{ "metadata": { "name": "", "signature": "sha256:ef697e1839b00d035b30ea4b91450cf0f9e97465900cb0b1cd7f2b91a0691a8c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Try out R-W learning of various number marking systems using data from Ramscar, et al. (2011)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import pandas.rpy.common as com\n", "import numpy as np\n", "from sklearn.feature_extraction import DictVectorizer\n", "\n", "import ndl\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "%load_ext rmagic\n", "\n", "%matplotlib inline\n", "\n", "%precision 2\n", "pd.set_option('display.precision', 2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "library(ndl)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "This is ndl version 0.2.16. \n", "For an overview of the package, type 'help(\"ndl.package\")'.\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "data = com.load_data('numbers')\n", "data['Cues'] = [x.split('_') for x in data['Cues']]\n", "data['Number'] = data['Outcomes']\n", "data" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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CuesOutcomesFrequencyNumber
1 [size, shape, color, 1, exactly1] 1 455 1
2 [size, shape, color, 1, 2, exactly2] 2 205 2
3 [size, shape, color, 1, 2, 3, exactly3] 3 107 3
4 [size, shape, color, 1, 2, 3, 4, exactly4] 4 60 4
5 [size, shape, color, 1, 2, 3, 4, 5, exactly5] 5 50 5
6 [size, shape, color, 1, 2, 3, 4, 5, 6, exactly6] 6 36 6
7 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, exac... 7 21 7
8 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, e... 8 20 8
9 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 9 13 9
10 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 10 16 10
11 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 11 3 11
12 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 12 4 12
13 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 13 2 13
14 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 14 2 14
15 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 15 4 15
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15 rows \u00d7 4 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " Cues Outcomes Frequency \\\n", "1 [size, shape, color, 1, exactly1] 1 455 \n", "2 [size, shape, color, 1, 2, exactly2] 2 205 \n", "3 [size, shape, color, 1, 2, 3, exactly3] 3 107 \n", "4 [size, shape, color, 1, 2, 3, 4, exactly4] 4 60 \n", "5 [size, shape, color, 1, 2, 3, 4, 5, exactly5] 5 50 \n", "6 [size, shape, color, 1, 2, 3, 4, 5, 6, exactly6] 6 36 \n", "7 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, exac... 7 21 \n", "8 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, e... 8 20 \n", "9 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 9 13 \n", "10 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 10 16 \n", "11 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 11 3 \n", "12 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 12 4 \n", "13 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 13 2 \n", "14 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 14 2 \n", "15 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... 15 4 \n", "\n", " Number \n", "1 1 \n", "2 2 \n", "3 3 \n", "4 4 \n", "5 5 \n", "6 6 \n", "7 7 \n", "8 8 \n", "9 9 \n", "10 10 \n", "11 11 \n", "12 12 \n", "13 13 \n", "14 14 \n", "15 15 \n", "\n", "[15 rows x 4 columns]" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "Singular, plural\n", "-------------------" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data['Outcomes'] = 'plural'\n", "data['Outcomes'][1] = 'singular'\n", "data" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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CuesOutcomesFrequencyNumber
