void BasicOperator::mult_dirac(const Eigen::MatrixXcd& matrix, Eigen::MatrixXcd& reordered, const size_t index) const { const vec_pdg_Corr op_Corr = global_data->get_lookup_corr(); const size_t rows = matrix.rows(); const size_t cols = matrix.cols(); const size_t col = cols/4; if( (rows != reordered.rows()) || (cols != reordered.cols()) ){ std::cout << "Error! In BasicOperator::mult_dirac: size of matrix and " "reordered must be equal" << std::endl; exit(0); } for(const auto& dirac : op_Corr[index].gamma){ if(dirac != 4){ for(size_t block = 0; block < 4; block++){ reordered.block(0, block * col, rows, col) = gamma[dirac].value[block] * matrix.block(0, gamma[dirac].row[block]*col, rows, col); } } } }
void CCorrelationFilters::build_mmcf(struct CDataStruct *img, struct CParamStruct *params, struct CFilterStruct *filt) { /* * This function calls the correlation filter design proposed in the following publications. * * A. Rodriguez, Vishnu Naresh Boddeti, B.V.K. Vijaya Kumar and A. Mahalanobis, "Maximum Margin Correlation Filter: A New Approach for Localization and Classification", IEEE Transactions on Image Processing, 2012. * * Vishnu Naresh Boddeti, "Advances in Correlation Filters: Vector Features, Structured Prediction and Shape Alignment" PhD thesis, Carnegie Mellon University, Pittsburgh, PA, USA, 2012. * * Vishnu Naresh Boddeti and B.V.K. Vijaya Kumar, "Maximum Margin Vector Correlation Filters," Arxiv 1404.6031 (April 2014). * * Notes: This currently the best performing Correlation Filter design, especially when the training sample size is larger than the dimensionality of the data. */ filt->params = *params; filt->filter.size_data = params->size_filt_freq; filt->filter.size_data_freq = params->size_filt_freq; filt->filter.num_elements_freq = img->num_elements_freq; params->num_elements_freq = img->num_elements_freq; filt->filter.data_freq = new complex<double>[img->num_elements_freq*img->num_channels]; Eigen::ArrayXcd filt_freq = Eigen::ArrayXcd::Zero(params->num_elements_freq*img->num_channels); // If not set default to 1 if (params->wpos < 1) params->wpos = 1; filt->params.wpos = params->wpos; compute_psd_matrix(img, params); Eigen::MatrixXcd Y = Eigen::MatrixXcd::Zero(img->num_elements_freq*img->num_channels,img->num_data); Eigen::MatrixXcd u = Eigen::MatrixXcd::Zero(img->num_data,1); Eigen::MatrixXd temp = Eigen::MatrixXd::Zero(img->num_data,img->num_data); Eigen::Map<Eigen::MatrixXcd> X(img->data_freq,img->num_elements_freq*img->num_channels,img->num_data); Eigen::ArrayXXcd temp1 = Eigen::ArrayXXcd::Zero(img->num_elements_freq,img->num_channels); Eigen::ArrayXXcd temp2 = Eigen::ArrayXXcd::Zero(img->num_elements_freq,img->num_channels); Eigen::Vector2i num_blocks_1, num_blocks_2; num_blocks_1 << img->num_channels,img->num_channels; num_blocks_2 << img->num_channels,1; for (int k=0;k<img->num_data;k++){ temp2 = X.block(0,k,img->num_elements_freq*img->num_channels,1).array(); temp2.resize(img->num_elements_freq,img->num_channels); fusion_matrix_multiply(temp1, img->Sinv, temp2, num_blocks_1, num_blocks_2); temp1.resize(img->num_elements_freq*img->num_channels,1); Y.block(0,k,img->num_elements_freq*img->num_channels,1) = temp1.matrix(); temp1.resize(img->num_elements_freq,img->num_channels); if (img->labels[k] == 1) { u(k) = std::complex<double>(params->wpos,0); } else { u(k) = std::complex<double>(-1,0); } } esvm::SVMClassifier libsvm; libsvm.setC(params->C); libsvm.setKernel(params->kernel_type); libsvm.setWpos(params->wpos); temp = (X.conjugate().transpose()*Y).real(); Eigen::Map<Eigen::MatrixXd> y(img->labels,img->num_data,1); libsvm.train(temp, y); temp.resize(0,0); int nSV; libsvm.getNSV(&nSV); Eigen::VectorXi sv_indices = Eigen::VectorXi::Zero(nSV); Eigen::VectorXd sv_coef = Eigen::VectorXd::Zero(nSV); libsvm.getSI(sv_indices); libsvm.getCoeff(sv_coef); for (int k=0; k<nSV; k++) { filt_freq += (Y.block(0,sv_indices[k]-1,img->num_elements_freq*img->num_channels,1)*sv_coef[k]).array(); } Y.resize(0,0); Eigen::Map<Eigen::ArrayXcd>(filt->filter.data_freq,img->num_elements_freq*img->num_channels) = filt_freq; filt->filter.num_data = 1; filt->filter.num_channels = img->num_channels; filt->filter.ptr_data.reserve(filt->filter.num_data); filt->filter.ptr_data_freq.reserve(filt->filter.num_data); ifft_data(&filt->filter); fft_data(&filt->filter); }
void CCorrelationFilters::build_otsdf(struct CDataStruct *img, struct CParamStruct *params, struct CFilterStruct *filt) { /* * This function implements the correlation filter design proposed in the following publications. * * [1] Optimal trade-off synthetic discriminant function filters for arbitrary devices, B.V.K.Kumar, D.W.Carlson, A.Mahalanobis - Optics Letters, 1994. * * [2] Jason Thornton, "Matching deformed and occluded iris patterns: a probabilistic model based on discriminative cues." PhD thesis, Carnegie Mellon University, Pittsburgh, PA, USA, 2007. * * [3] Vishnu Naresh Boddeti, Jonathon M Smereka, and B. V. K. Vijaya Kumar, "A comparative evaluation of iris and ocular recognition methods on challenging ocular images." IJCB, 2011. * * [4] A. Mahalanobis, B.V.K. Kumar, D. Casasent, "Minimum average correlation energy filters," Applied Optics, 1987 * * Notes: This filter design is good when the dimensionality of the data is greater than the training sample size. Setting the filter parameter params->alpha=0 results in the popular MACE filter. However, it is usually better to set alpha to a small number rather than setting it to 0. If you use MACE cite [4]. */ filt->params = *params; filt->filter.size_data = params->size_filt_freq; filt->filter.size_data_freq = params->size_filt_freq; filt->filter.num_elements_freq = img->num_elements_freq; params->num_elements_freq = img->num_elements_freq; filt->filter.data_freq = new complex<double>[img->num_elements_freq*img->num_channels]; Eigen::ArrayXcd filt_freq = Eigen::ArrayXcd::Zero(params->num_elements_freq*img->num_channels); // If not set default to 1 if (params->wpos < 1) params->wpos = 1; filt->params.wpos = params->wpos; compute_psd_matrix(img, params); Eigen::MatrixXcd Y = Eigen::MatrixXcd::Zero(img->num_elements_freq*img->num_channels,img->num_data); Eigen::MatrixXcd u = Eigen::MatrixXcd::Zero(img->num_data,1); Eigen::MatrixXcd