def __init__(self, prandom, nstates, cache_size=0):
"""
@param prandom: the probability of randomization per step
@param nstates: the number of possible states
@param cache_size: save this many observations
"""
if not (0 <= prandom <= 1):
raise ValueError('expected a probability')
self.prandom = prandom
self.nstates = nstates
self._get_ntrans = Util.Cache(self._get_uncached_ntrans, cache_size)
def __init__(self, randomization_rate, expected_coverage, cache_size=0):
"""
The number of residues includes a gap residue.
@param randomization_rate: the probability that a read is randomized
@param expected_coverage: the read expected read coverage at a good position
@param cache_size: the number of observations to remember
"""
CachedSuperstate.__init__(self, cache_size)
nresidues = 5
mutation_rate = 1e-6
state_hom = Homozygous(nresidues, randomization_rate, expected_coverage)
state_het = Heterozygous(nresidues, randomization_rate, expected_coverage)
self.states = [state_hom, state_het]
self.distribution = [1 - mutation_rate, mutation_rate]
self.state_names = ['hom', 'het']
# precalculate stuff for efficiency
self.get_posterior_distribution = Util.Cache(self._get_posterior_distribution, cache_size)
self.log_distribution = [math.log(p) for p in self.distribution]
def __init__(self, randomization_rate, expected_coverage, cache_size=0):
"""
The number of residues includes a gap residue.
@param randomization_rate: the probability that a read is randomized
@param expected_coverage: the read expected read coverage at a good position
@param cache_size: the number of observations to remember
"""
CachedSuperstate.__init__(self, cache_size)
nresidues = 5
low_coverage = 2
state_hom = Homozygous(nresidues, randomization_rate, expected_coverage)
state_het = Heterozygous(nresidues, randomization_rate, expected_coverage)
state_hom2x = Homozygous(nresidues, randomization_rate, expected_coverage*2)
state_het2x = Heterozygous(nresidues, randomization_rate, expected_coverage*2)
state_low = FlatState(nresidues, 2)
state_weird = FlatState(nresidues, expected_coverage*2)
state_high = FlatState(nresidues, expected_coverage*20)
self.states = [state_hom, state_het, state_hom2x, state_het2x, state_low, state_weird, state_high]
self.state_names = ['hom', 'het', 'hom2x', 'het2x', 'low', 'weird', 'high']
# precalculate stuff for efficiency
self.get_posterior_distribution = Util.Cache(self._get_posterior_distribution, cache_size)
self.log_nstates = math.log(len(self.states))
def __init__(self, cache_size=0):
self.get_annotated_log_likelihood = Util.Cache(self._get_annotated_log_likelihood, cache_size)
