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)
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)
p2.append(random.random())
p3.append(random.random())
p4.append(random.random())
g1 = lambda x: x / sum(p1)
p1 = map(g1, p1)
g2 = lambda x: x / sum(p2)
p2 = map(g2, p2)
g3 = lambda x: x / sum(p3)
p3 = map(g3, p3)
g4 = lambda x: x / sum(p4)
p4 = map(g4, p4)
mult = MultinomialDistribution(6, 25, p1, SNP)
mult2 = MultinomialDistribution(7, 25, p2, SNP)
phi = NormalDistribution(11.0, 4.0)
phi2 = NormalDistribution(11.0, 6.0)
pd1 = ProductDistribution([mult, mult2, phi, phi2])
mult3 = MultinomialDistribution(6, 25, p3, SNP)
mult4 = MultinomialDistribution(7, 25, p4, SNP)
phi3 = NormalDistribution(8.0, 5.0)
phi4 = NormalDistribution(15.0, 5.0)
pd2 = ProductDistribution([mult, mult2, phi, phi2])
m = MixtureModel(2, [0.5, 0.5], [pd1, pd2])
m.EM(d, 15, 0.05)
pdList.append(ProductDistribution(dList))
piList = [random(), random(), random()]
g = lambda x: x / sum(piList)
piList = map(g, piList)
mix = MixtureModel(3, piList, pdList)
dat = mix.sampleDataSet(1000)
pdList2 = []
for j in range(3):
dList2 = []
for i in range(10):
par2 = [random(), random(), random(), random(), random(), random()]
f = lambda x: x / sum(par2)
par2 = map(f, par2)
dList2.append(MultinomialDistribution(6, 6, par2))
pdList2.append(ProductDistribution(dList2))
piList2 = [random(), random(), random()]
g = lambda x: x / sum(piList2)
piList2 = map(g, piList2)
mix2 = MixtureModel(3, piList2, pdList2)
with Timer("time"):
mix2.EM(dat, 40, 0.1)
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=DIAG)
tmult2 = MultinomialDistribution(3, 4, [0.4, 0.3, 0.1, 0.2], alphabet=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=0)
##for i,s in enumerate(seq1):
## print "\n",i,":",s
## print "h1:",h1.hmm.loglikelihoods(s)
## print "h2:",h2.hmm.loglikelihoods(s)
#
#
##print tm
##print tm.components[0].distList[2].hmm
##print tm.components[1].distList[2].hmm
random(),
random(),
random(),
random(),
random()
]
g = lambda x: x / sum(piList2)
piList2 = map(g, piList2)
#print piList
#print sum(piList)
for i in range(10):
par2 = [random(), random(), random()]
f = lambda x: x / sum(par2)
par2 = map(f, par2)
# print par
dList2.append(MultinomialDistribution(6, 3, par2))
mix2 = MixtureModel(10, piList2, dList2)
mix2.EM(s, 50, 0.2)
print "----------- True Model--------- "
print mix
print "--------------------------------\n\n"
print "----------- Trained Model--------- "
print mix2
print "--------------------------------"
dat = DataSet()
dat.fromArray(seq3)
print "vorher ------\n", m4
pred = m4.cluster(dat,
nr_runs=5,
nr_init=9,
max_iter=30,
delta=0.1,
labels=None,
entropy_cutoff=None)
classes = m4.classify(dat)
m4.shortInitEM(dat, 5, 5, 5, 0.1)
m4.EM(seq3, 20, 0.1)
print "####Finish\n", m4
dat.printClustering(2, pred)
evaluate(pred, true)
# ----------------------------- Example 4 -----------------------------
m5 = MixtureModel(1, [1.0], [NormalDistribution(3.0, 2.5)])
seq4 = m5.sample(1800)
#print "var = ", variance(seq4)
m6 = MixtureModel(1, [1.0], [NormalDistribution(-1.5, 2.5)])
