void LVConvertModel(const model *model_in, LVlinear_model *model_out){
// Convert svm_model to LVsvm_model
model_out->nr_class = model_in->nr_class;
model_out->nr_feature = model_in->nr_feature;
model_out->bias = model_in->bias;
int nr_class = model_in->nr_class;
int nr_feature = model_in->nr_feature;
// Label
if (model_in->label != nullptr){
LVResizeNumericArrayHandle(model_out->label, nr_class);
MoveBlock(model_in->label, (*(model_out->label))->elt, nr_class * sizeof(int32_t));
(*model_out->label)->dimSize = model_in->nr_class;
}
else{
(*model_out->label)->dimSize = 0;
}
// w
int32_t nr_w = nr_feature * nr_class;
// If bias is present, the feature vector increases by one
if (model_in->bias >= 0)
nr_w += nr_class;
if (model_out->w != nullptr){
LVResizeNumericArrayHandle(model_out->w, nr_w);
MoveBlock(model_in->w, (*(model_out->w))->elt, nr_w * sizeof(double));
(*model_out->w)->dimSize = nr_w;
}
else{
(*model_out->w)->dimSize = 0;
}
}
double LVlinear_predict_values(lvError *lvErr, const LVlinear_model *model_in, const LVArray_Hdl<LVlinear_node> x_in, LVArray_Hdl<double> dec_values_out){
try{
// Input validation: Uninitialized model
if (model_in == nullptr || model_in->w == nullptr || (*model_in->w)->dimSize == 0)
throw LVException(__FILE__, __LINE__, "Uninitialized model passed to liblinear_predict_values.");
// Input validation: Empty feature vector
if (x_in == nullptr || (*x_in)->dimSize == 0)
throw LVException(__FILE__, __LINE__, "Empty feature vector passed to liblinear_predict_values.");
// Input validation: Final index -1?
if ((*x_in)->elt[(*x_in)->dimSize - 1].index != -1)
throw LVException(__FILE__, __LINE__, "The index of the last element of the feature vector needs to be -1 (liblinear_predict_values).");
// Convert LVsvm_model to svm_model
auto mdl = std::make_unique<model>();
LVConvertModel(*model_in, *mdl);
int nr_class = model_in->nr_class;
int solver = (model_in->param).solver_type;
// Set up output array
int nr_dec = 0;
if (nr_class <= 2){
if (solver == MCSVM_CS)
nr_dec = 2;
else
nr_dec = 1;
}
else {
nr_dec = nr_class;
}
LVResizeNumericArrayHandle(dec_values_out, nr_dec);
(*dec_values_out)->dimSize = nr_dec;
double predicted_label = predict_values(mdl.get(), reinterpret_cast<feature_node*>((*x_in)->elt), (*dec_values_out)->elt);
return predicted_label;
}
catch (LVException &ex) {
ex.returnError(lvErr);
(*dec_values_out)->dimSize = 0;
return std::nan("");
}
catch (std::exception &ex) {
LVException::returnStdException(lvErr, __FILE__, __LINE__, ex);
(*dec_values_out)->dimSize = 0;
return std::nan("");
}
catch (...) {
LVException ex(__FILE__, __LINE__, "Unknown exception has occurred");
ex.returnError(lvErr);
(*dec_values_out)->dimSize = 0;
return std::nan("");
}
}
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 model &model_in, LVlinear_model &model_out){
// Convert svm_model to LVsvm_model
model_out.nr_class = model_in.nr_class;
model_out.nr_feature = model_in.nr_feature;
model_out.bias = model_in.bias;
int nr_class = model_in.nr_class;
int nr_feature = model_in.nr_feature;
// Label
if (model_in.label != nullptr){
LVResizeNumericArrayHandle(model_out.label, nr_class);
MoveBlock(model_in.label, (*(model_out.label))->elt, nr_class * sizeof(int32_t));
(*model_out.label)->dimSize = model_in.nr_class;
}
else{
(*model_out.label)->dimSize = 0;
}
// n is equal to nr_feature, incremented if bias is present
int n = nr_feature;
if (model_in.bias >= 0)
n++;
// nr_w is equal to nr_class with one exception
int nr_w;
if (model_in.nr_class == 2 && model_in.param.solver_type != MCSVM_CS)
nr_w = 1;
else
nr_w = model_in.nr_class;
if (model_in.w != nullptr){
LVResizeNumericArrayHandle(model_out.w, n*nr_w);
MoveBlock(model_in.w, (*(model_out.w))->elt, nr_w * n * sizeof(double));
(*model_out.w)->dimSize = nr_w*n;
}
else{
(*model_out.w)->dimSize = 0;
}
}
double LVlinear_predict_probability(lvError *lvErr, const LVlinear_model *model_in, const LVArray_Hdl<LVlinear_node> x_in, LVArray_Hdl<double> prob_estimates_out){
try{
// Input validation: Uninitialized model
if (model_in == nullptr || model_in->w == nullptr || (*model_in->w)->dimSize == 0)
throw LVException(__FILE__, __LINE__, "Uninitialized model passed to liblinear_predict_probability.");
// Input validation: Empty feature vector
if (x_in == nullptr || (*x_in)->dimSize == 0)
throw LVException(__FILE__, __LINE__, "Empty feature vector passed to liblinear_predict_probability.");
// Input validation: Final index -1?
