void FreeFragment(struct s_polymer *fragment,struct s_mc_parms *parms)
{
int i;
for(i=0; i<parms->npol; i++)
{
FreeDoubleMatrix((fragment+i)->A,parms->nmul_local);
FreeDoubleMatrix((fragment+i)->G,parms->nmul_local);
FreeDoubleMatrix((fragment+i)->L,parms->nmul_local);
FreeDoubleMatrix((fragment+i)->Y,parms->nmul_local);
free((fragment+i)->d_ang );
free((fragment+i)->g_ang);
free((fragment+i)->back);
}
free(fragment);
}
ScaleHMM::~ScaleHMM()
{
//FILE_LOG(logDEBUG2) << __PRETTY_FUNCTION__;
FreeDoubleMatrix(this->A, this->N);
Free(this->scalefactoralpha);
FreeDoubleMatrix(this->scalealpha, this->T);
FreeDoubleMatrix(this->scalebeta, this->T);
FreeDoubleMatrix(this->densities, this->N);
// if (this->xvariate==MULTIVARIATE)
// {
// FreeDoubleMatrix(this->tdensities, this->T);
// }
FreeDoubleMatrix(this->gamma, this->N);
FreeDoubleMatrix(this->sumxi, this->N);
Free(this->proba);
Free(this->sumgamma);
for (int iN=0; iN<this->N; iN++)
{
//FILE_LOG(logDEBUG1) << "Deleting density functions";
delete this->densityFunctions[iN];
}
// Free(this->num_nonzero_A_into_state);
// FreeIntMatrix(this->index_nonzero_A_into_state, this->N);
}
ScaleHMM::~ScaleHMM()
{
//FILE_LOG(logDEBUG2) << __PRETTY_FUNCTION__;
FreeDoubleMatrix(this->A, this->N);
Free(this->scalefactoralpha);
FreeDoubleMatrix(this->scalealpha, this->T);
FreeDoubleMatrix(this->scalebeta, this->T);
// FreeDoubleMatrix(this->tdensities, this->T);
FreeDoubleMatrix(this->gamma, this->N);
FreeDoubleMatrix(this->sumxi, this->N);
Free(this->proba);
Free(this->sumgamma);
if (this->xvariate == UNIVARIATE)
{
FreeDoubleMatrix(this->densities, this->N);
for (int iN=0; iN<this->N; iN++)
{
//FILE_LOG(logDEBUG1) << "Deleting density functions";
delete this->densityFunctions[iN];
}
}
}
void ScaleHMM::EM(int* maxiter, int* maxtime, double* eps)
{
//FILE_LOG(logDEBUG2) << __PRETTY_FUNCTION__;
double logPold = -INFINITY;
double logPnew;
double** gammaold = CallocDoubleMatrix(this->N, this->T);
// Parallelization settings
// omp_set_nested(1);
// measuring the time
this->EMStartTime_sec = time(NULL);
// Print some initial information
if (this->xvariate == UNIVARIATE)
{
//FILE_LOG(logINFO) << "";
//FILE_LOG(logINFO) << "INITIAL PARAMETERS";
// this->print_uni_params();
this->print_uni_iteration(0);
}
else if (this->xvariate == MULTIVARIATE)
{
this->print_multi_iteration(0);
}
R_CheckUserInterrupt();
// Do the Baum-Welch and updates
int iteration = 0;
while (((this->EMTime_real < *maxtime) or (*maxtime < 0)) and ((iteration < *maxiter) or (*maxiter < 0)))
{
iteration++;
try { this->baumWelch(); } catch(...) { throw; }
logPnew = this->logP;
this->dlogP = logPnew - logPold;
if (this->xvariate == UNIVARIATE)
{
// clock_t clocktime = clock(), dtime;
// difference in posterior
//FILE_LOG(logDEBUG1) << "Calculating differences in posterior in EM()";
double postsum = 0.0;
for (int t=0; t<this->T; t++)
{
for (int iN=0; iN<this->N; iN++)
{
postsum += fabs(this->gamma[iN][t] - gammaold[iN][t]);
gammaold[iN][t] = this->gamma[iN][t];
}
}
this->sumdiff_posterior = postsum;
// dtime = clock() - clocktime;
// //FILE_LOG(logDEBUG) << "differences in posterior: " << dtime << " clicks";
}
R_CheckUserInterrupt();
// Print information about current iteration
if (this->xvariate == UNIVARIATE)
{
this->print_uni_iteration(iteration);
}
else if (this->xvariate == MULTIVARIATE)
{
this->print_multi_iteration(iteration);
}
// Check convergence
if((fabs(this->dlogP) < *eps) && (this->dlogP < INFINITY)) //it has converged
{
//FILE_LOG(logINFO) << "Convergence reached!\n";
Rprintf("Convergence reached!\n");
break;
}
else
{ // not converged
this->EMTime_real = difftime(time(NULL),this->EMStartTime_sec);
