Direct Graphical Models  v.1.5.2
DirectGraphicalModels::CTrainNodeGM Class Reference

Gaussian Model training class. More...

#include <TrainNodeGM.h>

Inheritance diagram for DirectGraphicalModels::CTrainNodeGM:
Collaboration diagram for DirectGraphicalModels::CTrainNodeGM:

Public Member Functions

 CTrainNodeGM (byte nStates, word nFeatures)
 Constructor. More...
 
 ~CTrainNodeGM (void)
 
Mat getNodePotentials (const Mat &featureVector, float weight=1.0f)
 Returns the node potential, based on the feature vector. More...
 
- Public Member Functions inherited from DirectGraphicalModels::CTrainNodeGMM
 CTrainNodeGMM (byte nStates, word nFeatures, TrainNodeGMMParams params=TRAIN_NODE_GMM_PARAMS_DEFAULT)
 Constructor. More...
 
 CTrainNodeGMM (byte nStates, word nFeatures, byte maxGausses)
 Constructor. More...
 
virtual ~CTrainNodeGMM (void)
 
void reset (void)
 Resets class variables. 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 &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)
 
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...
 

Additional Inherited Members

- Protected Member Functions inherited from DirectGraphicalModels::CTrainNodeGMM
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 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

Gaussian Model training class.

This class realizes the generative training mechanism, based on the idea of approximating a the density of multi-dimensional random variables with a single multi-dimensional Gaussian function.

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 18 of file TrainNodeGM.h.

Constructor & Destructor Documentation

◆ CTrainNodeGM()

DirectGraphicalModels::CTrainNodeGM::CTrainNodeGM ( byte  nStates,
word  nFeatures 
)
inline

Constructor.

Parameters
nStatesNumber of states (classes)
nFeaturesNumber of features

Definition at line 26 of file TrainNodeGM.h.

◆ ~CTrainNodeGM()

DirectGraphicalModels::CTrainNodeGM::~CTrainNodeGM ( void  )
inline

Definition at line 27 of file TrainNodeGM.h.

Member Function Documentation

◆ getNodePotentials()

Mat DirectGraphicalModels::CTrainNodeGM::getNodePotentials ( const Mat &  featureVector,
float  weight = 1.0f 
)
inline

Returns the node potential, based on the feature vector.

This function calculates the potentials of the node, described with the sample featureVector ( \( \textbf{f} \)): \( nodePot_s = \mathcal{N}_s(\textbf{f}), \forall s \in \mathbb{S} \), where \(\mathbb{S}\) is the set of all states (classes). In other words, the indexes: \( s \in [0; nStates) \). Here \( \mathcal{N} \) is a Gaussian function kernel, described in class CNDGauss

Parameters
featureVectorMulti-dimensinal point \(\textbf{f}\): Mat(size: nFeatures x 1; type: CV_{XX}C1)
weightThe weighting parameter
Returns
Node potentials on success: Mat(size: nStates x 1; type: CV_32FC1); empty Mat() otherwise

Definition at line 38 of file TrainNodeGM.h.

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