def learn(self, model, inf=None):
model.regularization = 0.01
model.inter_node_pos_same_label_default = 0.1
p = mmln.estimate_p_values_inter_node(self.n)
for label1 in p:
for label2 in p[label1]:
if p[label1][label2] < 0.1:
model.inter_node_pos[(label1, label2)] = 2
elif p[label1][label2] < 0.2:
model.inter_node_pos[(label1, label2)] = 1
elif p[label1][label2] < 0.5:
model.inter_node_pos[(label1, label2)] = 0.5
def test_estimate_p_values_inter_node(self):
net = self._get_network()
p = mmln.estimate_p_values_inter_node(net, 100)
self.assertEqual(len(p), 2)