StandardScaler.py 文件源码

def transform(self, X, y=None, copy=None):
        """Perform standardization by centering and scaling

        Parameters
        ----------
        X : array-like with shape [n_samples, n_features]
            The data used to scale along the features axis.
        """
        check_is_fitted(self, 'std_')

        copy = copy if copy is not None else self.copy
        X = check_array(X, copy=copy, accept_sparse="csc",
                        dtype=np.float32, ensure_2d=False)
        if sparse.issparse(X):
            if self.center_sparse:
                for i in range(X.shape[1]):
                    X.data[X.indptr[i]:X.indptr[i + 1]] -= self.mean_[i]

            elif self.with_mean:
                raise ValueError(
                    "Cannot center sparse matrices: pass `with_mean=False` "
                    "instead. See docstring for motivation and alternatives.")

            else:
                pass

            if self.std_ is not None:
                inplace_column_scale(X, 1 / self.std_)
        else:
            if self.with_mean:
                X -= self.mean_
            if self.with_std:
                X /= self.std_
        return X