x
import pandasimport scipyimport numpyfrom sklearn.preprocessing import Binarizerfileurl = '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 separationX = array[:,0:4]Y = array[:,4]#treshold is set to 1.4binarizer = Binarizer(threshold=1.4).fit(X) binaryX = binarizer.transform(X)# summarize transformed datanumpy.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.]]
[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.]]