mdr/mdr.py
def fit_predict(self, features, class_labels):
        """Convenience function that fits the provided data then constructs predictions from the provided features.

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix
        class_labels: array-like {n_samples}
            List of true class labels

        Returns
        ----------
        array-like: {n_samples}
            Constructed features from the provided feature matrix

        """
        self.fit(features, class_labels)
        return self.predict(features)
def fit_predict(self, features, class_labels):
        """Convenience function that fits the provided data then constructs predictions from the provided features.

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix
        class_labels: array-like {n_samples}
            List of true class labels

        Returns
        ----------
        array-like: {n_samples}
            Constructed features from the provided feature matrix

        """
        self.fit(features, class_labels)
        return self.predict(features)
def fit_predict ( self , features , class_labels ) : self . fit ( features , class_labels ) return self . predict ( features )
Convenience function that fits the provided data then constructs predictions from the provided features.

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix
        class_labels: array-like {n_samples}
            List of true class labels

        Returns
        ----------
        array-like: {n_samples}
            Constructed features from the provided feature matrix
Convenience function that fits the provided data then constructs predictions from the provided features .