# train socal
paa = []
pab = []
pbb = []
for ind in tindv:
info = indv_geno[ind]
genotype = info[2]
if(genotype == 'aa'):
paa.append([float(info[0]), float(info[1])])
elif(genotype == 'ab'):
pab.append([float(info[0]), float(info[1])])
elif(genotype == 'bb'):
pbb.append([float(info[0]), float(info[1])])
trainer = socal_trainer(snp, paa, pab, pbb, c1, c2, c3)
# time execution in ms
t1 = time.time()
trainer.train()
trainer.rescue(c5)
t2 = time.time()
dt = (t2-t1)*1000.0
# get truth
info = indv_geno[vindv]
truth = info[2]
# get the ellipsoids - skip the ellipsoid if any cluster can't be
# estimated
ellipsoids = trainer.get_ellipsoids()
pbb = []
for line in train_data_file:
line = line.strip()
cols = line.split(',')
genotype = cols[2]
if(genotype == '1'):
paa.append([float(cols[0]), float(cols[1])])
elif(genotype == '2'):
pab.append([float(cols[0]), float(cols[1])])
elif(genotype == '3'):
pbb.append([float(cols[0]), float(cols[1])])
c1 = 1
c2 = 10
c3 = 100
trainer = socal_trainer('SNP_A-1643086', paa, pab, pbb, c1, c2, c3)
trainer.train()
trainer.rescue()
ellipsoids = trainer.get_ellipsoids()
e_aa = ellipsoids['aa']
print 'aa'
print e_aa['c']
print e_aa['E']
print
print 'ab'
e_ab = ellipsoids['ab']
print e_ab['c']
print e_ab['E']
print
print 'bb'
e_bb = ellipsoids['bb']