Method transform

sklearn/preprocessing/_data.py:1092–1135

Perform standardization by centering and scaling. Parameters ---------- X : {array-like, sparse matrix of shape (n_samples, n_features) The data used to scale along the features axis. copy : bool, default=None Copy the input X or not.

(self, X, copy=None)

        return self

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

        Parameters
        ----------
        X : {array-like, sparse matrix of shape (n_samples, n_features)
            The data used to scale along the features axis.
        copy : bool, default=None
            Copy the input X or not.

        Returns
        -------
        X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)
            Transformed array.
        """
        xp, _, X_device = get_namespace_and_device(X)
        check_is_fitted(self)

        copy = copy if copy is not None else self.copy
        X = validate_data(
            self,
            X,
            reset=False,
            accept_sparse="csr",
            copy=copy,
            dtype=supported_float_dtypes(xp, X_device),
            force_writeable=True,
            ensure_all_finite="allow-nan",
        )

        if sparse.issparse(X):
            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:
                inplace_column_scale(X, 1 / self.scale_)
        else:
            if self.with_mean:
                X -= xp.astype(self.mean_, X.dtype)
            if self.with_std:
                X /= xp.astype(self.scale_, X.dtype)
        return X

    def inverse_transform(self, X, copy=None):
        """Scale back the data to the original representation.