def create_network(max_parents, max_children, connections):
'''
Takes in max parents / chilldren per node constraints and network definition (connections).
Outputs model and list of nodes included in network.
'''
model = BayesianModel([connections[0]])
nodes_added = []
for edge in connections[1:]:
#whichever has more children
#Node A
try:
if edge[0] in nodes_added and edge[1] in nodes_added: #both already in network
if len(model.get_children(edge[0])) < max_children and len(model.get_children(edge[1])) < max_children:
if len(model.get_parents(edge[0])) < max_parents and len(model.get_parents(edge[1])) < max_parents:
add_first = random.choice([0,1])
model.add_edge(edge[add_first],edge[1-add_first])
if len(model.get_parents(edge[0])) < max_parents and len(model.get_parents(edge[1])) >= max_parents:
model.add_edge(edge[1],edge[0])
if len(model.get_parents(edge[0])) >= max_parents and len(model.get_parents(edge[1])) < max_parents:
model.add_edge(edge[0],edge[1])
if len(model.get_children(edge[0])) < max_children and len(model.get_children(edge[1])) >= max_children and len(model.get_parents(edge[1])) < max_parents:
model.add_edge(edge[0],edge[1])
if len(model.get_children(edge[0])) >= max_children and len(model.get_children(edge[1])) < max_children and len(model.get_parents(edge[0])) < max_parents:
model.add_edge(edge[1],edge[0])
elif edge[0] in nodes_added and edge[1] not in nodes_added: #Node A in network Node B not in network
if len(model.get_children(edge[0])) < max_children:
model.add_edge(edge[0],edge[1])
nodes_added.append(edge[1])
elif edge[0] not in nodes_added and edge[1] in nodes_added: #Node A not in network Node B in network
if len(model.get_children(edge[1])) < max_children:
model.add_edge(edge[1],edge[0])
nodes_added.append(edge[0])
else:
#neither in network, choose randomly
add_first = random.choice([0,1])
model.add_edge(edge[add_first],edge[1-add_first])
nodes_added.append(edge[add_first])
nodes_added.append(edge[1-add_first])
except ValueError: #catch non-DAG error
pass
#if node is leaf, connect to target
for node in nodes_added:
if node in model.get_leaves():
model.add_edge(node,'target')
return model, nodes_added
generator = get_eth_gan_generator(load_mode="CPU")
obs_traj = None
# sample_generator(generator, obs_traj)
path_node.transition = None
noisy_path_node.transition = None
# CREATING BAYESIAN MODEL (GRAPH)
cusumano_model = BayesianModel([(start_node, path_node),
(goal_node, path_node),
(path_node, noisy_path_node)])
# ATTENTION!!! =)
# Test functions. If these do not work you probably need to check python requirements.txt:
# this was tested using networkx 2.3 and pgmpy 0.1.7 from dev branch in github.
print("Root nodes: %s" % cusumano_model.get_roots())
print("Children of 'start': %s" % cusumano_model.get_children(start_node))
print("Leaf nodes: %s" % cusumano_model.get_leaves())
print("Parents of 'noisy_path': %s" %
cusumano_model.get_parents(noisy_path_node))
# FIXME: not working: we get error 'RandomChoice' object is not iterable'.
# print(cusumano_model.get_independencies())
nx.draw_networkx(cusumano_model,
arrowsize=15,
node_size=800,
node_color='#90b9f9')
plt.show()
# Defines whether to accept or reject the new sample
def acceptance(x, x_new):
