void Matrix::eig_sym(Matrix* U, Matrix* L, uint nb_eigenvectors) {
if(!is_square()) throw std::runtime_error("Matrix must be square");
Matrix _U,_L;
_U.init(height, width);
_L.init(1,width);
matrix_eigSym2_float(data, _U.data, _L.data, width);
matrix_sortEig_float(_U.data, _L.data, width);
*U = _U.trim_height(nb_eigenvectors);
*L = _L.trim_width(nb_eigenvectors);
}
void SparseVlatCluster::init(){
StandardVlatCluster::init();
size_t dim = getTensorDim(1);
if(meanTensors[1] && L.size() == 0){
U.resize(dim*dim);
L.resize(dim);
memcpy(&U[0], meanTensors[1], dim*dim*sizeof(float));
matrix_eigSym_float (&U[0], &L[0], dim);
matrix_sortEig_float (&U[0], &L[0], dim);
}
if(mainOrder == 1 || sparsityOn == SparseVlatCluster::sparsityOnDiag){
varDual.reserve(dim);
memset(&varDual[0],0,dim*sizeof(float));
}
else if(sparsityOn == SparseVlatCluster::sparsityOnFull){
varDual.reserve(dim*dim);
memset(&varDual[0],0,dim*dim*sizeof(float));
}
counter = 0;
}