CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter > Class Template Reference

#include <probabilisticregressiontree.h>

Inheritance diagram for CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >:

[legend]Collaboration diagram for CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >:

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Public Member Functions
	BinaryProbabilisticRegressionTree (const Vector< int > &DDomainSize, int CsplitDim, int RegDim)
virtual	~BinaryProbabilisticRegressionTree (void)
virtual int	InDim (void)
	Get the number of input dimensions.
virtual string	TypeName (void)
	Get the type of this object.
virtual void	Infer (void)
	Do the inference.
virtual void	Identify (void)
	Use the training data to learn a new structure.
virtual void	Prune (void)
	Prune the structure.
virtual int	SetOption (char name, char val)
	Set an option for the machine.
virtual void	SaveToStream (ostream &out)
	Output the structure data to a stream.
Protected Member Functions
void	PrintSizesTree (void)
Protected Attributes
BinaryProbabilisticRegressionTreeNode< T_Distribution, T_Regressor, T_Splitter > *	root
const Vector< int > &	dDomainSize
	list of discrete domain sizes
int	dsplitDim
	num of discrete variables
int	csplitDim
	num of continuous+split variables
int	regDim
	num of regression variables
int	emMaxIterations
	num of iterations to get convergence for EM
int	emRestarts
	num of restarts of EM to get a good initial starting point
int	min_no_datapoints
	the minimum number of datapoints in a node to split further
int	splitType
	type of split to be passed to splitter, splitter dependent
T_Distribution *	rootDistribution
int	inferMaxNodeId

template<class T_Distribution, class T_Regressor, class T_Splitter>
class CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >

Constructor & Destructor Documentation

template<class T_Distribution, class T_Regressor, class T_Splitter>

CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::BinaryProbabilisticRegressionTree ( const Vector< int > & DDomainSize,

int CsplitDim,

int RegDim

) [inline]

Definition at line 89 of file probabilisticregressiontree.h.

template<class T_Distribution, class T_Regressor, class T_Splitter>

virtual CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::~BinaryProbabilisticRegressionTree ( void ) [inline, virtual]

Definition at line 101 of file probabilisticregressiontree.h.

Member Function Documentation

template<class T_Distribution, class T_Regressor, class T_Splitter>

virtual void CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Identify ( void ) [inline, virtual]

Use the training data to learn a new structure.

Reimplemented from CLUS::Machine.
Definition at line 136 of file probabilisticregressiontree.h.

template<class T_Distribution, class T_Regressor, class T_Splitter>

virtual int CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::InDim ( void ) [inline, virtual]

Get the number of input dimensions.

Reimplemented from CLUS::Machine.
Definition at line 107 of file probabilisticregressiontree.h.

template<class T_Distribution, class T_Regressor, class T_Splitter>

virtual void CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Infer ( void ) [inline, virtual]

Do the inference.

Reimplemented from CLUS::Machine.
Definition at line 117 of file probabilisticregressiontree.h.

template<class T_Distribution, class T_Regressor, class T_Splitter>

void CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::PrintSizesTree ( void ) [inline, protected]

Definition at line 80 of file probabilisticregressiontree.h.
Referenced by CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Identify(), and CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Prune().

template<class T_Distribution, class T_Regressor, class T_Splitter>

virtual void CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Prune ( void ) [inline, virtual]

Prune the structure.

Reimplemented from CLUS::Machine.
Definition at line 182 of file probabilisticregressiontree.h.

template<class T_Distribution, class T_Regressor, class T_Splitter>

virtual void CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::SaveToStream ( ostream & out ) [inline, virtual]

Output the structure data to a stream.

Parameters:

out stream for output

Reimplemented from CLUS::Machine.
Definition at line 231 of file probabilisticregressiontree.h.

template<class T_Distribution, class T_Regressor, class T_Splitter>

virtual int CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::SetOption ( char * name,

char * val

) [inline, virtual]

Set an option for the machine.

Parameters:

name name of option to be set

val value of option

Reimplemented from CLUS::Machine.
Definition at line 210 of file probabilisticregressiontree.h.

template<class T_Distribution, class T_Regressor, class T_Splitter>

virtual string CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::TypeName ( void ) [inline, virtual]

Get the type of this object.

Returns:
name of object type as a string

Reimplemented from CLUS::Machine.
Definition at line 112 of file probabilisticregressiontree.h.
Referenced by CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::SaveToStream().