MinMaxScaler.py 文件源码

def transform(self, X):
        """Scaling features of X according to feature_range.

        Parameters
        ----------
        X : array-like with shape [n_samples, n_features]
            Input data that will be transformed.
        """
        check_is_fitted(self, 'scale_')

        X = check_array(X, accept_sparse="csc", copy=self.copy,
                        dtype=np.float32)

        if sparse.issparse(X):
            for i in range(X.shape[1]):
                X.data[X.indptr[i]:X.indptr[i + 1]] *= self.scale_[i]
                X.data[X.indptr[i]:X.indptr[i + 1]] += self.min_[i]
        else:
            X *= self.scale_
            X += self.min_
        return X