{
 "metadata": {
  "name": "Exploratory analysis"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Exploratory analysis\n",
      "\n",
      "In this section we'll explore the sample characteristics, and determine possible features for using in the classifyier later."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import book_classification as bc\n",
      "import shelve\n",
      "import pandas\n",
      "import numpy\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myShelf = shelve.open(\"storage_new.db\")\n",
      "aBookCollection = myShelf['aBookCollection']\n",
      "del myShelf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Word frequencies\n",
      "\n",
      "Let's look at the word distribution across books."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tokenizer = bc.BasicTokenizer()\n",
      "aPossibleFeatureAnalyzer = bc.PossibleFeatureAnalyzer.from_documents(tokenizer, (b.contents for b in aBookCollection))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aDataFrame = aPossibleFeatureAnalyzer.as_dataframe()\n",
      "print(aDataFrame.describe())\n",
      "countSeries = aDataFrame['Count']\n",
      "print(\"Skewness: {}\\nKurtosis: {}\".format(countSeries.skew(), countSeries.kurt()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                Count     Frequency\n",
        "count   161226.000000  1.612260e+05\n",
        "mean       180.786678  6.202474e-06\n",
        "std       6970.495073  2.391454e-04\n",
        "min          1.000000  3.430824e-08\n",
        "25%          1.000000  3.430824e-08\n",
        "50%          3.000000  1.029247e-07\n",
        "75%         15.000000  5.146237e-07\n",
        "max    2191113.000000  7.517324e-02\n",
        "Skewness: 230.19566383088508\n",
        "Kurtosis: 65966.18551912217"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It's not a well shaped distribution. Some numbers and a logarithmic box/density plot:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figsize(4, 5)\n",
      "boxplot(countSeries.apply(numpy.log))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "{'boxes': [<matplotlib.lines.Line2D at 0x7f7f27122c50>],\n",
        " 'caps': [<matplotlib.lines.Line2D at 0x7f7f27283d90>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7f2711d550>],\n",
        " 'fliers': [<matplotlib.lines.Line2D at 0x7f7f26dbf210>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7f26aa1790>],\n",
        " 'medians': [<matplotlib.lines.Line2D at 0x7f7f27278790>],\n",
        " 'whiskers': [<matplotlib.lines.Line2D at 0x7f7f2727a810>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7f27282310>]}"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAAE1CAYAAADDO42vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAEMhJREFUeJzt3X9sFGUex/HPlrYnXI/aFrpc2ioGJRQoQqBBSGrGyA81\nFjmoxGIC15X4l0bUiNF/bEm0IhoPNfoHiRX8wx8xERBME1CnYAhBRGujJuXAngVtI1DwsEpt7f2x\nt9utXZZ2OsvszvN+JYXp7Hb2G+DD88wzzzwT6O/v7xcAI2R4XQCAK4fAAwYh8IBBCDxgEAIPGITA\nAwZJGPhQKKRgMKiysrJB+19++WWVlpZq5syZevzxx5NaIAD3ZCZ6saamRg8++KDWrFkT3ffJJ59o\n165d+uqrr5SVlaWffvop6UUCcEfCFr6iokJ5eXmD9r322mt64oknlJWVJUmaOHFi8qoD4KoRn8Mf\nO3ZM+/fv10033STLsnTkyJFk1AUgCRJ26ePp7e1VV1eXDh06pM8++0yrVq3SiRMnhrwvEAi4UiCA\nkbvUjPkRB764uFgrVqyQJJWXlysjI0NnzpxRQUHBsD8Uqa+2tla1tbVelwEHEjW2I+7SL1++XB9/\n/LEkqbW1VT09PXHDDiD1JGzhq6ur1dTUpDNnzqikpEQbN25UKBRSKBRSWVmZsrOztX379itVK4BR\nCiTr9thAIECXPo3Zti3LsrwuAw4kyh6BB3wmUfaYWgsYhMADBiHwgEEIPGAQAg8YhMADBiHwgEEI\nPGAQAg8YhMADBiHwgEEIPGAQAg8YhMADBiHwgEEIPOKyba8rQDIQeMRF4P2JwAMGGfEy1fAv2x5o\n2evqBvZbVvgL6Y/AI+rPwWZZev+hSw8YhMAjLrrw/sQy1YDPsEw1AEkEHjBKwsCHQiEFg0GVlZUN\nee2FF15QRkaGzp49m7TiALgrYeBramrU2Ng4ZH97e7v27t2ra6+9NmmFwVvMtPOnhIGvqKhQXl7e\nkP2PPPKInnvuuaQVBe8ReH8a8Tn8zp07VVxcrFmzZiWjHgBJNKKZdt3d3XrmmWe0d+/e6L5El95q\nY6ZqWZbF44dTHFNr05Nt27KH2SW77HX4trY2VVZWqqWlRS0tLVq0aJHGjRsnSTp58qSKiop0+PBh\nFRYWDj4w1+HTWm0tU2vTVaLsjaiFLysrU2dnZ/T76667Tp9//rny8/NHVyGAKyLhOXx1dbUWLlyo\n1tZWlZSUqKGhYdDrgUAgqcXBO3Th/YmptYDPMLUWgCQCDxiFwAMGIfCAQQg8YBACDxiEwAMGIfCA\nQQg84uL2WH8i8IiLwPsTgQcMwpNnEMX98P7HzTOIy7Lo1qcrbp7BiJ086XUFSAa69IiK7dIfPz6w\n4g1dev+gS4+4Jk2SOjq8rgJOuLbEFfztX/+SduwIb3d2DrTqy5dL69d7VhZcRAuPuPLzJR4qlJ5o\n4TEssefwXV2cw/sRLTziKiqSTp3yugo4QQuPYYlt4X/4gRbej2jhEdf110v//rfXVcAJWngMC9fh\n/Y8WHnHl5EgXLnhdBZyghcewxLbwv/xCC+9HBB5RX345+IaZyPbVVxN4v7hslz4UCmnPnj0qLCxU\nS0uLJOmxxx7T7t27lZ2drSlTpqihoUG5ubmDD0yXPq1lZkq9vV5XASdGdbdcTU2NGhsbB+1bsmSJ\nvv76azU3N2vq1Kmqr693p1J