Project 8 Identifying number of unique chars of unknown dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('arabic-data.csv',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13440, 1024)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0].values.reshape((32,32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x26c985bb438>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dkfSyCtonZjZelcC94u6vZ8OF75NqfbRqn2TbHvakufVqRfg/lDTdzL5nZhMk/USVyUAL\nZWYTzWzS2duSfiSpp/ZauRoVE6Ke/c+VuUsF7BMzM0krJe1292cHlQrdJ6k+it4nhU2aW9QRzPOO\nZs5X5UjqPkl/26IerlblTMNOSR8X2Yektaq8fDypyjGPxZL+TJXLnu2VtFnSZS3q458lfSRpV/af\nrbOAPuaq8hJ2l6Qd2c/8ovdJjT4K3SeS/lzSf2Tb65H0ZDbe1P3BJ/yAoKIf8APCIvxAUIQfCIrw\nA0ERfiAowg8ERfiBoAg/ENT/AKw6J4hS7B1yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26c985856a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data.iloc[0].values.reshape((32,32)), cmap=plt.cm.binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-25588804856.7\n",
      "-25260670356.1\n",
      "-24946795989.6\n",
      "-24680907894.5\n",
      "-24450499294.7\n"
     ]
    }
   ],
   "source": [
    "cost = []\n",
    "\n",
    "for i in range(5,10):\n",
    "    kmeans = KMeans(n_clusters=i, random_state=0,max_iter=10)\n",
    "    kmeans.fit(data)\n",
    "    c = kmeans.score(data)\n",
    "    print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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