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import pandas import scipy import numpy from sklearn.preprocessing import Binarizer fileurl = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data' names = ['septal_length','sepal_with','petal_length','pedal_width','class'] data = pandas.read_csv(fileurl, names=names) array = data.values # input/output component separation X = array[:,0:4] Y = array[:,4] #treshold is set to 1.4 binarizer = Binarizer(threshold=1.4).fit(X) binaryX = binarizer.transform(X) # summarize transformed data numpy.set_printoptions(precision=2) print(binaryX[0:20,:]) #Anything below the threshold is made into a 0.
[[1. 1. 0. 0.]
[1. 1. 0. 0.]
[1. 1. 0. 0.]
[1. 1. 1. 0.]
[1. 1. 0. 0.]
[1. 1. 1. 0.]
[1. 1. 0. 0.]
[1. 1. 1. 0.]
[1. 1. 0. 0.]
[1. 1. 1. 0.]
[1. 1. 1. 0.]
[1. 1. 1. 0.]
[1. 1. 0. 0.]
[1. 1. 0. 0.]
[1. 1. 0. 0.]
[1. 1. 1. 0.]
[1. 1. 0. 0.]
[1. 1. 0. 0.]
[1. 1. 1. 0.]
[1. 1. 1. 0.]]