4yrYHHhPd1zewzW2y/nHZwFdUVCgvL2/QvsWLFysjI/yj8+fP10nu\npfSFSJc+EvDI9pdfelcT3DXqc/jXX39d1dXVbtQCj61fP3CTTCBAy+5Howr8008/rezsbK1evTru\n67WRYV5JlmXJYuQnpcW27hKj9OnCtm3Zw/zfeVjX4dva2lRZWRkdtJOkN954Q1u3btVHH32kq666\nauiBGbRLO//4h/TJJ+Ht8+elyDjsLbdI77/vXV0YGdevwzc2Nmrz5s1qamqKG3akp9hQBwLSuXPe\n1YLkuGwLX11draamJp0+fVrBYFB1dXWqr69XT0+P8vPzJUkLFizQq6++OvjAtPBpLRCQ+OtLT4my\nx9RaRP15meqnngpvcw6fXphai2F57z1p9+6B7994I/z76dME3i9o4RFXRob0xx9eVwEnaOExLLFd\n+v5+Lsv5EQ+iAAxCC48ozuH9jxYeMAgtPKKqqqQJE8LbdXXSP/8Z3qZ19w8CjygWwPA/Ao+opqbB\nt8JGtvPyeNSUXxB4RD30kHTjjeHturqBkNO6+weBRxSj9P5H4BHFoJ3/MbUWURUV0mefhbcvXpT+\n8pfwdnm5dOCAd3VhZJhai2GZMEGKLG9w8eLAdqTVR/oj8IgqKgpfgpPCK95EtouKvKsJ7qJLj6jx\n46X//nfo/r/9Tfr55ytfD5wZ1br0MMett4bXsYusZRfZvvVWb+uCe+jSI4ouvf8ReETt3y/FPlMk\nsr1/vzf1wH0EHlG5ueFnyknhR01Ftv/02ECkMQKPKC7L+R+BR1Rz8+DR+Mh2c7M39cB9BB5ROTnh\nxSulcJc+sp2T411NcBeBR9TNNw+06v/5j1RcPLAf/sDEG0SNGyf9+uvQ/WPHSt3dV74eOMPEGwzL\npUbjGaX3j4SBD4VCCgaDKisri+47e/asFi9erKlTp2rJkiU6xxMHfWPlSunaa8Nf0sD2ypXe1gX3\nJOzSHzhwQDk5OVqzZk30UdEbNmzQhAkTtGHDBm3atEldXV169tlnhx6YLn3aYS69Pzju0ldUVCgv\nL2/Qvl27dmnt2rWSpLVr12rHjh0ulQmv/fWvI9uP9DPiUfrOzk4Fg0FJUjAYVGdn5yXfWxt5VpEk\ny7JksXRKSps4Ufrpp/B2X580ZszAfqQu27Zlxy43nMBlR+nb2tpUWVkZ7dLn5eWpq6sr+np+fr7O\nnj079MB06dPO3/8udXQM3T9pkvTjj1e+Hjjj6ih9MBhUx///Vfz4448qLCwcXXVIGT09I9uP9DPi\nwC9btkzbtm2TJG3btk3Lly93vSh44/z5ke1H+knYpa+urlZTU5NOnz6tYDCojRs36q677tKqVav0\n/fffa/LkyXr33Xd1deTG6dgD06VPO1lZUm/v0P2ZmdLvv1/5euBMouwx0w5RBQVSnOEY5edLZ85c\n+XrgDDPtAEiihUcMuvT+QAsPQBKBR4x4rXui/Ug/BB5R2dkj24/0Q+ARdamVbVjxxj8YtENUIHDp\n1/irTB8M2gGQROARI3J33HD3I/0QeET19Y1sP9IPgQcMQuABgxB4wCAEHjAIgQcMQuABgxB4wCAE\nHjAIgQcMQuABgxB4wCAEHjAIgQcMQuABgxB4wCAEHjCI48DX19drxowZKisr0+rVq3Xx4kU36wKQ\nBI4C39bWpq1bt+ro0aNqaWlRX1+f3n77bbdrA+CyTCc/NH78eGVlZam7u1tjxoxRd3e3ioqK3K4N\ngMscBT4/P1+PPvqorrnmGo0dO1ZLly7VokWLhryvtrY2um1ZlizLclongEuwbVu2bQ/rvY7WpT9+\n/LgqKyt14MAB5ebm6u6771ZVVZXuvffegQOzLn3aYV16f3B9XfojR45o4cKFKigoUGZmplasWKGD\nBw+OqkgAyeco8NOmTdOhQ4f066+/qr+/X/v27dP06dPdrg2AyxwF/sYbb9SaNWs0b948zZo1S5J0\n//33u1oYAPfxbDlEcQ7vDzxbDoAkAg8YhcADBiHwgEEIPGAQAg8YhMADBiHwgEEIPGAQAg8YhMAD\nBiHwgEEIPGAQAg8YhMADBiHwgEEcrVqL9Ba45EoXvRpoAwKSIoso/KFAYOg/FRY4ST8E3kDDCWog\nIPX3R/5jGKOB8COd0aUHDELgAYMQeMTF6bk/EXjEFfOUMPgIy1QjrvCgnddVwAmWqQYgicADRiHw\ngEEcB/7cuXOqqqpSaWmppk+frkOHDrlZF4AkcDzT7qGHHtIdd9yh9957T729vfrll1/crAsee+op\nrytAMjgapT9//rzmzJmjEydOXPrAjNIDnkiUPUct/HfffaeJEyeqpqZGzc3Nmjt3rrZs2aJx48YN\nel9tzMVcy7JkWZaTjwOQgG3bsm17WO911MIfOXJECxYs0MGDB1VeXq7169dr/Pjx2rhx48CBaeEB\nT7h+Hb64uFjFxcUqLy+XJFVVVeno0aPOKwRwRTgK/KRJk1RSUqLW1lZJ0r59+zRjxgxXCwPgPsdT\na5ubm7Vu3Tr19PRoypQpamhoUG5u7sCB6dKntdpa5tOnq0TZYy494mIuffpiLj0ASQQeMAqBBwxC\n4AGDEHjExVx6f2KUHvAZRukBSCLwgFEIPGAQAg8YhMAjLubR+xOj9IiLufTpi1F6AJIIPGAUAg8Y\nhMADBiHwiIu59P7EKD3gM4zSA5BE4AGjEHjAIAQeMAiBR1zMpfcnRukRF3Pp0xej9AAkjTLwfX19\nmjNnjiorK92qB0ASjSrwW7Zs0fTp0xUIBNyqB0ASOQ78yZMn9eGHH2rdunWcqwNpwnHgH374YW3e\nvFkZGQwD+BFz6f0p08kP7d69W4WFhZozZ45s277k+2pjru1YliXLspx8HDzAZbn0Ydt2whzGcnRZ\n7sknn9Sbb76pzMxM/fbbb/r555+1cuVKbd++feDAXJYDPJHU58M3NTXp+eef1wcffDDsDwWQPEm/\nDs8oPZAemGkH+Awz7TBiDNr5Ey084mIuffqihQcgicADRiHwgEEIPGAQAo+4mEvvT4zSAz7DKD0A\nSQQeMAqBBwxC4AGDEHjExVx6f2KUHnExlz59MUoPQBKBB4xC4AGDEHjAIATeh/Lzw4Nuo/mSRn+M\n/Hxv/xwwFKP0PpQqI+ypUodpGKUHIInAA0Yh8IBBCDxgEAIPGITAAwZxHPj29nbdcsstmjFjhmbO\nnKmXXnrJzboAJIHj6/AdHR3q6OjQ7NmzdeHCBc2dO1c7duxQaWlp+MBch/dMqlz/TpU6TJOU6/CT\nJk3S7NmzJUk5OTkqLS3VDz/84PRwAK4AV87h29ra9MUXX2j+/PluHA5AkmSO9gAXLlxQVVWVtmzZ\nopycnEGv1cYsm2JZlizLGu3HAfgT27Zl2/aw3juqufS///677rzzTt1+++1av3794ANzDu+ZVDl3\nTpU6TJMoe44D39/fr7Vr16qgoEAvvvjiiD4UyZUqQUuVOkyTlMB/+umnuvnmmzVr1iwF/n8/ZX19\nvW677bbLfiiSK1WClip1mCYpgR/NhyK5UiVoqVKHabg9FoAkAg8YhcADBiHwgEEIPGAQAg8YhMAD\nBiHwgEEIPGAQAg8YhMADBhn1/fBIPf0KSAGvq5D6Y35FaiDwPhRQf0rctBIIEPdUQ5ceMAiBBwxC\n4AGDEHjAIAQeMAiBBwxC4AGDEHjAIAQeMAiBBwxC4AGDEHjAIAQeMIjjwDc2NmratGm64YYbtGnT\nJjdrQgoY7uOHkV4cBb6vr08PPPCAGhsb9c033+itt97St99+63Zt8BCB9ydHgT98+LCuv/56TZ48\nWVlZWbrnnnu0c+dOt2sD4DJHgT916pRKSkqi3xcXF+vUqVOuFQUgORyteBN5Hrxb74P73Pijr6ur\nS4k64B5HgS8qKlJ7e3v0+/b2dhUXFw96D8+GB1KPoy79vHnzdOzYMbW1tamnp0fvvPOOli1b5nZt\nAFzmqIXPzMzUK6+8oqVLl6qvr0/33XefSktL3a4NgMsC/fS9ESMUCmnPnj0qLCxUS0uL1+XAZcy0\nwyA1NTVqbGz0ugwkCYHHIBUVFcrLy/O6DCQJgQcMQuABgxB4wCAEHjAIgccg1dXVWrhwoVpbW1VS\nUqKGhgavS4KLuA4PGIQWHjAIgQcMQuABgxB4wCAEHjAIgQcM8j+yl8kz8d6+6AAAAABJRU5ErkJg\ngg==\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figsize(6, 4)\n",
