{
 "metadata": {
  "name": "",
  "signature": "sha256:74685a070bc4882c0a034427d8f7ebd724c9f491fcf9a7100d846db6dbd77f43"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "import math\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def barplot(vec):\n",
      "    plt.bar(range(len(vec)), vec)\n",
      "    plt.xlim( (-0.2, len(vec)+0.2) )\n",
      "    plt.ylim( (min(0,min(vec)-0.3), max(vec)+0.3) )\n",
      "    plt.axhline(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scores = [2.0, -1.0]\n",
      "barplot(scores)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD7CAYAAACRxdTpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkv8D2A9N4nS4OjIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10642d5d0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "escores = [math.exp(s) for s in scores]\n",
      "escores"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[7.38905609893065, 0.36787944117144233]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "barplot(escores)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAAD7CAYAAABZqT4/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACxtJREFUeJzt3V2orWlBwPH/mjkO6nykUozZCMcujITwg5gGm5oVdKFS\n1kUXdRMYRReBEhQmBG6voisppJtEGbP0YiRJ0j7xHQ1pKJ0xP/sQDDU1aWzmmESWu4u155zjcZ+9\n3zVnrb2fvdfvB4u9zlrv7P3My3P++znP+ioAAAAAAAAAAAA49xY3+g3uu+++/QcffHATYwHYJQ9W\ny8PuuOmGv/ODD7a/v39il9e//vUn+vPOysV5cV6cl7N1Tqr7rtfVGw4zAJslzACDOXNhXi6Xpz2E\nITkvh3NeDue8fLuRzskNP/hX7R/slwAw02KxqOs0+MytmAHOO2EGGMyF0x7Auu6441lduvTV0x4G\nT9Lttz+zxx9/9LSHAUM7c3vMq30Ze9pn1yKPSYA9ZoAzRZgBBiPMAIMRZoDBCDPAYOaE+fuqh6+6\nPFa9epuDAthl6z5d7qbqC9Xd1ecObvN0Odbg6XJQm3263I9Xn+lKlAHYsHXD/LPVH21jIACsrLOV\ncUurbYwXVF+56nZbGazBVgbU0VsZ67xXxsurD/etUa5qb2/v8vXlcjnU+5oCjGCapqZpmnXsOivm\nd1bvq+6/5nYrZtZgxQx19Ip5bphvrf61el516Zr7hJk1CDPUZsJ8FGFmDcIM5d3lAM4UYQYYjDAD\nDEaYAQYjzACDEWaAwQgzwGCEGWAwwgwwGGEGGIwwAwxGmAEGI8wAgxFmgMEIM8BghBlgMMIMMBhh\nBhiMMAMMRpgBBjMnzM+oHqg+VX2yumerIwLYcRdmHPM71Xurnzk4/tatjghgxx360dlX+Y7q4ep7\njzhm/yQ/jn71kd8n9/PYtEUnOV9gVKuWHd7g47Yynld9pXpr9ZHq96unb3JwAHyr48J8oXpJ9XsH\nX/+r+o1tDwpglx23x/z5g8vfHfz5gQ4J897e3uXry+Wy5XK5mdEBnBPTNDVN06xjj9tjrvpA9YvV\nP1V71dOq1151vz1m1mCPGeroPeY5YX5h9ebqluoz1auqx666X5hZgzBD3XiYjyPMrEGYoW7sWRkA\nnDBhBhiMMAMMRpgBBiPMAIMRZoDBCDPAYIQZYDDCDDAYYQYYjDADDEaYAQYjzACDEWaAwQgzwGCE\nGWAwwgwwGGEGGIwwAwxGmAEGc2HmcZ+tHq/+r/pGdfe2BgSw6+aGeb9aVo9ubygA1HpbGYd+zDYA\nmzU3zPvVX1V/X/3S9oYDwNytjB+uvlh9V/WX1aerD25rUAC7bG6Yv3jw9SvVH7d68O9ymPf29i4f\nuFwuWy6XmxkdwDkxTVPTNM06ds6+8dOrm6tL1a3VX1RvOPhatb+/v7/+KJ+kxWLRameFs2nRSc4X\nGNWqZYc3eM6K+c5Wq+Qnjv/DrkQZgA3bxDMtrJhZgxUz1NErZq/8AxiMMAMMRpgBBiPMAIMRZoDB\nCDPAYIQZYDDCDDAYYQYYjDADDEaYAQYjzACDEWaAwQgzwGCEGWAwwgwwGGEGGIwwAwxGmAEGI8wA\ng5kb5purh6v3bHEsADQ/zK+pPpmPpwbYujlhvqt6RfXmrvNR2wBszpwwv7H69eqbWx4LAB0f5p+o\n/r3V/rLVMsAJuHDM/S+tXtlqK+Op1R3V26qfv/qgvb29y9eXy2XL5XKTYwQ486ZpapqmWceuswq+\nr/q16ievuX1/f//kHhNcLBZ5DPIsW3SS8wVGtWrZ4Q1e93nM/kYBbNkm9o2tmFmDFTPUZlfMAGyZ\nMAMMRpgBBiPMAIMRZoDBCDPAYIQZYDDCDDAYYQYYjDADDEaYAQYjzACDEWaAwQgzwGCEGWAwwgww\nGGEGGIwwAwxGmAEGI8wAg5kT5qdWD1WPVJ+sfmurIwLYcRdmHPPf1Y9VXz84/m+qew++ArBhc7cy\nvn7w9Zbq5urR7QwHgLlhvqnVVsaXq/e32tIAYAvmhvmb1Yuqu6ofrZbbGhDArpuzx3y1x6o/rX6w\nmp64cW9v7/IBy+Wy5XJ54yMDOEemaWqaplnHLmYc853V/1b/WT2t