本文整理汇总了C++中Booster类的典型用法代码示例。如果您正苦于以下问题：C++ Booster类的具体用法？C++ Booster怎么用？C++ Booster使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了Booster类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: XGBoosterUpdateInteract
DLLEXPORT void XGBoosterUpdateInteract( void *handle, void *dtrain, const char *action ){
Booster *bst = static_cast<Booster*>(handle);
DMatrix *dtr = static_cast<DMatrix*>(dtrain);
bst->CheckInit(); dtr->CheckInit();
std::string act( action );
bst->UpdateInteract( act, *dtr );
}示例2: XGBoosterBoostOneIter
DLLEXPORT void XGBoosterBoostOneIter( void *handle, void *dtrain,
float *grad, float *hess, size_t len, int bst_group ){
Booster *bst = static_cast<Booster*>(handle);
DMatrix *dtr = static_cast<DMatrix*>(dtrain);
bst->CheckInit(); dtr->CheckInit();
bst->BoostOneIter( *dtr, grad, hess, len, bst_group );
} 示例3: predictWithChannel
// Prediction
// Accepts a single integral image/channel, thus only to be used for testing
// Assumes that predPtr is already allocated, of same size as imgPtr
DLL_EXPORT
void predictWithChannel( void *modelPtr, ImagePixelType *imgPtr,
IntegralImagePixelType *chImgPtr,
int width, int height, int depth,
PredictionPixelType *predPtr )
{
Matrix3D<PredictionPixelType> predMatrix;
predMatrix.fromSharedData( predPtr, width, height, depth );
// create roi for image, no GT available
ROIData roi;
roi.init( imgPtr, 0, 0, 0, width, height, depth );
// get the precomputed integral images
ROIData::IntegralImageType ii;
ii.fromSharedData(chImgPtr, width, height, depth);
roi.addII( ii.internalImage().data() );
MultipleROIData allROIs;
allROIs.add( shared_ptr_nodelete(ROIData, &roi) );
Booster adaboost;
adaboost.setModel( *((BoosterModel *) modelPtr) );
adaboost.predict<false>( allROIs, &predMatrix );
}示例4: trainWithChannels
// input: multiple imgPtr, gtPtr (arrays of pointers)
// multiple img sizes
// returns a BoosterModel *
// -- BEWARE: this function is a mix of dirty tricks right now
DLL_EXPORT
void * trainWithChannels( ImagePixelType **imgPtr,
void **evecPtr,
GTPixelType **gtPtr,
int *width, int *height, int *depth,
int numStacks,
IntegralImagePixelType **chImgPtr,
int numChannels, double zAnisotropyFactor,
int numStumps,
int gtNegativeLabel, int gtPositiveLabel,
int debugOutput )
{
BoosterModel *modelPtr = 0;
try
{
std::unique_ptr<ROIData[]> rois(new ROIData[numStacks]); // TODO: remove
MultipleROIData allROIs;
std::unique_ptr<ROIData::IntegralImageType[]> ii(new ROIData::IntegralImageType[numStacks*numChannels]); // TODO: remove
for (int i=0; i < numStacks; i++)
{
rois[i].setGTNegativeSampleLabel(gtNegativeLabel);
rois[i].setGTPositiveSampleLabel(gtPositiveLabel);
rois[i].init( imgPtr[i], gtPtr[i], 0, 0, width[i], height[i], depth[i],
zAnisotropyFactor, 0.0, (const ROIData::RotationMatrixType *) evecPtr[i] );
for (int ch=0; ch < numChannels; ch++)
{
ii[i*numChannels+ch].fromSharedData(chImgPtr[i*numChannels+ch], width[i], height[i], depth[i]);
rois[i].addII( ii[i*numChannels+ch].internalImage().data() );
}
allROIs.add( shared_ptr_nodelete(ROIData, &rois[i]) );
}
BoosterInputData bdata;
bdata.init( shared_ptr_nodelete(MultipleROIData, &allROIs) );
bdata.showInfo();
Booster adaboost;
adaboost.setShowDebugInfo( debugOutput != 0 );
adaboost.train( bdata, numStumps );
// create by copying
modelPtr = new BoosterModel( adaboost.model() );
}
catch( std::exception &e )
{
printf("Error training: %s\n", e.what());
delete modelPtr;
return 0;
}
return modelPtr;
}示例5: XGBoosterUpdateOneIter
void XGBoosterUpdateOneIter(void *handle, int iter, void *dtrain) {
Booster *bst = static_cast<Booster*>(handle);
DataMatrix *dtr = static_cast<DataMatrix*>(dtrain);
bst->CheckInitModel();
bst->CheckInit(dtr);
bst->UpdateOneIter(iter, *dtr);
}示例6: XGBoosterSaveModel
int XGBoosterSaveModel(BoosterHandle handle, const char *fname) {
API_BEGIN();
Booster *bst = static_cast<Booster*>(handle);
bst->CheckInitModel();
bst->SaveModel(fname, false);
API_END();
}示例7: XGBoosterBoostOneIter
void XGBoosterBoostOneIter(void *handle, void *dtrain,
float *grad, float *hess, bst_ulong len) {
Booster *bst = static_cast<Booster*>(handle);
DataMatrix *dtr = static_cast<DataMatrix*>(dtrain);
bst->CheckInitModel();
bst->CheckInit(dtr);
bst->BoostOneIter(*dtr, grad, hess, len);
}示例8: predictWithChannels
// Prediction for a single ROI
// Accepts an arbitrary number of integral images/channels.
