IvmoiU/uAppUQ6f0LPHIGeMQS94BWkHi4FbRjumYTO62JB6PkIYQ57q0/XQyaikpKA1ZSVNWXzprCbQbs0jXun/UaRx0OHxKSwCRqPZDUay02UNX3b1irXUVGU6mmUgRyA6AcJHnS0IWIeBkBKLzL7Kih8H7Qt4FsPBS8gc+/zny94Fpn3lH9LNm0HFH2OzLoYqYcaNA3DkEToX7EEQ1y5tw5p1wGLKXgIQyI587h2la9DCaOh/Oi8IzeHB2dMw2oy8cygs7CZTKWJtawGIyaDAYfJjMNsxm4ycVKzFkRbwuSsAWSYDw1FUSKv0Q52GuxDkcKCLHwNfLvB1AYRcz/COtB/gXumP0AHzC13gvsvdPd8/wrQgKmCHtCykM7vEFHXV6oOwnEV0jWVwK4VASK+JEVAeMenNuGq7ifwzdrVuH0+hBCYjUbu6XcyLWJiK/X8I43s3I0Ply3BF2I1JoBPSqZv28LwiV/RPbUJY4cMZfrWLRwoLGBgm7Zc0f0ENmdnsSM3hy7JKXRLbcLbi/7h9UULgsoyAIOr8WGjKEr1NNpADiBsZyJsoQfdpHthyTL6EFx/+BNrybJzvl3+1aGVDeSWHsjYxyH/eRBGQAdDEiJhPKIS/dBPDBjEBR07M3XLv5gMRs7v2ClgZeb+ggLWZ6TTPDaWLhWs2Eyy29HCBPFDdClZk36Q9Rnp/LRxPV9dfCm9mjUvPd+7eQt6Nz88VfKuficzf/cuFu/bE1BOs5hYHObIZnNUFCW8Rh3Iy2VsRsgFOsIIxtaEXJ2Jsdw536EYHJcjbeeDdxUYYsHUrUrJuXo0bVaaA+UQXUqemDWDHzaux2I0ouk6HRKT+HT4SBLs9qAyNF3nzcX/hNnuOZgmJU6fjyf/msmUq64t99oWsbGY9gt8R/SXZxQV8eK8OTw7+OxKPlFRlJpovH3kFRD2ESWt5ICj/pWZjmvB0Awoe96MiKr6vHNhiEJYTwFTN1YdPMA7ixfyxaoV1c7bPWHNKn7etAGPplHo8eD0+diQmcF906eGvD7X5QrKTV4ZGzIzQs6cOURKyW+bNwUEcQCPrvHTpg1Vfp6iKNVzzLbIhTEVEj5G5v4XZIF/OoixFSLhHYTBgkz8DJl7N3jX+wO+sEHM80j3EmTOf0DPBUsfRMwDCFP4/mD/VMh9SGzcP3MR07duxq1pWIxGXpw/lw/OG86ANmlVqvvnq1YEBWavrrNg907y3S5irYGDvLFWKyZhwENwULYYjehShpzRYjWZKtyjM9xMmPI+ABRFiaxjNpADCEsfSJkL2jbAgjC1PnzO2ASRNAmpHfAv7ze2RRaMBefXh1eLumchPYsg6WeEqVVQ+dK9EJn3MOg56FLj8uapzN85GKfPUbrg5s6pv7LkptuwhpihcsiSfXt4ZcE8/s3KpGVMbNiWvEEIir3eoEBuNhq5odeJfLJiWcAHgM1k4u1zz2ddxkE+WLYkYBGQ1Wjk0q7ldwMJIejfshUL9+xGP6JVbhSCAW3ahr1PUZTIOma7Vg4RwoAwtQ8I4odI31bwbQGRALIIir8ss+Rf+jMoFn0U4t5dyJxbQd8PuDAIL72S9vHFGVP89x2h7GDhkRbt2c11P33P0n17yXe7WZ+ZQZ7bFfLaJIeDJlHRIc/9t/+p3HJiH6LNFkwGAymOKJ4ffDZnHteO2/v0Z2j7jliNRmIsFqxGI2e0actjpw0MW69Dnh10FrFWK7aSDyK7yUS8zcboMwZXeK+iKJFxTLfIw5F6nj8Ie9cfzppoPRv/r6tsBkUfeFcEl1E8gbJJsswGSQtHAT0T01mZfXgzY1HOCpwX58+tcLm8sWTTiBfPPCegBb3q4AHWph+kRUwsp7duwz39T+HOvv1x+nxEmc2l15oMBsYOGcpDp5zOluws0uITaBFbuSmOafEJ/HXtKH7YuI4NGRl0S23CxZ27EmsNP8dcUZTIUoH8CFLLBO8aZNGH4F0DeP27CQG4//T/HESE3rRZ2xnyeh1BU0cRZJfeTZ/m4WfCbMoKl2rAzyAEp7RszVMDB3NcQqK/qj4fN/36I8v370dKidFgIN5mY/IlV9AsJoZoS+DUwOX79/HekoUs27+PPLcbh8nMZd2O5+FTTy+3y+eQOJuNG3qeVOF1iqLUDhXIAd29GPIeAn0f/t6mUAN4LhDRJdkLj2yVWxHRtwZfbulfsiNR4BJ9s0FnQ04KNpMJAbwz7IJyg2XTqGh25IVPDaBLSWpUVGkQB/hw2RKW7tuHWytpyWvg8nm5b/rvfDPy8oD7P1q2hNcWzsd9xOBksc/LN2tXs68gnw/OH46Ukj+3b2XCmtUU+7wM79iZkV27YzFGftNoRVGq7pgP5LpzBuTdy+HWczmLZoQDbKeD81f8y+xTEHFjEObuwZfaRyKLPgHdy+EuFjtO40Au6zGUGIuVYR06kmgvP4vhPf1O5rFZM8JOHzQJQUqZfvHJ69ccDuIlNClZvn9fwKyWrOJixi6cHzJ3i1vzMWfndvbk5/HJiuVMXreGYp//d7Tm4AF+3LSBCSMuq3BWi6Iota9GgVwIcSkwGugC9JVS1u1GnJVQmglR2BGG6OBzBc8QusukLBNYB2KIexYZO8Y/+Cniw87qEIZoSP4RWfguuGb6PwQc15DouILbmlY++A3v3JWMoiKenz839HOE4LJugR8kXi3ch5EIOLdo7x4sRmPYJFwWo5HFe/fwzdpVAS12p8/H+ox0Zm7byrntO1T6XRRFqR01bU6tBUYAoaNMPZOexcjMs5AZg5Dp/dCzb0bq2UjpQ/q2In27y0l3W5aldG9PISwIQ0KFKzSFIRFD7JMYUudgSJmKIeqqSi3NL6tlXDzRYZa892nekrT4wI2bh3boiDlES7ltfHxAHvOyfeVleTSNzOIiDCHqXOz1MnvHtspUX1GUWlajFrmUcgNQpSXndUX6diGzbyagj9ozH5k5EvR8EFpJf3dls/RpoGeBsWkt1LZ8Ehk2tWzzmJigY/f2O5k5O7aTUVxEsdeLrWRhT9mNmE9u2QqzIXQ/t8Vo5Kzj2tE2PrE0C+KRzAYDyWqTZUU5KtRZH7kQ4hbgFoDWrYPnbEeaLP6K4C4TH+h7Sy4orVnJn4oykXiRxZMQcU9HsJaVc1qrNiFXUNpNZi7qHLw5RrzNzrSrr+P3zf+ydP9e2sYnMKJL16D+eLPRyOcXjeT6n7/H6fXi1jR0KbGZTNzQ40Tu6X8KUkrMRkPQr9JoMHBp1+Mj+p6KolRPhVu9CSFmAqGaoY9LKX8uuWY28EBl+8jrZKu37JvBM6eSVxs4/Jl2KGKF+L1Yh2JIeLPmlauGqZs3cd/0aYB/Ob3FaGRE5248PejMGn8j8uk6i/bupsjjoV+LVsTZAleGrs9IZ9QvP1LocfufJeHls8/h3PYda/RcRVGqJtxWbxW2yKWUZ9VOlWqZpQ94FhK8gCcE4YD4txDGFkhhhYwhBGVFxIGw1X42P5+us3TfXoq9Xvo0b0FMycKaoR06cWKzFvy2eRNOn5eBbdrSLbVJBaVVjslg4NRWbcKe75qSyvwbb2HNwQO4NY0eTZpWan65oih1o9H+axSOy5HFn4GucXj6nwn/DvZlWtvShzD3RBiiEYAefS8Uvo1/QwgJ2MHcFWzn1Gqd16Qf5Mafv8ft00D4g/roMwZzWTd/F0aT6Ghu7FU3C282Z2Xx/LzZLN23j3ibjVG9TuLaHr1C9pcrilK/ajr98GLgbSAF+E0IsVJKWbvRrpKEIQ6SfkIWvgXuWSCiwHYRFH0OFHLk3G6ibw2YmmiIvglpORFZPBFkPsI2DGxDy91js6Y8msZ1P35Hbpk8KqPnzOL4Jk0r3Dgiknbl5TJi8gSKvR4kUOT18MqCv9mTn8cTAwbVWT0URamcms5a+RH4MUJ1iThhTEXEPRtwTDpGIgvfB888MCQiom5ChGhpC8uJCMuJdVVV5u3aGXIbNo+mMXndGp6qwyRUHy5bgsvnDfje4vT5+HrNKu7s2594W/DmFYqi1J9jZlmelBK9+Dtk9v+B6xcwNkfEPhUyiNeHQo875K70upTkukJnO6wtKw7sRwtRF4vRyPacnDqti6IoFTt2AnnRB5D/DGi7/KsyPYuQWVcjvRvru2oA9G/ZCm+IKYYOs5kh7drXaV3aJ4SeO+7RtEpnRVQUpe4cE4FcShcUfUDZBFbgQha+XR9VCpIaFc2dffphL0mmBeAwmTk+tQlnH1e3gfzWk/oEJcSyGo0MbnscqWHynSuKUn8a7ayVANp+Qi+NlOBdV+Ht0rcLfFvB1C7kBhSRcmffk+nTvCUT166mwOPh/I6dOK9DpzpPTNUttQkfnj+cJ2fNZF9hAUYhGNGlG/9TA52KclQ6NgK5IQVkmD0kTeHnT0vpRubeA+75JRtMeJHWUxHxbyFE+XlKqqtfy1b0axm8bVx5tmRn8W9WJmnxCXRNSQ15jZSSnzZu4OMVS8l1uRjQJo17+p1M0+jgJf4Ap7dO46/rRlHo8WAzmTCrlLWKctQ6NgK5sIOpI/jWlDlhQ0TfEfY2WTDWH8Rx+3cJAnDPRxa8ioh9rNaqW1lun4/bf/+Ff/bsxiQMaFKna0oqnw4fGZQQ6+UFf/PFqpU4S1LRfr9+LTO2bWHa1deHzZkihChdkKQoytGrUfeRS+lB+nYj854C3+bgC6LvRVj6hi/A+S3BK0PdJcfr3xuLFrBg925cPh+FXg9On4816Qd5avafAdflOJ18tnJ5aRAH8ElJocfD5yuX13W1FUWJsEYbyPWiL5Dp/ZGZ54NrMv5VmmWE2GszgCw7OFrB8To2aV3wBhIeTWPKvxsDdrXflJUZcjcfj6axYM+uWq+noii1q1EGcumaCgVjQRYSPFPlCL5N5RdkDrMgyHx07E8ZblNmTUq0I6YyNomODjm10SAErePiaq1+iqLUjcYZyAvfpdwADoAAU6fyr4h9yr+0n0NL880gohCx/4tALWvu1FZtQs73PiG1ScDgZNv4BI5PbRK02YTVaGRUr6BEaoqiNDCNMpCjpVfiIisi+vZyrxDmTojk38FxDZj7g+MaRPLvCHP5HwB15ckBA4m1WLGWBG2L0UiU2cJzg4OzNI47/yJOa90Gi9GI3WQiye7gjXPOo3uEMigqilJ/KsxHXhtqOx+5nj0KPH+HOFPSejV1QcQ+ibBUrotEuuciiz4G7QBYTkZE34aoh52CQsl2FvPN2tWsOniAzknJXH18T5pEh1+0k+tyUuD20CI2VmUyVJQGJlw+8gYfyKVvB7j/BExgG4IwNkN61yOzruRwGloAM0TfjSH61iqVrxd9DQUvc7irxgQiGpH8y1ETzBVFOTaEC+QNumtFL3wPmXkBsuA1ZMGryIwh6MWTEeauiKRJYBmAf6q8AAxQ+C56zi1IWXbTiNCkdEPhqwT2t/tAFiILP4j8CymKolRDgw3k0vsvFH6Af563t+R/3ZD/DFLLQJg7lwxUHtqP0w24wP0PsvC9kjLWIp0/I71rQmYexLc9zNN94FkQ+ZdSFEWphga7slO6fid4c2UAAe6ZSPsl4J4R4ho3FE9E9ywC33pKA72xMzLq/8C7HmFqCbbzwZAEMtQzgFruVskoLuLpObOYsW0rAGcd146nzhhMiiOqVp+rKErD02ADub+VHa5/X+Lf0i147rT/dD541xCwL6dvBeStAXxI7FDwGiLxS7CcDJ5/Aq/Fjoi6ueavEIZX07hk8jfsLyzAVzL/e/rWLaw+eIA/r7lR5T1RFCVAg+1aEbahQKjEVRKsZyKEDUydQ5w34A/wofrJDy2wcYIsQObej4h/3R/Msfi7akQUxDyEsJ4emRc5wpbsLP7310xGTJ7AgSOCOPj378xxOpm5fWvEn6soSsPWYFvkwtwZGX0zFI7DH4AN/j8xjyKM/rnRIu45ZPbVJd0jHsDmT6Al8yv3EG0PyEIMiR8htQzQs8DUFiEin0hq1vZt3Dn1V7yaFnJ3HoBir5ct2VkRf7aiKA1bgw3kAIbou5C2oeD6E4QJrEMQpsMpYIW5KyRPQxZP8i/HN/dEOC5D5v63ZLAyTNdLKQn4uzGEMQWMtbMBsqbrPDxzWtgl94c4zGbaJSTVSh0URWm4ahTIhRCvABfgb+5uBW6QUuZGomKVroOpPUSH30FHGJsgYu4OPBj7FDLzYqCY8MHcAKYO/gBey3bm5eKsIIibhCDBZmdwWlsW7N5FZnERJzVrobZeUxSlxi3yGcCjUkqfEOIl4FHg4ZpXq5bpGfi7Y44M4gJ/n7sOwgLC5u8frwNRZktAf3hZZoOBs45rz80n9uasLz8lz+3P5OjTdS7t2p0xA89EqFWainLMqlEgl1JOP+LHhcAlNatO3ZD5zxGc1laCIQkRdZ1/aqH1zFrbBaisJtHRHJ/ahJVldq+3m0w8M+gsLu7cFSEE5379GQeKCgNS1H6/YT19WrTkgo6hBnYVRTkWRHLWyo3A1AiWV3vCpa/V94HjOoRtaJ0F8UPeHXYBxyUk4jCbibZYsBqNXNq1e2kQ356bw668vIAgDuD0efliVQV51RVFadQqbJELIWYCoVa/PC6l/Lnkmsfx91V8XU45twC3ALRuXXsbGFeKIR70zODjIgYh6mdGZmpUNNOuvo7VBw9wsKiQE5o0DdhP0+n1YgzTfVLkqVzKAUVRGqcKA7mU8qzyzgshrgfOB86U5WTgklKOA8aBP2lW1aoZYVE3QcGbBOZQsUPUDfVVI8C/R2aPps1CnuuYlOxfCOQNXGlqNZo4r+PRkVZXUZT6UaPmpxDiXOAh4EIpZXFkqlT7hOMGiLoO/7zyKP//2s4Fzwr0gz3R0wegF36ClBVNT6w7JoOBV84+F5vJhKlkgwiHyUzruDiu7xFmJyNFUY4JNUpjK4TYAliBQ6tUFkop/1PRfbWdj7yypHSCth8pNci+DGQxh5f928A+Y+hM/gAADjtJREFUAkPc6GqV7dN15uzYzu78PLqlptK7WYuIzCzZnpvDN2tWsb+wgAFt2nJhx85YTQ16OYCiKJXUaPORR4KeNwackzi8RP8QKyJ1DsKQWKXy9hcUcNl3E8l1OfHqOiaDgc7JKXx50SXYzeaKC1AURQmhwecj13170XMfQM+6Fr3wU/Ry5l1XmXclwUEc/3xy37YqF/fgzGnsLyygyOvFo2kUe72sSz/IO0sW1ryuiqIoZTSIQK4XfwuZg8D1C3gXQuELkNEPXS+KzANM7Qn5q5AeMLasUlFFHg+L9+4Jmibo1jS+W7+u0uXUxzclRVEapqO+c1VKL+Q/GeJEHuQ9CglvhblPgiwEYUeI8l9TRN2EdE0ncBaLFawDqrydW7iEV0C5qzcB3D4fLy/4m0nr1uD0eunVtBlPDzqLrimpVaqDoijHlqO+RS7dfxE2H4p7dsjDunMKMuN0ZHo/ZHpv9ILX/AOaYQhzJ0TCh2Bsiz9JlhXsFyHix1a5vrFWK12TUyg7rGk2GBjWoWO5997z/+3de3CcdbnA8e+z796ySZO0JU2vIEKFBuRaekCdATtQCx2ngqLlojCiBW8Fh+PYA+NhPIxHFM/BMz0y6ihalaIFUThTW06plTK1Q4u1SC1IWxB6Cw02tybbze6+j3+8b9pks0may17e5PnMdLL5vbv7Pvm1+/TN7/39nt+6Nax6+S90ptMosL3xENc//phVPDTGDKjsEzk6tJkemtoErfeAexhvf81O6FiJtg+clCV2KaG6Z5Apf0Lq/0yo5n6vpvkwPHjV1VTHYlT4s0kSkQjTJ1Rz92Xv7/c1+9taee7NN0hle4/VJzMZFv5iJbc9/STvdAZmhqcxpojKfmhF4vPR1u7NIHLEPtinSY+uoG8dlSR0/gKdcOegtcQllBh2rN1mT57Mc7d+lqde3cXfW1o4b+pUFp4xe8Bpgq83NxN1HFLZvr85uCjPv/UmNz75K5656VYrkGWM6aX8E7k4aPV/QtvynAO1UPPNvi/I7Ov/zdwW8DedKLTqWIxPnn/hST//jImT6MqTxLtlXJdD7e1sO3iAeTOGdgPWGDO2lX0iBwglrsONvg+OfhfctyH2QSRxc/66KJGz8+9wLxEY4nzwQlq75zUe2rKZfW2txMNhauMV1FdW0Xj0KF1u/oSuwL62VkvkxpheApHIAULhqVD7wKDPk6ovo0e203t4pQKqliFSHotxfr1rJ1/7w4bjOwKlsllaUykcEZxQiKgIXXlmv7iqNJxS+I0ujDHBUv43O4dIoucjk1ZC5GKQhDcTpfp+QpW3lDo0wJsW+a0/Pp93W7esKhnXZf67z6S+sqpXtcOYE+ZfZsxkjk1FNMbkCMwV+VBI9EJk8mOlDiOvjnSa5mTuzdgTXFW2HtzP2htv4cE/Ps+zb+wl5oT5xDnv5XNz5xUxUmNMUIzJRF7OEpEIiUiY9gFqiNfE4qzd8xrVsTj3X3ElC8440ytha4wxeQR2aMVNrsVtuhK3scH7mvxdqUM6KSER7rh43vE55rliTpiD7W08sHkTj+z4E1/d8AyLVv2MtlSqyJEaY4IikIncTa6B1q9C9i0g431tXY6bXIO67eix9WjqOVTLc+ecO+bO4wuXXEplJHJ8BWjcCRNzHCojYVLZ7PEx9M50mrdaW/jfrVZwyxiTXyDL2LpN8yG7v+8Bmeit5JQI3mS9EDLx+0j0kmGfq5CyrktnOk1bKkVjRzsT4wmuWbUy73zy+soqttx2ewmiNMaUi/7K2AZzjDx7IH+7NvtfUz2alkLd5lFZsTlSzckk/7VlM2v3vEY4JHx0zjl8ad5lzKiuZkZ1Nc3JZL9VD7t3BTLGmFzBzA6hoVUk7K+4VjGlMhmuW72Kx3e9TPOxJE2dnfxkx3Y++dsnjifviRUVnDulnlDOEvyY43B9w7mlCNsYEwDBTORVd4FU5DT2M6tDXa+cbZ/mFtyOn+K2fg3tfMLb9q2A1u3dTVNnB+kepWxT2SyvvtPEi4dO/IbxPwsXUZeopDISJeo4JCIRLpg6jdsvLs/hIWNM6QVyaCWUuBYXF44+5FU5DE2B+DWQXO3vu9mTC7EP9GrR9G70yBLQNHAMTf4fHF0Bk59EnMkFifmlxkY60+k+7VnXZVfTYS6Z7i27n1ldw6ZbP8PGv7/OgfZ2zquv56Kp061QljGmX4FM5KqKxC6HikVAFJEQqi6a3QepLUAnIEAcKm9DnOm9X9+63L9K7x6P7gS3Cz36HSRfIa5RcHptLRXhMMmcFZ3hkMPM6ppebRHHYcEZswsShzFm7Anc0Ioe2+BtGtF0Bfr2XLTtHlSPIRJCar+H1H4bYld7G0NM+hGhCct6v97tgMwuTiTxbhk4tr5gcS8+u4Go4/TacMIRoTYe5/LTTi/YeY0xY9+IrshF5H5gMV6x8MPArap6cDQCy0e7dqAtX6ZXQazkGtTtQCau8Kohxhcg8QUDBO1An/17uo8VrqhWdSzG49ffwL+uX8eupsMAzJsxk+9ctdBmpBhjRmRE88hFpFpV2/zHy4AGVb1jsNcNdx652/w5SP2evlfTUaRuI4RqAAcR78anus2gWcQ5pff7HPksdG0Geg5zxKDyU4QmfGXIcQ1VeyqFEwrxUuMhVu18ifauLhbNPovFZ80hakvxjTH9KMg88u4