void LVlinear_train(lvError *lvErr, const LVlinear_problem *prob_in, const LVlinear_parameter *param_in, LVlinear_model * model_out){ try{ // Input verification: Problem dimensions if ((*(prob_in->x))->dimSize != (*(prob_in->y))->dimSize) throw LVException(__FILE__, __LINE__, "The problem must have an equal number of labels and feature vectors (x and y)."); //-- Convert problem std::unique_ptr<problem> prob(new problem); uint32_t nr_nodes = (*(prob_in->y))->dimSize; prob->l = nr_nodes; prob->y = (*(prob_in->y))->elt; // Create and array of pointers (sparse datastructure) std::unique_ptr<feature_node*[]> x(new feature_node*[nr_nodes]); prob->x = x.get(); auto x_in = prob_in->x; for (unsigned int i = 0; i < (*x_in)->dimSize; i++){ // Assign the innermost svm_node array pointers to the array of pointers auto xi_in_Hdl = (*x_in)->elt[i]; x[i] = reinterpret_cast<feature_node*>((*xi_in_Hdl)->elt); } //-- Convert parameters std::unique_ptr<parameter> param(new parameter()); LVConvertParameter(param_in, param.get()); // Verify parameters const char * param_check = check_parameter(prob.get(), param.get()); if (param_check != nullptr) throw LVException(__FILE__, __LINE__, "Parameter check failed with the following error: " + std::string(param_check)); // Train model model *result = train(prob.get(), param.get()); // Copy model to LabVIEW memory LVConvertModel(result, model_out); // Release memory allocated by train free_model_content(result); } catch (LVException &ex) { ex.returnError(lvErr); // To avoid LabVIEW reading and utilizing bad memory, the dimension sizes of arrays is set to zero (*(model_out->label))->dimSize = 0; (*(model_out->w))->dimSize = 0; } catch (std::exception &ex) { LVException::returnStdException(lvErr, __FILE__, __LINE__, ex); (*(model_out->label))->dimSize = 0; (*(model_out->w))->dimSize = 0; } catch (...) { LVException ex(__FILE__, __LINE__, "Unknown exception has occurred"); ex.returnError(lvErr); (*(model_out->label))->dimSize = 0; (*(model_out->w))->dimSize = 0; } }
void LVlinear_cross_validation(lvError *lvErr, const LVlinear_problem *prob_in, const LVlinear_parameter *param_in, const int32_t nr_fold, LVArray_Hdl<double> target_out){ try{ // Input verification: Problem dimensions if ((*(prob_in->x))->dimSize != (*(prob_in->y))->dimSize) throw LVException(__FILE__, __LINE__, "The problem must have an equal number of labels and feature vectors (x and y)."); // Convert LVsvm_problem to svm_problem std::unique_ptr<problem> prob(new problem); uint32_t nr_nodes = (*(prob_in->y))->dimSize; prob->l = nr_nodes; prob->y = (*(prob_in->y))->elt; // Create and array of pointers (sparse datastructure) std::unique_ptr<feature_node*[]> x(new feature_node*[nr_nodes]); prob->x = x.get(); auto x_in = prob_in->x; for (unsigned int i = 0; i < (*x_in)->dimSize; i++){ // Assign the innermost svm_node array pointers to the array of pointers auto xi_in_Hdl = (*x_in)->elt[i]; x[i] = reinterpret_cast<feature_node*>((*xi_in_Hdl)->elt); } // Assign parameters to svm_parameter std::unique_ptr<parameter> param(new parameter()); LVConvertParameter(param_in, param.get()); // Verify parameters const char * param_check = check_parameter(prob.get(), param.get()); if (param_check != nullptr) throw LVException(__FILE__, __LINE__, "Parameter check failed with the following error: " + std::string(param_check)); // Allocate room in target_out LVResizeNumericArrayHandle(target_out, nr_nodes); // Run cross validation cross_validation(prob.get(), param.get(), nr_fold, (*target_out)->elt); (*target_out)->dimSize = nr_nodes; } catch (LVException &ex) { ex.returnError(lvErr); (*target_out)->dimSize = 0; } catch (std::exception &ex) { LVException::returnStdException(lvErr, __FILE__, __LINE__, ex); (*target_out)->dimSize = 0; } catch (...) { LVException ex(__FILE__, __LINE__, "Unknown exception has occurred"); ex.returnError(lvErr); (*target_out)->dimSize = 0; } }
