def get_bayesian_networks(self):
bayesian_network = GraphUtils.read_graph(self._network_name + '-' + str(self._max_parents))
bayesian_networks = []
if bayesian_network == None:
# Generate a tree (graph) with required number of nodes.
bayesian_network = self._generate_initial_graph()
bayesian_network.name = self._network_name + '-' + str(self._max_parents)
GraphUtils.write_graph(bayesian_network)
# theoretical bound is infinity but this also does well
num_iterations = 4 * self._num_nodes * self._num_nodes
# Since connectedness is only defined for undirected graphs
# so we have to keep a copy of the bayesian network
# except that all the edges are undirected
undirected_BN = bayesian_network.to_undirected()
bayesian_network.name = self._network_name
# Repeat for a large number of times.
for i in xrange(self._num_BNs):
count, i, j = 0, 0, 0;
while count < num_iterations:
edge = (i, j) = GraphUtils.get_random_edge(self._node_names)
if bayesian_network.has_edge(*edge):
# If (i,j) is in the graph, remove it
GraphUtils.apply_action(bayesian_network, undirected_BN, edge, 'remove', self._max_parents)
else:
# If the edge (i,j) is not in the graph, add it.
GraphUtils.apply_action(bayesian_network, undirected_BN, edge, 'add', self._max_parents)
count += 1
bayesian_networks.append(deepcopy(bayesian_network))
# Return the obtained graph
return bayesian_networks
def _get_feasible_local_solutions(self, bayesian_network, undirected_graph, edge):
local_solutions_action_pairs = []
# Calculate all possible local solutions by applying
# all the possible actions.
temp_bn = deepcopy(bayesian_network)
temp_graph = deepcopy(undirected_graph)
for action in self._actions_list:
# print action + 'ing', edge, ' in ', bayesian_network.edges()
is_feasible = GraphUtils.apply_action(temp_bn, temp_graph, (edge), action, 2)
if not is_feasible:
# If the action was not feasible then try again
# print 'Infeasible action.. trying with different action'
continue
if self._tabu_list_contains(temp_bn):
# If generated solution is already in the tabu list then try again
# print 'Solution already in tabu list trying again'
continue
# print 'Got ', temp_bn.edges()
local_solutions_action_pairs.append((temp_bn, action))
temp_bn = deepcopy(bayesian_network)
temp_graph = deepcopy(undirected_graph)
return local_solutions_action_pairs