temp = Eigen::MatrixXcd::Zero(img->num_data,img->num_data); Eigen::MatrixXd tmp = Eigen::MatrixXd::Zero(img->num_data,img->num_data); Eigen::Map<Eigen::MatrixXcd> X(img->data_freq,img->num_elements_freq*img->num_channels,img->num_data); Eigen::ArrayXXcd temp1 = Eigen::ArrayXXcd::Zero(img->num_elements_freq,img->num_channels); Eigen::ArrayXXcd temp2 = Eigen::ArrayXXcd::Zero(img->num_elements_freq,img->num_channels); Eigen::Vector2i num_blocks_1, num_blocks_2; num_blocks_1 << img->num_channels,img->num_channels; num_blocks_2 << img->num_channels,1; for (int k=0;k<img->num_data;k++){ temp2 = X.block(0,k,img->num_elements_freq*img->num_channels,1).array(); temp2.resize(img->num_elements_freq,img->num_channels); fusion_matrix_multiply(temp1, img->Sinv, temp2, num_blocks_1, num_blocks_2); temp1.resize(img->num_elements_freq*img->num_channels,1); Y.block(0,k,img->num_elements_freq*img->num_channels,1) = temp1.matrix(); temp1.resize(img->num_elements_freq,img->num_channels); if (img->labels[k] == 1) { u(k) = std::complex<double>(params->wpos,0); } else { u(k) = std::complex<double>(1,0); } } temp = X.conjugate().transpose()*Y; temp = temp.ldlt().solve(u); filt_freq = Y*temp; Y.resize(0,0); Eigen::Map<Eigen::ArrayXcd>(filt->filter.data_freq,img->num_elements_freq*img->num_channels) = filt_freq; filt->filter.num_data = 1; filt->filter.num_channels = img->num_channels; filt->filter.ptr_data.reserve(filt->filter.num_data); filt->filter.ptr_data_freq.reserve(filt->filter.num_data); ifft_data(&filt->filter); fft_data(&filt->filter); }
// ----------------------------------------------------------------------------- // ----------------------------------------------------------------------------- void LapH::OperatorsForMesons::build_rvdaggervr( const LapH::RandomVector& rnd_vec) { // check of vdaggerv is already build if(not is_vdaggerv_set){ std::cout << "\n\n\tCaution: vdaggerv is not set and rvdaggervr cannot be" << " computed\n\n" << std::endl; exit(0); } clock_t t2 = clock(); std::cout << "\tbuild rvdaggervr:"; for(auto& rvdvr_level1 : rvdaggervr) for(auto& rvdvr_level2 : rvdvr_level1) for(auto& rvdvr_level3 : rvdvr_level2) rvdvr_level3 = Eigen::MatrixXcd::Zero(4*dilE, 4*dilE); #pragma omp parallel for schedule(dynamic) for(size_t t = 0; t < Lt; t++){ // rvdaggervr is calculated by multiplying vdaggerv with the same quantum // numbers with random vectors from right and left. for(const auto& op : operator_lookuptable.rvdaggervr_lookuptable){ Eigen::MatrixXcd vdv; if(op.need_vdaggerv_daggering == false) vdv = vdaggerv[op.id_vdaggerv][t]; else vdv = vdaggerv[op.id_vdaggerv][t].adjoint(); size_t rid = 0; int check = -1; Eigen::MatrixXcd M; // Intermediate memory for(const auto& rnd_id : operator_lookuptable.ricQ2_lookup[op.id_ricQ_lookup].rnd_vec_ids){ if(check != rnd_id.first){ // this avoids recomputation M = Eigen::MatrixXcd::Zero(nb_ev, 4*dilE); for(size_t block = 0; block < 4; block++){ for(size_t vec_i = 0; vec_i < nb_ev; vec_i++) { size_t blk = block + (vec_i + nb_ev * t) * 4; M.block(0, vec_i%dilE + dilE*block, nb_ev, 1) += vdv.col(vec_i) * rnd_vec(rnd_id.first, blk); }} } for(size_t