// Assumes that predPtr is already allocated, of same size as imgPtr
// Returns 0 if ok
DLL_EXPORT
int predictWithChannels( void *modelPtr, ImagePixelType *imgPtr,
void *eigVecImgPtr,
int width, int height, int depth,
IntegralImagePixelType **chImgPtr,
int numChannels, double zAnisotropyFactor,
int useEarlyStopping,
int useROI, int x1, int y1, int z1, int x2, int y2, int z2, // used only if useROI != 0
PredictionPixelType *predPtr )
{
Matrix3D<PredictionPixelType> predMatrix;
predMatrix.fromSharedData( predPtr, width, height, depth );
// create roi for image, no GT available
ROIData roi;
roi.init( imgPtr, 0, 0, 0, width, height, depth, zAnisotropyFactor, 0.0, (const ROIData::RotationMatrixType *) eigVecImgPtr );
std::unique_ptr<ROIData::IntegralImageType[]> ii(new ROIData::IntegralImageType[numChannels]); // TODO: remove
for (int ch=0; ch < numChannels; ch++)
{
ii[ch].fromSharedData(chImgPtr[ch], width, height, depth);
roi.addII( ii[ch].internalImage().data() );
}
MultipleROIData allROIs;
allROIs.add( shared_ptr_nodelete(ROIData, &roi) );
try
{
Booster adaboost;
adaboost.setModel( *((BoosterModel *) modelPtr) );
if(useEarlyStopping != 0)
{
if (useROI)
{
ROICoordinates subROI;
subROI.x1 = x1; subROI.y1 = y1; subROI.z1 = z1;
subROI.x2 = x2; subROI.y2 = y2; subROI.z2 = z2;
adaboost.predictWithFeatureOrdering<true>( allROIs, &predMatrix, 0, IIBOOST_NUM_THREADS, &subROI );
}
else
adaboost.predictWithFeatureOrdering<true>( allROIs, &predMatrix );
}
else
adaboost.predictWithFeatureOrdering<false>( allROIs, &predMatrix );
}
catch( std::exception &e )
{
printf("Error in prediction: %s\n", e.what());
return -1;
}
return 0;
}示例9: XGBoosterUpdateOneIter
int XGBoosterUpdateOneIter(BoosterHandle handle,
int iter,
DMatrixHandle dtrain) {
API_BEGIN();
Booster *bst = static_cast<Booster*>(handle);
DataMatrix *dtr = static_cast<DataMatrix*>(dtrain);
bst->CheckInitModel();
bst->CheckInit(dtr);
bst->UpdateOneIter(iter, *dtr);
API_END();
}示例10: XGBoosterEvalOneIter
DLLEXPORT void XGBoosterEvalOneIter( void *handle, int iter, void *dmats[], const char *evnames[], size_t len ){
Booster *bst = static_cast<Booster*>(handle);
bst->CheckInit();
std::vector<std::string> names;
std::vector<const xgboost::regrank::DMatrix*> mats;
for( size_t i = 0; i < len; ++i ){
mats.push_back( static_cast<DMatrix*>(dmats[i]) );
names.push_back( std::string( evnames[i]) );
}
bst->EvalOneIter( iter, mats, names, stderr );
}示例11: XGBoosterEvalOneIter
const char* XGBoosterEvalOneIter(void *handle, int iter, void *dmats[],
const char *evnames[], bst_ulong len) {
Booster *bst = static_cast<Booster*>(handle);
std::vector<std::string> names;
std::vector<const DataMatrix*> mats;
for (bst_ulong i = 0; i < len; ++i) {
mats.push_back(static_cast<DataMatrix*>(dmats[i]));
names.push_back(std::string(evnames[i]));
}
bst->CheckInitModel();
bst->eval_str = bst->EvalOneIter(iter, mats, names);
return bst->eval_str.c_str();
}示例12: XGBoosterBoostOneIter
int XGBoosterBoostOneIter(BoosterHandle handle,
DMatrixHandle dtrain,
float *grad,
float *hess,
bst_ulong len) {
API_BEGIN();
Booster *bst = static_cast<Booster*>(handle);
DataMatrix *dtr = static_cast<DataMatrix*>(dtrain);
bst->CheckInitModel();
bst->CheckInit(dtr);
bst->BoostOneIter(*dtr, grad, hess, len);
API_END();
}示例13: train
// input: multiple imgPtr, gtPtr (arrays of pointers)
// multiple img sizes
// returns a BoosterModel *
// -- BEWARE: this function is a mix of dirty tricks right now
DLL_EXPORT
void * train( ImagePixelType **imgPtr, GTPixelType **gtPtr,
int *width, int *height, int *depth,
int numStacks,
int numStumps, int debugOutput )
{
BoosterModel *modelPtr = 0;
try
{
std::unique_ptr<ROIData[]> rois(new ROIData[numStacks]); // TODO: remove
std::unique_ptr<ROIData::IntegralImageType[]> ii(new ROIData::IntegralImageType[numStacks]); // TODO: remove
MultipleROIData allROIs;
for (int i=0; i < numStacks; i++)
{
rois[i].init( imgPtr[i], gtPtr[i], 0, 0, width[i], height[i], depth[i] );
// raw image to integral image
// TODO: this should be removed and passed directly to train()
ii[i].compute( rois[i].rawImage );
rois[i].addII( ii[i].internalImage().data() );
allROIs.add( shared_ptr_nodelete(ROIData, &rois[i]) );
}
BoosterInputData bdata;
bdata.init( shared_ptr_nodelete(MultipleROIData, &allROIs) );
bdata.showInfo();
Booster adaboost;
adaboost.setShowDebugInfo( debugOutput != 0 );
adaboost.train( bdata, numStumps );
// create by copying
modelPtr = new BoosterModel( adaboost.model() );
}
catch( std::exception &e )
{
printf("Error training: %s\n", e.what());
delete modelPtr;
return 0;
}
return modelPtr;
}示例14: predictIndividualWeakLearnersWithChannels
DLL_EXPORT
int predictIndividualWeakLearnersWithChannels( void *modelPtr, ImagePixelType *imgPtr,
void *eigVecImgPtr,
int width, int height, int depth,
IntegralImagePixelType **chImgPtr,
int numChannels, double zAnisotropyFactor,
WLPredictionPixelType **predPtr)
{
BoosterModel* model = static_cast<BoosterModel*>(modelPtr);
int numWL = model->size();
Matrix3D<WLPredictionPixelType> predMatrix[numWL]; // TODO: remove
for(int i=0; i < numWL; ++i)
predMatrix[i].fromSharedData(predPtr[i], width, height, depth);
// create roi for image, no GT available
ROIData roi;
roi.init( imgPtr, 0, 0, 0, width, height, depth, zAnisotropyFactor, 0.0, (const ROIData::RotationMatrixType *) eigVecImgPtr );
ROIData::IntegralImageType ii[numChannels]; // TODO: remove
for (int ch=0; ch < numChannels; ch++)
{
ii[ch].fromSharedData(chImgPtr[ch], width, height, depth);
roi.addII( ii[ch].internalImage().data() );
}
MultipleROIData allROIs;
allROIs.add( shared_ptr_nodelete(ROIData, &roi) );
try
{
Booster adaboost;
adaboost.setModel(*model);
adaboost.predictIndividualWeakLearners(allROIs, predMatrix);
}
catch( std::exception &e )
{
printf("Error in prediction: %s\n", e.what());
return -1;
}
return 0;
}示例15: XGBoosterEvalOneIter
int XGBoosterEvalOneIter(BoosterHandle handle,
int iter,
DMatrixHandle dmats[],
const char *evnames[],
bst_ulong len,
const char **out_str) {
API_BEGIN();
Booster *bst = static_cast<Booster*>(handle);
std::vector<std::string> names;
std::vector<const DataMatrix*> mats;
for (bst_ulong i = 0; i < len; ++i) {
mats.push_back(static_cast<DataMatrix*>(dmats[i]));
names.push_back(std::string(evnames[i]));
}
bst->CheckInitModel();
bst->eval_str = bst->EvalOneIter(iter, mats, names);
*out_str = bst->eval_str.c_str();
API_END();
}