def setUp(self):
skel = GraphSkeleton()
skel.load("unittestdict.txt")
skel.toporder()
nodedata = NodeData.load("unittestdict.txt")
self.bn = DiscreteBayesianNetwork(nodedata)
agg = SampleAggregator()
agg.aggregate(self.bn.randomsample(50))
self.rseq = agg.seq
self.ravg = agg.avg
self.fn = TableCPDFactorization(self.bn)
evidence = dict(Letter='weak')
agg.aggregate(self.fn.gibbssample(evidence, 51))
self.gseq = agg.seq
self.gavg = agg.avg
def GenProbTable(network):
#Generates Generic probability table for a Bayesian Network.
#Used for initialization of Dyn. Disc. BN
agg = SampleAggregator()
r = agg.aggregate(network.randomsample(5000))
return r
# Compute the approximate probability distribution by generating samples
# load nodedata and graphskeleton
nd = NodeData()
skel = GraphSkeleton()
nd.load("../tests/unittestdict.txt")
skel.load("../tests/unittestdict.txt")
# topologically order graphskeleton
skel.toporder()
# load bayesian network
bn = DiscreteBayesianNetwork(skel, nd)
# build aggregator
agg = SampleAggregator()
# average samples
result = agg.aggregate(bn.randomsample(10))
# output - toggle comment to see
#print json.dumps(result, indent=2)
# (8) --------------------------------------------------------------------------
# Learn the CPDs of a discrete Bayesian network, given data and a structure:
# say I have some data
data = bn.randomsample(200)
# and a graphskeleton
skel = GraphSkeleton()
