Microsoft Sherwood Random Forest training class. More...

#include <TrainNodeMsRF.h>

Inheritance diagram for DirectGraphicalModels::CTrainNodeMsRF:

Collaboration diagram for DirectGraphicalModels::CTrainNodeMsRF:

Public Member Functions
	CTrainNodeMsRF (byte nStates, word nFeatures, TrainNodeMsRFParams params=TRAIN_NODE_MS_RF_PARAMS_DEFAULT)
	Constructor. More...

	CTrainNodeMsRF (byte nStates, word nFeatures, int maxSamples)
	Constructor. More...

virtual	~CTrainNodeMsRF (void)

void	reset (void)
	Resets class variables. More...

void	save (const std::string &path, const std::string &name=std::string(), short idx=-1) const
	Saves the training data. More...

void	load (const std::string &path, const std::string &name=std::string(), short idx=-1)
	Loads the training data. More...

void	addFeatureVec (const Mat &featureVector, byte gt)
	Adds new feature vector. More...

void	train (bool doClean=false)
	Random model training. More...

Public Member Functions inherited from DirectGraphicalModels::CTrainNode
	CTrainNode (byte nStates, word nFeatures)
	Constructor. More...

virtual	~CTrainNode (void)

void	addFeatureVec (const Mat &featureVectors, const Mat &gt)
	Adds a block of new feature vectors. More...

void	addFeatureVec (const vec_mat_t &featureVectors, const Mat &gt)
	Adds a block of new feature vectors. More...

Mat	getNodePotentials (const Mat &featureVector, float weight=1.0f) const
	Returns the node potential, based on the feature vector. More...

Public Member Functions inherited from DirectGraphicalModels::ITrain
	ITrain (byte nStates, word nFeatures)
	Constructor. More...

virtual	~ITrain (void)

word	getNumFeatures (void) const
	Returns number of features. More...

Public Member Functions inherited from DirectGraphicalModels::CBaseRandomModel
	CBaseRandomModel (byte nStates)
	Constructor. More...

virtual	~CBaseRandomModel (void)

byte	getNumStates (void) const
	Returns number of features. More...

Protected Member Functions
void	saveFile (FILE *pFile) const
	Saves the random model into the file. More...

void	loadFile (FILE *pFile)
	Loads the random model from the file. More...

void	calculateNodePotentials (const Mat &featureVector, Mat &potential, Mat &mask) const
	Calculates the node potential, based on the feature vector. More...

Protected Member Functions inherited from DirectGraphicalModels::CBaseRandomModel
std::string	generateFileName (const std::string &path, const std::string &name, short idx) const
	Generates name of the data file for storing random model parameters. More...

Protected Attributes
std::auto_ptr< sw::Forest< sw::LinearFeatureResponse, sw::HistogramAggregator > >	m_pRF
	Random Forest classifier. More...

Protected Attributes inherited from DirectGraphicalModels::ITrain
word	m_nFeatures
	The number of features (length of the feature vector) More...

Protected Attributes inherited from DirectGraphicalModels::CBaseRandomModel
byte	m_nStates
	The number of states (classes) More...

Detailed Description

Microsoft Sherwood Random Forest training class.

This class is based on the Sherwood C++ code library for decision forests v.1.0.0

In order to use the Sherwood library, DGM must be built with the USE_SHERWOOD flag

Author: Sergey G. Kosov, serge.nosp@m.y.ko.nosp@m.sov@p.nosp@m.roje.nosp@m.ct-10.nosp@m..de

Definition at line 51 of file TrainNodeMsRF.h.

Constructor & Destructor Documentation

DirectGraphicalModels::CTrainNodeMsRF::CTrainNodeMsRF	(	byte	nStates,
		word	nFeatures,
		TrainNodeMsRFParams	params = `TRAIN_NODE_MS_RF_PARAMS_DEFAULT`
	)

Constructor.

Parameters

nStates	Number of states (classes)
nFeatures	Number of features
params	Random Forest parameters (Ref. TrainNodeMsRFParams)

Definition at line 21 of file TrainNodeMsRF.cpp.

DirectGraphicalModels::CTrainNodeMsRF::CTrainNodeMsRF	(	byte	nStates,
		word	nFeatures,
		int	maxSamples
	)

Constructor.

Parameters

nStates	Number of states (classes)
nFeatures	Number of features
maxSamples	Maximum number of samples to be used in training. Default value 0 means using all the samples. If another value is specified, the class for training will use maxSamples random samples from the whole amount of samples, added via addFeatureVec() function

Note: This implementation of the random forest is not weighted

Definition at line 27 of file TrainNodeMsRF.cpp.

