Esempi in Python per pnlExCreateRndArHMM

Esempio n. 1

#!/usr/bin/env python
import openpnl
import numpy as np

np.random.seed(1337)
# Still broken ...

print " *** SET UP MODEL **"
model = openpnl.pnlExCreateRndArHMM()
model.GetGraph().Dump()
pArHMM = openpnl.CDBN_Create(model)
pInfEng = openpnl.C1_5SliceJtreeInfEngine_Create(pArHMM)

# generate random slices
print " *** SET UP RANDOM SLICES  **"
nTimeSeries = 500
nSlices = np.random.randint(3, 20, nTimeSeries)
print nSlices

print " *** GENERATE SAMPLES *** "
evidencesOut = pArHMM.GenerateSamples2(nSlices)
pDBN = openpnl.CDBN.Create(openpnl.pnlExCreateRndArHMM())
print evidencesOut
print pDBN

print " *** LEARNING *** "
pLearn = openpnl.CEMLearningEngineDBN.Create(pDBN)
pLearn.SetData(evidencesOut)
pLearn.Learn()

print " *** model before learning ... "

Esempio n. 2

#!/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)

Esempio n. 3

File: qa2_dbn_complex.py Progetto: PyOpenPNL/PyOpenPNL

#!/usr/bin/env python
import openpnl
import numpy as np
np.random.seed(1337)
# Still broken ...

print " *** SET UP MODEL **"
model = openpnl.pnlExCreateRndArHMM()
model.GetGraph().Dump()
pArHMM = openpnl.CDBN_Create(model)
pInfEng = openpnl.C1_5SliceJtreeInfEngine_Create(pArHMM)

# generate random slices
print " *** SET UP RANDOM SLICES  **"
nTimeSeries = 500
nSlices = np.random.randint(3,20,nTimeSeries);
print nSlices

print " *** GENERATE SAMPLES *** "
evidencesOut = pArHMM.GenerateSamples2( nSlices );
pDBN = openpnl.CDBN.Create(openpnl.pnlExCreateRndArHMM());
print evidencesOut 
print pDBN

print " *** LEARNING *** "
pLearn = openpnl.CEMLearningEngineDBN.Create( pDBN );
pLearn.SetData(evidencesOut)
pLearn.Learn()

print " *** model before learning ... "
for i in range(0,4):