adaptive_graph_lasso.py 文件源码

def _inverse_squared_weights(self, estimator):
        n_features, _ = estimator.precision_.shape
        lam = np.zeros((n_features, n_features))
        mask = estimator.precision_ != 0
        lam[mask] = 1. / (np.abs(estimator.precision_[mask]) ** 2)
        mask_0 = estimator.precision_ == 0
        lam[mask_0] = np.max(lam[mask].flat)  # non-zero in appropriate range
        lam[np.diag_indices(n_features)] = 0
        return lam