#include "ocvrs_common.hpp"
#include <opencv2/ml.hpp>
#include "ml_types.hpp"
extern "C" {
Result_void cv_ml_createConcentricSpheresTestSet_int_int_int_const__OutputArrayR_const__OutputArrayR(int nsamples, int nfeatures, int nclasses, const cv::_OutputArray* samples, const cv::_OutputArray* responses) {
try {
cv::ml::createConcentricSpheresTestSet(nsamples, nfeatures, nclasses, *samples, *responses);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result_void cv_ml_randMVNormal_const__InputArrayR_const__InputArrayR_int_const__OutputArrayR(const cv::_InputArray* mean, const cv::_InputArray* cov, int nsamples, const cv::_OutputArray* samples) {
try {
cv::ml::randMVNormal(*mean, *cov, nsamples, *samples);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result_void cv_ml_ANN_MLP_setTrainMethod_int_double_double(cv::ml::ANN_MLP* instance, int method, double param1, double param2) {
try {
instance->setTrainMethod(method, param1, param2);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_ANN_MLP_getTrainMethod_const(const cv::ml::ANN_MLP* instance) {
try {
int ret = instance->getTrainMethod();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_ANN_MLP_setActivationFunction_int_double_double(cv::ml::ANN_MLP* instance, int type, double param1, double param2) {
try {
instance->setActivationFunction(type, param1, param2);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result_void cv_ml_ANN_MLP_setLayerSizes_const__InputArrayR(cv::ml::ANN_MLP* instance, const cv::_InputArray* _layer_sizes) {
try {
instance->setLayerSizes(*_layer_sizes);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_ANN_MLP_getLayerSizes_const(const cv::ml::ANN_MLP* instance) {
try {
cv::Mat ret = instance->getLayerSizes();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::TermCriteria> cv_ml_ANN_MLP_getTermCriteria_const(const cv::ml::ANN_MLP* instance) {
try {
cv::TermCriteria ret = instance->getTermCriteria();
return Ok<cv::TermCriteria>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::TermCriteria>))
}
Result_void cv_ml_ANN_MLP_setTermCriteria_TermCriteria(cv::ml::ANN_MLP* instance, cv::TermCriteria* val) {
try {
instance->setTermCriteria(*val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_getBackpropWeightScale_const(const cv::ml::ANN_MLP* instance) {
try {
double ret = instance->getBackpropWeightScale();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_setBackpropWeightScale_double(cv::ml::ANN_MLP* instance, double val) {
try {
instance->setBackpropWeightScale(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_getBackpropMomentumScale_const(const cv::ml::ANN_MLP* instance) {
try {
double ret = instance->getBackpropMomentumScale();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_setBackpropMomentumScale_double(cv::ml::ANN_MLP* instance, double val) {
try {
instance->setBackpropMomentumScale(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_getRpropDW0_const(const cv::ml::ANN_MLP* instance) {
try {
double ret = instance->getRpropDW0();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_setRpropDW0_double(cv::ml::ANN_MLP* instance, double val) {
try {
instance->setRpropDW0(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_getRpropDWPlus_const(const cv::ml::ANN_MLP* instance) {
try {
double ret = instance->getRpropDWPlus();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_setRpropDWPlus_double(cv::ml::ANN_MLP* instance, double val) {
try {
instance->setRpropDWPlus(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_getRpropDWMinus_const(const cv::ml::ANN_MLP* instance) {
try {
double ret = instance->getRpropDWMinus();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_setRpropDWMinus_double(cv::ml::ANN_MLP* instance, double val) {
try {
instance->setRpropDWMinus(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_getRpropDWMin_const(const cv::ml::ANN_MLP* instance) {
try {
double ret = instance->getRpropDWMin();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_setRpropDWMin_double(cv::ml::ANN_MLP* instance, double val) {
try {
instance->setRpropDWMin(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_getRpropDWMax_const(const cv::ml::ANN_MLP* instance) {
try {
double ret = instance->getRpropDWMax();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_setRpropDWMax_double(cv::ml::ANN_MLP* instance, double val) {
try {
instance->setRpropDWMax(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_getAnnealInitialT_const(const cv::ml::ANN_MLP* instance) {
try {
double ret = instance->getAnnealInitialT();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_setAnnealInitialT_double(cv::ml::ANN_MLP* instance, double val) {
try {
instance->setAnnealInitialT(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_getAnnealFinalT_const(const cv::ml::ANN_MLP* instance) {
try {
double ret = instance->getAnnealFinalT();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_setAnnealFinalT_double(cv::ml::ANN_MLP* instance, double val) {
try {
instance->setAnnealFinalT(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_getAnnealCoolingRatio_const(const cv::ml::ANN_MLP* instance) {
try {
double ret = instance->getAnnealCoolingRatio();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_setAnnealCoolingRatio_double(cv::ml::ANN_MLP* instance, double val) {
try {
instance->setAnnealCoolingRatio(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_ANN_MLP_getAnnealItePerStep_const(const cv::ml::ANN_MLP* instance) {
try {
int ret = instance->getAnnealItePerStep();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_ANN_MLP_setAnnealItePerStep_int(cv::ml::ANN_MLP* instance, int val) {
try {
instance->setAnnealItePerStep(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result_void cv_ml_ANN_MLP_setAnnealEnergyRNG_const_RNGR(cv::ml::ANN_MLP* instance, const cv::RNG* rng) {
try {
instance->setAnnealEnergyRNG(*rng);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_ANN_MLP_getWeights_const_int(const cv::ml::ANN_MLP* instance, int layerIdx) {
try {
cv::Mat ret = instance->getWeights(layerIdx);
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Ptr<cv::ml::ANN_MLP>*> cv_ml_ANN_MLP_create() {
try {
cv::Ptr<cv::ml::ANN_MLP> ret = cv::ml::ANN_MLP::create();
return Ok(new cv::Ptr<cv::ml::ANN_MLP>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::ANN_MLP>*>))
}
Result<cv::Ptr<cv::ml::ANN_MLP>*> cv_ml_ANN_MLP_load_const_StringR(const char* filepath) {
try {
cv::Ptr<cv::ml::ANN_MLP> ret = cv::ml::ANN_MLP::load(cv::String(filepath));
return Ok(new cv::Ptr<cv::ml::ANN_MLP>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::ANN_MLP>*>))
}
Result<double> cv_ml_ANN_MLP_ANNEAL_getAnnealInitialT_const(const cv::ml::ANN_MLP_ANNEAL* instance) {
try {
double ret = instance->getAnnealInitialT();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_ANNEAL_setAnnealInitialT_double(cv::ml::ANN_MLP_ANNEAL* instance, double val) {
