splearn/preprocessing/data.py
def transform(self, Z):
        """Perform standardization by centering and scaling
        Parameters
        ----------
        Z : DictRDD containing (X, y) pairs
            X - Training vector
            y - Target labels
        Returns
        -------
        C : DictRDD containing (X, y) pairs
            X - Training vector standardized
            y - Target labels
        """
        X = Z[:, 'X'] if isinstance(Z, DictRDD) else Z
        check_rdd(X, (np.ndarray, sp.spmatrix))

        if check_rdd_dtype(X, (sp.spmatrix)):
            if self.with_mean:
                raise ValueError(
                    "Cannot center sparse matrices: pass `with_mean=False` "
                    "instead. See docstring for motivation and alternatives.")
            if self.scale_ is not None:
                def mapper(X):
                    inplace_column_scale(X, 1 / self.scale_)
                    return X
        else:
            if self.with_mean:
                if self.with_std:
                    def mapper(X):
                        X -= self.mean_
                        X /= self.scale_
                        return X
                else:
                    def mapper(X):
                        X -= self.mean_
                        return X
            else:
                if self.with_std:
                    def mapper(X):
                        X /= self.scale_
                        return X
                else:
                    raise ValueError("Need with_std or with_mean ")
        return Z.transform(mapper, column="X")
def transform(self, Z):
        """Perform standardization by centering and scaling
        Parameters
        ----------
        Z : DictRDD containing (X, y) pairs
            X - Training vector
            y - Target labels
        Returns
        -------
        C : DictRDD containing (X, y) pairs
            X - Training vector standardized
            y - Target labels
        """
        X = Z[:, 'X'] if isinstance(Z, DictRDD) else Z
        check_rdd(X, (np.ndarray, sp.spmatrix))

        if check_rdd_dtype(X, (sp.spmatrix)):
            if self.with_mean:
                raise ValueError(
                    "Cannot center sparse matrices: pass `with_mean=False` "
                    "instead. See docstring for motivation and alternatives.")
            if self.scale_ is not None:
                def mapper(X):
                    inplace_column_scale(X, 1 / self.scale_)
                    return X
        else:
            if self.with_mean:
                if self.with_std:
                    def mapper(X):
                        X -= self.mean_
                        X /= self.scale_
                        return X
                else:
                    def mapper(X):
                        X -= self.mean_
                        return X
            else:
                if self.with_std:
                    def mapper(X):
                        X /= self.scale_
                        return X
                else:
                    raise ValueError("Need with_std or with_mean ")
        return Z.transform(mapper, column="X")
Perform standardization by centering and scaling
        Parameters
        ----------
        Z : DictRDD containing (X, y) pairs
            X - Training vector
            y - Target labels
        Returns
        -------
        C : DictRDD containing (X, y) pairs
            X - Training vector standardized
            y - Target labels