{ "metadata": { "name": "", "signature": "sha256:d763939098b397efb1edfdab2b980b0df702f3bd2785c6c7484c604b12783b50" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Discriminative learning\n", "=======================" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import ndl\n", "\n", "%matplotlib inline\n", "\n", "%precision 3\n", "pd.set_option('display.precision', 3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "data = pd.DataFrame()\n", "data['Cues'] = [('A','X'),('B','X')]\n", "data['Outcomes'] = ['yes', 'no']\n", "data['Frequency'] = [1,1]\n", "data" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", " | Cues | \n", "Outcomes | \n", "Frequency | \n", "
---|---|---|---|
0 | \n", "(A, X) | \n", "yes | \n", "1 | \n", "
1 | \n", "(B, X) | \n", "no | \n", "1 | \n", "