double RateGammaInvar::optimizeParameters(double gradient_epsilon) {
int ndim = getNDim();
// return if nothing to be optimized
if (ndim == 0)
return phylo_tree->computeLikelihood();
if (verbose_mode >= VB_MED)
cout << "Optimizing " << name << " model parameters by " << optimize_alg << " algorithm..." << endl;
if (optimize_alg.find("EM_RR") != string::npos) {
return randomRestartOptimization(gradient_epsilon);
} else if (optimize_alg.find("Brent") != string::npos || phylo_tree->aln->frac_const_sites == 0.0 || isFixPInvar() || isFixGammaShape()) {
double lh = phylo_tree->computeLikelihood();
cur_optimize = 0;
double gamma_lh = RateGamma::optimizeParameters(gradient_epsilon);
ASSERT(gamma_lh >= lh-0.1);
cur_optimize = 1;
double invar_lh = -DBL_MAX;
invar_lh = RateInvar::optimizeParameters(gradient_epsilon);
ASSERT(invar_lh >= gamma_lh-0.1);
cur_optimize = 0;
return invar_lh;
} else if (optimize_alg.find("EM") != string::npos) {
return optimizeWithEM(gradient_epsilon);
} else if (optimize_alg.find("BFGS") != string::npos) {
//if (freq_type == FREQ_ESTIMATE) scaleStateFreq(false);
double *variables = new double[ndim+1];
double *upper_bound = new double[ndim+1];
double *lower_bound = new double[ndim+1];
bool *bound_check = new bool[ndim+1];
double score;
// by BFGS algorithm
setVariables(variables);
setBounds(lower_bound, upper_bound, bound_check);
score = -minimizeMultiDimen(variables, ndim, lower_bound, upper_bound, bound_check, max(gradient_epsilon, TOL_GAMMA_SHAPE));
getVariables(variables);
phylo_tree->clearAllPartialLH();
score = phylo_tree->computeLikelihood();
delete [] bound_check;
delete [] lower_bound;
delete [] upper_bound;
delete [] variables;
return score;
} else {
string errMsg = "Unknown optimization algorithm: " + optimize_alg;
outError(errMsg.c_str());
return 0.0;
}
}
double NGSRateCat::optimizeParameters(double epsilon) {
int ndim = getNDim();
// return if nothing to be optimized
if (ndim == 0) return 0.0;
cout << "Optimizing " << name << " model parameters..." << endl;
double *variables = new double[ndim+1];
double *upper_bound = new double[ndim+1];
double *lower_bound = new double[ndim+1];
bool *bound_check = new bool[ndim+1];
int i;
double score;
// by BFGS algorithm
setVariables(variables);
for (i = 1; i <= ndim; i++) {
//cout << variables[i] << endl;
lower_bound[i] = 1e-4;
upper_bound[i] = 100.0;
bound_check[i] = false;
}
for (i = ndim-ncategory+2; i <= ndim; i++)
upper_bound[i] = 1.0;
//packData(variables, lower_bound, upper_bound, bound_check);
score = -minimizeMultiDimen(variables, ndim, lower_bound, upper_bound, bound_check, max(epsilon, 1e-6));
getVariables(variables);
delete [] bound_check;
delete [] lower_bound;
delete [] upper_bound;
delete [] variables;
return score;
}
/**
optimize parameters. Default is to optimize gamma shape
@return the best likelihood
*/
double RateFree::optimizeParameters(double gradient_epsilon) {
int ndim = getNDim();
// return if nothing to be optimized
if (ndim == 0)
return phylo_tree->computeLikelihood();
if (verbose_mode >= VB_MED)
cout << "Optimizing " << name << " model parameters by " << optimize_alg << " algorithm..." << endl;
// TODO: turn off EM algorithm for +ASC model
if ((optimize_alg.find("EM") != string::npos && phylo_tree->getModelFactory()->unobserved_ptns.empty()))
if (fix_params == 0)
return optimizeWithEM();
//if (freq_type == FREQ_ESTIMATE) scaleStateFreq(false);
double *variables = new double[ndim+1];
double *upper_bound = new double[ndim+1];
double *lower_bound = new double[ndim+1];
bool *bound_check = new bool[ndim+1];
double score;
// score = optimizeWeights();
int left = 1, right = 2;
if (fix_params == 1) // fix proportions
right = 1;
if (optimize_alg.find("1-BFGS") != string::npos) {
left = 0;
right = 0;
}
// changed to Wi -> Ri by Thomas on Sept 11, 15
for (optimizing_params = right; optimizing_params >= left; optimizing_params--) {
ndim = getNDim();
// by BFGS algorithm
setVariables(variables);
setBounds(lower_bound, upper_bound, bound_check);
// if (optimizing_params == 2 && optimize_alg.find("-EM") != string::npos)
// score = optimizeWeights();
// else
if (optimize_alg.find("BFGS-B") != string::npos)
score = -L_BFGS_B(ndim, variables+1, lower_bound+1, upper_bound+1, max(gradient_epsilon, TOL_FREE_RATE));
else
score = -minimizeMultiDimen(variables, ndim, lower_bound, upper_bound, bound_check, max(gradient_epsilon, TOL_FREE_RATE));
getVariables(variables);
// sort the rates in increasing order
if (sorted_rates)
quicksort(rates, 0, ncategory-1, prop);
phylo_tree->clearAllPartialLH();
score = phylo_tree->computeLikelihood();
}
optimizing_params = 0;
delete [] bound_check;
delete [] lower_bound;
delete [] upper_bound;
delete [] variables;
return score;
}