try {
instance->setAnnealInitialT(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_ANNEAL_getAnnealFinalT_const(const cv::ml::ANN_MLP_ANNEAL* instance) {
try {
double ret = instance->getAnnealFinalT();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_ANNEAL_setAnnealFinalT_double(cv::ml::ANN_MLP_ANNEAL* instance, double val) {
try {
instance->setAnnealFinalT(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ANN_MLP_ANNEAL_getAnnealCoolingRatio_const(const cv::ml::ANN_MLP_ANNEAL* instance) {
try {
double ret = instance->getAnnealCoolingRatio();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ANN_MLP_ANNEAL_setAnnealCoolingRatio_double(cv::ml::ANN_MLP_ANNEAL* instance, double val) {
try {
instance->setAnnealCoolingRatio(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_ANN_MLP_ANNEAL_getAnnealItePerStep_const(const cv::ml::ANN_MLP_ANNEAL* instance) {
try {
int ret = instance->getAnnealItePerStep();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_ANN_MLP_ANNEAL_setAnnealItePerStep_int(cv::ml::ANN_MLP_ANNEAL* instance, int val) {
try {
instance->setAnnealItePerStep(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result_void cv_ml_ANN_MLP_ANNEAL_setAnnealEnergyRNG_const_RNGR(cv::ml::ANN_MLP_ANNEAL* instance, const cv::RNG* rng) {
try {
instance->setAnnealEnergyRNG(*rng);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_Boost_getBoostType_const(const cv::ml::Boost* instance) {
try {
int ret = instance->getBoostType();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_Boost_setBoostType_int(cv::ml::Boost* instance, int val) {
try {
instance->setBoostType(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_Boost_getWeakCount_const(const cv::ml::Boost* instance) {
try {
int ret = instance->getWeakCount();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_Boost_setWeakCount_int(cv::ml::Boost* instance, int val) {
try {
instance->setWeakCount(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_Boost_getWeightTrimRate_const(const cv::ml::Boost* instance) {
try {
double ret = instance->getWeightTrimRate();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_Boost_setWeightTrimRate_double(cv::ml::Boost* instance, double val) {
try {
instance->setWeightTrimRate(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Ptr<cv::ml::Boost>*> cv_ml_Boost_create() {
try {
cv::Ptr<cv::ml::Boost> ret = cv::ml::Boost::create();
return Ok(new cv::Ptr<cv::ml::Boost>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::Boost>*>))
}
Result<cv::Ptr<cv::ml::Boost>*> cv_ml_Boost_load_const_StringR_const_StringR(const char* filepath, const char* nodeName) {
try {
cv::Ptr<cv::ml::Boost> ret = cv::ml::Boost::load(cv::String(filepath), cv::String(nodeName));
return Ok(new cv::Ptr<cv::ml::Boost>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::Boost>*>))
}
Result<int> cv_ml_DTrees_getMaxCategories_const(const cv::ml::DTrees* instance) {
try {
int ret = instance->getMaxCategories();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_setMaxCategories_int(cv::ml::DTrees* instance, int val) {
try {
instance->setMaxCategories(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_getMaxDepth_const(const cv::ml::DTrees* instance) {
try {
int ret = instance->getMaxDepth();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_setMaxDepth_int(cv::ml::DTrees* instance, int val) {
try {
instance->setMaxDepth(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_getMinSampleCount_const(const cv::ml::DTrees* instance) {
try {
int ret = instance->getMinSampleCount();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_setMinSampleCount_int(cv::ml::DTrees* instance, int val) {
try {
instance->setMinSampleCount(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_getCVFolds_const(const cv::ml::DTrees* instance) {
try {
int ret = instance->getCVFolds();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_setCVFolds_int(cv::ml::DTrees* instance, int val) {
try {
instance->setCVFolds(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<bool> cv_ml_DTrees_getUseSurrogates_const(const cv::ml::DTrees* instance) {
try {
bool ret = instance->getUseSurrogates();
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result_void cv_ml_DTrees_setUseSurrogates_bool(cv::ml::DTrees* instance, bool val) {
try {
instance->setUseSurrogates(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<bool> cv_ml_DTrees_getUse1SERule_const(const cv::ml::DTrees* instance) {
try {
bool ret = instance->getUse1SERule();
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result_void cv_ml_DTrees_setUse1SERule_bool(cv::ml::DTrees* instance, bool val) {
try {
instance->setUse1SERule(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<bool> cv_ml_DTrees_getTruncatePrunedTree_const(const cv::ml::DTrees* instance) {
try {
bool ret = instance->getTruncatePrunedTree();
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result_void cv_ml_DTrees_setTruncatePrunedTree_bool(cv::ml::DTrees* instance, bool val) {
try {
instance->setTruncatePrunedTree(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<float> cv_ml_DTrees_getRegressionAccuracy_const(const cv::ml::DTrees* instance) {
try {
float ret = instance->getRegressionAccuracy();
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result_void cv_ml_DTrees_setRegressionAccuracy_float(cv::ml::DTrees* instance, float val) {
try {
instance->setRegressionAccuracy(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_DTrees_getPriors_const(const cv::ml::DTrees* instance) {
try {
cv::Mat ret = instance->getPriors();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result_void cv_ml_DTrees_setPriors_const_MatR(cv::ml::DTrees* instance, const cv::Mat* val) {
try {
instance->setPriors(*val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<const std::vector<int>*> cv_ml_DTrees_getRoots_const(const cv::ml::DTrees* instance) {
try {
const std::vector<int> ret = instance->getRoots();
return Ok(new const std::vector<int>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<const std::vector<int>*>))
}
Result<const std::vector<cv::ml::DTrees::Node>*> cv_ml_DTrees_getNodes_const(const cv::ml::DTrees* instance) {
try {
const std::vector<cv::ml::DTrees::Node> ret = instance->getNodes();
return Ok(new const std::vector<cv::ml::DTrees::Node>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<const std::vector<cv::ml::DTrees::Node>*>))
}
Result<const std::vector<cv::ml::DTrees::Split>*> cv_ml_DTrees_getSplits_const(const cv::ml::DTrees* instance) {
try {
const std::vector<cv::ml::DTrees::Split> ret = instance->getSplits();
return Ok(new const std::vector<cv::ml::DTrees::Split>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<const std::vector<cv::ml::DTrees::Split>*>))
}
Result<const std::vector<int>*> cv_ml_DTrees_getSubsets_const(const cv::ml::DTrees* instance) {