+vPqDdVfH9y/v7+/v/4on6TF\nYlGd3M9j0xad5HyBUa1adniD56yYv7u6v9W2x03VH3QlygBs2JwV83GsmFmDFTPU0Stmr/wDGIww\nAwxGmAEGI8wAgxFmgMEIM8BghBlgMMIMMBhhBhiMMAMMRpgBBiPMAIMRZoDBCDPAYIQZYDDCDDAY\nYQYYjDADDEaYAQYjzACDmRPm51bvrz5Rfbx69VZHBLDj5nxK9rMPLo9Ut1Ufrn66+tTB/T4lmzX4\nlGyoG/+U7C+1inLV11oF+TkbGRkA32bdPeaL1YurhzY/FABqvTDfVj1QvabVyhmALbgw87inVO+q\n3l69+9o79/b2Ll9fLpctl8sNDA3g/JimqWmaZh0758G/RXV/9R/Vrx5yvwf/WIMH/6COfvBvTpjv\nrT5Q/UNXivi66s8OrgszaxBmqBsP83GEmTUIM9SNP10OgBMkzACDEWaAwQgzwGCEGWAwwgwwGGEG\nGIwwAwxGmAEGI8wAgxFmgMEIM8BghBlgMMIMMBhhBhiMMAMMRpgBBiPMAIMRZoDBCDPAYOaE+S3V\nl6uPbXksADQvzG+tXrbtgQCwMifMH6y+uu2BALBijxlgMMIMMJgLm/gme3t7l68vl8uWy+Umvi3A\nuTFNU9M0zTp2MfN7XqzeU/3AIfft7+/vz/w2N26xWFQn9/PYtEUnOV9gVKuWHd7gOVsZ76g+VD2/\n+lz1qo2NDIBvM3fFfBQrZtZgxQx14ytmAE6QMAMMRpgBBiPMAIMRZoDBCDPAYIQZYDDCDDAYYQYY\njDADDEaYAQYjzACDEWaAwQgzwGA28gkmMKo77nhWly75LOGz6Pbbn9njjz962sM4Fd6PmRN2su/H\nbL6cZef7vbu9HzPAGSLMAIMRZoDBCDPAYOaE+WXVp6t/rl673eEAcFyYb67e1CrOL6h+rvr+bQ+K\nJ2M67QFwpkynPYDhTNN02kO47Lgw3139S/XZ6hvVO6uf2vKYeFKm0x4AZ8p02gMYzlkK8/dUn7vq\nz58/uA2ALTkuzOf32d0AgzrulX/3VHut9pirXld9s/rtq455pHrhxkcGcL59tHrRk/kPL1SfqS5W\nt7SKsAf/AE7Zy6t/bPUg4OtOeSwAAMAT5ryw5XcP7v9o9eITGtdpO+68LKvHqocPLr95YiM7PW+p\nvlx97IhjdnGuHHdelu3eXHlu9f7qE9XHq1df57hdnC/HurnV1snF6ikdvrf9iuq9B9d/qPrbkxrc\nKZpzXpbVn5zoqE7fj7T6y3O9AO3iXKnjz8uy3Zsrz+7KA263tdqmHa4to75XxpwXtryyuv/g+kPV\nM6o7T2h8p2XuC3428T7bZ8kHq6PeDX8X50odf15q9+bKl1otaKq+Vn2qes41x5z6fBk1zHNe2HLY\nMXdteVynbc552a9e2uqfYO9t9VL6XbeLc2WOXZ8rF1v9i+Kha24/9fky6kdLzX1hy7W/7c/7C2Lm\n/P99pNU+2tdbPaPm3dXztzmoM2LX5socuzxXbqseqF7TauV8rVOdL6OumL/QasI84bmtfmsddcxd\nB7edZ3POy6VWf9Gq3tdqL/pZ2x/a0HZxrsyxq3PlKdW7qre3+mV0LfPlOua8sOXqDfp72o0HdOac\nlzu78tv+7lb70bvgYvMe/NuVufKEi13/vOziXFlUb6veeMQxuzxfjnXYC1t++eDyhDcd3P/R6iUn\nOrrTc9x5+ZVWTwN6pPpQq4l13r2j+rfqf1rtDf5C5kodf152ca7c2+ptJR7pytMEX575AgAAAAAA\nAAAAAAAAwEj+H1LKI4+5UOL9AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1064c5790>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Z = sum(escores)\n",
      "Z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "7.756935540102093"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "probs = [escore/Z for escore in escores]\n",
      "probs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[0.9525741268224331, 0.04742587317756678]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scores = [2, 0.0000001]\n",
      "escores = [math.exp(s) for s in scores]\n",
      "probs = [es/sum(escores) for es in escores]\n",
      "plt.figure(figsize=(20,6))\n",
      "plt.subplot(131); barplot(scores)\n",
      "plt.subplot(132); barplot(escores)\n",
      "plt.subplot(133); barplot(probs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAABH0AAAFrCAYAAABMofWVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106888810>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# IN-CLASS PAIRWISE AGREEMENT RATES\n",
      "# num_agree: count_of_people_having_that_numagree\n",
      "counts = {10: 12, 9:13, 8:8, 7:6 }\n",
      "\n",
      "sum(weight*numright for numright,weight in counts.items())  \\\n",
      " / sum(counts.values()) / 10.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "0.8"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}