def main(): r = Rule(0) r.c = 1.5 r.x = np.array([True, False, True]) input = np.array([1, 0.5, 1]) output = quantitativeInference([[r]], input) print(output)
def top_rbm_extract(W): n_hidden = W.shape[0] n_labels = W.shape[1] rules = [] for i in range(n_labels): for j in range(n_hidden): r = Rule(i) r.x = [None for _ in range(n_hidden)] r.x[j] = True r.c = np.exp(W[j][i]) rules.append(r) return rules