CvGBTrees Class Reference

#include <ml.hpp>

Inheritance diagram for CvGBTrees:

Public Types
enum	{ SQUARED_LOSS =0, ABSOLUTE_LOSS, HUBER_LOSS =3, DEVIANCE_LOSS }

Public Member Functions
	CvGBTrees ()
	CvGBTrees (const CvMat *trainData, int tflag, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, const CvMat *varType=0, const CvMat *missingDataMask=0, CvGBTreesParams params=CvGBTreesParams())
virtual	~CvGBTrees ()
virtual bool	train (const CvMat *trainData, int tflag, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, const CvMat *varType=0, const CvMat *missingDataMask=0, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
virtual bool	train (CvMLData *data, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
virtual float	predict_serial (const CvMat *sample, const CvMat *missing=0, CvMat *weakResponses=0, CvSlice slice=CV_WHOLE_SEQ, int k=-1) const
virtual float	predict (const CvMat *sample, const CvMat *missing=0, CvMat *weakResponses=0, CvSlice slice=CV_WHOLE_SEQ, int k=-1) const
virtual void	clear ()
virtual float	calc_error (CvMLData *_data, int type, std::vector< float > *resp=0)
virtual void	write (CvFileStorage *fs, const char *name) const
virtual void	read (CvFileStorage *fs, CvFileNode *node)
	CvGBTrees (const cv::Mat &trainData, int tflag, const cv::Mat &responses, const cv::Mat &varIdx=cv::Mat(), const cv::Mat &sampleIdx=cv::Mat(), const cv::Mat &varType=cv::Mat(), const cv::Mat &missingDataMask=cv::Mat(), CvGBTreesParams params=CvGBTreesParams())
virtual bool	train (const cv::Mat &trainData, int tflag, const cv::Mat &responses, const cv::Mat &varIdx=cv::Mat(), const cv::Mat &sampleIdx=cv::Mat(), const cv::Mat &varType=cv::Mat(), const cv::Mat &missingDataMask=cv::Mat(), CvGBTreesParams params=CvGBTreesParams(), bool update=false)
virtual float	predict (const cv::Mat &sample, const cv::Mat &missing=cv::Mat(), const cv::Range &slice=cv::Range::all(), int k=-1) const
Public Member Functions inherited from CvStatModel
	CvStatModel ()
virtual	~CvStatModel ()
virtual void	save (const char *filename, const char *name=0) const
virtual void	load (const char *filename, const char *name=0)

Protected Member Functions
virtual void	find_gradient (const int k=0)
virtual void	change_values (CvDTree *tree, const int k=0)
virtual float	find_optimal_value (const CvMat *_Idx)
virtual void	do_subsample ()
void	leaves_get (CvDTreeNode **leaves, int &count, CvDTreeNode *node)
CvDTreeNode **	GetLeaves (const CvDTree *dtree, int &len)
virtual bool	problem_type () const
virtual void	write_params (CvFileStorage *fs) const
virtual void	read_params (CvFileStorage *fs, CvFileNode *fnode)
int	get_len (const CvMat *mat) const

Protected Attributes
CvDTreeTrainData *	data
CvGBTreesParams	params
CvSeq **	weak
CvMat *	orig_response
CvMat *	sum_response
CvMat *	sum_response_tmp
CvMat *	sample_idx
CvMat *	subsample_train
CvMat *	subsample_test
CvMat *	missing
CvMat *	class_labels
cv::RNG *	rng
int	class_count
float	delta
float	base_value
Protected Attributes inherited from CvStatModel
const char *	default_model_name

Member Enumeration Documentation

anonymous enum

anonymous enum

Enumerator
SQUARED_LOSS
ABSOLUTE_LOSS
HUBER_LOSS
DEVIANCE_LOSS

Constructor & Destructor Documentation

CvGBTrees() [1/3]

CvGBTrees::CvGBTrees

(

)

CvGBTrees() [2/3]

CvGBTrees::CvGBTrees	(	const CvMat *	trainData,
		int	tflag,
		const CvMat *	responses,
		const CvMat *	varIdx = 0,
		const CvMat *	sampleIdx = 0,
		const CvMat *	varType = 0,
		const CvMat *	missingDataMask = 0,
		CvGBTreesParams	params = CvGBTreesParams()
	)

~CvGBTrees()

virtual CvGBTrees::~CvGBTrees ( )

virtual

CvGBTrees() [3/3]

CvGBTrees::CvGBTrees	(	const cv::Mat &	trainData,
		int	tflag,
		const cv::Mat &	responses,
		const cv::Mat &	varIdx = cv::Mat(),
		const cv::Mat &	sampleIdx = cv::Mat(),
		const cv::Mat &	varType = cv::Mat(),
		const cv::Mat &	missingDataMask = cv::Mat(),
		CvGBTreesParams	params = CvGBTreesParams()
	)

