Microsoft Sherwood Random Forest training class. More...

#include <TrainNodeMsRF.h>

Inheritance diagram for DirectGraphicalModels::CTrainNodeMsRF:

Collaboration diagram for DirectGraphicalModels::CTrainNodeMsRF:

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

	CTrainNodeMsRF (byte nStates, word nFeatures, size_t 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)=default

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

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

Mat	getNodePotentials (const Mat &featureVectors, const Mat &weights=Mat(), float Z=0.0f) const
	Returns a block of node potentials, based on the block of feature vector. More...

Mat	getNodePotentials (const vec_mat_t &featureVectors, const Mat &weights=Mat(), float Z=0.0f) const
	Returns a block of node potentials, based on the block of feature vector. More...

Mat	getNodePotentials (const Mat &featureVector, float weight, float Z=0.0f) 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)=default

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 states (classes) 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...

Private Member Functions
void	init (TrainNodeMsRFParams params)

Private Attributes
std::unique_ptr< sw::Forest< sw::LinearFeatureResponse, sw::HistogramAggregator > >	m_pRF
	Random Forest classifier. More...

std::unique_ptr< CSamplesAccumulator >	m_pSamplesAcc
	Samples Accumulator. More...

std::unique_ptr< sw::TrainingParameters >	m_pParams

Additional Inherited Members
Static Public Member Functions inherited from DirectGraphicalModels::CTrainNode
static std::shared_ptr< CTrainNode >	create (byte nodeRandomModel, byte nStates, word nFeatures)
	Factory method returning node trainer object. 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 52 of file TrainNodeMsRF.h.

Constructor & Destructor Documentation

◆ CTrainNodeMsRF() [1/2]

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 19 of file TrainNodeMsRF.cpp.

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◆ CTrainNodeMsRF() [2/2]

DirectGraphicalModels::CTrainNodeMsRF::CTrainNodeMsRF	(	byte	nStates,
		word	nFeatures,
		size_t	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 25 of file TrainNodeMsRF.cpp.

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◆ ~CTrainNodeMsRF()

DirectGraphicalModels::CTrainNodeMsRF::~CTrainNodeMsRF ( void )

virtual

Definition at line 45 of file TrainNodeMsRF.cpp.

Member Function Documentation

◆ addFeatureVec()

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 66 of file TrainNodeMsRF.cpp.

◆ calculateNodePotentials()

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 109 of file TrainNodeMsRF.cpp.

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◆ init()

void DirectGraphicalModels::CTrainNodeMsRF::init ( TrainNodeMsRFParams params )

private

Definition at line 32 of file TrainNodeMsRF.cpp.

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◆ load()

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 60 of file TrainNodeMsRF.cpp.

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◆ loadFile()

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 90 of file TrainNodeMsRF.h.

◆ reset()

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 48 of file TrainNodeMsRF.cpp.

◆ save()

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 54 of file TrainNodeMsRF.cpp.

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◆ saveFile()

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 89 of file TrainNodeMsRF.h.

◆ train()

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 71 of file TrainNodeMsRF.cpp.

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Member Data Documentation

◆ m_pParams

std::unique_ptr<sw::TrainingParameters> DirectGraphicalModels::CTrainNodeMsRF::m_pParams

private

Definition at line 101 of file TrainNodeMsRF.h.

◆ m_pRF

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

private

Random Forest classifier.

Definition at line 99 of file TrainNodeMsRF.h.

◆ m_pSamplesAcc

std::unique_ptr<CSamplesAccumulator> DirectGraphicalModels::CTrainNodeMsRF::m_pSamplesAcc

private

Samples Accumulator.

Definition at line 100 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

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ CTrainNodeMsRF() [1/2]

◆ CTrainNodeMsRF() [2/2]

◆ ~CTrainNodeMsRF()

Member Function Documentation

◆ addFeatureVec()

◆ calculateNodePotentials()

◆ init()

◆ load()

◆ loadFile()

◆ reset()

◆ save()

◆ saveFile()

◆ train()

Member Data Documentation

◆ m_pParams

◆ m_pRF

◆ m_pSamplesAcc