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Try out R-W learning of various number marking systems using data from Ramscar, et al. (2011).





In [1]:



    


import pandas as pd
import pandas.rpy.common as com
import numpy as np
from sklearn.feature_extraction import DictVectorizer

import ndl

%load_ext autoreload
%autoreload 2

%load_ext rmagic

%matplotlib inline

%precision 2
pd.set_option('display.precision', 2)



    











In [2]:



    


%%R
library(ndl)



    


















    








This is ndl version 0.2.16. 
For an overview of the package, type 'help("ndl.package")'.


















In [3]:



    


data = com.load_data('numbers')
data['Cues'] = [x.split('_') for x in data['Cues']]
data['Number'] = data['Outcomes']
data



    


















    
Out[3]:










  
    

      
      Cues
      Outcomes
      Frequency
      Number
    

  
  
    

      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
    

  



15 rows × 4 columns

Singular, plural





In [4]:



    


data['Outcomes'] = 'plural'
data['Outcomes'][1] = 'singular'
data



    


















    
Out[4]:










  
    

      
      Cues
      Outcomes
      Frequency
      Number
    

  
  
    

      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
    

  



15 rows × 4 columns



















In [7]:



    


W = ndl.rw(data,M=100)
W



    


















    
Out[7]:










  
    

      
      plural
      singular
    

  
  
    

      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
    

  



33 rows × 2 columns



















In [8]:



    


def activation(W):
    return pd.DataFrame([ndl.activation(c,W) for c in data.Cues],index=data.index)



    











In [9]:



    


activation(W)



    


















    
Out[9]:










  
    

      
      plural
      singular
    

  
  
    

      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
    

  



15 rows × 2 columns

Singular, dual, plural





In [10]:



    


data['Outcomes'] = 'plural'
data['Outcomes'][1] = 'singular'
data['Outcomes'][2] = 'dual'
W = ndl.rw(data,M=100)
activation(W)



    


















    
Out[10]:










  
    

      
      dual
      plural
      singular
    

  
  
    

      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
    

  



15 rows × 3 columns

Singular, dual, trial, plural





In [12]:



    


data['Outcomes'] = 'plural'
data['Outcomes'][1] = 'singular'
data['Outcomes'][2] = 'dual'
data['Outcomes'][3] = 'trial'
W = ndl.rw(data,M=100)
activation(W)



    


















    
Out[12]:










  
    

      
      dual
      plural
      singular
      trial
    

  
  
    

      1 
       0.2
       0.0
       0.7
       0.1
    

    

      2 
       0.5
       0.1
       0.3
       0.1
    

    

      3 
       0.3
       0.2
       0.2
       0.3
    

    

      4 
       0.2
       0.4
       0.2
       0.2
    

    

      5 
       0.2
       0.5
       0.2
       0.2
    

    

      6 
       0.2
       0.5
       0.2
       0.2
    

    

      7 
       0.2
       0.6
       0.2
       0.2
    

    

      8 
       0.2
       0.6
       0.2
       0.2
    

    

      9 
       0.2
       0.7
       0.2
       0.2
    

    

      10
       0.2
       0.7
       0.1
       0.1
    

    

      11
       0.2
       0.7
       0.2
       0.1
    

    

      12
       0.2
       0.8
       0.2
       0.1
    

    

      13
       0.2
       0.8
       0.2
       0.1
    

    

      14
       0.2
       0.8
       0.2
       0.1
    

    

      15
       0.2
       0.8
       0.2
       0.1
    

  



15 rows × 4 columns

Singular, dual, trial, quadral, plural





In [13]:



    


data['Outcomes'] = 'plural'
data['Outcomes'][1] = 'singular'
data['Outcomes'][2] = 'dual'
data['Outcomes'][3] = 'trial'
data['Outcomes'][4] = '4ial'
W = ndl.rw(data,M=100)
activation(W)



    


















    
Out[13]:










  
    

      
      4ial
      dual
      plural
      singular
      trial
    

  
  
    

      1 
       0.1
       0.1
       0.0
       0.8
       0.0
    

    

      2 
       0.1
       0.4
       0.1
       0.3
       0.1
    

    

      3 
       0.1
       0.2
       0.2
       0.3
       0.2
    

    

      4 
       0.3
       0.1
       0.3
       0.2
       0.1
    

    

      5 
       0.2
       0.1
       0.4
       0.2
       0.1
    

    

      6 
       0.2
       0.1
       0.4
       0.2
       0.1
    

    

      7 
       0.2
       0.1
       0.5
       0.2
       0.1
    

    

      8 
       0.2
       0.1
       0.5
       0.2
       0.1
    

    

      9 
       0.2
       0.1
       0.5
       0.2
       0.1
    

    

      10
       0.2
       0.1
       0.5
       0.2
       0.1
    

    

      11
       0.2
       0.1
       0.5
       0.2
       0.1
    

    

      12
       0.2
       0.1
       0.5
       0.2
       0.1
    

    

      13
       0.2
       0.1
       0.5
       0.2
       0.1
    

    

      14
       0.2
       0.1
       0.5
       0.2
       0.1
    

    

      15
       0.2
       0.1
       0.5
       0.2
       0.1
    

  



15 rows × 5 columns

Dual, non-dual





In [16]:



    


data['Outcomes'] = 'notdual'
data['Outcomes'][1] = 'notdual'
data['Outcomes'][2] = 'dual'
W = ndl.rw(data,M=100)
activation(W)



    


















    
Out[16]:










  
    

      
      dual
      notdual
    

  
  
    

      1 
       0.1
       0.9
    

    

      2 
       0.3
       0.7
    

    

      3 
       0.1
       0.9
    

    

      4 
       0.1
       0.9
    

    

      5 
       0.1
       0.9
    

    

      6 
       0.1
       0.9
    

    

      7 
       0.1
       1.0
    

    

      8 
       0.1
       1.0
    

    

      9 
       0.1
       1.0
    

    

      10
       0.1
       1.0
    

    

      11
       0.1
       1.0
    

    

      12
       0.1
       1.0
    

    

      13
       0.1
       1.0
    

    

      14
       0.1
       1.0
    

    

      15
       0.1
       1.0
    

  



15 rows × 2 columns



















In [ ]:

Content source: rmalouf/learning

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