double LVlinear_predict(lvError *lvErr, const struct LVlinear_model *model_in, const LVArray_Hdl<LVlinear_node> x_in){
try{
// Convert LVsvm_model to svm_model
std::unique_ptr<model> model(new model);
LVConvertModel(model_in, model.get());
double label = predict(model.get(), reinterpret_cast<feature_node*>((*x_in)->elt));
return label;
}
catch (LVException &ex) {
ex.returnError(lvErr);
return std::nan("");
}
catch (std::exception &ex) {
LVException::returnStdException(lvErr, __FILE__, __LINE__, ex);
return std::nan("");
}
catch (...) {
LVException ex(__FILE__, __LINE__, "Unknown exception has occurred");
ex.returnError(lvErr);
return std::nan("");
}
}
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;
}
}
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_save_model(lvError *lvErr, const char *path_in, const LVlinear_model *model_in){
try{
errno = 0;
// Convert LVsvm_model to svm_model
auto mdl = std::make_unique<model>();
LVConvertModel(*model_in, *mdl);
int err = save_model(path_in, mdl.get());
if (err == -1){
// Allocate room for output error message (truncated if buffer is too small)
const size_t bufSz = 256;
char buf[bufSz] = "";
std::string errstr;
#if defined(_WIN32) || defined(_WIN64)
if (strerror_s(buf, bufSz, errno) != 0)
errstr = buf;
else
errstr = "Unknown error";
#elif (_POSIX_C_SOURCE >= 200112L || _XOPEN_SOURCE >= 600) && ! _GNU_SOURCE
if (strerror_r(errno, buf, bufSz) != 0)
errstr = buf;
else
errstr = "Unknown error";
#else
char* gnuerr = strerror_r(errno, buf, bufSz);
if (gnuerr != nullptr)
errstr = gnuerr;
else
errstr = "Unknown error";
#endif
errno = 0;
throw LVException(__FILE__, __LINE__, "Model load operation failed (" + errstr + ").");
}
}
catch (LVException &ex) {
ex.returnError(lvErr);
}
catch (std::exception &ex) {
LVException::returnStdException(lvErr, __FILE__, __LINE__, ex);
}
catch (...) {
LVException ex(__FILE__, __LINE__, "Unknown exception has occurred");
ex.returnError(lvErr);
}
}
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("");
}
}
double LVlinear_predict(lvError *lvErr, const struct LVlinear_model *model_in, const LVArray_Hdl<LVlinear_node> x_in){
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.");
// Input validation: Empty feature vector
if (x_in == nullptr || (*x_in)->dimSize == 0)
throw LVException(__FILE__, __LINE__, "Empty feature vector passed to liblinear_predict.");
// 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).");
// Convert LVsvm_model to svm_model
auto mdl = std::make_unique<model>();
LVConvertModel(*model_in, *mdl);
double label = predict(mdl.get(), reinterpret_cast<feature_node*>((*x_in)->elt));
return label;
}
catch (LVException &ex) {
ex.returnError(lvErr);
return std::nan("");
}
catch (std::exception &ex) {
LVException::returnStdException(lvErr, __FILE__, __LINE__, ex);
return std::nan("");
}
catch (...) {
LVException ex(__FILE__, __LINE__, "Unknown exception has occurred");
ex.returnError(lvErr);
return std::nan("");
}
}
void LVlinear_load_model(lvError *lvErr, const char *path_in, LVlinear_model *model_out){
try{
errno = 0;
model *mdl = load_model(path_in);
if (mdl == nullptr){
// Allocate room for output error message (truncated if buffer is too small)
const size_t bufSz = 256;
char buf[bufSz] = "";
std::string errstr;
#if defined(_WIN32) || defined(_WIN64)
if (strerror_s(buf, bufSz, errno) != 0)
errstr = buf;
else
errstr = "Unknown error";
#elif (_POSIX_C_SOURCE >= 200112L || _XOPEN_SOURCE >= 600) && ! _GNU_SOURCE
if (strerror_r(errno, buf, bufSz) != 0)
errstr = buf;
else
errstr = "Unknown error";
#else
char* gnuerr = strerror_r(errno, buf, bufSz);
if (gnuerr != nullptr)
errstr = gnuerr;
else
errstr = "Unknown error";
#endif
errno = 0;
throw LVException(__FILE__, __LINE__, "Model load operation failed (" + errstr + ").");
}
else{
// liblinear returns uninitialized values for the parameters (except solver type)
(mdl->param).C = 0;
(mdl->param).eps = 0;
(mdl->param).init_sol = nullptr;
(mdl->param).nr_weight = 0;
(mdl->param).p = 0;
(mdl->param).weight = nullptr;
(mdl->param).weight_label = nullptr;
LVConvertModel(*mdl, *model_out);
free_model_content(mdl);
}
}
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);
}
}
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);
}
}