#!/usr/bin/env python
import openpnl
#print dir(openpnl)
model = openpnl.pnlExCreateRndArHMM()
#model = openpnl.pnlExCreateKjaerulfsBNet()
pArHMM = openpnl.CDBN_Create(model)
pInfEng = openpnl.C1_5SliceJtreeInfEngine_Create(pArHMM)
nTimeSlices = 5
# set up evidence ...
pEvidences = openpnl.newCEvidences(nTimeSlices)
for i in range(0,nTimeSlices):
ev = openpnl.mkEvidence( pArHMM, [1], [1.0] );
openpnl.assignEvidence( pEvidences, ev, i )
pInfEng.DefineProcedure(openpnl.ptSmoothing, nTimeSlices)
pInfEng.EnterEvidence(pEvidences, nTimeSlices)
pInfEng.Smoothing()
queryPrior = [0]
#queryPrior = openpnl.intVector([0])
queryPriorSize = 1
slice_ = 0
pInfEng.MarginalNodes(queryPrior, queryPriorSize, slice_)
pQueryJPD = pInfEng.GetQueryJPD();
mdl = pQueryJPD.GetModelDomain()
mat = pQueryJPD.GetMatrix(openpnl.matTable)
#!/usr/bin/env python
import openpnl
pWSBnet = openpnl.pnlExCreateWaterSprinklerBNet()
pWSBnet.GetGraph().Dump()
pEvidForWS = openpnl.mkEvidence(pWSBnet, [0], [1])
pNaiveInf = openpnl.CNaiveInfEngine.Create(pWSBnet)
pNaiveInf.EnterEvidence(pEvidForWS)
nodes = [1, 3]
pNaiveInf.MarginalNodes(nodes)
pMarg = pNaiveInf.GetQueryJPD()
obsVls = openpnl.pConstValueVector()
#print obsVls
#pEvidForWS.GetObsNodesWithValues( openpnl.toIntVector(nodes), obsVls )
print openpnl.convertVector(
pMarg.GetMatrix(openpnl.matTable).ConvertToDense().GetVector())
]:
parents = pGraph.GetParents(node);
print "node: ", node, " parents: ", parents
domain = list(parents) + [node]
cCPD = openpnl.CTabularCPD.Create( pBNet.GetModelDomain() , openpnl.toConstIntVector(domain) )
cCPD.AllocMatrix( cpdvals, openpnl.matTable )
cCPD.NormalizeCPD()
pBNet.AttachFactor(cCPD)
# Set up the inference engine
infEngine = openpnl.CPearlInfEngine.Create( pBNet );
# Problem 1 P(W|C=0)
ev = openpnl.mkEvidence( pBNet, [0], [0] )
infEngine.EnterEvidence(ev)
infEngine.pyMarginalNodes( [3], 0 )
infEngine.GetQueryJPD().Dump()
m = openpnl.convertVector(infEngine.GetQueryJPD().GetMatrix(openpnl.matTable).ConvertToDense().GetVector())
index = np.arange(0,2,1)
plt.figure()
plt.bar(index, m)
plt.title("Marginal of P(WetGrass|Cloudy=False)")
plt.ylabel("P(WetGress = x)");
plt.xticks(index+0.5, ('WetGrass=False', "WetGrass=True"))
# Problem 1 P(W|C=1)
ev = openpnl.mkEvidence( pBNet, [0], [1] )
infEngine.EnterEvidence(ev)
#!/usr/bin/env python import openpnl pWSBnet = openpnl.pnlExCreateWaterSprinklerBNet() pWSBnet.GetGraph().Dump() pEvidForWS = openpnl.mkEvidence(pWSBnet, [0], [1]) pNaiveInf = openpnl.CNaiveInfEngine.Create(pWSBnet) pNaiveInf.EnterEvidence(pEvidForWS) nodes = [1, 3] pNaiveInf.MarginalNodes(nodes) pMarg = pNaiveInf.GetQueryJPD() obsVls = openpnl.pConstValueVector() # print obsVls # pEvidForWS.GetObsNodesWithValues( openpnl.toIntVector(nodes), obsVls ) print openpnl.convertVector(pMarg.GetMatrix(openpnl.matTable).ConvertToDense().GetVector())