try {
const std::vector<int> ret = instance->getSubsets();
return Ok(new const std::vector<int>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<const std::vector<int>*>))
}
Result<cv::Ptr<cv::ml::DTrees>*> cv_ml_DTrees_create() {
try {
cv::Ptr<cv::ml::DTrees> ret = cv::ml::DTrees::create();
return Ok(new cv::Ptr<cv::ml::DTrees>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::DTrees>*>))
}
Result<cv::Ptr<cv::ml::DTrees>*> cv_ml_DTrees_load_const_StringR_const_StringR(const char* filepath, const char* nodeName) {
try {
cv::Ptr<cv::ml::DTrees> ret = cv::ml::DTrees::load(cv::String(filepath), cv::String(nodeName));
return Ok(new cv::Ptr<cv::ml::DTrees>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::DTrees>*>))
}
Result<double> cv_ml_DTrees_Node_getPropValue_const(const cv::ml::DTrees::Node* instance) {
try {
double ret = instance->value;
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_DTrees_Node_setPropValue_double(cv::ml::DTrees::Node* instance, double val) {
try {
instance->value = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_Node_getPropClassIdx_const(const cv::ml::DTrees::Node* instance) {
try {
int ret = instance->classIdx;
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_Node_setPropClassIdx_int(cv::ml::DTrees::Node* instance, int val) {
try {
instance->classIdx = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_Node_getPropParent_const(const cv::ml::DTrees::Node* instance) {
try {
int ret = instance->parent;
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_Node_setPropParent_int(cv::ml::DTrees::Node* instance, int val) {
try {
instance->parent = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_Node_getPropLeft_const(const cv::ml::DTrees::Node* instance) {
try {
int ret = instance->left;
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_Node_setPropLeft_int(cv::ml::DTrees::Node* instance, int val) {
try {
instance->left = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_Node_getPropRight_const(const cv::ml::DTrees::Node* instance) {
try {
int ret = instance->right;
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_Node_setPropRight_int(cv::ml::DTrees::Node* instance, int val) {
try {
instance->right = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_Node_getPropDefaultDir_const(const cv::ml::DTrees::Node* instance) {
try {
int ret = instance->defaultDir;
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_Node_setPropDefaultDir_int(cv::ml::DTrees::Node* instance, int val) {
try {
instance->defaultDir = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_Node_getPropSplit_const(const cv::ml::DTrees::Node* instance) {
try {
int ret = instance->split;
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_Node_setPropSplit_int(cv::ml::DTrees::Node* instance, int val) {
try {
instance->split = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
void cv_DTrees_Node_delete(cv::ml::DTrees::Node* instance) {
delete instance;
}
Result<cv::ml::DTrees::Node*> cv_ml_DTrees_Node_Node() {
try {
cv::ml::DTrees::Node* ret = new cv::ml::DTrees::Node();
return Ok<cv::ml::DTrees::Node*>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::ml::DTrees::Node*>))
}
Result<int> cv_ml_DTrees_Split_getPropVarIdx_const(const cv::ml::DTrees::Split* instance) {
try {
int ret = instance->varIdx;
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_Split_setPropVarIdx_int(cv::ml::DTrees::Split* instance, int val) {
try {
instance->varIdx = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<bool> cv_ml_DTrees_Split_getPropInversed_const(const cv::ml::DTrees::Split* instance) {
try {
bool ret = instance->inversed;
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result_void cv_ml_DTrees_Split_setPropInversed_bool(cv::ml::DTrees::Split* instance, bool val) {
try {
instance->inversed = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<float> cv_ml_DTrees_Split_getPropQuality_const(const cv::ml::DTrees::Split* instance) {
try {
float ret = instance->quality;
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result_void cv_ml_DTrees_Split_setPropQuality_float(cv::ml::DTrees::Split* instance, float val) {
try {
instance->quality = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_Split_getPropNext_const(const cv::ml::DTrees::Split* instance) {
try {
int ret = instance->next;
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_Split_setPropNext_int(cv::ml::DTrees::Split* instance, int val) {
try {
instance->next = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<float> cv_ml_DTrees_Split_getPropC_const(const cv::ml::DTrees::Split* instance) {
try {
float ret = instance->c;
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result_void cv_ml_DTrees_Split_setPropC_float(cv::ml::DTrees::Split* instance, float val) {
try {
instance->c = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_DTrees_Split_getPropSubsetOfs_const(const cv::ml::DTrees::Split* instance) {
try {
int ret = instance->subsetOfs;
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_DTrees_Split_setPropSubsetOfs_int(cv::ml::DTrees::Split* instance, int val) {
try {
instance->subsetOfs = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
void cv_DTrees_Split_delete(cv::ml::DTrees::Split* instance) {
delete instance;
}
Result<cv::ml::DTrees::Split*> cv_ml_DTrees_Split_Split() {
try {
cv::ml::DTrees::Split* ret = new cv::ml::DTrees::Split();
return Ok<cv::ml::DTrees::Split*>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::ml::DTrees::Split*>))
}
Result<int> cv_ml_EM_getClustersNumber_const(const cv::ml::EM* instance) {
try {
int ret = instance->getClustersNumber();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_EM_setClustersNumber_int(cv::ml::EM* instance, int val) {
try {
instance->setClustersNumber(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_EM_getCovarianceMatrixType_const(const cv::ml::EM* instance) {
try {
int ret = instance->getCovarianceMatrixType();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_EM_setCovarianceMatrixType_int(cv::ml::EM* instance, int val) {
try {
instance->setCovarianceMatrixType(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::TermCriteria> cv_ml_EM_getTermCriteria_const(const cv::ml::EM* instance) {
try {
cv::TermCriteria ret = instance->getTermCriteria();
return Ok<cv::TermCriteria>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::TermCriteria>))
}
Result_void cv_ml_EM_setTermCriteria_const_TermCriteriaR(cv::ml::EM* instance, const cv::TermCriteria* val) {
try {
instance->setTermCriteria(*val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_EM_getWeights_const(const cv::ml::EM* instance) {
try {
cv::Mat ret = instance->getWeights();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_EM_getMeans_const(const cv::ml::EM* instance) {
try {
