ML_prax4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[0.1,0.6], [0.15,0.71], [0.08,0.9], [0.16,0.85], [0.2,0.3], [0.25,0.5], [0.24,0.1], [0.3,0.2],])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.1 , 0.6 ],\n",
       "       [0.15, 0.71],\n",
       "       [0.08, 0.9 ],\n",
       "       [0.16, 0.85],\n",
       "       [0.2 , 0.3 ],\n",
       "       [0.25, 0.5 ],\n",
       "       [0.24, 0.1 ],\n",
       "       [0.3 , 0.2 ]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a57/qExb9fmHjXzdX2TghsnlC5QM32ffLvPWE6stsnEy7o3n9vVWvqYfrvwN4Z/P6IeA7bDkZ1e+XdtbPRrBeAk5sGT8Qz//brP+gPP8nWq7/GjDfvP4BbjyhepUenlCt/D/erXABPg78W/Mv8OPNsSfYeJUC8Ats/F/6f4AfAZda7vs7bJxIuQL8dtVr6eX6gV8CFpt/wReBR6peyz6t/2vAvwPfbF4uHLDnf9v1H6Dn/4+BS821P9cafzb+NfMSUAfu7eW8/Q1VSSqQx9wlqUDGXZIKZNwlqUDGXZIKZNwlqUDGXZIKZNwlqUDGXZIK9P8hT0BImXHftgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids = np.array([X[0], X[7]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=2, init=centroids, n_init=1)\n",
    "model = kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True,\n",
       "    init=array([[0.1, 0.6],\n",
       "       [0.3, 0.2]]), max_iter=300,\n",
       "    n_clusters=2, n_init=1, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.148     , 0.712     ],\n",
       "       [0.24666667, 0.2       ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NVYeokOt3/a6/PQ7OzJ6hJlVW7qWJiL5WPhJcKtfv+l1/Z63fwzKSVCDLXZIKZLmPnWuqDlAx1z+xuf4O4zF3SSqQe+6SVCDLvQURsSgiVkfEmoi4tMntfxYRP4+I/oh434Dbzo2IR+uXc9uXeuyMcv07I2JZ/bKkfanHTgvrvzgiVkXEioj4YUQc3HDbRHj+X2n9E+H5vyAiHqqv8d6IOKzhts/U77c6It7d1uCZ6eUVLkA38BjwBmAKsBw4bMCc+cBbgRuB9zWMHwCsrf+5f/36/lWvqV3rr9/2QtVraMP6TwCm169/HLhlgj3/Tdc/gZ7//RquLwb+p379sPr8qcCC+s/pbld299yHdjSwJjPXZuYO4GbgtMYJmfmrzFwB7Bpw33cD38/M32fm08D3gUXtCD2GRrP+ErSy/nsyc0t9cymw+1RGE+X539v6S9DK+p9r2NwX2P1C5mnAzZm5PTMfB9bUf15bWO5Dmw2sa9heXx8b7/t2itGuYVpE9EXE0oj4y7GN1hbDXf95wJ0jvG8nGs36YYI8/xHxiYh4DPgqcNFw7jtePIfq0KLJWKtvMRrNfTvFaNcwLzM3RMQbgP+NiIcy87ExytYOLa8/Is4GeoHjhnvfDjaa9cMEef4z8yrgqoj4APBZ4NxW7zte3HMf2npgbsP2HGBDG+7bKUa1hszcUP9zLfAj4MixDNcGLa0/Ik4ELgMWZ+b24dy3w41m/RPm+W9wM7D7N5Rqn/+qX7Do9Au1327WUntBZPcLKofvZe4NDH5B9XFqL6btX79+QNVrauP69wem1q/PAh5lwItRnX5pZf3UCusxYOGA8Qnx/L/C+ifK87+w4fp7gb769cPZ8wXVtbTxBdXK/+O9Gi7AycAj9b/Al9XHrqC2lwLwdmr/l94MPAWsbLjvR6i9kLIG+HDVa2nn+oFjgIfqf8EfAs6rei3jtP4fAE8Ay+qXJRPs+W+6/gn0/H8DWFlf+z2N5U/tt5nHgNXAe9qZ20+oSlKBPOYuSQWy3CWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBLHdJKtD/A9QoOTxqRVVIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], c=kmeans.labels_, cmap='rainbow')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}