skl_utils.py 文件源码

def fit(self, X, y):
        sdim, fdim = X.shape
        for i in range(self.n_estimators):
            ridge = Ridge(alpha=self.alpha, normalize=self.normalize, random_state=self.random_state)
            fidx = self._random_feature_idx(fdim, self.random_state+i*100)
            sidx = self._random_sample_idx(sdim, self.random_state+i*10)
            X_tmp = X[sidx][:,fidx]
            if self.poly:
                X_tmp = PolynomialFeatures(degree=2).fit_transform(X_tmp)[:,1:]
            ridge.fit(X_tmp, y[sidx])
            self.ridge_list[i] = ridge
            self.feature_idx_list[i] = fidx
        return self