cv::Mat ret = instance->getMeans();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result_void cv_ml_EM_getCovs_const_vector_Mat_R(const cv::ml::EM* instance, std::vector<cv::Mat>* covs) {
try {
instance->getCovs(*covs);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<float> cv_ml_EM_predict_const_const__InputArrayR_const__OutputArrayR_int(const cv::ml::EM* instance, const cv::_InputArray* samples, const cv::_OutputArray* results, int flags) {
try {
float ret = instance->predict(*samples, *results, flags);
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result<cv::Vec2d> cv_ml_EM_predict2_const_const__InputArrayR_const__OutputArrayR(const cv::ml::EM* instance, const cv::_InputArray* sample, const cv::_OutputArray* probs) {
try {
cv::Vec2d ret = instance->predict2(*sample, *probs);
return Ok<cv::Vec2d>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Vec2d>))
}
Result<bool> cv_ml_EM_trainEM_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(cv::ml::EM* instance, const cv::_InputArray* samples, const cv::_OutputArray* logLikelihoods, const cv::_OutputArray* labels, const cv::_OutputArray* probs) {
try {
bool ret = instance->trainEM(*samples, *logLikelihoods, *labels, *probs);
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result<bool> cv_ml_EM_trainE_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(cv::ml::EM* instance, const cv::_InputArray* samples, const cv::_InputArray* means0, const cv::_InputArray* covs0, const cv::_InputArray* weights0, const cv::_OutputArray* logLikelihoods, const cv::_OutputArray* labels, const cv::_OutputArray* probs) {
try {
bool ret = instance->trainE(*samples, *means0, *covs0, *weights0, *logLikelihoods, *labels, *probs);
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result<bool> cv_ml_EM_trainM_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(cv::ml::EM* instance, const cv::_InputArray* samples, const cv::_InputArray* probs0, const cv::_OutputArray* logLikelihoods, const cv::_OutputArray* labels, const cv::_OutputArray* probs) {
try {
bool ret = instance->trainM(*samples, *probs0, *logLikelihoods, *labels, *probs);
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result<cv::Ptr<cv::ml::EM>*> cv_ml_EM_create() {
try {
cv::Ptr<cv::ml::EM> ret = cv::ml::EM::create();
return Ok(new cv::Ptr<cv::ml::EM>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::EM>*>))
}
Result<cv::Ptr<cv::ml::EM>*> cv_ml_EM_load_const_StringR_const_StringR(const char* filepath, const char* nodeName) {
try {
cv::Ptr<cv::ml::EM> ret = cv::ml::EM::load(cv::String(filepath), cv::String(nodeName));
return Ok(new cv::Ptr<cv::ml::EM>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::EM>*>))
}
Result<int> cv_ml_KNearest_getDefaultK_const(const cv::ml::KNearest* instance) {
try {
int ret = instance->getDefaultK();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_KNearest_setDefaultK_int(cv::ml::KNearest* instance, int val) {
try {
instance->setDefaultK(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<bool> cv_ml_KNearest_getIsClassifier_const(const cv::ml::KNearest* instance) {
try {
bool ret = instance->getIsClassifier();
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result_void cv_ml_KNearest_setIsClassifier_bool(cv::ml::KNearest* instance, bool val) {
try {
instance->setIsClassifier(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_KNearest_getEmax_const(const cv::ml::KNearest* instance) {
try {
int ret = instance->getEmax();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_KNearest_setEmax_int(cv::ml::KNearest* instance, int val) {
try {
instance->setEmax(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_KNearest_getAlgorithmType_const(const cv::ml::KNearest* instance) {
try {
int ret = instance->getAlgorithmType();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_KNearest_setAlgorithmType_int(cv::ml::KNearest* instance, int val) {
try {
instance->setAlgorithmType(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<float> cv_ml_KNearest_findNearest_const_const__InputArrayR_int_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::ml::KNearest* instance, const cv::_InputArray* samples, int k, const cv::_OutputArray* results, const cv::_OutputArray* neighborResponses, const cv::_OutputArray* dist) {
try {
float ret = instance->findNearest(*samples, k, *results, *neighborResponses, *dist);
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result<cv::Ptr<cv::ml::KNearest>*> cv_ml_KNearest_create() {
try {
cv::Ptr<cv::ml::KNearest> ret = cv::ml::KNearest::create();
return Ok(new cv::Ptr<cv::ml::KNearest>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::KNearest>*>))
}
Result<cv::Ptr<cv::ml::KNearest>*> cv_ml_KNearest_load_const_StringR(const char* filepath) {
try {
cv::Ptr<cv::ml::KNearest> ret = cv::ml::KNearest::load(cv::String(filepath));
return Ok(new cv::Ptr<cv::ml::KNearest>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::KNearest>*>))
}
Result<double> cv_ml_LogisticRegression_getLearningRate_const(const cv::ml::LogisticRegression* instance) {
try {
double ret = instance->getLearningRate();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_LogisticRegression_setLearningRate_double(cv::ml::LogisticRegression* instance, double val) {
try {
instance->setLearningRate(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_LogisticRegression_getIterations_const(const cv::ml::LogisticRegression* instance) {
try {
int ret = instance->getIterations();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_LogisticRegression_setIterations_int(cv::ml::LogisticRegression* instance, int val) {
try {
instance->setIterations(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_LogisticRegression_getRegularization_const(const cv::ml::LogisticRegression* instance) {
try {
int ret = instance->getRegularization();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_LogisticRegression_setRegularization_int(cv::ml::LogisticRegression* instance, int val) {
try {
instance->setRegularization(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_LogisticRegression_getTrainMethod_const(const cv::ml::LogisticRegression* instance) {
try {
int ret = instance->getTrainMethod();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_LogisticRegression_setTrainMethod_int(cv::ml::LogisticRegression* instance, int val) {
try {
instance->setTrainMethod(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_LogisticRegression_getMiniBatchSize_const(const cv::ml::LogisticRegression* instance) {
try {
int ret = instance->getMiniBatchSize();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_LogisticRegression_setMiniBatchSize_int(cv::ml::LogisticRegression* instance, int val) {
try {
instance->setMiniBatchSize(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::TermCriteria> cv_ml_LogisticRegression_getTermCriteria_const(const cv::ml::LogisticRegression* instance) {
try {
cv::TermCriteria ret = instance->getTermCriteria();
return Ok<cv::TermCriteria>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::TermCriteria>))
}
Result_void cv_ml_LogisticRegression_setTermCriteria_TermCriteria(cv::ml::LogisticRegression* instance, cv::TermCriteria* val) {
try {
instance->setTermCriteria(*val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<float> cv_ml_LogisticRegression_predict_const_const__InputArrayR_const__OutputArrayR_int(const cv::ml::LogisticRegression* instance, const cv::_InputArray* samples, const cv::_OutputArray* results, int flags) {
try {
float ret = instance->predict(*samples, *results, flags);
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result<cv::Mat*> cv_ml_LogisticRegression_get_learnt_thetas_const(const cv::ml::LogisticRegression* instance) {
try {
cv::Mat ret = instance->get_learnt_thetas();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Ptr<cv::ml::LogisticRegression>*> cv_ml_LogisticRegression_create() {
try {
cv::Ptr<cv::ml::LogisticRegression> ret = cv::ml::LogisticRegression::create();
return Ok(new cv::Ptr<cv::ml::LogisticRegression>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::LogisticRegression>*>))
}
Result<cv::Ptr<cv::ml::LogisticRegression>*> cv_ml_LogisticRegression_load_const_StringR_const_StringR(const char* filepath, const char* nodeName) {
try {
cv::Ptr<cv::ml::LogisticRegression> ret = cv::ml::LogisticRegression::load(cv::String(filepath), cv::String(nodeName));
return Ok(new cv::Ptr<cv::ml::LogisticRegression>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::LogisticRegression>*>))
}
Result<float> cv_ml_NormalBayesClassifier_predictProb_const_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int(const cv::ml::NormalBayesClassifier* instance, const cv::_InputArray* inputs, const cv::_OutputArray* outputs, const cv::_OutputArray* outputProbs, int flags) {
try {
float ret = instance->predictProb(*inputs, *outputs, *outputProbs, flags);
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result<cv::Ptr<cv::ml::NormalBayesClassifier>*> cv_ml_NormalBayesClassifier_create() {
try {
cv::Ptr<cv::ml::NormalBayesClassifier> ret = cv::ml::NormalBayesClassifier::create();
return Ok(new cv::Ptr<cv::ml::NormalBayesClassifier>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::NormalBayesClassifier>*>))
}
Result<cv::Ptr<cv::ml::NormalBayesClassifier>*> cv_ml_NormalBayesClassifier_load_const_StringR_const_StringR(const char* filepath, const char* nodeName) {
try {
cv::Ptr<cv::ml::NormalBayesClassifier> ret = cv::ml::NormalBayesClassifier::load(cv::String(filepath), cv::String(nodeName));
return Ok(new cv::Ptr<cv::ml::NormalBayesClassifier>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::NormalBayesClassifier>*>))
}
Result<double> cv_ml_ParamGrid_getPropMinVal_const(const cv::ml::ParamGrid* instance) {
try {
double ret = instance->minVal;
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ParamGrid_setPropMinVal_double(cv::ml::ParamGrid* instance, double val) {
try {
instance->minVal = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ParamGrid_getPropMaxVal_const(const cv::ml::ParamGrid* instance) {
try {
double ret = instance->maxVal;
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ParamGrid_setPropMaxVal_double(cv::ml::ParamGrid* instance, double val) {
try {
instance->maxVal = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_ParamGrid_getPropLogStep_const(const cv::ml::ParamGrid* instance) {
try {
double ret = instance->logStep;
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_ParamGrid_setPropLogStep_double(cv::ml::ParamGrid* instance, double val) {
try {
instance->logStep = val;
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
void cv_ParamGrid_delete(cv::ml::ParamGrid* instance) {
delete instance;
}
Result<cv::ml::ParamGrid*> cv_ml_ParamGrid_ParamGrid() {
try {
cv::ml::ParamGrid* ret = new cv::ml::ParamGrid();
return Ok<cv::ml::ParamGrid*>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::ml::ParamGrid*>))
}
Result<cv::ml::ParamGrid*> cv_ml_ParamGrid_ParamGrid_double_double_double(double _minVal, double _maxVal, double _logStep) {
try {
cv::ml::ParamGrid* ret = new cv::ml::ParamGrid(_minVal, _maxVal, _logStep);
return Ok<cv::ml::ParamGrid*>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::ml::ParamGrid*>))
}
Result<cv::Ptr<cv::ml::ParamGrid>*> cv_ml_ParamGrid_create_double_double_double(double minVal, double maxVal, double logstep) {
try {
cv::Ptr<cv::ml::ParamGrid> ret = cv::ml::ParamGrid::create(minVal, maxVal, logstep);
return Ok(new cv::Ptr<cv::ml::ParamGrid>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::ParamGrid>*>))
}
Result<bool> cv_ml_RTrees_getCalculateVarImportance_const(const cv::ml::RTrees* instance) {
try {
bool ret = instance->getCalculateVarImportance();
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result_void cv_ml_RTrees_setCalculateVarImportance_bool(cv::ml::RTrees* instance, bool val) {
try {
instance->setCalculateVarImportance(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_RTrees_getActiveVarCount_const(const cv::ml::RTrees* instance) {
try {
int ret = instance->getActiveVarCount();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_RTrees_setActiveVarCount_int(cv::ml::RTrees* instance, int val) {
try {
instance->setActiveVarCount(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::TermCriteria> cv_ml_RTrees_getTermCriteria_const(const cv::ml::RTrees* instance) {
try {
cv::TermCriteria ret = instance->getTermCriteria();
return Ok<cv::TermCriteria>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::TermCriteria>))
}
Result_void cv_ml_RTrees_setTermCriteria_const_TermCriteriaR(cv::ml::RTrees* instance, const cv::TermCriteria* val) {
try {
instance->setTermCriteria(*val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_RTrees_getVarImportance_const(const cv::ml::RTrees* instance) {
try {
cv::Mat ret = instance->getVarImportance();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result_void cv_ml_RTrees_getVotes_const_const__InputArrayR_const__OutputArrayR_int(const cv::ml::RTrees* instance, const cv::_InputArray* samples, const cv::_OutputArray* results, int flags) {
try {
instance->getVotes(*samples, *results, flags);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_RTrees_getOOBError_const(const cv::ml::RTrees* instance) {
try {
double ret = instance->getOOBError();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result<cv::Ptr<cv::ml::RTrees>*> cv_ml_RTrees_create() {
try {
cv::Ptr<cv::ml::RTrees> ret = cv::ml::RTrees::create();
return Ok(new cv::Ptr<cv::ml::RTrees>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::RTrees>*>))
}
Result<cv::Ptr<cv::ml::RTrees>*> cv_ml_RTrees_load_const_StringR_const_StringR(const char* filepath, const char* nodeName) {
try {
