Function thundersvm_predict_sub

src/thundersvm/svm_interface_api.cpp:114–158

        return;
    }
    void thundersvm_predict_sub(DataSet& predict_dataset, CMDParser& parser, char* model_file_path, char* output_file_path){
        fstream file;
        file.open(model_file_path, std::fstream::in);
        string feature, svm_type;
        file >> feature >> svm_type;
        CHECK_EQ(feature, "svm_type");
        SvmModel *model = nullptr;
        Metric *metric = nullptr;
        if (svm_type == "c_svc") {
            model = new SVC();
            metric = new Accuracy();
        } else if (svm_type == "nu_svc") {
            model = new NuSVC();
            metric = new Accuracy();
        } else if (svm_type == "one_class") {
            model = new OneClassSVC();
            //todo determine a metric
        } else if (svm_type == "epsilon_svr") {
            model = new SVR();
            metric = new MSE();
        } else if (svm_type == "nu_svr") {
            model = new NuSVR();
            metric = new MSE();
        }

#ifdef USE_CUDA
        CUDA_CHECK(cudaSetDevice(parser.gpu_id));
#endif

        model->set_max_memory_size_Byte(parser.param_cmd.max_mem_size);
        model->load_from_file(model_file_path);
        file.close();
        file.open(output_file_path, fstream::out);

        vector<float_type> predict_y;
        predict_y = model->predict(predict_dataset.instances(), -1);
        for (int i = 0; i < predict_y.size(); ++i) {
            file << predict_y[i] << std::endl;
        }
        file.close();

        if (metric) {
            LOG(INFO) << metric->name() << " = " << metric->score(predict_y, predict_dataset.y());
        }
    }

    void thundersvm_predict(int argc, char **argv){
        CMDParser parser;