def prototype_adjacency(self, n_block_features, alpha):
        """Build a new graph.

        Doc for ".create(n_features, alpha)"

        Parameters
        -----------
        n_features : int

        alpha : float (0,1)
            The complexity / sparsity factor.
            This is (1 - alpha_0) in sklearn.datasets.make_sparse_spd_matrix
            where alpha_0 is the probability that a coefficient is zero.

        Returns
        -----------
        (n_features, n_features) matrices: covariance, precision, adjacency
        """
        return make_sparse_spd_matrix(
            n_block_features,
            alpha=np.abs(1.0 - alpha),
            smallest_coef=self.spd_low,
            largest_coef=self.spd_high,
            random_state=self.prng,
        )
Build a new graph.

        Doc for ".create(n_features, alpha)"

        Parameters
        -----------
        n_features : int

        alpha : float (0,1)
            The complexity / sparsity factor.
            This is (1 - alpha_0) in sklearn.datasets.make_sparse_spd_matrix
            where alpha_0 is the probability that a coefficient is zero.

        Returns
        -----------
        (n_features, n_features) matrices: covariance, precision, adjacency