cv::Ptr<cv::ml::RTrees> ret = cv::ml::RTrees::load(cv::String(filepath), cv::String(nodeName));
return Ok(new cv::Ptr<cv::ml::RTrees>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::RTrees>*>))
}
Result<int> cv_ml_SVM_getType_const(const cv::ml::SVM* instance) {
try {
int ret = instance->getType();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_SVM_setType_int(cv::ml::SVM* instance, int val) {
try {
instance->setType(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_SVM_getGamma_const(const cv::ml::SVM* instance) {
try {
double ret = instance->getGamma();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_SVM_setGamma_double(cv::ml::SVM* instance, double val) {
try {
instance->setGamma(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_SVM_getCoef0_const(const cv::ml::SVM* instance) {
try {
double ret = instance->getCoef0();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_SVM_setCoef0_double(cv::ml::SVM* instance, double val) {
try {
instance->setCoef0(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_SVM_getDegree_const(const cv::ml::SVM* instance) {
try {
double ret = instance->getDegree();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_SVM_setDegree_double(cv::ml::SVM* instance, double val) {
try {
instance->setDegree(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_SVM_getC_const(const cv::ml::SVM* instance) {
try {
double ret = instance->getC();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_SVM_setC_double(cv::ml::SVM* instance, double val) {
try {
instance->setC(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_SVM_getNu_const(const cv::ml::SVM* instance) {
try {
double ret = instance->getNu();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_SVM_setNu_double(cv::ml::SVM* instance, double val) {
try {
instance->setNu(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<double> cv_ml_SVM_getP_const(const cv::ml::SVM* instance) {
try {
double ret = instance->getP();
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result_void cv_ml_SVM_setP_double(cv::ml::SVM* instance, double val) {
try {
instance->setP(val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_SVM_getClassWeights_const(const cv::ml::SVM* instance) {
try {
cv::Mat ret = instance->getClassWeights();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result_void cv_ml_SVM_setClassWeights_const_MatR(cv::ml::SVM* instance, const cv::Mat* val) {
try {
instance->setClassWeights(*val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::TermCriteria> cv_ml_SVM_getTermCriteria_const(const cv::ml::SVM* instance) {
try {
cv::TermCriteria ret = instance->getTermCriteria();
return Ok<cv::TermCriteria>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::TermCriteria>))
}
Result_void cv_ml_SVM_setTermCriteria_const_TermCriteriaR(cv::ml::SVM* instance, const cv::TermCriteria* val) {
try {
instance->setTermCriteria(*val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_SVM_getKernelType_const(const cv::ml::SVM* instance) {
try {
int ret = instance->getKernelType();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_SVM_setKernel_int(cv::ml::SVM* instance, int kernelType) {
try {
instance->setKernel(kernelType);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result_void cv_ml_SVM_setCustomKernel_const_Ptr_Kernel_R(cv::ml::SVM* instance, const cv::Ptr<cv::ml::SVM::Kernel>* _kernel) {
try {
instance->setCustomKernel(*_kernel);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<bool> cv_ml_SVM_trainAuto_const_Ptr_TrainData_R_int_ParamGrid_ParamGrid_ParamGrid_ParamGrid_ParamGrid_ParamGrid_bool(cv::ml::SVM* instance, const cv::Ptr<cv::ml::TrainData>* data, int kFold, cv::ml::ParamGrid* Cgrid, cv::ml::ParamGrid* gammaGrid, cv::ml::ParamGrid* pGrid, cv::ml::ParamGrid* nuGrid, cv::ml::ParamGrid* coeffGrid, cv::ml::ParamGrid* degreeGrid, bool balanced) {
try {
bool ret = instance->trainAuto(*data, kFold, *Cgrid, *gammaGrid, *pGrid, *nuGrid, *coeffGrid, *degreeGrid, balanced);
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result<bool> cv_ml_SVM_trainAuto_const__InputArrayR_int_const__InputArrayR_int_Ptr_ParamGrid__Ptr_ParamGrid__Ptr_ParamGrid__Ptr_ParamGrid__Ptr_ParamGrid__Ptr_ParamGrid__bool(cv::ml::SVM* instance, const cv::_InputArray* samples, int layout, const cv::_InputArray* responses, int kFold, cv::Ptr<cv::ml::ParamGrid>* Cgrid, cv::Ptr<cv::ml::ParamGrid>* gammaGrid, cv::Ptr<cv::ml::ParamGrid>* pGrid, cv::Ptr<cv::ml::ParamGrid>* nuGrid, cv::Ptr<cv::ml::ParamGrid>* coeffGrid, cv::Ptr<cv::ml::ParamGrid>* degreeGrid, bool balanced) {
try {
bool ret = instance->trainAuto(*samples, layout, *responses, kFold, *Cgrid, *gammaGrid, *pGrid, *nuGrid, *coeffGrid, *degreeGrid, balanced);
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result<cv::Mat*> cv_ml_SVM_getSupportVectors_const(const cv::ml::SVM* instance) {
try {
cv::Mat ret = instance->getSupportVectors();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_SVM_getUncompressedSupportVectors_const(const cv::ml::SVM* instance) {
try {
cv::Mat ret = instance->getUncompressedSupportVectors();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<double> cv_ml_SVM_getDecisionFunction_const_int_const__OutputArrayR_const__OutputArrayR(const cv::ml::SVM* instance, int i, const cv::_OutputArray* alpha, const cv::_OutputArray* svidx) {
try {
double ret = instance->getDecisionFunction(i, *alpha, *svidx);
return Ok<double>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<double>))
}
Result<cv::ml::ParamGrid*> cv_ml_SVM_getDefaultGrid_int(int param_id) {
try {
cv::ml::ParamGrid ret = cv::ml::SVM::getDefaultGrid(param_id);
return Ok(new cv::ml::ParamGrid(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::ml::ParamGrid*>))
}
Result<cv::Ptr<cv::ml::ParamGrid>*> cv_ml_SVM_getDefaultGridPtr_int(int param_id) {
try {
cv::Ptr<cv::ml::ParamGrid> ret = cv::ml::SVM::getDefaultGridPtr(param_id);
return Ok(new cv::Ptr<cv::ml::ParamGrid>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::ParamGrid>*>))
}
Result<cv::Ptr<cv::ml::SVM>*> cv_ml_SVM_create() {
try {
cv::Ptr<cv::ml::SVM> ret = cv::ml::SVM::create();
return Ok(new cv::Ptr<cv::ml::SVM>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::SVM>*>))
}
Result<cv::Ptr<cv::ml::SVM>*> cv_ml_SVM_load_const_StringR(const char* filepath) {
try {
cv::Ptr<cv::ml::SVM> ret = cv::ml::SVM::load(cv::String(filepath));
return Ok(new cv::Ptr<cv::ml::SVM>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::SVM>*>))
}
Result<int> cv_ml_SVM_Kernel_getType_const(const cv::ml::SVM::Kernel* instance) {
try {
int ret = instance->getType();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_SVM_Kernel_calc_int_int_const_floatX_const_floatX_floatX(cv::ml::SVM::Kernel* instance, int vcount, int n, const float* vecs, const float* another, float* results) {
try {
