sklearn 归一化 和 标准化

from sklearn.preprocessing import MinMaxScaler,MaxAbsScaler,StandardScaler,Normalizer  # pip install sklearn

data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]  # 数据

scaler1 = MinMaxScaler()
print(scaler1.fit_transform(data))  # 归一化,缩放到0和1之间

[[0.   0.  ]
 [0.25 0.25]
 [0.5  0.5 ]
 [1.   1.  ]]

scaler2 = StandardScaler()
print(scaler2.fit_transform(data))  # 标准化,缩放到均值为0，方差为1

[[-1.18321596 -1.18321596]
 [-0.50709255 -0.50709255]
 [ 0.16903085  0.16903085]
 [ 1.52127766  1.52127766]]

scaler3 = MaxAbsScaler()
print(scaler3.fit_transform(data))  # 归一化,缩放到-1和1之间

[[-1.          0.11111111]
 [-0.5         0.33333333]
 [ 0.          0.55555556]
 [ 1.          1.        ]]

scaler4 = Normalizer()
print(scaler4.fit_transform(data))  # 归一化,缩放到-1和1之间，保留原始数据的分布

[[-0.4472136   0.89442719]
 [-0.08304548  0.99654576]
 [ 0.          1.        ]
 [ 0.05547002  0.99846035]]