Naive Bayes training class. More...

#include <TrainNodeNaiveBayes.h>

Inheritance diagram for DirectGraphicalModels::CTrainNodeNaiveBayes:

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Collaboration diagram for DirectGraphicalModels::CTrainNodeNaiveBayes:

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Public Member Functions
	CTrainNodeNaiveBayes (byte nStates, word nFeatures)
	Constructor. More...

	~CTrainNodeNaiveBayes (void)

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

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

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

IPDF *	getPDF (byte state, word feature) const
	Returns the normalized probability density function for specific state (class) and feature. More...

void	smooth (int nIt=1)

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)

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

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

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

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

virtual 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
IPDF ***	m_pPDF
	The 1D PDF for node potentials. 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

Naive Bayes training class.

This class implements the naive Bayes classifier, which is based on strong (naive) independence assumptions between the features.

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 29 of file TrainNodeNaiveBayes.h.

Constructor & Destructor Documentation

DirectGraphicalModels::CTrainNodeNaiveBayes::CTrainNodeNaiveBayes	(	byte	nStates,
		word	nFeatures
	)

Constructor.

Parameters

nStates	Number of states (classes)
nFeatures	Number of features

Definition at line 9 of file TrainNodeNaiveBayes.cpp.

DirectGraphicalModels::CTrainNodeNaiveBayes::~CTrainNodeNaiveBayes ( void )

Definition at line 35 of file TrainNodeNaiveBayes.cpp.

Member Function Documentation

void DirectGraphicalModels::CTrainNodeNaiveBayes::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 68 of file TrainNodeNaiveBayes.cpp.

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void DirectGraphicalModels::CTrainNodeNaiveBayes::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 ( \( \textbf{f} \)):

\[ nodePot_s = prior_s\cdot\prod_{f\in\mathbb{F}} (H_{s,f}.data[\textbf{f}_f] / H_{s,f}.n); \forall s\in\mathbb{S}, \]

where \(\mathbb{S}\) and \(\mathbb{F}\) are sets of all states (classes) and features correspondently. In other words, the indexes: \( s \in [0; nStates) \) and \( f \in [0; nFeatures) \). Here \( H.data[256] \) is a 1D histogram, \( H.n \) is the number of entries in histogram, i.e. \( H.n = \sum^{255}_{i = 0} H.data[i] \). And \( \textbf{f}_f \in [0; 255], \forall f \in [0; nFeatures) \), i.e. has (type: CV_8UC1).

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 124 of file TrainNodeNaiveBayes.cpp.

IPDF* DirectGraphicalModels::CTrainNodeNaiveBayes::getPDF	(	byte	state,
		word	feature
	)		const

inline

Returns the normalized probability density function for specific state (class) and feature.

Parameters

state	The state (class)
feature	The feature

Returns: The probability density function

Definition at line 51 of file TrainNodeNaiveBayes.h.

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

protectedvirtual

Loads the random model from the file.

Allows to re-use the class.

Parameters

pFile Pointer to the file, opened for reading.

Reimplemented from DirectGraphicalModels::CPrior.

Definition at line 114 of file TrainNodeNaiveBayes.cpp.

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void DirectGraphicalModels::CTrainNodeNaiveBayes::reset ( void )

virtual

Resets class variables.

Allows to re-use the class.

Reimplemented from DirectGraphicalModels::CPrior.

Definition at line 50 of file TrainNodeNaiveBayes.cpp.

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

protectedvirtual

Saves the random model into the file.

Allows to re-use the class.

Parameters

pFile Pointer to the file, opened for writing.

Reimplemented from DirectGraphicalModels::CPrior.

Definition at line 104 of file TrainNodeNaiveBayes.cpp.

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void DirectGraphicalModels::CTrainNodeNaiveBayes::smooth ( int nIt = 1 )

Definition at line 96 of file TrainNodeNaiveBayes.cpp.

void DirectGraphicalModels::CTrainNodeNaiveBayes::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 91 of file TrainNodeNaiveBayes.cpp.

Member Data Documentation

IPDF*** DirectGraphicalModels::CTrainNodeNaiveBayes::m_pPDF

protected

The 1D PDF for node potentials.

Definition at line 76 of file TrainNodeNaiveBayes.h.

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

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

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation