def f_nll(n, m): P = m.edge_probabilities(n) w = P / (1.0 - P) A = n.as_dense() return approximate_conditional_nll(A, w)
def f_nll(n, m): P = m.edge_probabilities(n) w = P / (1.0 - P) A = np.array(n.adjacency_matrix()) return approximate_conditional_nll(A, w)