Function sparse_model_scikit

src/thundersvm/thundersvm-scikit.cpp:41–128

    }

    void sparse_model_scikit(int row_size, float* val, int* row_ptr, int* col_ptr, float* label,
                                  int svm_type, int kernel_type, int degree, float gamma, float coef0,
                                  float cost, float nu, float epsilon, float tol, int probability,
                                  int weight_size, int* weight_label, float* weight,
                                  int verbose, int max_iter, int n_cores, int max_mem_size,
                                  int gpu_id,
                                  int* n_features, int* n_classes, int* succeed, SvmModel* model){
#ifdef USE_CUDA
        CUDA_CHECK(cudaSetDevice(gpu_id));
#endif

        succeed[0] = 1;
        if(verbose)
            el::Loggers::reconfigureAllLoggers(el::ConfigurationType::Enabled, "true");
        else
            el::Loggers::reconfigureAllLoggers(el::ConfigurationType::Enabled, "false");

        if (n_cores > 0) {
            omp_set_num_threads(n_cores);
        } else if (n_cores != -1) {
            LOG(ERROR) << "n_jobs must be positive or -1";
        }

        DataSet train_dataset;
        train_dataset.load_from_sparse(row_size, val, row_ptr, col_ptr, label);
//        SvmModel* model;
//        switch (svm_type){
//            case SvmParam::C_SVC:
//                model = new SVC();
//                break;
//            case SvmParam::NU_SVC:
//                model = new NuSVC();
//                break;
//            case SvmParam::ONE_CLASS:
//                model = new OneClassSVC();
//                break;
//            case SvmParam::EPSILON_SVR:
//                model = new SVR();
//                break;
//            case SvmParam::NU_SVR:
//                model = new NuSVR();
//                break;
//        }
        model->set_max_iter(max_iter);

        //todo add this to check_parameter method
        if (svm_type == SvmParam::NU_SVC) {
            train_dataset.group_classes();
            for (int i = 0; i < train_dataset.n_classes(); ++i) {
                int n1 = train_dataset.count()[i];
                for (int j = i + 1; j < train_dataset.n_classes(); ++j) {
                    int n2 = train_dataset.count()[j];
                    if (nu * (n1 + n2) / 2 > min(n1, n2)) {
                        printf("specified nu is infeasible\n");
                        succeed[0] = -1;
                    }
                }
            }