def __log_complete_likelihood1__(self, phi, mu_r, mu_v, new_state=0): llh = [] if self.cnv: self.compute_n_genomes(0,new_state) # maternal mu = (self.nr * mu_r + self.nv*(1-mu_r) ) / (self.nr+ self.nv) llh.append(u.log_binomial_likelihood(self.a, self.d, mu) + log(0.5) + self._log_bin_norm_const) self.compute_n_genomes(1,new_state) # paternal mu = (self.nr * mu_r + self.nv*(1-mu_r) ) / (self.nr+ self.nv) llh.append(u.log_binomial_likelihood(self.a, self.d, mu) + log(0.5) + self._log_bin_norm_const) llh = u.logsumexp(ll) else: ## CNV datum or SSM with no CNV mu = (1 - phi) * mu_r + phi*mu_v # (mu_r=0.999, mu_v=0.5) llh = u.log_binomial_likelihood(self.a, self.d, mu) + self._log_bin_norm_const return llh
def __log_complete_likelihood1__(self, phi, mu_r, mu_v, new_state=0): llh = [] if self.cnv: self.compute_n_genomes(0,new_state) # maternal mu = (self.nr * mu_r + self.nv*(1-mu_r) ) / (self.nr+ self.nv) llh.append(u.log_binomial_likelihood(self.a, self.d, mu) + log(0.5) + self._log_bin_norm_const) self.compute_n_genomes(1,new_state) # paternal mu = (self.nr * mu_r + self.nv*(1-mu_r) ) / (self.nr+ self.nv) llh.append(u.log_binomial_likelihood(self.a, self.d, mu) + log(0.5) + self._log_bin_norm_const) llh = u.logsumexp(ll) else: ## CNV datum or SSM with no CNV mu = (1 - phi) * mu_r + phi*mu_v # (mu_r=0.999, mu_v=0.5) llh = u.log_binomial_likelihood(self.a, self.d, mu) + self._log_bin_norm_const return llh
def __log_complete_likelihood__(self, phi, mu_r, mu_v, tp, new_state=0): if self.cnv: ll = [] poss_n_genomes = self.compute_n_genomes(tp,new_state) poss_n_genomes = [x for x in poss_n_genomes if x[1] > 0] for (nr,nv) in poss_n_genomes: mu = (nr * mu_r + nv*(1-mu_r) ) / (nr+ nv) ll.append(u.log_binomial_likelihood(self.a[tp], self.d[tp], mu) + log(1.0/len(poss_n_genomes)) + self._log_bin_norm_const[tp]) if len(poss_n_genomes) == 0: ll.append(log(1e-99)) # to handle cases with zero likelihood llh = u.logsumexp(ll) else: ## CNV datum mu = (1 - phi) * mu_r + 0.5*phi llh = u.log_binomial_likelihood(self.a[tp], self.d[tp], mu) + self._log_bin_norm_const[tp] return llh
def __log_complete_likelihood__(self, phi, mu_r, mu_v, tp, new_state=0): if self.cnv: ll = [] poss_n_genomes = self.compute_n_genomes(tp,new_state) poss_n_genomes = [x for x in poss_n_genomes if x[1] > 0] for (nr,nv) in poss_n_genomes: mu = (nr * mu_r + nv*(1-mu_r) ) / (nr+ nv) ll.append(u.log_binomial_likelihood(self.a[tp], self.d[tp], mu) + log(1.0/len(poss_n_genomes)) + self._log_bin_norm_const[tp]) if len(poss_n_genomes) == 0: ll.append(log(1e-99)) # to handle cases with zero likelihood llh = u.logsumexp(ll) else: ## CNV datum mu = (1 - phi) * mu_r + phi*mu_v # (mu_r=0.999, mu_v=0.5) llh = u.log_binomial_likelihood(self.a[tp], self.d[tp], mu) + self._log_bin_norm_const[tp] return llh
def __log_complete_likelihood__(self, phi, mu_r, mu_v, tp):
mu = (1 - phi) * mu_r + phi * mu_v
return u.log_binomial_likelihood(self.a[tp], self.d[tp],
mu) + self._log_bin_norm_const[tp]
def calcLogLikelihoodBinom(self, mu, tp, sd_d=0): return u.log_binomial_likelihood(self.a[tp], self.d[tp], mu) + self._log_bin_norm_const[tp]
def __log_complete_likelihood__(self, phi, mu_r, mu_v, tp):
mu = (1 - phi) * mu_r + phi * mu_v
return u.log_binomial_likelihood(self.a[tp], self.d[tp], mu) + self._log_bin_norm_const[tp]