1 [size, shape, color, 1, exactly1] singular 455 1
2 [size, shape, color, 1, 2, exactly2] plural 205 2
3 [size, shape, color, 1, 2, 3, exactly3] plural 107 3
4 [size, shape, color, 1, 2, 3, 4, exactly4] plural 60 4
5 [size, shape, color, 1, 2, 3, 4, 5, exactly5] plural 50 5
6 [size, shape, color, 1, 2, 3, 4, 5, 6, exactly6] plural 36 6
7 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, exac... plural 21 7
8 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, e... plural 20 8
9 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 13 9
10 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 16 10
11 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 3 11
12 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 4 12
13 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 2 13
14 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 2 14
15 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 4 15
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15 rows \u00d7 4 columns

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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " Cues Outcomes Frequency \\\n", "1 [size, shape, color, 1, exactly1] singular 455 \n", "2 [size, shape, color, 1, 2, exactly2] plural 205 \n", "3 [size, shape, color, 1, 2, 3, exactly3] plural 107 \n", "4 [size, shape, color, 1, 2, 3, 4, exactly4] plural 60 \n", "5 [size, shape, color, 1, 2, 3, 4, 5, exactly5] plural 50 \n", "6 [size, shape, color, 1, 2, 3, 4, 5, 6, exactly6] plural 36 \n", "7 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, exac... plural 21 \n", "8 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, e... plural 20 \n", "9 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 13 \n", "10 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 16 \n", "11 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 3 \n", "12 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 4 \n", "13 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 2 \n", "14 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 2 \n", "15 [size, shape, color, 1, 2, 3, 4, 5, 6, 7, 8, 9... plural 4 \n", "\n", " Number \n", "1 1 \n", "2 2 \n", "3 3 \n", "4 4 \n", "5 5 \n", "6 6 \n", "7 7 \n", "8 8 \n", "9 9 \n", "10 10 \n", "11 11 \n", "12 12 \n", "13 13 \n", "14 14 \n", "15 15 \n", "\n", "[15 rows x 4 columns]" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "W = ndl.rw(data,M=100)\n", "W" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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pluralsingular
1 9.3e-02 1.2e-01
10 2.0e-03-2.8e-03
11 2.0e-03-2.8e-03
12 2.0e-03-2.8e-03
13 0.0e+00 0.0e+00
14 0.0e+00 0.0e+00
15 0.0e+00 0.0e+00
2 2.2e-01-1.2e-01
3 1.3e-01-7.4e-02
4 7.9e-02-5.2e-02
5 4.7e-02-3.1e-02
6 4.5e-02-2.9e-02
7 4.0e-02-2.3e-02
8 2.4e-02-1.4e-02
9 5.3e-03-6.0e-03
color 9.3e-02 1.2e-01
exactly1-1.3e-01 2.4e-01
exactly10 0.0e+00 0.0e+00
exactly11 0.0e+00 0.0e+00
exactly12 2.0e-03-2.8e-03
exactly13 0.0e+00 0.0e+00
exactly14 0.0e+00 0.0e+00
exactly15 0.0e+00 0.0e+00
exactly2 9.5e-02-5.0e-02
exactly3 4.6e-02-2.2e-02
exactly4 3.2e-02-2.1e-02
exactly5 1.8e-03-2.0e-03
exactly6 4.9e-03-5.6e-03
exactly7 1.6e-02-9.0e-03
exactly8 1.9e-02-8.0e-03
exactly9 3.4e-03-3.2e-03
shape 9.3e-02 1.2e-01
size 9.3e-02 1.2e-01
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33 rows \u00d7 2 columns

