if random() > .5:
seq[start:start + K] = site
else:
seq[start:start + K] = rev_comp_site
return seq
p_binding_site = .01
order = 1
num_background_states = 2
# try to learn pssm for these sequences
seqs = [gen_sequence() for i in xrange(num_seqs)]
def bw_callback(LL):
print LL
traits = PssmTraits(K, p_binding_site, order, num_background_states,
create_background_model)
pssm_learner = PssmLearner(seqs, traits)
#pssm_learner.bw_callback = bw_callback
multiple_learner = MultiplePssmLearner(pssm_learner, 'test_multiple')
found, discarded = multiple_learner.learn_pssms(max_to_find=2,
max_attempts=2)
print 'Found %d models, discarded %d' % (len(found), len(discarded))
for model, sites, counts in found:
print '# sites: %d' % len(sites)
dist = traits.pssm_dist(model)
IC = information_content(dist)
print 'IC: %f' % IC
#image.show()
def gen_sequence():
seq = random_sequence(seq_length)
if random() > .5:
seq[start:start+K] = site
else:
seq[start:start+K] = rev_comp_site
return seq
p_binding_site = .01
order = 1
num_background_states = 2
# try to learn pssm for these sequences
seqs = [ gen_sequence() for i in xrange(num_seqs) ]
def bw_callback(LL):
print LL
traits = PssmTraits(K, p_binding_site, order, num_background_states, create_background_model)
pssm_learner = PssmLearner(seqs, traits)
#pssm_learner.bw_callback = bw_callback
multiple_learner = MultiplePssmLearner(pssm_learner, 'test_multiple')
found, discarded = multiple_learner.learn_pssms(max_to_find = 2, max_attempts = 2)
print 'Found %d models, discarded %d' % (len(found), len(discarded))
for model, sites, counts in found:
print '# sites: %d' % len(sites)
dist = traits.pssm_dist(model)
IC = information_content(dist)
print 'IC: %f' % IC
#image.show()
