def _predict(self, features):
        """Predict matches and non-matches.

        Parameters
        ----------
        features : numpy.ndarray
            The data to predict the class of.

        Returns
        -------
        numpy.ndarray
            The predicted classes.
        """

        from sklearn.exceptions import NotFittedError

        try:
            prediction = self.kernel.predict_classes(features)[:, 0]
        except NotFittedError:
            raise NotFittedError(
                "{} is not fitted yet. Call 'fit' with appropriate "
                "arguments before using this method.".format(
                    type(self).__name__
                )
            )

        return prediction
def _predict ( self , features ) : from sklearn . exceptions import NotFittedError try : prediction = self . kernel . predict_classes ( features ) [ : , 0 ] except NotFittedError : raise NotFittedError ( "{} is not fitted yet. Call 'fit' with appropriate " "arguments before using this method." . format ( type ( self ) . __name__ ) ) return prediction
Predict matches and non-matches.

        Parameters
        ----------
        features : numpy.ndarray
            The data to predict the class of.

        Returns
        -------
        numpy.ndarray
            The predicted classes.