block_x = 0; block_x < 4; block_x++){ for(size_t block_y = 0; block_y < 4; block_y++){ for(size_t vec_y = 0; vec_y < nb_ev; ++vec_y) { size_t blk = block_y + (vec_y + nb_ev * t) * 4; rvdaggervr[op.id][t][rid].block( dilE*block_y + vec_y%dilE, dilE*block_x, 1, dilE) += M.block(vec_y, dilE*block_x, 1, dilE) * std::conj(rnd_vec(rnd_id.second, blk)); }}} check = rnd_id.first; rid++; } }}// time and operator loops end here t2 = clock() - t2; std::cout << std::setprecision(1) << "\t\tSUCCESS - " << std::fixed << ((float) t2)/CLOCKS_PER_SEC << " seconds" << std::endl; }
// ----------------------------------------------------------------------------- // ----------------------------------------------------------------------------- void LapH::Quarklines::build_Q2L(const Perambulator& peram, const OperatorsForMesons& meson_operator, const std::vector<QuarklineQ2Indices>& ql_lookup, const std::vector<RandomIndexCombinationsQ2>& ric_lookup){ std::cout << "\tcomputing Q2L:"; clock_t time = clock(); #pragma omp parallel { Eigen::MatrixXcd M = Eigen::MatrixXcd::Zero(4 * dilE, 4 * nev); for(size_t t1 = 0; t1 < Lt; t1++){ for(size_t t2 = 0; t2 < Lt/dilT; t2++){ for(size_t op = 0; op < ql_lookup.size(); op++){ size_t nb_rnd = ric_lookup[(ql_lookup[op]). id_ric_lookup].rnd_vec_ids.size(); for(size_t rnd1 = 0; rnd1 < nb_rnd; rnd1++){ Q2L[t1][t2][op][rnd1].setZero(); } } }} #pragma omp for schedule(dynamic) for(size_t t1 = 0; t1 < Lt; t1++){ for(const auto& qll : ql_lookup){ size_t rnd_counter = 0; int check = -1; for(const auto& rnd_id : ric_lookup[qll.id_ric_lookup].rnd_vec_ids){ if(check != rnd_id.first){ // this avoids recomputation for(size_t row = 0; row < 4; row++){ for(size_t col = 0; col < 4; col++){ if(!qll.need_vdaggerv_dag) M.block(col*dilE, row*nev, dilE, nev) = peram[rnd_id.first].block((t1*4 + row)*nev, (t1/dilT*4 + col)*dilE, nev, dilE).adjoint() * meson_operator.return_vdaggerv(qll.id_vdaggerv, t1); else M.block(col*dilE, row*nev, dilE, nev) = peram[rnd_id.first].block((t1*4 + row)*nev, (t1/dilT*4 + col)*dilE, nev, dilE).adjoint() * meson_operator.return_vdaggerv(qll.id_vdaggerv, t1).adjoint(); // gamma_5 trick if( ((row + col) == 3) || (abs(row - col) > 1) ) M.block(col*dilE, row*nev, dilE, nev) *= -1.; }} } for(size_t t2 = 0; t2 < Lt/dilT; t2++){ Q2L[t1][t2][qll.id][rnd_counter].setZero(4*dilE, 4*dilE); const size_t gamma_id = qll.gamma[0]; for(size_t block_dil = 0; block_dil < 4; block_dil++) { const cmplx value = gamma[gamma_id].value[block_dil]; const size_t gamma_index = gamma[gamma_id].row[block_dil]; for(size_t row = 0; row < 4; row++){ for(size_t col = 0; col < 4; col++){ Q2L[t1][t2][qll.id][rnd_counter]. block(row*dilE, col*dilE, dilE, dilE) += value * M.block(row*dilE, block_dil*nev, dilE, nev) * peram[rnd_id.second].block( (t1*4 + gamma_index)*nev, (t2*4 + col)*dilE, nev, dilE); }} } } check = rnd_id.first; rnd_counter++; } }} } // pragma omp ends time = clock() - time; std::cout << "\t\t\tSUCCESS - " << ((float) time) / CLOCKS_PER_SEC << " seconds" << std::endl; }
