double E_rate(const vector<vector<double> >& P, int c,const substitution::MultiModel& smodel)
{
double R=0;
for(int m=0;m<smodel.n_base_models();m++)
R += P[c][m]*smodel.base_model(m).rate();
return R;
}
void estimate_tree(const alignment& A,
SequenceTree& T,
substitution::MultiModel& smodel,
const vector<int>& parameters)
{
//------- Estimate branch lengths -------------//
log_branch_likelihood score2(A,smodel,T,parameters);
// Initialize starting point
optimize::Vector start(0.1, T.n_branches() + parameters.size());
for(int b=0;b<T.n_branches();b++)
start[b] = log(T.branch(b).length());
for(int i=0;i<parameters.size();i++)
start[i+T.n_branches()] = smodel.get_parameter_value_as<Double>( parameters[i] );
// optimize::Vector end = search_gradient(start,score);
// optimize::Vector end = search_basis(start,score);
optimize::Vector end = search_gradient(start,score2,1e-3);
for(int b=0;b<T.n_branches();b++)
T.branch(b).set_length(exp(end[b]));
for(int i=0;i<parameters.size();i++)
smodel.set_parameter_value(parameters[i], end[i+T.n_branches()]);
smodel.set_rate(1);
}
double getSimilarity(double t,substitution::MultiModel& SM)
{
double S = 0;
for(int m=0;m<SM.n_base_models();m++) {
// We can't use models with more than one part
Matrix Q = SM.transition_p(t,0,m);
double Sm = 0;
for(int i=0;i<Q.size1();i++)
Sm += SM.base_model(m).frequencies()[i]*Q(i,i);
S += Sm * SM.distribution()[m];
}
return S;
}
void analyze_rates(const alignment& A,const SequenceTree& T,
const substitution::MultiModel& smodel)
{
if (smodel.n_base_models() == 1) return;
data_partition DP("DP",A,T,smodel);
vector< vector<double> > rate_probs = substitution::get_model_probabilities_by_alignment_column(DP);
vector<double> prior_bin_f = smodel.distribution();
vector<double> post_bin_f = get_post_rate_probs(rate_probs);
show_rate_probs(cout,rate_probs,smodel);
double prior_rate=0;
double post_rate=0;
for(int i=0;i<prior_bin_f.size();i++) {
prior_rate += prior_bin_f[i]*smodel.base_model(i).rate();
post_rate += post_bin_f[i]*smodel.base_model(i).rate();
}
for(int i=0;i<smodel.n_base_models();i++)
cout<<" rate"<<i<<" = "<<smodel.base_model(i).rate();
cout<<endl<<endl;
cout<<" Prior rate = "<<prior_rate<<endl;;
for(int i=0;i<smodel.n_base_models();i++)
cout<<" prior_bin_f"<<i<<" = "<<prior_bin_f[i];
cout<<endl<<endl;
cout<<" Posterior rate = "<<post_rate<<endl;;
for(int i=0;i<smodel.n_base_models();i++)
cout<<" post_bin_f"<<i<<" = "<<post_bin_f[i];
cout<<endl<<endl;
for(int i=0;i<smodel.n_base_models();i++)
cout<<" odds_ratio"<<i<<" = "<<post_bin_f[i]/prior_bin_f[i];
cout<<endl<<endl;
}
void show_frequencies(std::ostream& o,const substitution::MultiModel& MModel) {
const alphabet& a = MModel.Alphabet();
if (MModel.n_base_models() == 1) {
const valarray<double>& f = MModel.base_model(0).frequencies();
for(int i=0;i<a.size();i++)
o<<"f"<<a.lookup(i)<<" = "<<f[i]<<"\n";
}
else {
for(int i=0;i<a.size();i++) {
double total = 0;
for(int m=0;m<MModel.n_base_models();m++) {
const valarray<double>& f = MModel.base_model(m).frequencies();
o<<"f"<<a.lookup(i)<<m+1<<" = "<<f[i]<<" ";
total += MModel.distribution()[m] * f[i];
}
o<<"f"<<a.lookup(i)<<" = "<<total<<"\n";
}
}
}
Multi_Likelihood_Cache::Multi_Likelihood_Cache(const substitution::MultiModel& MM)
:C(0),
M(MM.n_base_models()),
S(MM.n_states())
{ }
