Method transform

numpy_ml/preprocessing/general.py:216–242

Standardize features by removing the mean and scaling to unit variance. For a sample `x`, the standardized score is calculated as: .. math:: z = (x - u) / s where `u` is the mean of the training samples or zero if `with_mean` is False, and `s`

(self, X)

        self._is_fit = True

    def transform(self, X):
        """
        Standardize features by removing the mean and scaling to unit variance.

        For a sample `x`, the standardized score is calculated as:

        .. math::

            z = (x - u) / s

        where `u` is the mean of the training samples or zero if `with_mean` is
        False, and `s` is the standard deviation of the training samples or 1
        if `with_std` is False.

        Parameters
        ----------
        X : :py:class:`ndarray <numpy.ndarray>` of shape `(N, C)`
            An array of N samples, each with dimensionality `C`.

        Returns
        -------
        Z : :py:class:`ndarray <numpy.ndarray>` of shape `(N, C)`
            The feature-wise standardized version of `X`.
        """
        if not self._is_fit:
            raise Exception("Must call `fit` before using the `transform` method")
        return (X - self._mean) / self._std

    def inverse_transform(self, Z):
        """