In [1]:

    

%pylab inline
from classy import *


    

    




Populating the interactive namespace from numpy and matplotlib
Version:  0.0.15

In [2]:

    

data=load_excel('data/iris.xls',verbose=True)


    

    




iris.data 151 5
150 vectors of length 4
Feature names: 'petal length in cm', 'petal width in cm', 'sepal length in cm', 'sepal width in cm'
Target values given.
Target names: 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'
Mean:  [ 3.75866667  1.19866667  5.84333333  3.054     ]
Median:  [ 4.35  1.3   5.8   3.  ]
Stddev:  [ 1.75852918  0.76061262  0.82530129  0.43214658]

In [3]:

    

data_train=extract_features(data,[2,3])


    

In [4]:

    

C=CSC()


    

In [5]:

    

timeit(reset=True)
C.fit(data_train.vectors,data_train.targets)
print("Training time: ",timeit())


    

    




Time Reset
Training time:  0.06154680252075195 seconds 

In [6]:

    

C.percent_correct(data_train.vectors,data_train.targets)


    

    
Out[6]:





92.0

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