def get_two_taxon_neg_ll(
model,
subs_counts,
log_counts, codon_distn,
ts, tv, syn, nonsyn, compo, asym_compo,
natural_theta,
):
"""
Get the negative log likelihood.
This function does not use the logarithms.
It is mostly for computing the hessian;
otherwise the version with the logarithms would probably be better.
The first param group is the model implementation.
The second param group is the data.
The third param group consists of data summaries.
The fourth param group consists of design matrices related to genetic code.
The fifth param group consist of free parameters of the model.
"""
branch_length = natural_theta[0]
natural_model_theta = natural_theta[1:]
distn = model.get_distn(
log_counts, codon_distn,
ts, tv, syn, nonsyn, compo, asym_compo,
natural_model_theta,
)
pre_Q = model.get_pre_Q(
log_counts, codon_distn,
ts, tv, syn, nonsyn, compo, asym_compo,
natural_model_theta,
)
neg_ll = -markovutil.get_branch_ll(
subs_counts, pre_Q, distn, branch_length)
print neg_ll
return neg_ll
def get_two_taxon_neg_ll_log_theta(
model,
subs_counts,
log_counts, codon_distn,
ts, tv, syn, nonsyn, compo, asym_compo,
theta,
):
"""
Get the negative log likelihood.
This function uses the logarithms of the model parameters.
The first param group is the model implementation.
The second param group is the data.
The third param group consists of data summaries.
The fourth param group consists of design matrices related to genetic code.
The fifth param group consist of free parameters of the model.
"""
branch_length = algopy.exp(theta[0])
model_theta = theta[1:]
distn = model.get_distn(
log_counts, codon_distn,
ts, tv, syn, nonsyn, compo, asym_compo,
model_theta,
)
pre_Q = model.get_pre_Q(
log_counts, codon_distn,
ts, tv, syn, nonsyn, compo, asym_compo,
model_theta,
)
neg_ll = -markovutil.get_branch_ll(
subs_counts, pre_Q, distn, branch_length)
return neg_ll