if ((*x_in)->elt[(*x_in)->dimSize - 1].index != -1)
throw LVException(__FILE__, __LINE__, "The index of the last element of the feature vector needs to be -1 (liblinear_predict_probability).");
// Convert LVsvm_model to svm_model
auto mdl = std::make_unique<model>();
LVConvertModel(*model_in, *mdl);
// Check probability model
int valid_probability = check_probability_model(mdl.get());
if (!valid_probability)
throw LVException(__FILE__, __LINE__, "The selected solver type does not support probability output.");
// Allocate room for probability estimates
LVResizeNumericArrayHandle(prob_estimates_out, mdl->nr_class);
(*prob_estimates_out)->dimSize = mdl->nr_class;
double highest_prob_label = predict_probability(mdl.get(), reinterpret_cast<feature_node*>((*x_in)->elt), (*prob_estimates_out)->elt);
return highest_prob_label;
}
catch (LVException &ex) {
ex.returnError(lvErr);
(*prob_estimates_out)->dimSize = 0;
return std::nan("");
}
catch (std::exception &ex) {
LVException::returnStdException(lvErr, __FILE__, __LINE__, ex);
(*prob_estimates_out)->dimSize = 0;
return std::nan("");
}
catch (...) {
LVException ex(__FILE__, __LINE__, "Unknown exception has occurred");
ex.returnError(lvErr);
(*prob_estimates_out)->dimSize = 0;
return std::nan("");
}
}
double LVlinear_predict_values(lvError *lvErr, const LVlinear_model *model_in, const LVArray_Hdl<LVlinear_node> x_in, LVArray_Hdl<double> dec_values_out){
try{
// Convert LVsvm_model to svm_model
std::unique_ptr<model> model(new model);
LVConvertModel(model_in, model.get());
int nr_class = model_in->nr_class;
int solver = (model_in->param).solver_type;
// Set up output array
int nr_dec = 0;
if (nr_class <= 2){
if (solver == MCSVM_CS)
nr_dec = 2;
else
nr_dec = 1;
}
else {
nr_dec = nr_class;
}
LVResizeNumericArrayHandle(dec_values_out, nr_dec);
(*dec_values_out)->dimSize = nr_dec;
double predicted_label = predict_values(model.get(), reinterpret_cast<feature_node*>((*x_in)->elt), (*dec_values_out)->elt);
return predicted_label;
}
catch (LVException &ex) {
ex.returnError(lvErr);
(*dec_values_out)->dimSize = 0;
return std::nan("");
}
catch (std::exception &ex) {
LVException::returnStdException(lvErr, __FILE__, __LINE__, ex);
(*dec_values_out)->dimSize = 0;
return std::nan("");
}
catch (...) {
LVException ex(__FILE__, __LINE__, "Unknown exception has occurred");
ex.returnError(lvErr);
(*dec_values_out)->dimSize = 0;
return std::nan("");
}
}
double LVlinear_predict_probability(lvError *lvErr, const LVlinear_model *model_in, const LVArray_Hdl<LVlinear_node> x_in, LVArray_Hdl<double> prob_estimates_out){
try{
// Convert LVsvm_model to svm_model
std::unique_ptr<model> model(new model);
LVConvertModel(model_in, model.get());
// Check probability model
int valid_probability = check_probability_model(model.get());
if (!valid_probability)
throw LVException(__FILE__, __LINE__, "The model does not support probability output.");
// Allocate room for probability estimates
LVResizeNumericArrayHandle(prob_estimates_out, model->nr_class);
(*prob_estimates_out)->dimSize = model->nr_class;
double highest_prob_label = predict_probability(model.get(), reinterpret_cast<feature_node*>((*x_in)->elt), (*prob_estimates_out)->elt);
return highest_prob_label;
}
catch (LVException &ex) {
ex.returnError(lvErr);
(*prob_estimates_out)->dimSize = 0;
return std::nan("");
}
catch (std::exception &ex) {
LVException::returnStdException(lvErr, __FILE__, __LINE__, ex);
(*prob_estimates_out)->dimSize = 0;
return std::nan("");
}
catch (...) {
LVException ex(__FILE__, __LINE__, "Unknown exception has occurred");
ex.returnError(lvErr);
(*prob_estimates_out)->dimSize = 0;
return std::nan("");
}
}
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: Nonempty problem
if (prob_in->x == nullptr || (*(prob_in->x))->dimSize == 0)
throw LVException(__FILE__, __LINE__, "Empty problem passed to liblinear_crossvalidation.");
// 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 LVsvm_problem to svm_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 (libsvm_crossvalidation).");
// 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 (largestIndex > prob->n)
prob->n = largestIndex;
}
// n increases by one if bias is present
if (prob_in->bias >= 0)
prob->n++;
// Assign parameters to svm_parameter
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));
// 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;
}
}
