from pymix import mixture
import random
from pymix.parse import readMixture
from pymix.util.alphabet import Alphabet
from pymix.util.dataset import DataSet
VNTR = Alphabet(['.', '2/4', '2/7', '3/4', '3/7', '4/4', '4/6', '4/7', '4/8', '4/9', '7/7'])
DIAG = Alphabet(['.', '0', '8', '1'])
data = DataSet()
# iq.txt = iq and achievement test fields from pheno.txt
# drd4_len.txt = drd4 vntr types, only number of repeats
data.fromFiles(["filt_WISC_WIAT_DISC_134.txt"]) # ,"DRD4_134_len.txt"
m = readMixture('pheno_best.py')
print "Without deterministic anealing:"
m.randMaxEM(data, 100, 30, 0.1, tilt=0, silent=0)
print "\nWith deterministic annealing:"
m.randMaxEM(data, 100, 30, 0.1, tilt=1, silent=0)
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')
print m2
print m2.prior