      "countSeries.apply(numpy.log).plot(kind='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f7f26a01ed0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD9CAYAAAC85wBuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see that the words at the extremes are very rare or stopwords."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = aDataFrame.sort(columns='Count')\n",
      "print(df.head(5))\n",
      "print(df.tail(5))\n",
      "print(len(df))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "        Count     Frequency          Word\n",
        "80612       1  3.430824e-08   unwelcomely\n",
        "114598      1  3.430824e-08  heterozygous\n",
        "51599       1  3.430824e-08       myoides\n",
        "51600       1  3.430824e-08       anglic\u00e9\n",
        "114596      1  3.430824e-08       yasnaya\n",
        "          Count  Frequency  Word\n",
        "136836   355794   0.012207   his\n",
        "60894    398451   0.013670   was\n",
        "46061    472727   0.016218  that\n",
        "44419   1178084   0.040418   and\n",
        "50522   2191113   0.075173   the\n",
        "161226\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figsize(10, 5)\n",
      "resolution = 100\n",
      "plot([x/resolution for x in range(resolution)], [math.log(countSeries.quantile(x/resolution)) for x in range(resolution)])\n",
      "#plot([x/resolution for x in range(resolution)], [countSeries.quantile(x/resolution) for x in range(resolution)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f7f0ae3e8d0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAkwAAAE1CAYAAAAVob9XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4hQggwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Prunning some words\n",
      "\n",
      "Now we'll remove some of the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anotherPossibleFeatureAnalyzer = aPossibleFeatureAnalyzer.prune_last_words(20).prune_less_occurrences_than(500)\n",
      "anotherDataFrame = anotherPossibleFeatureAnalyzer.as_dataframe()\n",
      "print(anotherDataFrame.describe())\n",
      "\n",
      "anotherCountSeries = anotherDataFrame['Count']\n",
      "print(\"Skewness: {}\\nKurtosis: {}\".format(anotherCountSeries.skew(), anotherCountSeries.kurt()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "               Count    Frequency\n",
        "count    5473.000000  5473.000000\n",
        "mean     3233.204458     0.000183\n",
        "std      8165.130393     0.000461\n",
        "min       500.000000     0.000028\n",
        "25%       718.000000     0.000041\n",
        "50%      1178.000000     0.000067\n",
        "75%      2390.000000     0.000135\n",
        "max    123844.000000     0.006999\n",
        "Skewness: 7.597174876456923\n",
        "Kurtosis: 74.41778635368166\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = anotherDataFrame.sort(columns='Count')\n",
      "print(df.head(5))\n",
      "print(df.tail(5))\n",
      "print(len(df))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      Count  Frequency       Word\n",
        "834     500   0.000028    designs\n",
        "2631    500   0.000028  obviously\n",
        "1622    500   0.000028    holland\n",
        "2193    500   0.000028  horseback\n",
        "2224    500   0.000028  withdrawn\n",
        "       Count  Frequency   Word\n",
        "3340  107906   0.006098    are\n",
        "5149  117655   0.006649  there\n",
        "4095  119119   0.006732    one\n",
        "593   120227   0.006794   said\n",
        "2913  123844   0.006999   were\n",
        "5473\n"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anotherCountSeries.apply(numpy.log).plot(kind='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f7f0add4550>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAE1CAYAAACr9+cKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnR4sQmou268AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#plt.xscale('log')\n",
