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
