from sklearn.preprocessing import OneHotEncoder

#数据预处理二元化OneHotEncoder模型
def test_OneHotEncoder():
    X=[[1,2,3,4,5],
          [5,4,3,2,1],
          [3,3,3,3,3,],
          [1,1,1,1,1]]
    print("before transform:",X)
    encoder=OneHotEncoder(sparse=False)
    encoder.fit(X)
    print("active_features_:",encoder.active_features_)
    print("feature_indices_:",encoder.feature_indices_)
    print("n_values_:",encoder.n_values_)
    print("after transform:",encoder.transform([[1,2,3,4,5]]))
    
# 调用 test_OneHotEncoder
test_OneHotEncoder()