instance->calc(vcount, n, vecs, another, results);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_SVMSGD_getWeights(cv::ml::SVMSGD* instance) {
try {
cv::Mat ret = instance->getWeights();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<float> cv_ml_SVMSGD_getShift(cv::ml::SVMSGD* instance) {
try {
float ret = instance->getShift();
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result<cv::Ptr<cv::ml::SVMSGD>*> cv_ml_SVMSGD_create() {
try {
cv::Ptr<cv::ml::SVMSGD> ret = cv::ml::SVMSGD::create();
return Ok(new cv::Ptr<cv::ml::SVMSGD>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::SVMSGD>*>))
}
Result<cv::Ptr<cv::ml::SVMSGD>*> cv_ml_SVMSGD_load_const_StringR_const_StringR(const char* filepath, const char* nodeName) {
try {
cv::Ptr<cv::ml::SVMSGD> ret = cv::ml::SVMSGD::load(cv::String(filepath), cv::String(nodeName));
return Ok(new cv::Ptr<cv::ml::SVMSGD>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::SVMSGD>*>))
}
Result_void cv_ml_SVMSGD_setOptimalParameters_int_int(cv::ml::SVMSGD* instance, int svmsgdType, int marginType) {
try {
instance->setOptimalParameters(svmsgdType, marginType);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_SVMSGD_getSvmsgdType_const(const cv::ml::SVMSGD* instance) {
try {
int ret = instance->getSvmsgdType();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_SVMSGD_setSvmsgdType_int(cv::ml::SVMSGD* instance, int svmsgdType) {
try {
instance->setSvmsgdType(svmsgdType);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_SVMSGD_getMarginType_const(const cv::ml::SVMSGD* instance) {
try {
int ret = instance->getMarginType();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_SVMSGD_setMarginType_int(cv::ml::SVMSGD* instance, int marginType) {
try {
instance->setMarginType(marginType);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<float> cv_ml_SVMSGD_getMarginRegularization_const(const cv::ml::SVMSGD* instance) {
try {
float ret = instance->getMarginRegularization();
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result_void cv_ml_SVMSGD_setMarginRegularization_float(cv::ml::SVMSGD* instance, float marginRegularization) {
try {
instance->setMarginRegularization(marginRegularization);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<float> cv_ml_SVMSGD_getInitialStepSize_const(const cv::ml::SVMSGD* instance) {
try {
float ret = instance->getInitialStepSize();
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result_void cv_ml_SVMSGD_setInitialStepSize_float(cv::ml::SVMSGD* instance, float InitialStepSize) {
try {
instance->setInitialStepSize(InitialStepSize);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<float> cv_ml_SVMSGD_getStepDecreasingPower_const(const cv::ml::SVMSGD* instance) {
try {
float ret = instance->getStepDecreasingPower();
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result_void cv_ml_SVMSGD_setStepDecreasingPower_float(cv::ml::SVMSGD* instance, float stepDecreasingPower) {
try {
instance->setStepDecreasingPower(stepDecreasingPower);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::TermCriteria> cv_ml_SVMSGD_getTermCriteria_const(const cv::ml::SVMSGD* instance) {
try {
cv::TermCriteria ret = instance->getTermCriteria();
return Ok<cv::TermCriteria>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::TermCriteria>))
}
Result_void cv_ml_SVMSGD_setTermCriteria_const_TermCriteriaR(cv::ml::SVMSGD* instance, const cv::TermCriteria* val) {
try {
instance->setTermCriteria(*val);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<int> cv_ml_StatModel_getVarCount_const(const cv::ml::StatModel* instance) {
try {
int ret = instance->getVarCount();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result<bool> cv_ml_StatModel_empty_const(const cv::ml::StatModel* instance) {
try {
bool ret = instance->empty();
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result<bool> cv_ml_StatModel_isTrained_const(const cv::ml::StatModel* instance) {
try {
bool ret = instance->isTrained();
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result<bool> cv_ml_StatModel_isClassifier_const(const cv::ml::StatModel* instance) {
try {
bool ret = instance->isClassifier();
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result<bool> cv_ml_StatModel_train_const_Ptr_TrainData_R_int(cv::ml::StatModel* instance, const cv::Ptr<cv::ml::TrainData>* trainData, int flags) {
try {
bool ret = instance->train(*trainData, flags);
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result<bool> cv_ml_StatModel_train_const__InputArrayR_int_const__InputArrayR(cv::ml::StatModel* instance, const cv::_InputArray* samples, int layout, const cv::_InputArray* responses) {
try {
bool ret = instance->train(*samples, layout, *responses);
return Ok<bool>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<bool>))
}
Result<float> cv_ml_StatModel_calcError_const_const_Ptr_TrainData_R_bool_const__OutputArrayR(const cv::ml::StatModel* instance, const cv::Ptr<cv::ml::TrainData>* data, bool test, const cv::_OutputArray* resp) {
try {
float ret = instance->calcError(*data, test, *resp);
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result<float> cv_ml_StatModel_predict_const_const__InputArrayR_const__OutputArrayR_int(const cv::ml::StatModel* instance, const cv::_InputArray* samples, const cv::_OutputArray* results, int flags) {
try {
float ret = instance->predict(*samples, *results, flags);
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result<float> cv_ml_TrainData_missingValue() {
try {
float ret = cv::ml::TrainData::missingValue();
return Ok<float>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<float>))
}
Result<int> cv_ml_TrainData_getLayout_const(const cv::ml::TrainData* instance) {
try {
int ret = instance->getLayout();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result<int> cv_ml_TrainData_getNTrainSamples_const(const cv::ml::TrainData* instance) {
try {
int ret = instance->getNTrainSamples();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result<int> cv_ml_TrainData_getNTestSamples_const(const cv::ml::TrainData* instance) {
try {
int ret = instance->getNTestSamples();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result<int> cv_ml_TrainData_getNSamples_const(const cv::ml::TrainData* instance) {
try {
int ret = instance->getNSamples();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result<int> cv_ml_TrainData_getNVars_const(const cv::ml::TrainData* instance) {
try {
int ret = instance->getNVars();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result<int> cv_ml_TrainData_getNAllVars_const(const cv::ml::TrainData* instance) {
try {
int ret = instance->getNAllVars();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result_void cv_ml_TrainData_getSample_const_const__InputArrayR_int_floatX(const cv::ml::TrainData* instance, const cv::_InputArray* varIdx, int sidx, float* buf) {
try {
instance->getSample(*varIdx, sidx, buf);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_TrainData_getSamples_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getSamples();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getMissing_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getMissing();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getTrainSamples_const_int_bool_bool(const cv::ml::TrainData* instance, int layout, bool compressSamples, bool compressVars) {
try {
cv::Mat ret = instance->getTrainSamples(layout, compressSamples, compressVars);
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getTrainResponses_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getTrainResponses();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getTrainNormCatResponses_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getTrainNormCatResponses();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getTestResponses_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getTestResponses();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getTestNormCatResponses_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getTestNormCatResponses();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getResponses_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getResponses();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getNormCatResponses_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getNormCatResponses();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getSampleWeights_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getSampleWeights();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getTrainSampleWeights_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getTrainSampleWeights();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getTestSampleWeights_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getTestSampleWeights();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getVarIdx_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getVarIdx();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getVarType_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getVarType();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getVarSymbolFlags_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getVarSymbolFlags();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<int> cv_ml_TrainData_getResponseType_const(const cv::ml::TrainData* instance) {
try {
int ret = instance->getResponseType();
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result<cv::Mat*> cv_ml_TrainData_getTrainSampleIdx_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getTrainSampleIdx();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getTestSampleIdx_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getTestSampleIdx();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result_void cv_ml_TrainData_getValues_const_int_const__InputArrayR_floatX(const cv::ml::TrainData* instance, int vi, const cv::_InputArray* sidx, float* values) {
try {
instance->getValues(vi, *sidx, values);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result_void cv_ml_TrainData_getNormCatValues_const_int_const__InputArrayR_intX(const cv::ml::TrainData* instance, int vi, const cv::_InputArray* sidx, int* values) {
try {
instance->getNormCatValues(vi, *sidx, values);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_TrainData_getDefaultSubstValues_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getDefaultSubstValues();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<int> cv_ml_TrainData_getCatCount_const_int(const cv::ml::TrainData* instance, int vi) {
try {
int ret = instance->getCatCount(vi);
return Ok<int>(ret);
} OCVRS_CATCH(OCVRS_TYPE(Result<int>))
}
Result<cv::Mat*> cv_ml_TrainData_getClassLabels_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getClassLabels();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getCatOfs_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getCatOfs();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getCatMap_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getCatMap();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result_void cv_ml_TrainData_setTrainTestSplit_int_bool(cv::ml::TrainData* instance, int count, bool shuffle) {
try {
instance->setTrainTestSplit(count, shuffle);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result_void cv_ml_TrainData_setTrainTestSplitRatio_double_bool(cv::ml::TrainData* instance, double ratio, bool shuffle) {
try {
instance->setTrainTestSplitRatio(ratio, shuffle);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result_void cv_ml_TrainData_shuffleTrainTest(cv::ml::TrainData* instance) {
try {
instance->shuffleTrainTest();
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_TrainData_getTestSamples_const(const cv::ml::TrainData* instance) {
try {
cv::Mat ret = instance->getTestSamples();
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result_void cv_ml_TrainData_getNames_const_vector_String_R(const cv::ml::TrainData* instance, std::vector<cv::String>* names) {
try {
instance->getNames(*names);
return Ok();
} OCVRS_CATCH(OCVRS_TYPE(Result_void))
}
Result<cv::Mat*> cv_ml_TrainData_getSubVector_const_MatR_const_MatR(const cv::Mat* vec, const cv::Mat* idx) {
try {
cv::Mat ret = cv::ml::TrainData::getSubVector(*vec, *idx);
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Mat*> cv_ml_TrainData_getSubMatrix_const_MatR_const_MatR_int(const cv::Mat* matrix, const cv::Mat* idx, int layout) {
try {
cv::Mat ret = cv::ml::TrainData::getSubMatrix(*matrix, *idx, layout);
return Ok(new cv::Mat(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Mat*>))
}
Result<cv::Ptr<cv::ml::TrainData>*> cv_ml_TrainData_loadFromCSV_const_StringR_int_int_int_const_StringR_char_char(const char* filename, int headerLineCount, int responseStartIdx, int responseEndIdx, const char* varTypeSpec, char delimiter, char missch) {
try {
cv::Ptr<cv::ml::TrainData> ret = cv::ml::TrainData::loadFromCSV(cv::String(filename), headerLineCount, responseStartIdx, responseEndIdx, cv::String(varTypeSpec), delimiter, missch);
return Ok(new cv::Ptr<cv::ml::TrainData>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::TrainData>*>))
}
Result<cv::Ptr<cv::ml::TrainData>*> cv_ml_TrainData_create_const__InputArrayR_int_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(const cv::_InputArray* samples, int layout, const cv::_InputArray* responses, const cv::_InputArray* varIdx, const cv::_InputArray* sampleIdx, const cv::_InputArray* sampleWeights, const cv::_InputArray* varType) {
try {
cv::Ptr<cv::ml::TrainData> ret = cv::ml::TrainData::create(*samples, layout, *responses, *varIdx, *sampleIdx, *sampleWeights, *varType);
return Ok(new cv::Ptr<cv::ml::TrainData>(ret));
} OCVRS_CATCH(OCVRS_TYPE(Result<cv::Ptr<cv::ml::TrainData>*>))
}
}