def getRandomMixture(G, p, KL_lower, KL_upper, dtypes='discgauss', M=4,seed = None):
# if seed:
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# #print '*** seed=',seed
#
# else: # XXX debug
# seed = random.randint(1,9000000)
# mixextend.set_gsl_rng_seed(seed)
# random.seed(seed)
# #print '*** seed=',seed
#M = 4 # Alphabet size for discrete distributions
min_sigma = 0.1 # minimal std for Normal
max_sigma = 1.0 # maximal std for Normal
min_mu = -5.0 # minimal mean
max_mu = 8.0 # maximal mean
if dtypes == 'disc':
featureTypes = [0] * p
elif dtypes == 'gauss':
featureTypes = [1] * p
elif dtypes == 'discgauss':
# discrete or Normal features for now, chosen uniformly
# 0 discrete, 1 Normal
featureTypes = [ random.choice( (0, 1) ) for i in range(p) ]
else:
raise TypeError
#print featureTypes
C = []
for j in range(p):
c_j = []
for i in range(G):
#print i,j
if featureTypes[j] == 0:
acc = 0
while acc == 0:
cand = DiscreteDistribution(M, random_vector(M) )
#print 'cand:',cand
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(d,cand)
if KL_dist > KL_upper or KL_dist < KL_lower:
#print ' *', cand, 'rejected:', d , KL_dist
acc = 0
break
c_j.append(cand)
elif featureTypes[j] == 1:
acc = 0
while acc == 0:
mu = random.uniform(min_mu, max_mu)
sigma = random.uniform(min_sigma, max_sigma)
cand = NormalDistribution(mu, sigma )
#print 'cand:',cand
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(d,cand)
if KL_dist > KL_upper or KL_dist < KL_lower:
#print ' *', cand, 'rejected:', d , KL_dist
acc = 0
c_j.append(cand)
else:
RuntimeError
C.append(c_j)
# print '\n'
# for cc in C:
# print cc
comps = []
for i in range(G):
comps.append( ProductDistribution( [ C[j][i] for j in range(p) ] ) )
pi = get_random_pi(G,0.1)
m = MixtureModel(G,pi, comps,struct=1)
m.updateFreeParams()
return m
def getRandomMixture(G,
p,
KL_lower,
KL_upper,
dtypes='discgauss',
M=4,
seed=None):
# if seed:
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# #print '*** seed=',seed
#
# else: # XXX debug
# seed = random.randint(1,9000000)
# mixextend.set_gsl_rng_seed(seed)
# random.seed(seed)
# #print '*** seed=',seed
#M = 4 # Alphabet size for discrete distributions
min_sigma = 0.1 # minimal std for Normal
max_sigma = 1.0 # maximal std for Normal
min_mu = -5.0 # minimal mean
max_mu = 8.0 # maximal mean
if dtypes == 'disc':
featureTypes = [0] * p
elif dtypes == 'gauss':
featureTypes = [1] * p
elif dtypes == 'discgauss':
# discrete or Normal features for now, chosen uniformly
# 0 discrete, 1 Normal
featureTypes = [random.choice((0, 1)) for i in range(p)]
else:
raise TypeError
#print featureTypes
C = []
for j in range(p):
c_j = []
for i in range(G):
#print i,j
if featureTypes[j] == 0:
acc = 0
while acc == 0:
cand = DiscreteDistribution(M, random_vector(M))
#print 'cand:',cand
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(d, cand)
if KL_dist > KL_upper or KL_dist < KL_lower:
#print ' *', cand, 'rejected:', d , KL_dist
acc = 0
break
c_j.append(cand)
elif featureTypes[j] == 1:
acc = 0
while acc == 0:
mu = random.uniform(min_mu, max_mu)
sigma = random.uniform(min_sigma, max_sigma)
cand = NormalDistribution(mu, sigma)
#print 'cand:',cand
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(d, cand)
if KL_dist > KL_upper or KL_dist < KL_lower:
#print ' *', cand, 'rejected:', d , KL_dist
acc = 0
c_j.append(cand)
else:
RuntimeError
C.append(c_j)
# print '\n'
# for cc in C:
# print cc
comps = []
for i in range(G):
comps.append(ProductDistribution([C[j][i] for j in range(p)]))
pi = get_random_pi(G, 0.1)
m = MixtureModel(G, pi, comps, struct=1)
m.updateFreeParams()
return m
2, [0.5, 0.5],
[NormalDistribution(1.0, 1.0),
NormalDistribution(0.5, 1.0)],
compFix=[0, 2])
mix4 = MixtureModel(
2, [0.1, 0.9],
[NormalDistribution(2.0, 1.0),
NormalDistribution(1.5, 1.0)],
compFix=[0, 2])
pd2 = ProductDistribution([mix3, mix4, mult2])
m7 = MixtureModel(2, [0.4, 0.6], [pd1, pd2])
seq6 = m7.sampleSet(80)
p = random_vector(4)
mult3 = MultinomialDistribution(3, 4, p)
mix5 = MixtureModel(
2, [0.3, 0.7],
[NormalDistribution(-1.0, 1.0),
NormalDistribution(-2.0, 1.0)],
compFix=[0, 2])
mix6 = MixtureModel(
2, [0.8, 0.2],
[NormalDistribution(-2.0, 1.0),
NormalDistribution(2.0, 1.0)],
compFix=[0, 2])
pd3 = ProductDistribution([mix5, mix6, mult3])
p = random_vector(4)
mult4 = MultinomialDistribution(3, 4, p)
piPrior = DirichletDistribution(G, [1.0] * G)
compPrior = []
for i in range(2):
compPrior.append(DirichletDistribution(4, [1.02, 1.02, 1.02, 1.02]))
for i in range(2):
compPrior.append(NormalGammaDistribution(1.0, 2.0, 3.0, 4.0))
mixPrior = MixturePrior(0.7, 0.7, piPrior, compPrior)
DNA = Alphabet(['A', 'C', 'G', 'T'])
comps = []
for i in range(G):
dlist = []
for j in range(2):
phi = random_vector(4)
dlist.append(DiscreteDistribution(4, phi, DNA))
for j in range(2):
mu = j + 1.0
sigma = j + 0.5
dlist.append(NormalDistribution(mu, sigma))
comps.append(ProductDistribution(dlist))
pi = random_vector(G)
m = BayesMixtureModel(G, pi, comps, mixPrior, struct=1)
mixture.writeMixture(m, 'test.bmix')
m2 = readMixture('test.bmix')
mult1 = MultinomialDistribution(3, 4, [0.25, 0.25, 0.25, 0.25]) mix1 = MixtureModel(2, [0.3, 0.7], [NormalDistribution(-2.0, 1.0), NormalDistribution(-2.0, 1.0)], compFix=[0, 2]) mix2 = MixtureModel(2, [0.8, 0.2], [NormalDistribution(-3.0, 1.0), NormalDistribution(3.0, 1.0)], compFix=[0, 2]) pd1 = ProductDistribution([mix1, mix2, mult1]) mult2 = MultinomialDistribution(3, 4, [0.2, 0.1, 0.5, 0.2]) mix3 = MixtureModel(2, [0.5, 0.5], [NormalDistribution(1.0, 1.0), NormalDistribution(0.5, 1.0)], compFix=[0, 2]) mix4 = MixtureModel(2, [0.1, 0.9], [NormalDistribution(2.0, 1.0), NormalDistribution(1.5, 1.0)], compFix=[0, 2]) pd2 = ProductDistribution([mix3, mix4, mult2]) m7 = MixtureModel(2, [0.4, 0.6], [pd1, pd2]) seq6 = m7.sampleSet(80) p = random_vector(4) mult3 = MultinomialDistribution(3, 4, p) mix5 = MixtureModel(2, [0.3, 0.7], [NormalDistribution(-1.0, 1.0), NormalDistribution(-2.0, 1.0)], compFix=[0, 2]) mix6 = MixtureModel(2, [0.8, 0.2], [NormalDistribution(-2.0, 1.0), NormalDistribution(2.0, 1.0)], compFix=[0, 2]) pd3 = ProductDistribution([mix5, mix6, mult3]) p = random_vector(4) mult4 = MultinomialDistribution(3, 4, p) mix7 = MixtureModel(2, [0.5, 0.5], [NormalDistribution(-1.0, 1.0), NormalDistribution(-3.0, 1.0)], compFix=[0, 2]) mix8 = MixtureModel(2, [0.1, 0.9], [NormalDistribution(2.5, 1.0), NormalDistribution(0.5, 1.0)], compFix=[0, 2]) pd4 = ProductDistribution([mix3, mix4, mult4]) m8 = MixtureModel(2, [0.5, 0.5], [pd3, pd4]) m8.randParams(seq6) m8.EM(seq6, 15, 0.3)