      "df['Count'].hist(log=True, bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f7f0ad78a90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAE4CAYAAAAad4STAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X9sVfX9x/HXdcVkmTDFaZ29dRRaKJUKjjIWEuI1hpFu\ngpu/2rKIKW5DDZsuGzH+ZVlU7BYTN1g2hoouaCVRl7opV3R6NxO1BJCF73ACrndeQd1SQCpGfpTP\n94/aK7e09vD53HvP5/Q+H0kj5/Teez59CeHNPa97TswYYwQAAIBQnRH2AgAAAMBQBgAA4AWGMgAA\nAA8wlAEAAHiAoQwAAMADDGUAAAAeYCgDAADwAEMZAACABwoylB0+fFizZs3Ss88+W4iXBwAAGHUK\nMpT98pe/VFNTUyFeGgAAYFQKNJQtWbJE5eXlqq+vz9mfTCZVW1urmpoatbe3S5JeeOEF1dXV6bzz\nzsv/agEAAEapWJB7X77yyis666yztHjxYu3YsUOS1NfXpylTpujFF19URUWFZs2apY6ODj322GM6\nfPiwdu7cqS9+8Yv605/+pFgsVvAfBAAAIMrKgjxo7ty5SqfTOfs2b96s6upqTZgwQZLU3Nyszs5O\n3X333ZKkRx99VOeddx4DGQAAQACBhrKh7N27V5WVldnteDyurq6u7PaNN974uc9nWAMAAFES4OSi\nE+uifz6GKmMMX5Zfd911V+hriOoX2ZEf+UX3i/zILqyvYrAeyioqKpTJZLLbmUxG8Xg8L4vCyAaf\nTkZwZOeG/NyQnxvys0d2/rMeyhoaGrR7926l02kdPXpUGzZs0MKFC0/rNdra2pRKpWyXAAAAUFCp\nVEptbW1FOVagT1+2tLTob3/7m3p6enT++efrF7/4hVpbW7Vx40bdfvvt6uvr00033aQ777wz+IFj\nsaK9HTgapVIpJRKJsJcRSWTnhvzckJ8b8rNHdm6KMbcEGsoKcmCGMgAAEBHFmFu492VEcdrXHtm5\nIT835OeG/OyRnf9CHcrolAEAAJ951ykryIE5fQkAACKC05cAAAAlgqEsojjta4/s3JCfG/JzQ372\nyM5/dMoAAACGQacMAADAI3TKAAAASgRDWURx2tce2bkhPzfk54b87JGd/+iUAQAADINOGQAAgEfo\nlAEAAJQIhrKI4rSvPbJzQ35uyM8N+dkjO/8xlAEAAHiAThkAAMAIRn2njE9fAgAAnxXz05ehD2WJ\nRCLMJUQWw6w9snNDfm7Izw352SM7O4lEojSGMgAAAPSjUwYAADCCUd8pAwAAQD+GsoiiG2CP7NyQ\nnxvyc0N+9sjOfwxlAAAAHgi1U3bXXXcpkUjwCUwAAOClVCqlVCqlFStWFLxTRtEfAABgBMWYW8oK\n+uojeOONN7Rp06acfbFYTIsXL9YFF1wQ0qqiIZVK8Q6jJbJzQ35uyM8N+dkjO/+FOpStWfOI1q79\nP8ViDdl9ZWXPqLKyUi0tLSGuDAAAoLhCPX25dOlPtGbNREm3ZfePHduiNWsWMpQBAABvcJ0yAACA\nEsFQFlFcb8Ye2bkhPzfk54b87JGd/xjKAAAAPBBq0X/Lli5JJ8JcQmTxCRp7ZOeG/NyQnxvys0d2\ndgauU1YMob5T1tAwW1J1mEsAAAAYViKRUFtbW1GOxenLiKIbYI/s3JCfG/JzQ372yM5/DGUAAAAe\nYCiLKLoB9sjODfm5IT835GeP7PzHUAYAAOABhrKIohtgj+zckJ8b8nNDfvbIzn8MZQAAAB5gKIso\nugH2yM4N+bkhPzfkZ4/s/MdQBgAA4AGGsoiiG2CP7NyQnxvyc0N+9sjOfwxlAAAAHmAoiyi6AfbI\nzg35uSE/N+Rnj+z8xw3JAQAAhsENyTEiugH2yM4N+bkhPzfkZ4/s7HBDcgAAgBLDUBZRdAPskZ0b\n8nNDfm7Izx7Z+Y+hDAAAwAMMZRFFN8Ae2bkhPzfk54b87JGd/xjKAAAAPMBQFlF0A+yRnRvyc0N+\nbsjPHtn5j6EMAADAAwxlEUU3wB7ZuSE/N+TnhvzskZ3/GMoAAAA8wFAWUXQD7JGdG/JzQ35uyM8e\n2fmPoQwAAMADDGURRTfAHtm5IT835OeG/OyRnf8YygAAADzAUBZRdAPskZ0b8nNDfm7Izx7Z+S/v\nQ9m//vUv3XLLLbr++uv10EMP5fvlAQAARqW8D2W1tbX63e9+pyeeeELPP/98vl8en6IbYI/s3JCf\nG/JzQ372yM5/gYayJUuWqLy8XPX19Tn7k8mkamtrVVNTo/b29uz+P//5z/rOd76j5ubm/K4WAABg\nlIoZY8xID3rllVd01llnafHixdqxY4ckqa+vT1OmTNGLL76oiooKzZo1Sx0dHZo6dWr2eVdddZU6\nOzuHPnAspqVLf6I1ayZKui27f+zYFq1Zs1AtLS2OPxoAAEB+xGIxBRiZnJQFedDcuXOVTqdz9m3e\nvFnV1dWaMGGCJKm5uVmdnZ3673//q6efflqffPKJLr/88nyvFwAAYFQKNJQNZe/evaqsrMxux+Nx\ndXV16bLLLtNll10W6DVefvkFSV+TdEDS2ZJmZL83cO574NMibOduP/DAA5oxY4Y364nS9sm9Ch/W\nE7Vt8iM/8ovm9sA+X9bj+/bArwe/KVVIgU5fSlI6ndaCBQuypy+feuopJZNJrV27VpK0fv16dXV1\nadWqVcEOzOlLJ6lUKvsbCKeH7NyQnxvyc0N+9sjOTTFOX55h+8SKigplMpnsdiaTUTwez8uiMDL+\nYNkjOzfk54b83JCfPbLzn/Xpy4aGBu3evVvpdFoXXnihNmzYoI6OjtN6jS1buiSdsF0CAABAQaVS\nqZxTmoUU6J2ylpYWzZkzR7t27VJlZaXWrVunsrIyrV69WvPnz1ddXZ2amppyPnkZREPDbEnVNusu\necX6DTIakZ0b8nNDfm7Izx7Z2UkkEmprayvKsQK9UzbcO2CNjY1qbGzM64IkacmSpVq0aFHOvrFj\nz9GhQ/vzfiwAAAAfBC765/3AsZhmzpytrVtnSfrswwFjx7aot/cJSYOXVfiCHQAAwMkGTl+uWLHC\n36J/PnD6EgAA+KyYpy9DHcpgj26APbJzQ35uyM8N+dkjO/8xlAEAAHiAThkAAMAw6JQBAAB4gE4Z\nRkQ3wB7ZuSE/N+TnhvzskZ3/GMoAAAA8EGqnbLgbktMpAwAAPinGDcmt732ZD9z7EgAA+My7e18W\nCkV/e3QD7JGdG/JzQ35uyM8e2dmh6A8AAFBi6JQBAACMoBidMt4pAwAA8ABDWUTRDbBHdm7Izw35\nuSE/e2TnPz59CQAAMIxifvqSThkAAMAI6JQBAACUCIayiKIbYI/s3JCfG/JzQ372yM5/DGUAAAAe\noFMGAAAwAu59CQAAECLufYkR0Q2wR3ZuyM8N+bkhP3tkZ4d7XwIAAJQYOmUAAAAj4DplAAAAJYKh\nLKLoBtgjOzfk54b83JCfPbLzX4SGsjLFYrGcr3Hjxoe9KAAAgLyIVKeMnhkAAAgDnTIAAIASEepQ\n1n/x2D1hLiGy6AbYIzs35OeG/NyQnz2ys5NKpUrjOmVcPBYAAPismBePpVMGAAAwAjplAAAAJYKh\nLKLoBtgjOzfk54b83JCfPbLzH0MZAACAB+iUAQAAjIBOGQAAQIlgKIsougH2yM4N+bkhPzfkZ4/s\n/MdQBgAA4AE6ZQAAACOgUwYAAFAiuPdlRNENsEd2bsjPDfm5IT97ZGeHe18CAAB4gHtf0ikDAAAe\noVMGAABQIhjKIopugD2yc0N+bsjPDfnZIzv/MZQBAAB4gE4ZAADACOiUjahMsVgs52vcuPFhLwoA\nAOC0RXwoO67+d88+++rtPRDukoqEboA9snNDfm7Izw352SM7/0V8KAMAABgdIt8po2cGAAAKjU4Z\nAABAiWAoiyi6AfbIzg35uSE/N+Rnj+z8x1AGAADgATplAAAAI6BTBgAAUCIYyiKKboA9snNDfm7I\nzw352SM7/5UV4kU7Ozv17LPP6tChQ7rppps0b968QhwGAABg1Chop+zgwYP6+c9/rgcffPDUA9Mp\nAwAAEeFVp2zJkiUqLy9XfX19zv5kMqna2lrV1NSovb0953t33323li1blp+VAgAAjGKBh7LW1lYl\nk8mcfX19fVq2bJmSyaR27typjo4OvfnmmzLG6I477lBjY6NmzJiR90WDboALsnNDfm7Izw352SM7\n/wXulM2dO1fpdDpn3+bNm1VdXa0JEyZIkpqbm9XZ2akXX3xRf/3rX3Xo0CHt2bNHS5cuHfI1X375\nBUlfk3RA0tmSTh7gUp/+NzFoX2LQ9wc/vkyxWCznO2PHnqNnnnm6/9US/c8f+M0Z1e3t27d7tR62\n2Wabbbb93h7gy3p83x749eDZp5BOq1OWTqe1YMEC7dixQ5L05JNP6vnnn9fatWslSevXr1dXV5dW\nrVo18oEL2CmjZwYAAPLJq07ZUAa/IwUAAAA7TkNZRUWFMplMdjuTySgejwd+/pYtXZL2uCyhZA1+\nOxrBkZ0b8nNDfm7Izx7Z2UmlUmprayvKsZyGsoaGBu3evVvpdFpHjx7Vhg0btHDhwtN4/mxJ1S5L\nAAAAKJhEIuHfUNbS0qI5c+Zo165dqqys1Lp161RWVqbVq1dr/vz5qqurU1NTk6ZOnVrI9eJTA4VE\nnD6yc0N+bsjPDfnZIzv/Bf70ZUdHx5D7Gxsb1djYmLcFFUbuJzLHjj1Hhw