k7qukb4YdXZk3+zmFg/7jE+AeAMJo7ArIHvaHUAR1TkNqH0QiDQBIzTfRIzeC2wSaBQQi5yJVXypo+N0mxGKs2LqF77+4jWTGuwG67cABVv/1ZVZd93Grq2KMGZIR3+wUkW8AnwJagb57r5143lJgKcCpp546vJNFL4LkG0DuysckuN0rPdOQyhnrzu5Gj9wMdb9HQrXeFfop66BrC2T3QXgORM4r2syQpo4OHt72Qq9t3ZKZNK+808S6vbv58HvOLkocxpixYdDBWRF5VkR25vmzGEBV71XVWcCjwBf7ex9V/aGqzlXVuXV1w9scQSpv9+ePDyPhagZNPnXivSSExN6PJJZ4NcyLOL3vhQP78o6Ld6bT/P/e3UWLwxgzNgx6Ra6qV57kez0K/A64b0QRDUDCs9Dah6H1Pv8KPAG0cXIjOsf8oZnSq47FkTz/GYVEmBjPXehkjDEDG9F0CRHpOdl5MfDqyMIZmHZtg5bb/SSeBpKc/LB8AoleVLjghuCymbOIhvuOg0cdhxvee34JIjLGBNlI57094A+z/AVYANw5CjHlpapoy1dAk3hJHGCgMrU9r3ij4NTDQNMSiyjiOPz8Ix9jir8UvyoaJR4O8/XL5zPH9uQ0xgzRSGetfHS0AhlUdj+4R07yyTWQWALH1gBpiC9Cqj6PSLSQEQ7JnLopbP70UrY3HiSZznDxtOlURssnPmNMcARmib4S4cSVeI7QNAi/C9xWiF+NJG5CQlVQfXcxQxwyJxQ6XmPFGGOGKzCJnI4f4C0gzRWDyqWEKm8qdkTGGFMWArE2XN1WSD5B3huboclIYknRYzLGmHIRiEROdn//dVAkfnxJvjHGjEfBSOTOTL92eK4QhM8qejjGGFNOApHIJVQDFdcC8ZwjUaRq0BpdxhgzpgUikQNI9b9D5WdAJgAC4TnIpB8jkTmlDs0YY0oqMLNWRMLIhGUwYZm3Q5BtfWaMMUCArsh7siRujDEnBDKRG2OMOcESuTHGBJwlcmOMCThL5MYYE3CWyI0xJuBEtbD7Jec9qUgTUB7b9RTOKcA7pQ6iDFg/eKwfPNYPnuH2w2mq2mfTgpIk8vFARF5U1bmljqPUrB881g8e6wfPaPeDDa0YY0zAWSI3xpiAs0ReOD8sdQBlwvrBY/3gsX7wjGo/2Bi5McYEnF2RG2NMwFkiN8aYgLNEPspE5HoR+auIuCIyN+fYv4nIHhH5m4h8qFQxFouILPR/1j0isrzU8RSLiDwiIodFZGePtkkisl5EdvtfJ5YyxmIQkVkislFEdvmfiTv99nHTFyISF5GtIvKS3wdf99tPF5EX/M/Gr0QkOpLzWCIffTuB64BNPRtFpAFYApwDLAQeljG82aj/s30PuBpoAG7w+2A8+Cne33FPy4ENqjob2OB/P9ZlgLtVtQG4FPiC/29gPPVFCpivqucDFwALReRS4FvAQ6p6JtAM3DaSk1giH2Wq+oqq/i3PocXAL1U1papvAHuAecWNrqjmAXtU9XVV7QJ+idcHY56qbgKO5DQvBlb6j1cCHylqUCWgqodUdbv/uB14BZjBOOoL9Rz1v434fxSYDzzht4+4DyyRF88MYF+P7/f7bWPVePt5B1Ovqof8x41AfSmDKTYReRdwIfAC46wvRMQRkR3AYWA9sBdoUdWM/5QRfzYCs9VbORGRZ4GpeQ7dq6pPFTseEyyqqiIybub9ikgV8GvgLlVt67nD13joC1XNAheISC3wG+Ds0T6HJfJhUNUrh/GyA8CsHt/P9NvGqvH28w7mbRGZpqqHRGQa3tXZmCciEbwk/qiqPuk3j8u+UNUWEdkIXAbUikjYvyof8WfDhlaK52lgiYjEROR0YDawtcQxFdI2YLZ/dz6Kd6P36RLHVEpPA7f4j28BxvxvbuJdev8YeEVV/7vHoXHTFyJS51+JIyIVwFV49wo2Ah/znzbiPrCVnaNMRK4FVgB1QAuwQ1U/5B+7F/g03t38u1R1bckCLQIRuQb4LuAAj6jqN0ocUlGIyGPAFXilSt8G7gN+C6wGTsUr4fxxVc29ITqmiMgHgOeBlwHXb74Hb5x8XPSFiJyHdzPTwbtwXq2q/yEi78abADAJ+DNws6qmhn0eS+TGGBNsNrRijDEBZ4ncGGMCzhK5McYEnCVyY4wJOEvkxhgTcJbIjTEm4CyRG2NMwP0TS/0gQpixEWIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis = fit_model[:,0]\n",
    "y_axis = fit_model[:,1]\n",
    "\n",
    "plt.scatter(x_axis, y_axis, c=iris.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Principal Component Analysis (PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "iris_df = pd.DataFrame(data=X,columns=iris.feature_names)\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.019004</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.328414</td>\n",
       "      <td>-1.397064</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.098217</td>\n",
       "      <td>-1.283389</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.249201</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0          -0.900681          1.019004          -1.340227         -1.315444\n",
       "1          -1.143017         -0.131979          -1.340227         -1.315444\n",
       "2          -1.385353          0.328414          -1.397064         -1.315444\n",
       "3          -1.506521          0.098217          -1.283389         -1.315444\n",
       "4          -1.021849          1.249201          -1.340227         -1.315444"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "x_std = StandardScaler().fit_transform(X)\n",
    "\n",
    "std_df = pd.DataFrame(data=x_std,columns=iris.feature_names)\n",
    "std_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "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>pc 1</th>\n",
       "      <th>pc 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.264703</td>\n",
       "      <td>0.480027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.080961</td>\n",