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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " plural singular\n", "1 9.3e-02 1.2e-01\n", "10 2.0e-03 -2.8e-03\n", "11 2.0e-03 -2.8e-03\n", "12 2.0e-03 -2.8e-03\n", "13 0.0e+00 0.0e+00\n", "14 0.0e+00 0.0e+00\n", "15 0.0e+00 0.0e+00\n", "2 2.2e-01 -1.2e-01\n", "3 1.3e-01 -7.4e-02\n", "4 7.9e-02 -5.2e-02\n", "5 4.7e-02 -3.1e-02\n", "6 4.5e-02 -2.9e-02\n", "7 4.0e-02 -2.3e-02\n", "8 2.4e-02 -1.4e-02\n", "9 5.3e-03 -6.0e-03\n", "color 9.3e-02 1.2e-01\n", "exactly1 -1.3e-01 2.4e-01\n", "exactly10 0.0e+00 0.0e+00\n", "exactly11 0.0e+00 0.0e+00\n", "exactly12 2.0e-03 -2.8e-03\n", "exactly13 0.0e+00 0.0e+00\n", "exactly14 0.0e+00 0.0e+00\n", "exactly15 0.0e+00 0.0e+00\n", "exactly2 9.5e-02 -5.0e-02\n", "exactly3 4.6e-02 -2.2e-02\n", "exactly4 3.2e-02 -2.1e-02\n", "exactly5 1.8e-03 -2.0e-03\n", "exactly6 4.9e-03 -5.6e-03\n", "exactly7 1.6e-02 -9.0e-03\n", "exactly8 1.9e-02 -8.0e-03\n", "exactly9 3.4e-03 -3.2e-03\n", "shape 9.3e-02 1.2e-01\n", "size 9.3e-02 1.2e-01\n", "\n", "[33 rows x 2 columns]" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "def activation(W):\n", " return pd.DataFrame([ndl.activation(c,W) for c in data.Cues],index=data.index)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "activation(W)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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pluralsingular
1 0.2 0.7
2 0.7 0.3
3 0.8 0.3
4 0.8 0.2
5 0.8 0.2
6 0.9 0.2
7 0.9 0.1
8 1.0 0.1
9 1.0 0.1
10 1.0 0.1
11 1.0 0.1
12 1.0 0.1
13 1.0 0.1
14 1.0 0.1
15 1.0 0.1
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15 rows \u00d7 2 columns

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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ " plural singular\n", "1 0.2 0.7\n", "2 0.7 0.3\n", "3 0.8 0.3\n", "4 0.8 0.2\n", "5 0.8 0.2\n", "6 0.9 0.2\n", "7 0.9 0.1\n", "8 1.0 0.1\n", "9 1.0 0.1\n", "10 1.0 0.1\n", "11 1.0 0.1\n", "12 1.0 0.1\n", "13 1.0 0.1\n", "14 1.0 0.1\n", "15 1.0 0.1\n", "\n", "[15 rows x 2 columns]" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "Singular, dual, plural\n", "----------------------" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data['Outcomes'] = 'plural'\n", "data['Outcomes'][1] = 'singular'\n", "data['Outcomes'][2] = 'dual'\n", "W = ndl.rw(data,M=100)\n", "activation(W)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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dualpluralsingular
1 0.1 0.1 0.8
2 0.4 0.3 0.3
3 0.2 0.5 0.3
4 0.2 0.6 0.3
5 0.1 0.7 0.2
6 0.1 0.7 0.2
7 0.1 0.7 0.2
8 0.1 0.8 0.2
9 0.1 0.8 0.2
10 0.1 0.8 0.2
11 0.1 0.8 0.2
12 0.1 0.8 0.2
13 0.1 0.8 0.2
14 0.1 0.8 0.2
15 0.1 0.8 0.2
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15 rows \u00d7 3 columns

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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " dual plural singular\n", "1 0.1 0.1 0.8\n", "2 0.4 0.3 0.3\n", "3 0.2 0.5 0.3\n", "4 0.2 0.6 0.3\n", "5 0.1 0.7 0.2\n", "6 0.1 0.7 0.2\n", "7 0.1 0.7 0.2\n", "8 0.1 0.8 0.2\n", "9 0.1 0.8 0.2\n", "10 0.1 0.8 0.2\n", "11 0.1 0.8 0.2\n", "12 0.1 0.8 0.2\n", "13 0.1 0.8 0.2\n", "14 0.1 0.8 0.2\n", "15 0.1 0.8 0.2\n", "\n", "[15 rows x 3 columns]" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "Singular, dual, trial, plural\n", "-----------------------------" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data['Outcomes'] = 'plural'\n", "data['Outcomes'][1] = 'singular'\n", "data['Outcomes'][2] = 'dual'\n", "data['Outcomes'][3] = 'trial'\n", "W = ndl.rw(data,M=100)\n", "activation(W)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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dualpluralsingulartrial
1 0.2 0.0 0.7 0.1
2 0.5 0.1 0.3 0.1
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4ialdualpluralsingulartrial
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dualnotdual
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