DirectGraphicalModels::CTrainNodeMsRF::~CTrainNodeMsRF ( void )

virtual

Definition at line 51 of file TrainNodeMsRF.cpp.

Member Function Documentation

void DirectGraphicalModels::CTrainNodeMsRF::addFeatureVec	(	const Mat &	featureVector,
		byte	gt
	)

virtual

Adds new feature vector.

Used to add a featureVector, corresponding to the ground-truth state (class) gt for training

Parameters

featureVector	Multi-dimensinal point: Mat(size: nFeatures x 1; type: CV_8UC1)
gt	Corresponding ground-truth state (class)

Implements DirectGraphicalModels::CTrainNode.

Definition at line 73 of file TrainNodeMsRF.cpp.

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void DirectGraphicalModels::CTrainNodeMsRF::calculateNodePotentials	(	const Mat &	featureVector,
		Mat &	potential,
		Mat &	mask
	)		const

protectedvirtual

Calculates the node potential, based on the feature vector.

This function calculates the potentials of the node, described with the sample featureVector, being in each state (belonging to each class). These potentials are united in the node potential vector:

\[nodePot[nStates] = f(\textbf{f}[nFeatures]).\]

Functions \( f \) must be implemented in derived classes.

Parameters

[in]	featureVector	Multi-dimensinal point \(\textbf{f}\): Mat(size: nFeatures x 1; type: CV_{XX}C1)
[in,out]	potential	Node potentials: Mat(size: nStates x 1; type: CV_32FC1). This parameter should be preinitialized and set to value 0.
[in,out]	mask	Relevant Node potentials: Mat(size: nStates x 1; type: CV_8UC1). This parameter should be preinitialized and set to value 1 (all potentials are relevant).

Implements DirectGraphicalModels::CTrainNode.

Definition at line 128 of file TrainNodeMsRF.cpp.

void DirectGraphicalModels::CTrainNodeMsRF::load	(	const std::string &	path,
		const std::string &	name = `std::string()`,
		short	idx = `-1`
	)

virtual

Loads the training data.

Allows to re-use the class. Loads data to the file: "<path><name>_<idx>.dat".

Parameters

path	Path to the folder, containing the data file.
name	Name of data file. If empty, will be generated automatically from the class name.
idx	Index of the data file. Negative value means no index.

Reimplemented from DirectGraphicalModels::CBaseRandomModel.

Definition at line 67 of file TrainNodeMsRF.cpp.

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void DirectGraphicalModels::CTrainNodeMsRF::loadFile ( FILE * pFile )

inlineprotectedvirtual

Loads the random model from the file.

Allows to re-use the class.

Parameters

pFile Pointer to the file, opened for reading.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 89 of file TrainNodeMsRF.h.

void DirectGraphicalModels::CTrainNodeMsRF::reset ( void )

virtual

Resets class variables.

Allows to re-use the class

Note: This function may be extremely slow

Todo:: Check! It may be very slow here

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 54 of file TrainNodeMsRF.cpp.

void DirectGraphicalModels::CTrainNodeMsRF::save	(	const std::string &	path,
		const std::string &	name = `std::string()`,
		short	idx = `-1`
	)		const

virtual

Saves the training data.

Allows to re-use the class. Stores data to the file: "<path><name>_<idx>.dat".

Parameters

path	Path to the destination folder.
name	Name of data file. If empty, will be generated automatically from the class name.
idx	Index of the destination file. Negative value means no index.

Reimplemented from DirectGraphicalModels::CBaseRandomModel.

Definition at line 61 of file TrainNodeMsRF.cpp.

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void DirectGraphicalModels::CTrainNodeMsRF::saveFile ( FILE * pFile ) const

inlineprotectedvirtual

Saves the random model into the file.

Allows to re-use the class.

Parameters

pFile Pointer to the file, opened for writing.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 88 of file TrainNodeMsRF.h.

void DirectGraphicalModels::CTrainNodeMsRF::train ( bool doClean = false )

virtual

Random model training.

Auxilary function for training - some derived classes may use this function inbetween training and classification phases

Note: This function must be called inbetween the training and classification phases

Parameters

doClean Flag indicating if the memory, keeping the trining data should be released after training

Reimplemented from DirectGraphicalModels::CTrainNode.

Definition at line 89 of file TrainNodeMsRF.cpp.

Member Data Documentation

std::auto_ptr<sw::Forest<sw::LinearFeatureResponse, sw::HistogramAggregator> > DirectGraphicalModels::CTrainNodeMsRF::m_pRF

protected

Random Forest classifier.

Definition at line 94 of file TrainNodeMsRF.h.

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

D:/Projects/DGM/modules/DGM/TrainNodeMsRF.h
D:/Projects/DGM/modules/DGM/TrainNodeMsRF.cpp

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation