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_matlab(int argc, char **argv){
CMDParser parser;
parser.parse_command_line(argc, argv);
char model_file_path[1024] = DATASET_DIR;
char predict_file_path[1024] = DATASET_DIR;
char output_file_path[1024] = DATASET_DIR;
strcat(model_file_path, parser.svmpredict_model_file_name);
strcat(predict_file_path, parser.svmpredict_input_file);
strcat(output_file_path, parser.svmpredict_output_file);
std::fstream file;
//FILE *fp;
//fp = fopen("model_file_path", "rb");
file.open(model_file_path, std::fstream::in);
string feature, svm_type;
//char feature[20];
//char svm_type[20];
//fscanf(fp, "%s", feature);
//fscanf(fp, "%s", 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->load_from_file(model_file_path);
//fclose(fp);
file.close();
//fp = fopen("output_file_path", "wb");
file.open(output_file_path, std::fstream::out);
DataSet predict_dataset;
predict_dataset.load_from_file(predict_file_path);
vector<float_type> predict_y;
predict_y = model->predict(predict_dataset.instances(), 10000);
for (int i = 0; i < predict_y.size(); ++i) {
//fprintf(fp, "%s\n", predict_y[i]);
file << predict_y[i] << std::endl;
}
//fclose(fp);
file.close();
if (metric) {
LOG(INFO) << metric->name() << " = " << metric->score(predict_y, predict_dataset.y());
}
}
