用 scikit-learn 实现 One-Hot Encoding

用 scikit-learn 实现 One-Hot Encoding

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]])