/*----------------------------------------------------------------------------------------------------------------------
| Constructor initializes the conditional likelihood vectors using `nRates' and `nStates', and initializes the
| `pMatrices' data member using `pMat'. The parameter `managePMatrices' determines whether to allocate space for the
| transition probability matrices.
*/
InternalData::InternalData(
PartitionModelShPtr partition, /**< is the PartitionModel object containing information about the number of states and rates for each subset */
CondLikelihoodStorageShPtr cla_storage)
:
state(-1),
cla_pool(cla_storage)
{
const unsigned num_subsets = partition->getNumSubsets();
pMatrices.resize(num_subsets);
for (unsigned i = 0; i < num_subsets; ++i)
{
const unsigned num_rates = partition->subset_num_rates[i];
const unsigned num_states = partition->subset_num_states[i];
pMatrices[i].Initialize(num_rates, num_states, num_states);
}
}
/*----------------------------------------------------------------------------------------------------------------------
| Calls proposeNewState(), then decides whether to accept or reject the proposed new state, calling accept() or
| revert(), whichever is appropriate.
|
| We actually generate a new mapping all the time (in proposeNewState). Then we change the branch length. The branch length
| change might be rejected.
*/
bool UnimapEdgeMove::update()
{
#if 1 || DISABLED_UNTIL_UNIMAP_WORKING_WITH_PARTITIONING
// The only case in which is_fixed is true occurs when the user decides to fix the edge lengths.
// A proposed UnimapEdgeMove cannot be accepted without changing edge lengths, so it is best to just bail out now.
if (is_fixed)
return false;
// std::cerr << "****** UnimapEdgeMove::update" << std::endl;
ChainManagerShPtr p = chain_mgr.lock();
PHYCAS_ASSERT(p);
//likelihood->fullRemapping(tree, rng, true); ///@TEMP!!!!
proposeNewState();
PartitionModelShPtr partModel = likelihood->getPartitionModel();
const unsigned nSubsets = partModel->getNumSubsets();
std::vector<double> uniformization_lambda(nSubsets);
for (unsigned i = 0; i < nSubsets; ++i)
{
ModelShPtr subMod = partModel->getModel(i);
uniformization_lambda[i] = subMod->calcUniformizationLambda();
}
bool is_internal_edge = origNode->IsInternal();
double prev_ln_prior = (is_internal_edge ? p->calcInternalEdgeLenPriorUnnorm(origEdgelen) : p->calcExternalEdgeLenPriorUnnorm(origEdgelen));
double curr_edgelen = r*origEdgelen;
double curr_ln_prior = (is_internal_edge ? p->calcInternalEdgeLenPriorUnnorm(curr_edgelen) : p->calcExternalEdgeLenPriorUnnorm(curr_edgelen));
double log_posterior_ratio = 0.0;
const double log_prior_ratio = curr_ln_prior - prev_ln_prior;
double log_likelihood_ratio = (double)mdot*log(r);
const double edgeLenDiff = (curr_edgelen - origEdgelen);
for (unsigned i = 0; i < nSubsets; ++i)
{
const unsigned numSites = partModel->getNumSites(i);
const double ul = uniformization_lambda[i];
PHYCAS_ASSERT(ul > 0.0);
log_likelihood_ratio -= numSites*ul*edgeLenDiff;
}
likelihood->incrementNumLikelihoodEvals();
if (is_standard_heating)
log_posterior_ratio = heating_power*(log_likelihood_ratio + log_prior_ratio);
else
log_posterior_ratio = heating_power*log_likelihood_ratio + log_prior_ratio;
double ln_accept_ratio = log_posterior_ratio + getLnHastingsRatio();
double lnu = std::log(rng->Uniform(FILE_AND_LINE));
/* std::cerr << " log_likelihood_ratio = " << log_likelihood_ratio << '\n';
std::cerr << " log_posterior_ratio = " << log_posterior_ratio << '\n';
std::cerr << " ln_accept_ratio = " << ln_accept_ratio << '\n';
*/
if (ln_accept_ratio >= 0.0 || lnu <= ln_accept_ratio)
{
p->setLastLnPrior(p->getLastLnPrior() + log_prior_ratio);
p->setLastLnLike(p->getLastLnLike() + log_likelihood_ratio);
accept();
return true;
}
else
{
curr_ln_like = p->getLastLnLike();
curr_ln_prior = p->getLastLnPrior();
revert();
return false;
}
#else
return true;
#endif
}