Member Function Documentation

calc_error()

virtual float CvGBTrees::calc_error ( CvMLData *  _data, int  type, std::vector< float > *  resp = 0  )

virtual

change_values()

virtual void CvGBTrees::change_values ( CvDTree *  tree, const int  k = 0  )

protectedvirtual

clear()

virtual void CvGBTrees::clear ( )

virtual

Reimplemented from CvStatModel.

do_subsample()

virtual void CvGBTrees::do_subsample ( )

protectedvirtual

find_gradient()

virtual void CvGBTrees::find_gradient ( const int  k = 0 )

protectedvirtual

find_optimal_value()

virtual float CvGBTrees::find_optimal_value ( const CvMat *  _Idx )

protectedvirtual

get_len()

int CvGBTrees::get_len ( const CvMat *  mat ) const

protected

GetLeaves()

CvDTreeNode** CvGBTrees::GetLeaves ( const CvDTree *  dtree, int &  len  )

protected

leaves_get()

void CvGBTrees::leaves_get ( CvDTreeNode **  leaves, int &  count, CvDTreeNode *  node  )

protected

predict() [1/2]

virtual float CvGBTrees::predict ( const CvMat *  sample, const CvMat *  missing = 0, CvMat *  weakResponses = 0, CvSlice  slice = CV_WHOLE_SEQ, int  k = -1  ) const

virtual

predict() [2/2]

virtual float CvGBTrees::predict ( const cv::Mat &  sample, const cv::Mat &  missing = cv::Mat(), const cv::Range &  slice = cv::Range::all(), int  k = -1  ) const

virtual

predict_serial()

virtual float CvGBTrees::predict_serial ( const CvMat *  sample, const CvMat *  missing = 0, CvMat *  weakResponses = 0, CvSlice  slice = CV_WHOLE_SEQ, int  k = -1  ) const

virtual

problem_type()

virtual bool CvGBTrees::problem_type ( ) const

protectedvirtual

read()

virtual void CvGBTrees::read ( CvFileStorage *  fs, CvFileNode *  node  )

virtual

Reimplemented from CvStatModel.

read_params()

virtual void CvGBTrees::read_params ( CvFileStorage *  fs, CvFileNode *  fnode  )

protectedvirtual

train() [1/3]

virtual bool CvGBTrees::train ( const CvMat *  trainData, int  tflag, const CvMat *  responses, const CvMat *  varIdx = 0, const CvMat *  sampleIdx = 0, const CvMat *  varType = 0, const CvMat *  missingDataMask = 0, CvGBTreesParams  params = CvGBTreesParams(), bool  update = false  )

virtual

train() [2/3]

virtual bool CvGBTrees::train ( CvMLData *  data, CvGBTreesParams  params = CvGBTreesParams(), bool  update = false  )

virtual

train() [3/3]

virtual bool CvGBTrees::train ( const cv::Mat &  trainData, int  tflag, const cv::Mat &  responses, const cv::Mat &  varIdx = cv::Mat(), const cv::Mat &  sampleIdx = cv::Mat(), const cv::Mat &  varType = cv::Mat(), const cv::Mat &  missingDataMask = cv::Mat(), CvGBTreesParams  params = CvGBTreesParams(), bool  update = false  )

virtual

write()

virtual void CvGBTrees::write ( CvFileStorage *  fs, const char *  name  ) const

virtual

Reimplemented from CvStatModel.

write_params()

virtual void CvGBTrees::write_params ( CvFileStorage *  fs ) const

protectedvirtual

Member Data Documentation

base_value

float CvGBTrees::base_value

protected

class_count

int CvGBTrees::class_count

protected

class_labels

CvMat* CvGBTrees::class_labels

protected

data

CvDTreeTrainData* CvGBTrees::data

protected

delta

float CvGBTrees::delta

protected

missing

CvMat* CvGBTrees::missing

protected

orig_response

CvMat* CvGBTrees::orig_response

protected

params

CvGBTreesParams CvGBTrees::params

protected

rng

cv::RNG* CvGBTrees::rng

protected

sample_idx

CvMat* CvGBTrees::sample_idx

protected

subsample_test

CvMat* CvGBTrees::subsample_test

protected

subsample_train

CvMat* CvGBTrees::subsample_train

protected

sum_response

CvMat* CvGBTrees::sum_response

protected

sum_response_tmp

CvMat* CvGBTrees::sum_response_tmp

protected

weak

CvSeq** CvGBTrees::weak

protected

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The documentation for this class was generated from the following file:

• ml/ml.hpp