def __init__(
self,
K,
p_binding_site,
background_order,
num_background_mosaics,
background_model_creator,
emission_dists = None,
prior_strength = 0.0,
interesting_ic_per_base = .3
):
BaseTraits.__init__(
self,
K,
p_binding_site,
background_order,
num_background_mosaics,
background_model_creator,
prior_strength = prior_strength
)
self.model_builder = hmm.pssm.ModelBuilder(self.order)
self.background_states = set(xrange(num_background_mosaics))
self.reverse_complements = set()
self.reverse_complements = dict(
(state, state - self.K)
for state in
xrange(num_background_mosaics + K, num_background_mosaics + 2*K)
)
self.emission_dists = emission_dists
self.prior_strength = prior_strength
self.interesting_ic_per_base = interesting_ic_per_base
if None != emission_dists and self.K != len(emission_dists):
raise RuntimeError('K and len(emission_dists) mismatch')
def __init__(
self,
K,
p_binding_site,
background_order,
num_background_mosaics,
background_model_creator,
emission_dists = None,
p_gap = 0.35,
gap_threshold = 0.1,
ic_threshold = 0.5,
prior_strength = 0.0
):
BaseTraits.__init__(
self,
K,
p_binding_site,
background_order,
num_background_mosaics,
background_model_creator,
prior_strength = prior_strength
)
self.K_prime = 2 * self.K - 1
self.model_builder = hmm.pssm.ModelBuilder(self.order)
self.background_states = range(num_background_mosaics)
self.reverse_complements = dict(
(state, state - self.K_prime)
for state in
xrange(num_background_mosaics + self.K_prime, num_background_mosaics + 2*self.K_prime)
)
self.emission_dists = emission_dists
self.p_gap = p_gap
self.gap_threshold = gap_threshold
self.ic_threshold = ic_threshold
def log_info(self, model, log):
model = hmm.as_model(model)
BaseTraits.log_info(self, model, log)
log.info('Adjusted IC/base = %.4f' % self.information_content_per_base(model))
A = model.A
for k in xrange(self.K):
p_gap = self.p_gap_for_model(model, k)
if p_gap > self.gap_threshold:
log.info('p(gap at %d) = %.4f' % (k, p_gap))
