text: import numpy as np
  
  
  class StandardScaler:
      def __init__(self):
          self.mean_ = None
          self.scale_ = None
  
      def fit(self, X):
          """根据训练数据集X获得数据的均值和方差"""
          assert X.ndim == 2, "The dimension of X must be 2"
          self.mean_ = np.array([np.mean(X[:, i]) for i in range(X.shape(1))])
          self.scale_ = np.array([np.std(X[:, i]) for i in range(X.shape(1))])
  
          return self
  
      def transform(self, X):
          """将X根据这个StandardScaler进行均值方差归一化处理"""
          assert X.ndim == 2, "The dimension of X must be 2"
          assert self.mean_ is not None and self.scale_ is not None, \
              "must fit before transform!"
          assert X.shape[1] == len(self.mean_),\
              "the feature number of X must be equal to mean_ and std_"
          resX = np.empty(shape=X.shape, dtype=float)
          for col in range(X.shape[1]):
              resX[:, col] = (X[:, col] - self.mean_[col]) / self.scale_[col]
          return resX
  