one_hot (獨熱)編碼，和dummy variable（啞變量）的區別：
one_hot 類別變量中n個不同類別轉換為n個變量
dummy variable 在某一設定的參考准則下，對n個不同的類別，轉換為n-1個變量
pandas 將標簽轉化為獨熱編碼
pd.get_dummies(df_NMF['cluster']).head(20)tensorflow 將標簽轉化為獨熱編碼
from keras.utils import to_categorical
encoded=to_categorical(df_NMF['cluster'])機器學習包的獨熱編碼使用
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
data = ['cold', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold', 'warm', 'hot']
values = np.array(data)
print(values)
# integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(values)
print(integer_encoded)
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
print(onehot_encoded)
# invert first example
inverted = label_encoder.inverse_transform([np.argmax(onehot_encoded[0, :])])
print(inverted)