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)

%%R
library(ndl)

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

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

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

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

activation(W)

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

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

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)

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