def testem(self):
# complex DataSet with HMM sequences and scalar data
dat = self.gen.sampleSet(100)
# sampling hmm data
seq1 = self.h1.hmm.sample(40, 10)
seq2 = self.h2.hmm.sample(60, 10)
seq1.merge(seq2)
data = mixtureHMM.SequenceDataSet()
data.fromGHMM(dat, [seq1])
data.internalInit(self.m)
tA = [[0.5, 0.2, 0.3], [0.2, 0.3, 0.5], [0.1, 0.5, 0.4]]
tB = [[0.2, 0.4, 0.1, 0.3], [0.5, 0.1, 0.2, 0.2],
[0.4, 0.3, 0.15, 0.15]]
tpi = [0.3, 0.3, 0.4]
th1 = mixtureHMM.getHMM(
mixtureHMM.ghmm.IntegerRange(0, 4),
mixtureHMM.ghmm.DiscreteDistribution(
mixtureHMM.ghmm.IntegerRange(0, 4)), tA, tB, tpi)
tA2 = [[0.5, 0.4, 0.1], [0.3, 0.2, 0.5], [0.3, 0.2, 0.5]]
tB2 = [[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.4, 0.4],
[0.2, 0.1, 0.6, 0.1]]
tpi2 = [0.3, 0.4, 0.3]
th2 = mixtureHMM.getHMM(
mixtureHMM.ghmm.IntegerRange(0, 4),
mixtureHMM.ghmm.DiscreteDistribution(
mixtureHMM.ghmm.IntegerRange(0, 4)), tA2, tB2, tpi2)
tn1 = mixture.NormalDistribution(-1.5, 1.5)
tn2 = mixture.NormalDistribution(9.0, 1.2)
tmult1 = mixture.MultinomialDistribution(3,
4, [0.1, 0.1, 0.55, 0.25],
alphabet=self.DIAG)
tmult2 = mixture.MultinomialDistribution(3,
4, [0.4, 0.3, 0.1, 0.2],
alphabet=self.DIAG)
tc1 = mixture.ProductDistribution([tn1, tmult1, th1])
tc2 = mixture.ProductDistribution([tn2, tmult2, th2])
tmpi = [0.7, 0.3]
tm = mixture.MixtureModel(2, tmpi, [tc1, tc2])
tm.EM(data, 80, 0.1, silent=1)
def testinternalinitcomplex(self):
# complex DataSet with HMM sequences
dat = self.gen.sampleSet(100)
# sampling hmm data
seq1 = self.h1.hmm.sample(40, 10)
seq2 = self.h2.hmm.sample(60, 10)
seq1.merge(seq2)
data = mixtureHMM.SequenceDataSet()
data.fromGHMM(dat, [seq1])
data.internalInit(self.m)
self.assertEqual(str(data.complexFeature), '[0, 0, 1]')
self.assertEqual(data.p, 5)
self.assertEqual(data.suff_p, 6)
def testgetinternalfeature(self):
# complex DataSet with HMM sequences
dat = self.gen.sampleSet(100)
# sampling hmm data
seq1 = self.h1.hmm.sample(40, 10)
seq2 = self.h2.hmm.sample(60, 10)
seq1.merge(seq2)
data = mixtureHMM.SequenceDataSet()
data.fromGHMM(dat, [seq1])
data.internalInit(self.m)
f0 = data.getInternalFeature(0)
self.assertEqual(isinstance(f0, numarray.numarraycore.NumArray), True)
f1 = data.getInternalFeature(1)
self.assertEqual(isinstance(f1, numarray.numarraycore.NumArray), True)
f2 = data.getInternalFeature(2)
self.assertEqual(isinstance(f2, ghmm.SequenceSet), True)
def testsimpleem(self):
# sampling hmm data
seq1 = self.h1.hmm.sample(40, 10)
seq2 = self.h2.hmm.sample(60, 10)
seq1.merge(seq2)
data = mixtureHMM.SequenceDataSet()
data.fromGHMM([], [seq1])
tA = [[0.5, 0.2, 0.3], [0.2, 0.3, 0.5], [0.1, 0.5, 0.4]]
tB = [[0.2, 0.4, 0.1, 0.3], [0.5, 0.1, 0.2, 0.2],
[0.4, 0.3, 0.15, 0.15]]
tpi = [0.3, 0.3, 0.4]
th1 = mixture.ProductDistribution([
mixtureHMM.getHMM(
mixtureHMM.ghmm.IntegerRange(0, 4),
mixtureHMM.ghmm.DiscreteDistribution(
mixtureHMM.ghmm.IntegerRange(0, 4)), tA, tB, tpi)
])
tA2 = [[0.5, 0.4, 0.1], [0.3, 0.2, 0.5], [0.3, 0.2, 0.5]]
tB2 = [[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.4, 0.4],
[0.2, 0.1, 0.6, 0.1]]
tpi2 = [0.3, 0.4, 0.3]
th2 = mixture.ProductDistribution([
mixtureHMM.getHMM(
mixtureHMM.ghmm.IntegerRange(0, 4),
mixtureHMM.ghmm.DiscreteDistribution(
mixtureHMM.ghmm.IntegerRange(0, 4)), tA2, tB2, tpi2)
])
mpi = [0.4, 0.6]
hm = mixture.MixtureModel(2, mpi, [th1, th2])
data.internalInit(hm)
hm.EM(data, 80, 0.1, silent=1)
def testinternalinitcomplexempty(self):
# complex DataSet with HMM sequences only
# sampling hmm data
seq1 = self.h1.hmm.sample(40, 10)
seq2 = self.h2.hmm.sample(60, 10)
seq1.merge(seq2)
data = mixtureHMM.SequenceDataSet()
data.fromGHMM([], [seq1])
self.assertRaises(AssertionError, data.internalInit, self.m)
c1 = mixture.ProductDistribution([self.h1])
c2 = mixture.ProductDistribution([self.h2])
mpi = [0.4, 0.6]
hm = mixture.MixtureModel(2, mpi, [c1, c2])
data.internalInit(hm)
self.assertEqual(str(data.complexFeature), '[1]')
self.assertEqual(data.p, 1)
self.assertEqual(data.suff_p, 1)
# mixture for sampling gc1 = mixture.ProductDistribution([n1, mult1]) gc2 = mixture.ProductDistribution([n2, mult2]) gen = mixture.MixtureModel(2, mpi, [gc1, gc2]) dat = gen.sampleSet(100) #print dat # sampling hmm data seq1 = h1.hmm.sample(40, 10) seq2 = h2.hmm.sample(60, 10) seq1.merge(seq2) data = mixtureHMM.SequenceDataSet() #data.fromGHMM(dat,[seq1]) data.fromGHMM(dat, [seq1]) data.internalInit(m) #print data.getInternalFeature(0) #print data.getInternalFeature(1) # -------- construct model to be trained ------------- tA = [[0.5, 0.2, 0.3], [0.2, 0.3, 0.5], [0.1, 0.5, 0.4]] tB = [[0.2, 0.4, 0.1, 0.3], [0.5, 0.1, 0.2, 0.2], [0.4, 0.3, 0.15, 0.15]] tpi = [0.3, 0.3, 0.4] th1 = mixtureHMM.getHMM(