from sklearn import preprocessing
import  numpy as np

enc = preprocessing.OneHotEncoder(categories='auto')
# 訓練onehot編碼，指定標籤
enc.fit([[1],[2],[3]])

# 將標籤轉換成 onehot編碼
result =enc.transform([[1],[3],[2]])
print(result.toarray())
#--------
# [[1. 0. 0.]
#  [0. 0. 1.]
#  [0. 1. 0.]]
#--------


# sortmax 結果轉 onehot
 

a = [[0.2,0.3,0.5],
     [0.7,0.3,0.5],
     [0.7,0.9,0.5]
    ]


# sortmax 結果轉 onehot
def props_to_onehot(props):
    if isinstance(props, list):
        props = np.array(props)
    a = np.argmax(props, axis=1)
    b = np.zeros((len(a), props.shape[1]))
    b[np.arange(len(a)), a] = 1
    return b


print
 
(props_to_onehot(a))
#----------
# [[0. 0. 1.]
#  [1. 0. 0.]
#  [0. 1. 0.]]
#---------


# 將onehot轉換成標籤
print("----softmax -> label ----")
print(enc.inverse_transform(props_to_onehot(a)))
#----------
# [[3]
#  [1]
#  [2]]
#-----------