       "      <td>-0.674134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.364229</td>\n",
       "      <td>-0.341908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.299384</td>\n",
       "      <td>-0.597395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.389842</td>\n",
       "      <td>0.646835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pc 1      pc 2\n",
       "0 -2.264703  0.480027\n",
       "1 -2.080961 -0.674134\n",
       "2 -2.364229 -0.341908\n",
       "3 -2.299384 -0.597395\n",
       "4 -2.389842  0.646835"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "pc = pca.fit_transform(x_std)\n",
    "principal_df = pd.DataFrame(data = pc, columns = ['pc 1', 'pc 2'])\n",
    "\n",
    "principal_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = 3\n",
    "kmeans_pca = KMeans(n_clusters=K)\n",
    "kmeans_pca.fit(pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f68a3854978>"
      ]
     },
     "execution_count": 108,
     "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.scatter(pc[:,0],pc[:,1],c=kmeans_pca.labels_,cmap='Dark2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[39  0 11]\n",
      " [ 0 50  0]\n",
      " [14  0 36]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.78      0.76        50\n",
      "           1       1.00      1.00      1.00        50\n",
      "           2       0.77      0.72      0.74        50\n",
      "\n",
      "    accuracy                           0.83       150\n",
      "   macro avg       0.83      0.83      0.83       150\n",
      "weighted avg       0.83      0.83      0.83       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Y_pc = np.concatenate([np.repeat(1,50),np.repeat(0,50),np.repeat(2,50)])\n",
    "\n",
    "print(confusion_matrix(Y_pc,kmeans_pca.labels_))\n",
    "print(classification_report(Y_pc,kmeans_pca.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}