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 setUp(self):
# building generating models
self.DIAG = mixture.Alphabet(['.', '0', '8', '1'])
A = [[0.3, 0.6, 0.1], [0.0, 0.5, 0.5], [0.4, 0.2, 0.4]]
B = [[0.5, 0.2, 0.1, 0.2], [0.5, 0.4, 0.05, 0.05],
[0.8, 0.1, 0.05, 0.05]]
pi = [1.0, 0.0, 0.0]
self.h1 = mixtureHMM.getHMM(
mixtureHMM.ghmm.IntegerRange(0, 4),
mixtureHMM.ghmm.DiscreteDistribution(
mixtureHMM.ghmm.IntegerRange(0, 4)), A, B, pi)
A2 = [[0.5, 0.4, 0.1], [0.3, 0.2, 0.5], [0.3, 0.2, 0.5]]
B2 = [[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.4, 0.5], [0.2, 0.2, 0.3, 0.3]]
pi2 = [0.6, 0.4, 0.0]
self.h2 = mixtureHMM.getHMM(
mixtureHMM.ghmm.IntegerRange(0, 4),
mixtureHMM.ghmm.DiscreteDistribution(
mixtureHMM.ghmm.IntegerRange(0, 4)), A2, B2, pi2)
n1 = mixture.NormalDistribution(2.5, 0.5)
n2 = mixture.NormalDistribution(6.0, 0.8)
mult1 = mixture.MultinomialDistribution(3,
4, [0.23, 0.26, 0.26, 0.25],
alphabet=self.DIAG)
mult2 = mixture.MultinomialDistribution(3,
4, [0.7, 0.1, 0.1, 0.1],
alphabet=self.DIAG)
c1 = mixture.ProductDistribution([n1, mult1, self.h1])
c2 = mixture.ProductDistribution([n2, mult2, self.h2])
mpi = [0.4, 0.6]
self.m = mixture.MixtureModel(2, mpi, [c1, c2])
# mixture for sampling
gc1 = mixture.ProductDistribution([n1, mult1])
gc2 = mixture.ProductDistribution([n2, mult2])
self.gen = mixture.MixtureModel(2, mpi, [gc1, gc2])
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)
import mixture
import numpy
import random
import mixtureHMM
# building generating models
DIAG = mixture.Alphabet(['.', '0', '8', '1'])
A = [[0.3, 0.6, 0.1], [0.0, 0.5, 0.5], [0.4, 0.2, 0.4]]
B = [[0.5, 0.2, 0.1, 0.2], [0.5, 0.4, 0.05, 0.05], [0.8, 0.1, 0.05, 0.05]]
pi = [1.0, 0.0, 0.0]
h1 = mixtureHMM.getHMM(
mixtureHMM.ghmm.IntegerRange(0, 4),
mixtureHMM.ghmm.DiscreteDistribution(mixtureHMM.ghmm.IntegerRange(0, 4)),
A, B, pi)
#seq = h1.hmm.sample(10,50)
#print seq
A2 = [[0.5, 0.4, 0.1], [0.3, 0.2, 0.5], [0.3, 0.2, 0.5]]
B2 = [[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.4, 0.5], [0.2, 0.2, 0.3, 0.3]]
pi2 = [0.6, 0.4, 0.0]
h2 = mixtureHMM.getHMM(
mixtureHMM.ghmm.IntegerRange(0, 4),
mixtureHMM.ghmm.DiscreteDistribution(mixtureHMM.ghmm.IntegerRange(0, 4)),
A2, B2, pi2)
n1 = mixture.NormalDistribution(2.5, 0.5)
n2 = mixture.NormalDistribution(6.0, 0.8)
mult1 = mixture.MultinomialDistribution(3,