Python getHMM示例

示例#1

文件： test_HMMmixture.py 项目： minzastro/pymix

    def setUp(self):
        # building generating models
        self.DIAG = 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 = NormalDistribution(2.5, 0.5)
        n2 = NormalDistribution(6.0, 0.8)

        mult1 = MultinomialDistribution(3, 4, [0.23, 0.26, 0.26, 0.25], alphabet=self.DIAG)
        mult2 = MultinomialDistribution(3, 4, [0.7, 0.1, 0.1, 0.1], alphabet=self.DIAG)

        c1 = ProductDistribution([n1, mult1, self.h1])
        c2 = ProductDistribution([n2, mult2, self.h2])

        mpi = [0.4, 0.6]
        self.m = MixtureModel(2, mpi, [c1, c2])

        # mixture for sampling
        gc1 = ProductDistribution([n1, mult1])
        gc2 = ProductDistribution([n2, mult2])
        self.gen = MixtureModel(2, mpi, [gc1, gc2])

示例#2

文件： test_HMMmixture.py 项目： minzastro/pymix

    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 = 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 = 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 = MixtureModel(2, mpi, [th1, th2])

        data.internalInit(hm)

        hm.EM(data, 80, 0.1, silent=1)

示例#3

    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 = NormalDistribution(-1.5, 1.5)
        tn2 = NormalDistribution(9.0, 1.2)

        tmult1 = MultinomialDistribution(3,
                                         4, [0.1, 0.1, 0.55, 0.25],
                                         alphabet=self.DIAG)
        tmult2 = MultinomialDistribution(3,
                                         4, [0.4, 0.3, 0.1, 0.2],
                                         alphabet=self.DIAG)

        tc1 = ProductDistribution([tn1, tmult1, th1])
        tc2 = ProductDistribution([tn2, tmult2, th2])

        tmpi = [0.7, 0.3]
        tm = MixtureModel(2, tmpi, [tc1, tc2])

        tm.EM(data, 80, 0.1, silent=1)

示例#4

    def setUp(self):
        # building generating models
        self.DIAG = 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 = NormalDistribution(2.5, 0.5)
        n2 = NormalDistribution(6.0, 0.8)

        mult1 = MultinomialDistribution(3,
                                        4, [0.23, 0.26, 0.26, 0.25],
                                        alphabet=self.DIAG)
        mult2 = MultinomialDistribution(3,
                                        4, [0.7, 0.1, 0.1, 0.1],
                                        alphabet=self.DIAG)

        c1 = ProductDistribution([n1, mult1, self.h1])
        c2 = ProductDistribution([n2, mult2, self.h2])

        mpi = [0.4, 0.6]
        self.m = MixtureModel(2, mpi, [c1, c2])

        # mixture for sampling
        gc1 = ProductDistribution([n1, mult1])
        gc2 = ProductDistribution([n2, mult2])
        self.gen = MixtureModel(2, mpi, [gc1, gc2])

示例#5

    def testememptylist(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])

        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)

        c1 = ProductDistribution([th1])
        c2 = ProductDistribution([th2])

        mpi = [0.4, 0.6]
        hm = MixtureModel(2, mpi, [c1, c2])

        data.internalInit(hm)

        hm.EM(data, 40, 0.1, silent=1)

示例#6

文件： test_HMMmixture.py 项目： minzastro/pymix

    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 = NormalDistribution(-1.5, 1.5)
        tn2 = NormalDistribution(9.0, 1.2)

        tmult1 = MultinomialDistribution(3, 4, [0.1, 0.1, 0.55, 0.25], alphabet=self.DIAG)
        tmult2 = MultinomialDistribution(3, 4, [0.4, 0.3, 0.1, 0.2], alphabet=self.DIAG)

        tc1 = ProductDistribution([tn1, tmult1, th1])
        tc2 = ProductDistribution([tn2, tmult2, th2])

        tmpi = [0.7, 0.3]
        tm = MixtureModel(2, tmpi, [tc1, tc2])

        tm.EM(data, 80, 0.1, silent=1)

示例#7

文件： test_hmm.py 项目： minzastro/pymix

# building generating models
from pymix import mixtureHMM
from pymix.distributions.multinomial import MultinomialDistribution
from pymix.distributions.normal import NormalDistribution
from pymix.distributions.product import ProductDistribution
from pymix.models.mixture import MixtureModel
from pymix.util.alphabet import Alphabet

DIAG = 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 = NormalDistribution(2.5, 0.5)
n2 = NormalDistribution(6.0, 0.8)

mult1 = MultinomialDistribution(3, 4, [0.23, 0.26, 0.26, 0.25], alphabet=DIAG)
mult2 = MultinomialDistribution(3, 4, [0.7, 0.1, 0.1, 0.1], alphabet=DIAG)

c1 = ProductDistribution([n1, mult1, h1])
c2 = ProductDistribution([n2, mult2, h2])

示例#8

# building generating models
from pymix import mixtureHMM
from pymix.distributions.multinomial import MultinomialDistribution
from pymix.distributions.normal import NormalDistribution
from pymix.distributions.product import ProductDistribution
from pymix.models.mixture import MixtureModel
from pymix.util.alphabet import Alphabet

DIAG = 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 = NormalDistribution(2.5, 0.5)
n2 = NormalDistribution(6.0, 0.8)