7tD3E9AAAAuZxOX7oq\nXqfsuPo/kdn/1dt7oAjHLCy6AfbIzg35uSE/N+Rnj+zsRKZT5opOGQAA8FkxO2UFvffl5x64yNcp\ny903Rv3vnn2GU5oAAGA4xbhOWeBO2egycDrzM729XHMNAACEp0Q6ZaMP3QB7ZOeG/NyQnxvys0d2\nduiUAQAAeIBOWcE7ZdwfEwAABOf9vS8BAACQHwxlEUU3wB7ZuSE/N+TnhvzskZ3/Qv30ZX/R/0SY\nSwAAABhWKpUq2kBLp+ykfXTKAADAUOiUAQAAlAiGsoiiG2CP7NyQnxvyc0N+9sjOfwxlAAAAHgi1\nUzZz5mxt3TpL0qrsfjplAADAFwNF/xUrVozuThlX9AcAAD4r5hX9OX0ZUXQD7JGdG/JzQ35uyM8e\n2fmPoQwAAMADXKfspH10ygAAwFC4ThkAAECJYCiLKLoB9sjODfm5IT835GeP7PzHvS8BAACGwb0v\n6ZQBAACP0CkDAAAoEQxln2PcuPGKxWI5X+PGjQ97WZLoBrggOzfk54b83JCfPbLzX6idMt/19h7Q\n4NOcvb2xcBYDAABGNTplWWMkHR9ipXTPAAAodcXolPFOWdZxDT3MAQAAFB6dsoiiG2CP7NyQnxvy\nc0N+9sjOfwxlAAAAHgi1UzZz5mxt3TpL0qrs/jCvUxZ0H50yAABKw8DFY1esWDG6r1PW0DBbUnWY\nSwAAABhWIpFQW1tbUY7F6cuIohtgj+zckJ8b8nNDfvbIzn8MZQAAAB7gOmUW++iUAQBQWrj3JQAA\nQIlgKIsougH2yM4N+bkhPzfkZ4/s/MdQlgc+37gcAABEA52y097HPTIBACg1dMq8NHCPzJO/hlLG\nu2cAACAwhrKCOXV46+09kLdXpxtgj+zckJ8b8nNDfvbIzn8MZQAAAB7g3pdF3kfPDACA6ODelwAA\nAB7g3pcYEd0Ae2TnhvzckJ8b8rNHdv5jKAMAAPAA1ykr8j46ZQAARA/XKQMAACgRDGURRTfAHtm5\nIT835OeG/OyRnf8YyjzEvTQBACg9dMqKvC9I3LEYfTQAAHxCpwwAAKBEMJRFFN0Ae2TnhvzckJ8b\n8rNHdv5jKAMAAPAAnbIi76NTBgBA9NApG3XKHD5V6fJcAADgO4ayojqu/nfAPvvq7T1g+dyXT+O5\nOBm9Cjfk54b83JCfPbLzH0MZAACAB/I+lHV3d+sHP/iBrrvuuny/NHIkwl5AZCUSibCXEGnk54b8\n3JCfPbLzX96HsqqqKj344IP5flkAAIBRLdBQtmTJEpWXl6u+vj5nfzKZVG1trWpqatTe3l6QBWI4\nKVH+t0Ovwg35uSE/N+Rnj+z8F2goa21tVTKZzNnX19enZcuWKZlMaufOnero6NCbb75ZkEViOC4f\nHAAAAD4JNJTNnTtX55xzTs6+zZs3q7q6WhMmTNCYMWPU3Nyszs5O7d+/XzfffLO2b9/Ou2cFlQh7\nAZFFr8IN+bkhPzfkZ4/s/Fdm+8S9e/eqsrIyux2Px9XV1aXx48fr97//faDXePnlFyR9TdIBSWdL\nmnHSd1Of/jcxaF9i0PcHP36454+0ne/XD/p6ZZ9eLHYwt/UNvE098Ifw5O1x48af8o7aF794lp57\n7s9DPp5tttlmm222S2174NfpdFrFEviK/ul0WgsWLNCOHTskSU899ZSSyaTWrl0rSVq/fr26urq0\natWqYAcu0Sv6529fStLlQz5upP+lpX7HgFQqlf3Dh9NHfm7Izw352SM7N15f0b+iokKZTCa7nclk\nFI/H87IoAACAUmN9+rKhoUG7d+9WOp3WhRdeqA0bNqijo+O0XmPLli5JJ2yXUOISYS8gsviXohvy\nc0N+bsjPHtnZSaVSOac0CynQO2UtLS2aM2eOdu3apcrKSq1bt05lZWVavXq15s+fr7q6OjU1NWnq\n1KmndfCGhtmSqm3WDQAAUHCJREJtbW1FOVagoayjo0P79u3TkSNHlMlk1NraKklqbGzUW2+9pT17\n9ujOO+8s6EIxWCrsBURWsf7FM1qRnxvyc0N+9sjOf9adMgAAAORP4E9f5v3AsZhmzpytrVtnSfrs\nE5t8+tJ9H5++BAAgPwY6ZStWrPD305f5QKcMAAD4zLtOGXyUCnsBkUWvwg35uSE/N+Rnj+z8x1AG\nAADgATplo3CfD52yoW7lNHbsOTp0aH/ejgEAQKHRKUPk9Q9kJudr8JAGAIDv6JQhgFTYC4gsehVu\nyM8N+bkhP3tk5z+GMgAAAA+E2ilbuvQnWrNmoqTbsvvplLnv86FTxrXQAACjSSxW+L/DrG9Ing/c\nkLwQyj4diPoVo1w/VKm/GMfggwMAgELz7obkhULR30VqmP3HVexy/VCl/mIcw/Zno1fhhvzckJ8b\n8rNHdnYo+gMAAJQYOmWjft+p58Dz3fca7vWKcQw6agCAYihGp4x3ygAAADwQ6lDWX/TfE+YSIiyV\n59fr/4DAyV/jxo0/5VHjxo0/5XEuhnq9oY6bTz71Kob6+WOxM4uax+nyKb8oIj835GeP7OykUqnS\n6JRR9PdJ7gcEhivS57vUX+pX/h86z2Mq1TwAwDfFLPrTKRv1+4J3yoJ0tk7nuS6vV+genC+C5Rn9\nnxMAoo5OGQAAQIlgKIusVNgLiCx6FW7Izw35uSE/e2TnP4YyAAAAD9ApG/X76JT5jE4ZAEQD975E\nHpQ5XLbC5bn+CnofTe63CQDg3pcIIBXwcade6iI4l+f6K+hlOEr9ch3DoZfihvzckJ89srPDvS8B\nAABKDENZZCXCXgBKVCKRCHsJkUZ+bsjPHtn5j6EMAADAAwxlkZUKewEoUfRS3JCfG/KzR3b+YygD\nAADwAENZZCXCXgBKFL0UN+TnhvzskZ3/GMoAAAA8EOpQ1n/x2D1hLiHCUmEvwEL/xWhP/kL00Etx\nQ35uyM8e2dlJpVKlcZ0yLh5bakbnxWgBAKMXF49FAImwF4ASRS/FDfm5IT97ZOc/hjIAAAAPMJRF\nVirsBaBE0UtxQ35uyM8e2fmPoQwAAMADDGWRlQh7AShR9FLckJ8b8rNHdv5jKAMAAPAAQ1lkpcJe\nAEoUvRQ35OeG/OyRnf8YygAAADzAUBZZibAXgBJFL8UN+bkhP3tk5z+GMgAAAA9w78vISoW9AA8M\nvpfmmafcW3OofcFeK/jjxo0bf8qjxo0bH+hx+RT0mEM9Lmh248aNL2gvJYzcir2WKPd6fPj/E+X8\nwkZ2drj3JRDI4HtpHtOp99Ycal+Q1wr+uN7eA6c8qn/fyI/Lp6DHHOpxQbPz5WcoBp/W4gsyQSni\n3pcIIBH2AlCi6KW4IT835GeP7PzHUAYAAOABhrLISoW9AJQoeiluyM8N+dkjO/8xlAEAAHiAoSyy\nEmEvACWKXoob8nNDfvbIzn8MZQAAAB5gKIusVNgLQImil+KG/NyQnz2y8x9DGQAAgAcYyiIrEfYC\nUKLopbghPzfkZ4/s/MdQBgAA4AGGsshKhb0AlCh6KW7Izw352SM7/zGUAQAAeIChLLISYS8AJYpe\nihvyc0N+9sjOfwxlAAAAHsj7UHb48GHdeOON+tGPfqTHH3883y+PrFTYC0CJopfihvzckJ89svNf\n3oeyp59+Wtdff73+8Ic/6Jlnnsn3yyNre9gLQInavp3fey7Izw352SM7/wUaypYsWaLy8nLV19fn\n7E8mk6qtrVVNTY3a29slSXv37lVlZaUk6Qtf+EKel4vPHAx7AShRBw/ye88F+bkhP3tk579AQ1lr\na6uSyWTOvr6+Pi1btkzJZFI7d+5UR0eH3nzzTcXjcWUyGUnSiRMn8r9iAACAUSjQUDZ37lydc845\nOfs2b96s6upqTZgwQWPGjFFzc7M6Ozt19dVX66mnntKtt96qhQsXFmTRkKR02AtAiUqn02EvIdLI\nzw352SM7/8WMMSbIA9PptBYsWKAdO3ZIkp588kk9//zzWrt2rSRp/fr16urq0qpVq4IdOBazXDIA\nAEDxBRyZrJXZPtF1qCr0DwYAABAl1p++rKioyHbHJCmTySgej+dlUQAAAKXGeihraGjQ7t27lU6n\ndfToUW3YsIEOGQAAgKVAQ1lLS4vmzJmjXbt2qbKyUuvWrVNZWZlWr16t+fPnq66uTk1NTZo6dWqh\n1wsAADAqBRrKOjo6tG/fPh05ckSZTEatra2SpMbGRr311lvas2eP7rzzzkAHHOraZqUok8no8ssv\n18UXX6xp06bpN7/5jSRp//79mjdvniZPnqxvfetbOdeVWblypWpqalRbW6tNmzZl92/dulX19fWq\nqanRbbfdlt1/5MgRNTU1qaamRt/85