Metrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(3, 2, 1)\n",
    "x1 = np.array([1, 2, 3, 1, 5, 6, 5, 5, 6, 7, 8, 9, 7, 9])\n",
    "x2 = np.array([1, 3, 2, 2, 8, 6, 7, 6, 7, 1, 2, 1, 1, 3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 1 5 6 5 5 6 7 8 9 7 9]\n",
      " [1 3 2 2 8 6 7 6 7 1 2 1 1 3]]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([x1, x2])\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 1],\n",
       "       [5, 6],\n",
       "       [5, 5],\n",
       "       [6, 7],\n",
       "       [8, 9],\n",
       "       [7, 9],\n",
       "       [1, 3],\n",
       "       [2, 2],\n",
       "       [8, 6],\n",
       "       [7, 6],\n",
       "       [7, 1],\n",
       "       [2, 1],\n",
       "       [1, 3]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.reshape(len(x1), 2)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.xlim([0, 10])\n",
    "plt.ylim([0, 10])\n",
    "plt.title('Instances')\n",
    "plt.scatter(x1, x2)\n",
    "colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'b']\n",
    "markers = ['o', 's', 'D', 'v', '^', 'p', '*', '+']\n",
    "tests = [2, 3, 4, 5, 8]\n",
    "subplot_counter = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in tests:\n",
    "            subplot_counter += 1\n",
    "            plt.subplot(3, 2, subplot_counter)\n",
    "            kmeans_model = KMeans(n_clusters=t).fit(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  }
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