void LVConvertModel(const LVlinear_model *model_in, model *model_out){ // Assign the parameters LVConvertParameter(&model_in->param, &model_out->param); // Copy assignments model_out->nr_class = model_in->nr_class; model_out->nr_feature = model_in->nr_feature; model_out->bias = model_in->bias; // w if ((*(model_in->w))->dimSize > 0) model_out->w = (*(model_in->w))->elt; else model_out->w = nullptr; // label if ((*(model_in->label))->dimSize > 0) model_out->label = (*(model_in->label))->elt; else model_out->label = nullptr; }
void LVlinear_train(lvError *lvErr, const LVlinear_problem *prob_in, const LVlinear_parameter *param_in, LVlinear_model * model_out){ try{ // Input verification: Nonempty problem if (prob_in->x == nullptr || (*(prob_in->x))->dimSize == 0) throw LVException(__FILE__, __LINE__, "Empty problem passed to liblinear_train."); // Input verification: Problem dimensions if ((*(prob_in->x))->dimSize != (*(prob_in->y))->dimSize) throw LVException(__FILE__, __LINE__, "The problem must have an equal number of labels and feature vectors (x and y)."); uint32_t nr_nodes = (*(prob_in->y))->dimSize; // Input validation: Number of feature vectors too large (exceeds max signed int) if(nr_nodes > INT_MAX) throw LVException(__FILE__, __LINE__, "Number of feature vectors too large (grater than " + std::to_string(INT_MAX) + ")"); //-- Convert problem auto prob = std::make_unique<problem>(); prob->l = nr_nodes; prob->y = (*(prob_in->y))->elt; prob->n = 0; // Calculated later prob->bias = prob_in->bias; // Create and array of pointers (sparse datastructure) auto x = std::make_unique<feature_node*[]>(nr_nodes); prob->x = x.get(); auto x_in = prob_in->x; for (unsigned int i = 0; i < (*x_in)->dimSize; i++){ // Assign the innermost svm_node array pointers to the array of pointers auto xi_in_Hdl = (*x_in)->elt[i]; x[i] = reinterpret_cast<feature_node*>((*xi_in_Hdl)->elt); // Input validation: Final index -1? if ((*xi_in_Hdl)->elt[(*xi_in_Hdl)->dimSize - 1].index != -1) throw LVException(__FILE__, __LINE__, "The index of the last element of each feature vector needs to be -1 (liblinear_train)."); // Calculate the max index // This detail is not exposed in LabVIEW, as setting the wrong value causes a crash // Second to last element should contain the max index for that feature vector (as they are in ascending order). auto secondToLast = (*xi_in_Hdl)->dimSize - 2; // Ignoring -1 index auto largestIndex = (*xi_in_Hdl)->elt[secondToLast].index; if (secondToLast >= 0 && largestIndex > prob->n) prob->n = largestIndex; } //-- Convert parameters auto param = std::make_unique<parameter>(); LVConvertParameter(*param_in, *param); // Verify parameters const char * param_check = check_parameter(prob.get(), param.get()); if (param_check != nullptr) throw LVException(__FILE__, __LINE__, "Parameter check failed with the following error: " + std::string(param_check)); // Train model model *result = train(prob.get(), param.get()); // Copy model to LabVIEW memory LVConvertModel(*result, *model_out); // Release memory allocated by train free_model_content(result); } catch (LVException &ex) { (*(model_out->label))->dimSize = 0; (*(model_out->w))->dimSize = 0; (*(model_out->param).weight)->dimSize = 0; (*(model_out->param).weight_label)->dimSize = 0; ex.returnError(lvErr); } catch (std::exception &ex) { (*(model_out->label))->dimSize = 0; (*(model_out->w))->dimSize = 0; (*(model_out->param).weight)->dimSize = 0; (*(model_out->param).weight_label)->dimSize = 0; LVException::returnStdException(lvErr, __FILE__, __LINE__, ex); } catch (...) { (*(model_out->label))->dimSize = 0; (*(model_out->w))->dimSize = 0; (*(model_out->param).weight)->dimSize = 0; (*(model_out->param).weight_label)->dimSize = 0; LVException ex(__FILE__, __LINE__, "Unknown exception has occurred"); ex.returnError(lvErr); } }