jf1n//8p3g/YJH09fXp0ksv1YIFCySR3+k4ePCgrr32Wk2d\nOlV1dXXq6uoiv9OwcuVKXXzxxaqvr9eiRYt05MgR8hvGUNe6LFZWjz76qCZPnqzJkyfrj3/8Y4F/\n0sIYKr/ly5dr6tSpmj59uq6++mp9+OGH2e+RX67hrrUqSffff7/OOOMM7d+/P7sv1PxMER0/ftxM\nmjTJdHd3m6NHj5rp06ebnTt3FnMJ3njvvffMG2+8YYwxpre310yePNns3LnTLF++3LS3txtjjLnv\nvvvMHXfcYYwx5p///KeZPn26OXr0qOnu7jaTJk0yJ06cMMYYM2vWLNPV1WWMMaaxsdFs3LjRGGPM\nb3/7W3PLLbcYY4x54oknTFNTU1F/xmK4//77zaJFi8yCBQuMMYb8TsPixYvNQw89ZIwx5tixY+bg\nwYPkF1B3d7epqqoyn3zyiTHGmOuvv9488sgj5DeMv//972bbtm1m2rRp2X3FyKqnp8dMnDjRHDhw\nwBw4cCD766gZKr9NmzaZvr4+Y4wxd9xxB/l9jqHyM8aYd955x8yfP99MmDDB9PT0GGPCz6+oQ9mr\nr75q5s+fn91euXKlWblyZTGX4K2rrrrKvPDCC2bKlCnm/fffN8b0D25Tpkwxxhhz7733mvvuuy/7\n+Pnz55vXXnvN7Nu3z9TW1mb3d3R0mKVLl2Yf8/rrrxtj+v/S/cpXvlKsH6coMpmMueKKK8xLL71k\nrrzySmOMIb+ADh48aKqqqk7ZT37B9PT0mMmTJ5v9+/ebY8eOmSuvvNJs2rSJ/D5Hd3d3zl+Kxcjq\n8ccfNzfffHP2OUuXLjUdHR0F+gkLa3B+J3v66afN97//fWMM+Q1nqPyuvfZa849//CNnKAs7v7zf\n+/LznHwLJkmKx+Pau3dvMZfgpXQ6rTfeeEOzZ8/WBx98oPLycklSeXm5PvjgA0nSvn37cj7dOpDd\n4P0VFRXZTE/Ou6ysTF/+8pdz3qKNup/+9Kf61a9+pTPO+Oy3MfkF093drfPOO0+tra36+te/rh/+\n8Ic6fPgw+QU0fvx4/exnP9NFF12kCy+8UGeffbbmzZtHfqeh0Fn19PQM+1qjzcMPP6xvf/vbksgv\nqM7OTsXjcV1yySU5+8POr6hDGReMPdVHH32ka665Rr/+9a81duzYnO/FYjEyG8Zf/vIXnX/++br0\n0kuHveYd+Q3v+PHj2rZtm2699VZt27ZNX/rSl3TfffflPIb8hvf222/rgQceUDqd1r59+/TRRx9p\n/fr1OY8hv+DIyt4999yjM888U4sWLQp7KZHx8ccf695779WKFSuy+4b7e6TYijqUcW2zXMeOHdM1\n11yjG264Qd/97ncl9f+L8f3335ckvffeezr//PMlnZrdu+++q3g8roqKCr377run7B94zjvvvCOp\n/y/hDz/8UOPHjy/Kz1Zor776qp555hlVVVWppaVFL730km644QbyCygejysej2vWrFmSpGuvvVbb\ntm3TBRdcQH4BbNmyRXPmzNG5556rsrIyXX311XrttdfI7zQU+s/queeeO+r/znnkkUf03HPP6bHH\nHsvuI7+Rvf3220qn05o+fbqqqqr07rvvaubMmfrggw/Cz8/6BK2FY8eOmYkTJ5ru7m5z5MiRki76\nnzhxwtxwww3m9ttvz9m/fPny7PnslStXnlLePHLkiPn3v/9tJk6cmC0ffuMb3zCvv/66OXHixCnl\nw4Hz2R0dHZEuCn+eVCqV7ZSRX3Bz5841b731ljHGmLvuusssX76c/ALavn27ufjii83HH39sTpw4\nYRYvXmxWr15Nfp9jcKenGFn19PSYqqoqc+DAAbN///7sr6NocH4bN240dXV15n//+1/O48hvaJ/X\nyRuq6B9WfkUdyowx5rnnnjOTJ082kyZNMvfee2+xD++NV155xcRiMTN9+nQzY8YMM2PGDLNx40bT\n09NjrrjiClNTU2PmzZuX8z/wnnvuMZMmTTJTpkwxyWQyu3/Lli1m2rRpZtKkSebHP/5xdv8nn3xi\nrrvuOlMTU6QpAAAAyklEQVRdXW1mz55turu7i/kjFk0qlcp++pL8gtu+fbtpaGgwl1xyifne975n\nDh48SH6nob293dTV1Zlp06aZxYsXm6NHj5LfMJqbm81Xv/pVM2bMGBOPx83DDz9ctKwefvhhU11d\nbaqrq80jjzxSlJ833wbn99BDD5nq6mpz0UUXZf/+GPj0nzHkN9hAfmeeeWb299/JqqqqskOZMeHm\nF/iG5AAAACiconbKAAAAMDSGMgAAAA8wlAEAAHiAoQwAAMADDGUAAAAe+H+krYhpF1TwFwAAAABJ\nRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figsize(4, 5)\n",
      "boxplot(df['Count'].apply(numpy.log))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "{'boxes': [<matplotlib.lines.Line2D at 0x7f7f0ab8e050>],\n",
        " 'caps': [<matplotlib.lines.Line2D at 0x7f7f0abac210>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7f0abac910>],\n",
        " 'fliers': [<matplotlib.lines.Line2D at 0x7f7f0ab8ee50>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7f0ab95590>],\n",
        " 'medians': [<matplotlib.lines.Line2D at 0x7f7f0ab8e750>],\n",
        " 'whiskers': [<matplotlib.lines.Line2D at 0x7f7f0ab9e690>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7f0ab9ea50>]}"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAAE1CAYAAADDO42vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADxJJREFUeJzt3X9oXFXex/HPNEkbVm2TsE2CaWrZiDRN06TVWgimGdGI\nVqhBqjIR2k3wF6JUEP/RFSeCNCDSH6AIStog0qqLFtlowMgzY/8JLZSm0doE3SbdFluJMVk03WZb\n5/ljnrmT6M08yZ07vTP3vF8w9MzkZvIlzYdz7jnn3gnEYrGYABhhkdcFALh2CDxgEAIPGITAAwYh\n8IBBCDxgkJSBb29vV1lZmWpra63XXnjhBVVXV6uurk4PPvigJicnM14kAHekDHxbW5t6e3tnvXbP\nPffom2++0cDAgG655Rbt2rUrowUCcE/KwDc2Nqq4uHjWa83NzVq0KP5tmzZt0rlz5zJXHQBXpXUO\n39XVpS1btrhVC4AMy3f6ja+99poWL16s1tZW268HAgHHRQFIz1w75h0F/sCBA/rss8/05ZdfOvqh\nyH7hcFjhcNjrMuBAqs52wYHv7e3V66+/rmg0qsLCwrQKA3BtpTyHD4VCamho0NDQkCorK9XV1aVn\nn31Wv/zyi5qbm7V+/Xo9/fTT16pWAGkKZOry2EAgwJA+h0UiEQWDQa/LgAOpskfgAZ9JlT221gIG\nIfCAQQg8YBACDxiEwMNWJOJ1BcgEAg9bnZ1eV4BMIPCwdeKE1xUgExxfPAP/iUSSQ/mLF6XEVvpg\nMP5A7iPwsJw4MfvcPdEuKiLwfkHgYamvlyYm4u1oNBny+nrPSoLL2FoLWyUl0vi411XACbbWYsFK\nSryuAJlA4GHrjju8rgCZwDk8LDNn6bu7pVWr4m1m6f2Dc3jYCgbZbZerOIcHIIkhPWaYOaSPRtl4\n40cM6WGrokI6f97rKuAEt7jCgi1eLE1Pe10FnEiVPYb0sMwc0v/3vwzp/YjAw/L3v0v/+Efy+YED\n8X/Hxgi8XxB4WLZtk/7853i7o0P661/jbcLuHyzLAQZh0g62/vQnaWrK6yrgBJN2mJeZk3aXLjFp\n50f08LBVXi5duOB1FXCCHh7zwi2u/I9JO8AgDOlhKz9funLF6yrgBEN6zMuePdLhw/H21avJYXxL\ni/Tcc56VBRcxpAcMwpAetrh4JndxAwwsWCDgdQXIBM7hYZm5LDc9zbKcH9HDAwYh8IBBCDxgkJSB\nb29vV1lZmWpra63XPvroI9XU1CgvL0/Hjx/PeIEA3JNyWe7IkSO6/vrrtX37dg0ODkqSTp8+rUWL\nFunJJ5/UG2+8oQ0bNti/MctyOa2gIH6bK+QexzvtGhsbNTIyMuu11atXu1YYssvMWforV5il96OM\nLsuFE38xkoLBoIL81QCui0QiiszzY4KuWeCR/biJZW76fWfa0dEx57FsvIGFm1j6X1qBZ1LOX06c\nmP0Bkol2URGh94uUy3KhUEgNDQ0aGhpSZWWlurq6dPjwYVVWVqq/v1/333+/7rvvvmtVK4A0pezh\nDx48aPt6S0tLRoqBt+rrpYmJeDsaTfbq9fWelQSXcXksbAUCEv99uYk73mBeZq7DS6zD+xE9PCzr\n1kmnTsXbV69KeXnx9po10smT3tWFhaGHx7xs3iz9+9/x9uiotGJF8nX4A4GH5eabpVWr4u3R0WT7\n5pu9qghu4/JYwCD08LCwLOd/9PCAQQg8YBCG9LBwtZz/EXhYmKX3P4b0gEHo4WH57jtp5h3NEu3v\nvvOiGmQCgYeFG2D4H4GHhRtg+B+Bh4WNN/7HpB1gEHp4WBjS+x+BhyUajYc+IdEuLpaee86bmuAu\nAg9LU5P088/xdjSaPHdvavKuJriLO97AFve0y12pssekHSzPPBPfTpvYUptoP/OMdzXBXQQeMAjn\n8LCw087/CDwsLMv5H4GHhWU5/+McHjAIPTwsFRXx4bskTU4m2xUV3tUEd9HDAwahh4eFW1z5H4GH\nhTve+B9ba2GLrbW5i621mBe21vofPTxs0cPnLnp4zAs9vP8xaQfLV19J584lnyfaX33lTT1wHz08\nYJCUPXx7e7t6enpUWlqqwcFBSdL4+LgeeeQRjY6OatWqVfrwww9VlNiShZy2b1/ygpmODulvf4u3\nuXDGP1L28G1tbert7Z31Wmdnp5qbmzU8PKy77rpLnZ2dGS0Q107iarlE6BPtmRfUILelDHxjY6OK\ni4tnvfbpp59qx44dkqQdO3bo8OHDmasOgKsWPGl38eJFlZWVSZLKysp08eJF14uCN9hp539pzdIH\nAgEFAoE5vx4Oh612MBhUkJPBrMZe+twUiUQUmXnnkhQWHPiysjJduHBB5eXl+uGHH1RaWjrnsTMD\nj+xHD5+bft+ZdnR0zHnsgpfltm7dqu7ubklSd3e3WlpaFl4hAE+k3FobCoUUjUY1NjamsrIyvfrq\nq3rggQf08MMP6+zZsymX5dham3saG6Vjx+Lty5elJUvi7Y0bpSNHvKsLC5Mqe+ylh2XPHimx6BKN\nJj9xpqWFe9rlklTZY2stLJzD+x89PCx/+Usy5LFY/Io5KT5b/89/elUVFooeHvNSVyeNj8fbk5PS\n0qXJ1+EPBB6WsTHpP/9JPk+0x8a8qQfu42o5wCD08LBMTkpXriSfJ9qTk97UA/cReFja22cvy91x\nR7zN3ir/IPCwsCznfwQeloEB6cKF5PNEe2DAm3rgPgIPS12d9K9/xdujo1J5efJ1+AOBh+X8eWli\nIvk80T5/3pt64D4CD0tTk/Tzz/F2NCrV1ydfhz+wtRYWrpbzB7bWYl5Yh/c/engDzX1bso8l3fl/\n7SJJiRP6/5H04B+O5v83O3E9PBaMz5bLXXy2HBZsEX8ZvsR/K2xt3ep1BcgEhvSAzzCkByCJwGMO\nfKSAPzGkhy1m6XMXQ3oAkgg8YBQCDxiEwAMGIfCw9corXleATGCWHvAZZukBSCLwgFEIPGAQAg8Y\nhMDDFnvp/YlZethiL33uYpYegCQCDxiFwAMGIfCAQRwHfu/evaqtrdXatWu1d+9eN2tCFmAvvT85\nmqX/+uuvFQqFdOzYMRUUFOjee+/V22+/raqqquQbM0sPeML1WfrTp09r06ZNKiwsVF5enpqamvTx\nxx+nVSSAzHP02XJr167VSy+9pPHxcRUWFqqnp0e33377H44Lz9i9EQwGFQwGndYJYA6RSESRSGRe\nxzreeNPV1aW33npL1113nWpqarRkyRLt3r07+cYM6QFPZPyz5V588UWtXLlSTz311Lx+KIDMychO\nux9//FGSdPbsWX3yySdqbW11+lbIQuyl9yfHPfzmzZv1008/qaCgQLt379add9456+v08LmNvfS5\ni4+LxoIR+NzFxTMAJBF4wCgEHjAIgYct9tL7E5N2gM8waQdAEoEHjELgAYMQeMAgBB622EvvT8zS\nwxZba3MXs/QAJBF4wCgEHjAIgQcMQuBhi730/sQsPeAzzNIDkETgAaMQeMAgBB4wCIGHLfbS+xOz\n9LDFXvrcxSw9AEkEHjAKgQcMQuABgxB42GIvvT8xSw/4DLP0ACQReMAoBB4wCIEHDELgYYu99P7E\nLD1ssZc+dzFLD0ASgQeM4jjwu3btUk1NjWpra9Xa2qrLly+7WReADHAU+JGREb3zzjs6fvy4BgcH\ndfXqVR06dMjt2gC4LN/JNy1dulQFBQWamppSXl6epqamVFFR4XZt8BB76f3JUQ9fUlKi559/XitX\nrtSNN96ooqIi3X333W7XBg+xLOdPjnr477//Xnv27NHIyIiWLVumhx56SO+//74effTRWceFZ/zV\nBINBBYPBdGoFYCMSiSgSiczrWEfr8B988IG++OILvfvuu5Kk9957T/39/XrzzTeTb8w6POAJ19fh\nV69erf7+fl26dEmxWEx9fX1as2ZNWkUCyDxHga+rq9P27dt12223ad26dZKkJ554wtXCALiPrbWw\nFQ4zcZerUmWPwMMWe+lzF3vpAUgi8IBRCDxgEAIPGITA+1BJSXzSLZ2HlN73l5R4+zuAPWbpfSgb\nZtizoQZTMUsPQBKBB4xC4AGDEHjAIAQeMAiBBwxC4AGDEHjAIAQeMAiBBwxC4AGDEHjAIAQeMAiB\nBwxC4AGDEHjAIAQeMAiBBwxC4AGDEHjAIAQeMAiBBwxC4AGDEHjAIAQeMAiBBwxC4AGDEHjAIAQe\nMAiBBwxC4AGDOAr80NCQ1q9fbz2WLVumffv2uV0bHIopEP+Adg8fMQW8/jXARiA21yfHz9Nvv/2m\niooKHT16VJWVlck3TvGh9MisQEDy+lefDTWYKlX20h7S9/X1qaqqalbYAWSn/HTf4NChQ2ptbbX9\nWjgcttrBYFDBYDDdHwfgdyKRiCKRyLyOTWtIPz09rYqKCp06dUrLly+f/cYM6T2TDcPpbKjBVBkb\n0n/++ee69dZb/xB2ANkprcAfPHhQoVDIrVoAZJjjIf2vv/6qm266SWfOnNENN9zwxzdmSO+ZbBhO\nZ0MNpkqVvbSX5Zz8UGRWNoQtG2owVUaX5QDkDgIPGITAAwYh8IBBCDxgEAIPGITAAwYh8IBBCDxg\nEAIPGITAAwYh8IBBCDxgEAIPGCTte9ohOwU8vkt0cbG3Px/2CLwPuXEdOtez+xNDesAgBB4wCIEH\nDELgAYMQeNh65RWvK0AmcNdawGe4ay0ASQQeMAqBBwxC4AGDEHjYCoe9rgCZwCw9bLGXPncxSw9A\nEoEHjELgAYNwPbyBAvO8O8b/dxhzNLmHwBuIoJqLIT1gEAIPGITAAwYh8IBBHAd+YmJC27ZtU3V1\ntdasWaP+/n4364LHIpGI1yUgAxwHfufOndqyZYu+/fZbnTx5UtXV1W7WBY8ReH9ytCw3OTmpI0eO\nqLu7O/4m+flatmyZq4UBcJ+jHv7MmTNavny52tratGHDBj3++OOamppyuzYAbos5cOzYsVh+fn7s\n6NGjsVgsFtu5c2fs5ZdfnnWMJB48eHj0mIujIf2KFSu0YsUKbdy4UZK0bds2dXZ2zjqG3VxA9nE0\npC8vL1dlZaWGh4clSX19faqpqXG1MADuc3wDjIGBAT322GOanp5WVVWV9u/fz8QdkOUydscb5Kb2\n9nb19PSotLRUg4ODXpcDl7HTDrO0tbWpt7fX6zKQIQQeszQ2Nqq4uNjrMpAhBB4wCIEHDELgAYMQ\neMAgBB6zhEIhNTQ0aHh4WJWVldq/f7/XJcFFrMMDBqGHBwxC4AGDEHjAIAQeMAiBBwxC4AGD/C/M\n8qlqASAcZwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot([x/40 for x in range(40)], [math.log(anotherCountSeries.quantile(x/40)) for x in range(40)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f7f0ab23b50>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAQ0AAAE1CAYAAAD9O7XIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Word entropies\n",
