In [7]:

    

import numpy as np
from sklearn import preprocessing


    

In [8]:

    

# Example labels
labels = np.array([1,5,3,2,1,4,2,1,3])
print(labels)


    

    




[1 5 3 2 1 4 2 1 3]

In [9]:

    

# Create the encoder
lb = preprocessing.LabelBinarizer()


    

In [10]:

    

# Here the encoder finds the classes and assigns one-hot vectors 
lb.fit(labels)


    

    
Out[10]:





LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)

In [11]:

    

# And finally, transform the labels into one-hot encoded vectors
lb.transform(labels)


    

    
Out[11]:





array([[1, 0, 0, 0, 0],
       [0, 0, 0, 0, 1],
       [0, 0, 1, 0, 0],
       [0, 1, 0, 0, 0],
       [1, 0, 0, 0, 0],
       [0, 0, 0, 1, 0],
       [0, 1, 0, 0, 0],
       [1, 0, 0, 0, 0],
       [0, 0, 1, 0, 0]])