mdr/mdr_ensemble.py
def fit(self, features, classes):
        """Constructs the MDR ensemble from the provided training data

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix
        classes: array-like {n_samples}
            List of class labels for prediction

        Returns
        -------
        None

        """
        self.ensemble.fit(features, classes)

        # Construct the feature map from the ensemble predictions
        unique_rows = list(set([tuple(row) for row in features]))
        for row in unique_rows:
            self.feature_map[row] = self.ensemble.predict([row])[0]
def fit(self, features, classes):
        self.ensemble.fit(features, classes)

        # Construct the feature map from the ensemble predictions
        unique_rows = list(set([tuple(row) for row in features]))
        for row in unique_rows:
            self.feature_map[row] = self.ensemble.predict([row])[0]
def fit ( self , features , classes ) : self . ensemble . fit ( features , classes ) # Construct the feature map from the ensemble predictions unique_rows = list ( set ( [ tuple ( row ) for row in features ] ) ) for row in unique_rows : self . feature_map [ row ] = self . ensemble . predict ( [ row ] ) [ 0 ]
Constructs the MDR ensemble from the provided training data

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix
        classes: array-like {n_samples}
            List of class labels for prediction

        Returns
        -------
        None
Constructs the MDR ensemble from the provided training data