CvGBTrees Class Reference

#include <ml.hpp>

Inheritance diagram for CvGBTrees:
CvStatModel

List of all members.

Public Types

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

Public Member Functions

virtual float calc_error (CvMLData *_data, int type, std::vector< float > *resp=0)
virtual CV_WRAP void clear ()
CV_WRAP 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())
 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())
CV_WRAP CvGBTrees ()
virtual CV_WRAP float predict (const cv::Mat &sample, const cv::Mat &missing=cv::Mat(), const cv::Range &slice=cv::Range::all(), 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 read (CvFileStorage *fs, CvFileNode *node)
virtual CV_WRAP 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 bool train (CvMLData *data, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
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 void write (CvFileStorage *fs, const char *name) const
virtual ~CvGBTrees ()

Protected Member Functions

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

Protected Attributes

float base_value
int class_count
CvMatclass_labels
CvDTreeTrainDatadata
float delta
CvMatmissing
CvMatorig_response
CvGBTreesParams params
cv::RNGrng
CvMatsample_idx
CvMatsubsample_test
CvMatsubsample_train
CvMatsum_response
CvMatsum_response_tmp
CvSeq ** weak
CvMatweak_eval

Member Enumeration Documentation

anonymous enum
Enumerator:
SQUARED_LOSS 
ABSOLUTE_LOSS 
HUBER_LOSS 
DEVIANCE_LOSS 

Constructor & Destructor Documentation

CV_WRAP CvGBTrees::CvGBTrees (  ) 
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::~CvGBTrees (  )  [virtual]
CV_WRAP 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

virtual float CvGBTrees::calc_error ( CvMLData _data,
int  type,
std::vector< float > *  resp = 0 
) [virtual]
virtual void CvGBTrees::change_values ( CvDTree tree,
const int  k = 0 
) [protected, virtual]
virtual CV_WRAP void CvGBTrees::clear (  )  [virtual]

Reimplemented from CvStatModel.

virtual void CvGBTrees::do_subsample (  )  [protected, virtual]
virtual void CvGBTrees::find_gradient ( const int  k = 0  )  [protected, virtual]
virtual float CvGBTrees::find_optimal_value ( const CvMat _Idx  )  [protected, virtual]
CvDTreeNode** CvGBTrees::GetLeaves ( const CvDTree dtree,
int &  len 
) [protected]
void CvGBTrees::leaves_get ( CvDTreeNode **  leaves,
int &  count,
CvDTreeNode node 
) [protected]
virtual CV_WRAP 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]
virtual float CvGBTrees::predict ( const CvMat sample,
const CvMat missing = 0,
CvMat weakResponses = 0,
CvSlice  slice = CV_WHOLE_SEQ,
int  k = -1 
) const [virtual]
virtual bool CvGBTrees::problem_type (  )  const [protected, virtual]
virtual void CvGBTrees::read ( CvFileStorage fs,
CvFileNode node 
) [virtual]

Reimplemented from CvStatModel.

virtual void CvGBTrees::read_params ( CvFileStorage fs,
CvFileNode fnode 
) [protected, virtual]
virtual CV_WRAP 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]
virtual bool CvGBTrees::train ( CvMLData data,
CvGBTreesParams  params = CvGBTreesParams(),
bool  update = false 
) [virtual]
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]
virtual void CvGBTrees::write ( CvFileStorage fs,
const char *  name 
) const [virtual]

Reimplemented from CvStatModel.

virtual void CvGBTrees::write_params ( CvFileStorage fs  )  const [protected, virtual]

Member Data Documentation

float CvGBTrees::base_value [protected]
int CvGBTrees::class_count [protected]
float CvGBTrees::delta [protected]
CvMat* CvGBTrees::missing [protected]
cv::RNG* CvGBTrees::rng [protected]
CvSeq** CvGBTrees::weak [protected]

The documentation for this class was generated from the following file:
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Generated on Thu Dec 23 11:40:54 2010 for opencv by  doxygen 1.6.3