      "\n",
      "In this section we'll look at word entropies."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tokenizer = bc.BasicTokenizer()\n",
      "grouper = bc.BasicGrouper(500)\n",
      "entropies = {}\n",
      "for book in aBookCollection:\n",
      "    entropies[book] = bc.TokenEntropies.from_parts(grouper.parts_from(tokenizer.tokens_from(book.contents)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import functools\n",
      "total_entropy = functools.reduce(lambda x,y: x.combine(y), entropies.values())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfEntropies = pandas.DataFrame([[k,total_entropy[k],v/anotherPossibleFeatureAnalyzer._total] for (k,v) in anotherPossibleFeatureAnalyzer._counts.items()], columns=['Word', 'Entropy', 'Frequency'])\n",
      "#hist([v for k,v in total_entropy.items()], log=True)\n",
      "dfEntropies.Entropy.hist(log=True, bins=30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f7f0ab2c790>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAE4CAYAAABMu9ZRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot([x/20 for x in range(20)], [dfEntropies.Entropy.quantile(x/20) for x in range(20)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f7f0aa19650>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAE1CAYAAADqGSUBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIlLEcCAiRQwHIlLEcCAiRQwHIlL0/+RvopwX6AMCAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = []\n",
      "for k,v in anotherPossibleFeatureAnalyzer._counts.items():\n",
      "    data.append([k, v/anotherPossibleFeatureAnalyzer._total, total_entropy[k], v/anotherPossibleFeatureAnalyzer._total / total_entropy[k]])\n",
      "    #freq_entr_y2.append()\n",
      "    #freq_entr.append((v, total_entropy[k]))\n",
      "data = pandas.DataFrame(data, columns=['Word', 'Freq', 'Entropy', 'Both'])\n",
      "#(data.Freq * (1-data.Entropy)).plot()\n",
      "#blah = (data.Freq * (1-data.Entropy))\n",
      "#blah = data[data.Entropy > .01][data.Freq > .0001].sort(columns='Both')\n",
      "blah = data.sort(columns='Both')\n",
      "print(blah.head(20))\n",
      "boxplot(blah.Entropy)\n",
      "#boxplot(blah.Both)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                 Word      Freq   Entropy      Both\n",
        "2631        obviously  0.000028  0.562110  0.000050\n",
        "2224        withdrawn  0.000028  0.562110  0.000050\n",
        "1272        persisted  0.000028  0.563151  0.000050\n",
        "12           intently  0.000028  0.564123  0.000050\n",
        "2193        horseback  0.000028  0.560437  0.000050\n",
        "2197         pathetic  0.000028  0.560944  0.000050\n",
        "4614  merchantibility  0.000029  0.567355  0.000051\n",
        "2629          forlorn  0.000028  0.560729  0.000051\n",
        "554         protected  0.000028  0.562806  0.000051\n",
        "4692           crisis  0.000028  0.560382  0.000051\n",
        "4342         brightly  0.000029  0.564586  0.000051\n",
        "1709          planned  0.000028  0.561172  0.000051\n",
        "555          asserted  0.000028  0.561960  0.000051\n",
        "1826        examining  0.000028  0.558461  0.000051\n",
        "3949        perpetual  0.000028  0.557920  0.000051\n",
        "528           deepest  0.000029  0.564272  0.000051\n",
        "1209         perished  0.000028  0.559317  0.000051\n",
        "2819         gorgeous  0.000029  0.562434  0.000051\n",
        "1528          pleaded  0.000029  0.562184  0.000051\n",
        "4815           boyish  0.000028  0.557615  0.000051\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "{'boxes': [<matplotlib.lines.Line2D at 0x7f7f0a96f390>],\n",
        " 'caps': [<matplotlib.lines.Line2D at 0x7f7f0a969550>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7f0a969c50>],\n",
        " 'fliers': [<matplotlib.lines.Line2D at 0x7f7f0a9731d0>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7f0a9738d0>],\n",
        " 'medians': [<matplotlib.lines.Line2D at 0x7f7f0a96fa90>],\n",
        " 'whiskers': [<matplotlib.lines.Line2D at 0x7f7f0a9669d0>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7f0a966d90>]}"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAQAAAAE1CAYAAAAIxT54AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCADAYAQAYLD/A7p0jEcdqyeoAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}