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);
}
}
