CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter > Class Template Reference

#include <binaryprobabilisticdecisiontreenode.h>

Collaboration diagram for CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >:

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Public Member Functions
	BinaryProbabilisticDecisionTreeNode (int NodeId, const Vector< int > &DDomainSize, int CsplitDim)
	~BinaryProbabilisticDecisionTreeNode (void)
void	StartLearningEpoch (void)
void	LearnSample (const int Dvars, const double Cvars, int classlabel, double probability, double threshold)
bool	StopLearningEpoch (double minMass)
double	ProbabilityFirstClass (const int Dvars, const double Cvars, double probability, double threshold)
	Use Baesian decision mode.
void	InitializePruningStatistics (void)
void	UpdatePruningStatistics (const int Dvars, const double Cvars, int classlabel, double probability, double threshold)
void	FinalizePruningStatistics (void)
double	PruneSubtree (void)
	Returns the optimal cost for this subtree and cuts the subtree to optimal size.
void	SaveToStream (ostream &out)
Protected Types
enum	state { stable, split, bootstrap }
	the state of the node. At creation em. At load stable More...
Protected Attributes
int	nodeId
	unique identifier of the cluster for a regression tree
enum CLUS::BinaryProbabilisticDecisionTreeNode::state	State
	the state of the node. At creation em. At load stable
BinaryProbabilisticDecisionTreeNode< T_Splitter > *	Children [2]
	the children of this node
double	probFirstClass
	the probability to return first class, for the second probability is 1-probFirstClass
T_Splitter	Splitter
	Splitter for split criterion.
double	pruningError
	pruning statistics. Their ratio is the error
double	pruningTotalMass
	pruning statistics. Their ratio is the error
double	pruningTotalMassLeft
	pruning statistics. Their ratio is the error

template<class T_Splitter>
class CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >

Member Enumeration Documentation

template<class T_Splitter>

enum CLUS::BinaryProbabilisticDecisionTreeNode::state [protected]

the state of the node. At creation em. At load stable

Enumeration values:

stable

split

bootstrap

Definition at line 58 of file binaryprobabilisticdecisiontreenode.h.

Constructor & Destructor Documentation

template<class T_Splitter>

CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::BinaryProbabilisticDecisionTreeNode ( int NodeId,

const Vector< int > & DDomainSize,

int CsplitDim

) [inline]

Definition at line 73 of file binaryprobabilisticdecisiontreenode.h.

template<class T_Splitter>

CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::~BinaryProbabilisticDecisionTreeNode ( void ) [inline]

Definition at line 80 of file binaryprobabilisticdecisiontreenode.h.

Member Function Documentation

template<class T_Splitter>

void CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::FinalizePruningStatistics ( void ) [inline]

Definition at line 284 of file binaryprobabilisticdecisiontreenode.h.

template<class T_Splitter>

void CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::InitializePruningStatistics ( void ) [inline]

Definition at line 230 of file binaryprobabilisticdecisiontreenode.h.

template<class T_Splitter>

void CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::LearnSample ( const int * Dvars,

const double * Cvars,

int classlabel,

double probability,

double threshold

) [inline]

Definition at line 116 of file binaryprobabilisticdecisiontreenode.h.

template<class T_Splitter>

double CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::ProbabilityFirstClass ( const int * Dvars,

const double * Cvars,

double probability,

double threshold

) [inline]

Use Baesian decision mode.

Definition at line 202 of file binaryprobabilisticdecisiontreenode.h.

template<class T_Splitter>

double CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::PruneSubtree ( void ) [inline]

Returns the optimal cost for this subtree and cuts the subtree to optimal size.

Definition at line 290 of file binaryprobabilisticdecisiontreenode.h.

template<class T_Splitter>

void CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::SaveToStream ( ostream & out ) [inline]

Definition at line 341 of file binaryprobabilisticdecisiontreenode.h.

template<class T_Splitter>

void CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::StartLearningEpoch ( void ) [inline]

Definition at line 92 of file binaryprobabilisticdecisiontreenode.h.

template<class T_Splitter>

bool CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::StopLearningEpoch ( double minMass ) [inline]

Definition at line 147 of file binaryprobabilisticdecisiontreenode.h.

template<class T_Splitter>

void CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter >::UpdatePruningStatistics ( const int * Dvars,

const double * Cvars,

int classlabel,

double probability,

double threshold

) [inline]

Definition at line 242 of file binaryprobabilisticdecisiontreenode.h.