Update 40.md

源码错误，修改为正确的scale函数
请参照 https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing-scaler

docs/master/40.md

@@ -20,17 +20,16 @@
 函数 [`scale`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale "sklearn.preprocessing.scale") 为数组形状的数据集的标准化提供了一个快捷实现:
 
 ```py
+>>> import numpy as np
 >>> X_train = np.array([[ 1., -1.,  2.],
 ...                     [ 2.,  0.,  0.],
 ...                     [ 0.,  1., -1.]])
-...
->>> min_max_scaler = preprocessing.MinMaxScaler()
->>> X_train_minmax = min_max_scaler.fit_transform(X_train)
->>> X_train_minmax
-array([[0.5       , 0.        , 1.        ],
-       [1.        , 0.5       , 0.33333333],
-       [0.        , 1.        , 0.        ]])
-```
+>>> X_scaled = preprocessing.scale(X_train)
+
+>>> X_scaled
+array([[ 0.  ..., -1.22...,  1.33...],
+       [ 1.22...,  0.  ..., -0.26...],
+       [-1.22...,  1.22..., -1.06...]])
 
 经过缩放后的数据具有零均值以及标准方差: