void KPCA::compPrimalEnd ()
{
if (featureCount != dim)
retinThrowException("Wrong number of features");
for (size_t i=0;i<featureDim;i++)
primalMean[i] /= dim;
for (size_t j=0;j<eigenCount;j++)
primalValues[j] = (float)(eigens[firstEigen+j].getValue() / dim);
}
void SparseVlatCluster::setEigenDecomposition(const float* eigenVectors , const float* eigenValues, size_t vectorsDim, size_t vectorsSize){
if (vectorsDim != vectorsSize)
retinThrowException("Not square matrix");
if (vectorsDim != getTensorDim(1))
retinThrowException2("Invalid dim %d != %d", vectorsDim, getTensorDim(1));
U.resize(vectorsDim*vectorsDim);
L.resize(vectorsDim);
memcpy(&U[0], eigenVectors, vectorsDim*vectorsDim * sizeof(float));
memcpy(&L[0], eigenValues, vectorsDim * sizeof(float));
}
void KPCA::compPrimalBegin(size_t featureDim_,size_t firstEigen_,size_t eigenCount_)
{
featureCount = 0;
featureDim = featureDim_;
firstEigen = firstEigen_;
eigenCount = eigenCount_;
if (firstEigen+eigenCount > eigens.size())
retinThrowException("Invalid eigen dims");
primalFeature.resize(featureDim);
primalMean.resize(featureDim);
primalValues.resize(eigenCount);
primalVectors.resize(featureDim*eigenCount);
}
FeatureRef StandardPcaVlatBuilder::getVlat(string & format){
FeatureRef vlat;
size_t dim = getDim(format);
vlat = boost::make_shared<Feature>(dim);
float * data = vlat->data();
for(size_t i = 0 ; i < clusters.size(); i++){
FeatureRef subVlat = clusters[i]->getData(format);
memcpy(data, subVlat->data(), subVlat->dim()*sizeof(float));
data += subVlat->dim();
if((size_t)(data - vlat->data()) > dim)
retinThrowException("StandardPcaVlatCluster::getVlat => data - vlat->data() > dim");
}
return vlat;
}
void StandardPcaVlatCluster::add(const float * f, size_t dim){
if(dim != dimPrim)
retinThrowException("StandardPcaVlatCluster::add => dim != dimPrim");
counter++;
for(size_t i = 0 ; i<dimPrim ; i++)
{
(*featc)(i,0) = f[i] - (*meanPrim)(i);
}
for (size_t k = 0; k < dimPrim; k++) {
(featDual->data())[k] = vector_ps_double(featc->data(), proj->data()+k*dimPrim, dimPrim);
}
if(whitening)
vector_mul_double(featDual->data(), invsqrtvar->data(), dimPrim);
if(degre == 1)
vector_add_double(vlat->data(), featDual->data(), dimDual);
else
matrix_Cpaat_double(vlat->data(), featDual->data(), dimDual);
}