if (iteration == *maxiter)
{
//FILE_LOG(logINFO) << "Maximum number of iterations reached!";
Rprintf("Maximum number of iterations reached!\n");
break;
}
else if ((this->EMTime_real >= *maxtime) and (*maxtime >= 0))
{
//FILE_LOG(logINFO) << "Exceeded maximum time!";
Rprintf("Exceeded maximum time!\n");
break;
}
logPold = logPnew;
}
// // Check weights
// double weights [this->N];
// double sumweights=0;
// this->calc_weights(weights);
// for (int iN=0; iN<this->N; iN++)
// {
// //FILE_LOG(logERROR) << "weights["<<iN<<"] = " << weights[iN];
// sumweights += weights[iN];
// }
// //FILE_LOG(logERROR) << "sumweights = " << sumweights;
//
// Updating initial probabilities proba and transition matrix A
for (int iN=0; iN<this->N; iN++)
{
this->proba[iN] = this->gamma[iN][0];
//FILE_LOG(logDEBUG4) << "sumgamma["<<iN<<"] = " << sumgamma[iN];
if (this->sumgamma[iN] == 0)
{
//FILE_LOG(logINFO) << "Not reestimating A["<<iN<<"][x] because sumgamma["<<iN<<"] = 0";
// Rprintf("Not reestimating A[%d][x] because sumgamma[%d] = 0\n", iN, iN);
}
else
{
for (int jN=0; jN<this->N; jN++)
{
//FILE_LOG(logDEBUG4) << "sumxi["<<iN<<"]["<<jN<<"] = " << sumxi[iN][jN];
this->A[iN][jN] = this->sumxi[iN][jN] / this->sumgamma[iN];
if (std::isnan(this->A[iN][jN]))
{
//FILE_LOG(logERROR) << "updating transition probabilities";
//FILE_LOG(logERROR) << "A["<<iN<<"]["<<jN<<"] = " << A[iN][jN];
//FILE_LOG(logERROR) << "sumxi["<<iN<<"]["<<jN<<"] = " << sumxi[iN][jN];
//FILE_LOG(logERROR) << "sumgamma["<<iN<<"] = " << sumgamma[iN];
throw nan_detected;
}
}
}
}
if (this->xvariate == UNIVARIATE)
{
// clock_t clocktime = clock(), dtime;
//
// // Update all distributions independently
// for (int iN=0; iN<this->N; iN++)
// {
// this->densityFunctions[iN]->update(this->gamma[iN]);
// }
// Update distribution of independent states first, set others as multiples of 'monosomy'
// This loop assumes that the dependent negative binomial states come last and are consecutive
int xsomy = 1;
for (int iN=0; iN<this->N; iN++)
{
if (this->densityFunctions[iN]->get_name() == ZERO_INFLATION) {}
if (this->densityFunctions[iN]->get_name() == GEOMETRIC)
{
this->densityFunctions[iN]->update(this->gamma[iN]);
}
if (this->densityFunctions[iN]->get_name() == NEGATIVE_BINOMIAL)
{
if (xsomy==1)
{
//FILE_LOG(logDEBUG1) << "mean(state="<<iN<<") = " << this->densityFunctions[iN]->get_mean() << ", var(state="<<iN<<") = " << this->densityFunctions[iN]->get_variance();
this->densityFunctions[iN]->update_constrained(this->gamma, iN, this->N);
double mean1 = this->densityFunctions[iN]->get_mean();
double variance1 = this->densityFunctions[iN]->get_variance();
//FILE_LOG(logDEBUG1) << "mean(state="<<iN<<") = " << this->densityFunctions[iN]->get_mean() << ", var(state="<<iN<<") = " << this->densityFunctions[iN]->get_variance();
// Set others as multiples
for (int jN=iN+1; jN<this->N; jN++)
{
this->densityFunctions[jN]->set_mean(mean1 * (jN-iN+1));
this->densityFunctions[jN]->set_variance(variance1 * (jN-iN+1));
//FILE_LOG(logDEBUG1) << "mean(state="<<jN<<") = " << this->densityFunctions[jN]->get_mean() << ", var(state="<<jN<<") = " << this->densityFunctions[jN]->get_variance();
}
break;
}
xsomy++;
}
}
// dtime = clock() - clocktime;
// //FILE_LOG(logDEBUG) << "updating distributions: " << dtime << " clicks";
R_CheckUserInterrupt();
}
} /* main loop end */
//Print the last results
//FILE_LOG(logINFO) << "";
//FILE_LOG(logINFO) << "FINAL ESTIMATION RESULTS";
// this->print_uni_params();
// free memory
FreeDoubleMatrix(gammaold, this->N);
// Return values
*maxiter = iteration;
*eps = this->dlogP;
this->EMTime_real = difftime(time(NULL),this->EMStartTime_sec);
*maxtime = this->EMTime_real;
}
