Adjust all values in X to encode for NaNs and infinities in the data.

        Parameters
        ----------
        X : array-like, shape=(n_samples, n_feature)
            Input array of type int.

        Returns
        -------
        X : array-like, shape=(n_samples, n_feature)
            Input array without any NaNs or infinities.

def _matrix_adjust(self, X):
        """Adjust all values in X to encode for NaNs and infinities in the data.

        Parameters
        ----------
        X : array-like, shape=(n_samples, n_feature)
            Input array of type int.

        Returns
        -------
        X : array-like, shape=(n_samples, n_feature)
            Input array without any NaNs or infinities.
        """
        data_matrix = X.data if sparse.issparse(X) else X

        # Shift all values to specially encode for NAN/infinity/OTHER and 0
        #   Old value       New Value
        #   ---------       ---------
        #   N (0..int_max)  N + 3
        #   np.NaN          2
        #   infinity        2
        #   *other*         1
        #
        # A value of 0 is reserved, as that is specially handled in sparse
        # matrices.
        data_matrix += len(SPARSE_ENCODINGS) + 1
        data_matrix[~np.isfinite(data_matrix)] = SPARSE_ENCODINGS['NAN']

        return X