void PackedVlatCluster::add(const float* feature,size_t dim)
{
size_t meanDim = getTensorDim(1);
if (dim != meanDim)
retinThrowException2("Invalid feature dim %d != %d",dim,meanDim);
vector<double> temp(meanDim);
const float* mean = getMeanTensor(1);
if (mean) {
for (size_t i=0;i<dim;i++)
temp[i] = feature[i] - mean[i];
}
else {
for (size_t i=0;i<dim;i++)
temp[i] = feature[i];
}
pca.add(&temp[0],dim);
}
void SparseVlatCluster::add(const float* feature, size_t featureDim){
size_t meanDim = getTensorDim(1);
if (featureDim != meanDim)
retinThrowException2("Invalid feature dim %d != %d", featureDim, meanDim);
vector<float> featureCentred(meanDim);
const float* mean = getMeanTensor(1);
if (mean)
vector_linear_float(&featureCentred[0], feature, -1, mean, meanDim);
else
memcpy(&featureCentred[0],feature,meanDim*sizeof(float));
vector<float> featureDual(meanDim);
matrix_CpAtB_float(&featureDual[0], &U[0], &featureCentred[0], meanDim, meanDim, 1);
if(mainOrder == 1 || sparsityOn == SparseVlatCluster::sparsityOnDiag){
for(size_t i = 0 ; i < meanDim ; i++)
varDual[i] += featureDual[i]*featureDual[i];
}
else if(sparsityOn == SparseVlatCluster::sparsityOnFull){
vector<float> temp(meanDim*meanDim);
matrix_Cpaat_float(&temp[0], &featureDual[0], meanDim);
for(size_t i = 0 ; i < meanDim*meanDim ; i++)
temp[i] *= temp[i];
vector_add_float (&varDual[0], &featureDual[0], meanDim*meanDim);
}
vector<pair<size_t, float> > sortTable;
if (mainOrder == 1) {
if(sparsityOn == sparsityMaxValues){
sortTable.resize(meanDim);
for(size_t i = 0 ; i < meanDim ; i++){
sortTable[i].first = i;
sortTable[i].second = featureDual[i];
}
sort(sortTable.begin(), sortTable.end(), myfunction);
for(size_t i = sparsityDim ; i < meanDim ; i++){
featureDual[sortTable[i].first] = 0;
}
}
vector_add_float (&vlat[0], &featureDual[0], meanDim);
}
else if (mainOrder == 2) {
if(sparsityOn == sparsityMaxValues){
vector<float> temp(meanDim*meanDim);
memset(&temp[0],0,meanDim*meanDim*sizeof(float));
matrix_Cpaat_float(&temp[0], &featureDual[0], meanDim);
sortTable.resize(meanDim*meanDim);
for(size_t i = 0 ; i < meanDim*meanDim ; i++){
sortTable[i].first = i;
sortTable[i].second = temp[i];
}
sort(sortTable.begin(), sortTable.end(), myfunction);
for(size_t i = sparsityDim ; i < meanDim*meanDim ; i++){
temp[sortTable[i].first] = 0;
}
vector_add_float (&vlat[0], &temp[0], meanDim*meanDim);
}
else
matrix_Cpaat_float(&vlat[0], &featureDual[0], meanDim);
}
counter++;
}
