/// Compute Factor Analysis model to perform likelihood computation in the classical way
void FactorAnalysisStat::getFactorAnalysisModel(MixtureGD& FA,String& file) {
if (verbose) cout << "(FactorAnalysisStat) Compute Variance adapted Speaker Model"<<endl;
this->getTrueSpeakerModel(FA,file);
unsigned long loc=_ndxTable.locNb(file);
/// Compute sigma_s
RealVector <double> sigma_s;
sigma_s.setSize(_supervsize);
for (unsigned long i=0;i<_mixsize;i++)
for (unsigned long j=0;j<_vsize;j++)
sigma_s[i*_vsize+j]=FA.getDistrib(i).getCov(j)/(_tau+_matN(loc,i));
/// Compute sigma_s+*sigma_w
RealVector <double> sum,prod;
sum.setSize(_supervsize);prod.setSize(_supervsize);
for (unsigned long i=0;i<_mixsize;i++) {
for (unsigned long j=0;j<_vsize;j++) {
sum[i*_vsize+j]=sigma_s[i*_vsize+j]+FA.getDistrib(i).getCov(j);
//cout << "i,j"<<i<<","<<j<<" sigma_s "<<sigma_s[i*_vsize+j]<<" cov:"<<FA.getDistrib(i).getCov(j)<<" N"<<_N(loc,i)<<" "<<_tau<<endl;
}
}
for (unsigned long i=0;i<_mixsize;i++)
for (unsigned long j=0;j<_vsize;j++)
FA.getDistrib(i).setCov(sum[i*_vsize+j],j);
FA.computeAll();
}
// Plot the enrgy model
void plotEnergyDistrib(MixtureGD &mixt)
{
unsigned long distribCount = mixt.getDistribCount();
cout << "EnergyModel"<<endl;
for (unsigned long c=0; c<distribCount; c++)
cout << "Component["<<c<<"] Mean["<<mixt.getDistrib(c).getMean(0)<<
"] Cov["<<mixt.getDistrib(c).getCov(0)<<"] Weight["<<mixt.weight(c)<<"]"<<endl;
}
//-------------------------------------------------------------------------
void W::writeMixtureGDXml(const MixtureGD& m)
{
unsigned long i;
writeString("\n\t<MixtureGD");
writeAttribute("id", m.getId());
writeAttribute("distribCount", m.getDistribCount());
writeString(">");
for (i=0; i< m.getDistribCount(); i++)
{
DistribGD& d = m.getDistrib(i);
writeString("\n\t\t<DistribGD");
writeAttribute("i", i);
writeAttribute("dictIdx", d.dictIndex(K::k));
writeAttribute("weight", m.weight(i));
writeString("/>");
}
writeString("\n\t</MixtureGD>");
}
// find the lowest energy component...
unsigned long findMinEnergyDistrib(MixtureGD &mixt)
{
unsigned long distribCount = mixt.getDistribCount();
unsigned long cmpMin=0;
for (unsigned long c=1; c<distribCount; c++)
if (mixt.getDistrib(c).getMean(0)<mixt.getDistrib(cmpMin).getMean(0))
cmpMin=c;
if (verbose) cout << "Lowest component["<<cmpMin<<"] Mean["<<mixt.getDistrib(cmpMin).getMean(0)<<
"] Cov["<<mixt.getDistrib(cmpMin).getCov(0)<<"] Weight["<<mixt.weight(cmpMin)<<"]"<<endl;
return cmpMin;
}
//-------------------------------------------------------------------------
void W::writeMixtureGDRaw(const MixtureGD& m)
{
unsigned long i;
writeString("GD");
writeUInt4(m.getId().length());
writeString(m.getId());
writeUInt4(m.getDistribCount());
for (i=0; i< m.getDistribCount(); i++)
{
DistribGD& d = m.getDistrib(i);
writeUInt4(d.dictIndex(K::k));
writeDouble(m.weight(i));
}
}
TabWeight::TabWeight(const MixtureGD &model){
init(model,model.getDistribCount());
}
void TabWeight::init(const MixtureGD &model,double threshold){
_size=model.getDistribCount();
_tab=new TabWeightElem[_size];
_sortByWeight(model);
_nbTopDyn(threshold);
}
void TabWeight::init(const MixtureGD &model,unsigned long topDistribs){
_size=model.getDistribCount();
_tab=new TabWeightElem[_size];
_sortByWeight(model);
_nbTop=topDistribs;
}
// Main init function
double TopGauss::compute(MixtureGD & UBM,FeatureServer &fs,String & featureFilename,Config & config){
StatServer ss(config);
MixtureGDStat &acc=ss.createAndStoreMixtureStat(UBM);
unsigned long _mixsize=UBM.getDistribCount();
String labelSelectedFrames =config.getParam("labelSelectedFrames");
unsigned long begin=fs.getFirstFeatureIndexOfASource(featureFilename);
fs.seekFeature(begin);
SegServer segmentsServer;
LabelServer labelServer;
initializeClusters(featureFilename,segmentsServer,labelServer,config);
// __android_log_print(ANDROID_LOG_DEBUG, "TopGauss::compute", " Feature file %s \n", featureFilename.c_str());
verifyClusterFile(segmentsServer,fs,config);
unsigned long codeSelectedFrame=labelServer.getLabelIndexByString(labelSelectedFrames);
SegCluster& selectedSegments=segmentsServer.getCluster(codeSelectedFrame);
acc.resetLLK();
double topD=config.getParam("topGauss").toDouble();
if (verbose) {if(topD<1.0) cout << "LLK %="<< topD << "% ";else cout << "Top-"<<topD<<" ";}
// Class values
_nt=totalFrame(selectedSegments);
_nbg.setSize(_nt); _idx.setSize(0);_snsw.setSize(0); _snsl.setSize(0);
_nbg.setAllValues(0); _idx.setAllValues(0);_snsw.setAllValues(0.0);_snsl.setAllValues(0.0);
_nbgcnt=0;
Seg *seg; // current selected segment
selectedSegments.rewind();
unsigned long t=0; //cnt frames
while((seg=selectedSegments.getSeg())!=NULL){
unsigned long begin=seg->begin()+fs.getFirstFeatureIndexOfASource(seg->sourceName());
fs.seekFeature(begin);
Feature f;
for (unsigned long idxFrame=0;idxFrame<seg->length();idxFrame++){
fs.readFeature(f);
double llk=acc.computeAndAccumulateLLK(f,1.0,DETERMINE_TOP_DISTRIBS);
const LKVector &topV=ss.getTopDistribIndexVector();
double lk_tot=exp(llk);
double val=0.0;
if (topD<1.0) {
for(unsigned long j=0;j<_mixsize;j++){
if (val > topD*lk_tot) break;
val+=(topV[j].lk);
_nbg[t]++;
}
} else _nbg[t]=(unsigned long)topD;
_nbgcnt+=_nbg[t];
double snsw=1.0;
double snsl=lk_tot;
for(unsigned long j=0;j<_nbg[t];j++) {
_idx.addValue(topV[j].idx);
snsw -=UBM.weight(topV[j].idx);
snsl -=topV[j].lk;
}
_snsw.addValue(snsw);
if (snsl < EPS_LK)
_snsl.addValue(EPS_LK);
else _snsl.addValue(snsl);
t++;
}
}
if (t!=_nt) cout << "W: t("<<t<<") != _nt(" <<_nt<<")"<<endl;
return acc.getMeanLLK();
}
