base_pareto.py 文件源码

def predict(self, X):
        if not hasattr(self, "classes_"):        
            raise ValueError('fit')

        if self.normalize_:
            X = self._sc_X.fit_transform(X)

        X_ = self.transform(X)
        y_pred = self.estimator.predict(X_)
        return   self.classes_.take(np.asarray(y_pred, dtype=np.intp))

#        elif self.predict_with == 'all':
#
#            predict_ = []
#            
#            for mask in self.mask_:
#                self.estimator.fit(X=self.transform(self.X_, mask=mask), y=self.y_)
#                X_ = self.transform(X, mask=mask)
#                y_pred = self.estimator.predict(X_)
#                predict_.append(self.classes_.take(np.asarray(y_pred, dtype=np.intp)))
#            return np.asarray(predict_)