def update_bayesian_network_structure(theme, nodes, edges, cpts): data = select_theme_data(theme) skel = GraphSkeleton() skel.load_skel(nodes, edges) skel.toporder() learner = PGMLearner() bn, passed = learner.discrete_mle_estimateparams(skel, data) return bn.Vdata, passed
def create_bayesian_network_structure(theme):
file_name = "bayes/bayes" + str(theme) + ".txt"
data = select_theme_data(theme)
if not os.path.exists(file_name):
learner = PGMLearner()
result = learner.discrete_estimatebn(data, pvalparam=0.05, indegree=1)
assert len(data[0]) == len(result.V) # check number of nodes
print 'Nr of nodes ' + str(len(result.V))
print 'Nr of edges ' + str(len(result.E))
print 'Saved network'
save_network(file_name, result, theme)
nd = NodeData()
nd.load(file_name)
skel = GraphSkeleton()
skel.load(file_name)
skel.toporder()
bn = DiscreteBayesianNetwork(skel, nd)
return bn