//-------------------------------------------------------------------------
void A::deaccumulate(const Feature& f)
{
const unsigned long vectSize = f.getVectSize();
if (!_vectSizeDefined)
{
return;
}
else if (vectSize != _vectSize)
throw Exception("Incompatible vectSize ("
+ String::valueOf(vectSize) + "/"
+ String::valueOf(_vectSize) + ")", __FILE__, __LINE__);
const double* dataVect = f.getDataVector();
for (unsigned long i=0; i<_vectSize; i++)
{
const double v = dataVect[i];
_accVect[i] -= v;
_xaccVect[i] -= v*v;
}
_count--;
_computed = false;
_stdComputed = false;
}
void FactorAnalysisStat::computeAndAccumulateGeneralFAStats(SegCluster &selectedSegments,FeatureServer &fs,Config & config){
if (verbose) cout <<"(FactorAnalysisStat) Compute General FA Stats (Complete)" << endl;
double *N_h, *N, *S_X_h, *S_X,*ff;
_matN_h.setAllValues(0.0);
_matN.setAllValues(0.0);
_matS_X_h.setAllValues(0.0);
_matS_X.setAllValues(0.0);
N_h=_matN_h.getArray(); N=_matN.getArray(); S_X_h=_matS_X_h.getArray();S_X=_matS_X.getArray();
MixtureGD & UBM=_ms.getMixtureGD((unsigned long) 1);
MixtureGDStat &acc=_ss.createAndStoreMixtureStat(UBM);
// Compute Occupations and Statistics
acc.resetOcc();
Seg *seg;
selectedSegments.rewind();
String currentSource="";unsigned long loc=0;unsigned long sent=0;
while((seg=selectedSegments.getSeg())!=NULL){
unsigned long begin=seg->begin()+fs.getFirstFeatureIndexOfASource(seg->sourceName()); // Idx of the first frame of the current file in the feature server
if (currentSource!=seg->sourceName()) {
currentSource=seg->sourceName();
loc=_ndxTable.locNb(currentSource);
sent=_ndxTable.sessionNb(currentSource);
if (verbose)cout << "Processing speaker["<<currentSource<<"]"<< endl;
}
fs.seekFeature(begin);
Feature f;
if (!_topGauss) {
for (unsigned long idxFrame=0;idxFrame<seg->length();idxFrame++){
fs.readFeature(f);
acc.computeAndAccumulateOcc(f);
RealVector <double> aPost=acc.getOccVect();
ff=f.getDataVector();
for(unsigned long k=0;k<_mixsize;k++) {
N_h[sent*_mixsize+k]+=aPost[k];
N[loc*_mixsize+k] +=aPost[k];
for (unsigned long i=0;i<_vsize;i++) {
S_X_h[sent*_supervsize+(k*_vsize+i)]+=aPost[k]*ff[i];
S_X[loc*_supervsize+(k*_vsize+i)] +=aPost[k]*ff[i];
}
}
}
}
else throw Exception("ComputeGeneralStats TopGauss not done at this level",__FILE__,__LINE__);
}
};
/// Normalize features with a smooth mixture transformation o't=ot-sum(P(c|ot)Uc.x)
void FactorAnalysisStat::normalizeFeatures(SegCluster &selectedSegments,FeatureServer &fs,Config & config){
if (verbose) cout << "(FactorAnalysisStat) Normalize Features" << endl;
MixtureGD & clientMixture=_ms.getMixtureGD(1); // copy the UBM mixture
unsigned long nt=0;
RealVector <double> m_xh_1; m_xh_1.setSize(_supervsize);
double *_m_xh_1=m_xh_1.getArray();
Seg *seg; // current selectd segment
selectedSegments.rewind();
String currentSource="";
while((seg=selectedSegments.getSeg())!=NULL){
unsigned long begin=seg->begin()+fs.getFirstFeatureIndexOfASource(seg->sourceName());
if (currentSource!=seg->sourceName()) {
currentSource=seg->sourceName();
this->getUX(m_xh_1,currentSource);
this->getSpeakerModel(clientMixture,currentSource);
if (verbose)cout << "Processing speaker["<<currentSource<<"]"<< endl;
}
fs.seekFeature(begin);
Feature f;
if (!_topGauss) {
for (unsigned long idxFrame=0;idxFrame<seg->length();idxFrame++){
fs.readFeature(f,0);
double *ff=f.getDataVector();
double sum=0.0;
RealVector <double> P;
P.setSize(_mixsize);
double *Prob=P.getArray();
for(unsigned long k=0;k<_mixsize;k++) {
Prob[k]=clientMixture.weight(k)*clientMixture.getDistrib(k).computeLK(f);
sum+=Prob[k];
}
for(unsigned long k=0;k<_mixsize;k++)
Prob[k]/=sum;
for(unsigned long k=0;k<_mixsize;k++) {
for (unsigned long i=0;i<_vsize;i++)
ff[i]-= Prob[k]*_m_xh_1[k*_vsize+i];
}
fs.writeFeature(f);
nt++;
}
}
else {
throw Exception("no topgauss yet",__FILE__,__LINE__);
}
}
};
//-------------------------------------------------------------------------
// TODO : A OPTIMISER !!
//-------------------------------------------------------------------------
lk_t DistribGD::computeLK(const Feature& frame) const
{
if (frame.getVectSize() != _vectSize)
throw Exception("distrib vectSize ("
+ String::valueOf(_vectSize) + ") != feature vectSize ("
+ String::valueOf(frame.getVectSize()) + ")", __FILE__, __LINE__);
real_t tmp = 0.0;
real_t* m = _meanVect.getArray();
real_t* c = _covInvVect.getArray();
Feature::data_t* f = frame.getDataVector();
for (unsigned long i=0; i<_vectSize; i++)
tmp += (f[i] - m[i]) * (f[i] - m[i]) * c[i];
tmp = _cst * exp(-0.5*tmp);
if (ISNAN(tmp))
return EPS_LK;
return tmp;
}
//-------------------------------------------------------------------------
bool M::writeFeature(const Feature& f, unsigned long step)
{
bool ok;
if (!_useMask)
ok = _pInput->writeFeature(f, step);
else
{
if (_selectionSize != f.getVectSize())
throw Exception("Invalid feature mask : " + _mask,
__FILE__, __LINE__);
_feature.setVectSize(K::k, _pInput->getVectSize());
Feature::data_t* outputVect = _feature.getDataVector();
Feature::data_t* inputVect = f.getDataVector();
for (unsigned long i=0; i<_selectionSize; i++)
outputVect[_selection[i]] = inputVect[i];
ok = _pInput->writeFeature(_feature, step);
}
_error = _pInput->getError();
return ok;
}