void BasicOperator::init_operator(const char dilution, const LapH::VdaggerV& vdaggerv, const LapH::Perambulator& peram){ const int Lt = global_data->get_Lt(); const size_t nb_ev = global_data->get_number_of_eigen_vec(); const std::vector<quark> quarks = global_data->get_quarks(); const size_t nb_rnd = quarks[0].number_of_rnd_vec; const size_t dilE = quarks[0].number_of_dilution_E; const int dilT = quarks[0].number_of_dilution_T; const size_t Q2_size = 4 * dilE; const vec_pdg_Corr op_Corr = global_data->get_lookup_corr(); const size_t nb_op = op_Corr.size(); std::cout << "\n" << std::endl; clock_t time = clock(); #pragma omp parallel { Eigen::MatrixXcd M = Eigen::MatrixXcd::Zero(Q2_size, 4 * nb_ev); #pragma omp for schedule(dynamic) for(int t_0 = 0; t_0 < Lt; t_0++){ if(omp_get_thread_num() == 0) std::cout << "\tcomputing double quarkline: " << std::setprecision(2) << (float) t_0/Lt*100 << "%\r" << std::flush; for(const auto& op : op_Corr){ for(size_t rnd_i = 0; rnd_i < nb_rnd; ++rnd_i) { for(int t = 0; t < Lt/dilT; t++){ // new momentum -> recalculate M[0] // M only depends on momentum and displacement. first_vdv // prevents repeated calculation for different gamma structures if(op.first_vdv == true){ for(size_t col = 0; col < 4; ++col) { for(size_t row = 0; row < 4; ++row) { if(op.negative_momentum == false){ M.block(dilE * col, nb_ev * row, dilE, nb_ev) = (peram(1, rnd_i).block(nb_ev * (4 * t_0 + row), dilE * (4 * t + col), nb_ev, dilE)).adjoint() * vdaggerv.return_vdaggerv(op.id_vdv, t_0); } else { M.block(dilE * col, nb_ev * row, dilE, nb_ev) = (peram(1, rnd_i).block(nb_ev * (4 * t_0 + row), dilE * (4 * t + col), nb_ev, dilE)).adjoint() * // TODO: calculate V^daggerV Omega from op.negative_momentum // == false and multiply Omega from the left // and then (V^daggerV Omega)^dagger * Omega (vdaggerv.return_vdaggerv(op.id_vdv, t_0)).adjoint(); } // gamma_5 trick. It changes the sign of the two upper right and two // lower left blocks in dirac space if( ((row + col) == 3) || (abs(row - col) > 1) ) M.block(dilE * col, row * nb_ev, dilE, nb_ev) *= -1.; }}// loops over row and col end here }//if over same gamma structure ends here for(int ti = 0; ti < 3; ti++){ // getting the neighbour blocks const int tend = (Lt/dilT+t + ti - 1)%(Lt/dilT); for(size_t rnd_j = 0; rnd_j < nb_rnd; ++rnd_j) { if(rnd_i != rnd_j){ //dilution of d-quark from left for(size_t block_dil = 0; block_dil < 4; block_dil++){ cmplx value = 1.; value_dirac(op.id, block_dil, value); for(size_t col = 0; col < 4; col++){ for(size_t row = 0; row < 4; row++){ Q2[t_0][t][ti][op.id][rnd_i][rnd_j] .block(row*dilE, col*dilE, dilE, dilE) += value * M.block(row*dilE, block_dil* nb_ev, dilE, nb_ev) * peram(0, rnd_j) .block(4*nb_ev*t_0 + order_dirac(op.id, block_dil)*nb_ev, Q2_size*tend + col*dilE, nb_ev, dilE); }}}//dilution ends here }}}// loops over rnd_j and ti block end here }// loop over t ends here }// loop over rnd_i ends here }//loop operators }// loops over t_0 ends here }// pragma omp ends std::cout << "\tcomputing double quarkline: 100.00%" << std::endl; time = clock() - time; std::cout << "\t\tSUCCESS - " << ((float) time) / CLOCKS_PER_SEC << " seconds" << std::endl; }