scikit-learn 的 LabelBinarizer 函数可以很方便地把你的目标(labels)转化成独热编码向量。
示例:
import numpy as np from sklearn import preprocessing # Example labels 示例labels labels = np.array([1,5,3,2,1,4,2,1,3]) # Create the encoder 创建编码器 lb = preprocessing.LabelBinarizer() # Here the encoder finds the classes and assigns one-hot vectors # 编码器找到类别并分配 one-hot 向量 lb.fit(labels) # And finally, transform the labels into one-hot encoded vectors # 最后把目标(lables)转换成独热编码的(one-hot encoded)向量 lb.transform(labels)>>> 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]])