skl_utils.py 文件源码

def predict(self, X):
        y_pred = np.zeros((X.shape[0], self.n_estimators))
        for i in range(self.n_estimators):
            fidx = self.feature_idx_list[i]
            ridge = self.ridge_list[i]
            X_tmp = X[:,fidx]
            if self.poly:
                X_tmp = PolynomialFeatures(degree=2).fit_transform(X_tmp)[:,1:]
            y_pred[:,i] = ridge.predict(X_tmp)
        y_pred = np.mean(y_pred, axis=1)
        return y_pred