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

Nearest Neighbor training class. More...

#include <TrainNodeKNN.h>

Inheritance diagram for DirectGraphicalModels::CTrainNodeKNN:
Collaboration diagram for DirectGraphicalModels::CTrainNodeKNN:

Public Member Functions

 CTrainNodeKNN (byte nStates, word nFeatures, TrainNodeKNNParams params=TRAIN_NODE_KNN_PARAMS_DEFAULT)
 Constructor. More...
 
 CTrainNodeKNN (byte nStates, word nFeatures, size_t maxSamples)
 Constructor. More...
 
 ~CTrainNodeKNN (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 &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)
 
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

CKDTreem_pTree
 k-D Tree More...
 
CSamplesAccumulatorm_pSamplesAcc
 Samples Accumulator. 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

Nearest Neighbor training class.

This class implements the k-nearest neighbors classifier (k-NN), where the input consists of the k closest training samples in the feature space and the output depends on k-Nearest Neighbors.

This trainer is especially effective for low-dimentional feature spaces.

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 37 of file TrainNodeKNN.h.

Constructor & Destructor Documentation

◆ CTrainNodeKNN() [1/2]

DirectGraphicalModels::CTrainNodeKNN::CTrainNodeKNN ( byte  nStates,
word  nFeatures,
TrainNodeKNNParams  params = TRAIN_NODE_KNN_PARAMS_DEFAULT 
)

Constructor.

Parameters
nStatesNumber of states (classes)
nFeaturesNumber of features
paramsk-Nearest Neighbors parameters (Ref. TrainNodeKNNParams)

Definition at line 9 of file TrainNodeKNN.cpp.

◆ CTrainNodeKNN() [2/2]

DirectGraphicalModels::CTrainNodeKNN::CTrainNodeKNN ( byte  nStates,
word  nFeatures,
size_t  maxSamples 
)

Constructor.

Parameters
nStatesNumber of states (classes)
nFeaturesNumber of features
maxSamplesMaximum 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

Definition at line 15 of file TrainNodeKNN.cpp.

◆ ~CTrainNodeKNN()

DirectGraphicalModels::CTrainNodeKNN::~CTrainNodeKNN ( void  )

Definition at line 30 of file TrainNodeKNN.cpp.

Member Function Documentation

◆ addFeatureVec()

void DirectGraphicalModels::CTrainNodeKNN::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
featureVectorMulti-dimensinal point: Mat(size: nFeatures x 1; type: CV_8UC1)
gtCorresponding ground-truth state (class)

Implements DirectGraphicalModels::CTrainNode.

Definition at line 54 of file TrainNodeKNN.cpp.

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◆ calculateNodePotentials()

void DirectGraphicalModels::CTrainNodeKNN::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]featureVectorMulti-dimensinal point \(\textbf{f}\): Mat(size: nFeatures x 1; type: CV_{XX}C1)
[in,out]potentialNode potentials: Mat(size: nStates x 1; type: CV_32FC1). This parameter should be preinitialized and set to value 0.
[in,out]maskRelevant 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 81 of file TrainNodeKNN.cpp.

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◆ load()

void DirectGraphicalModels::CTrainNodeKNN::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
pathPath to the folder, containing the data file.
nameName of data file. If empty, will be generated automatically from the class name.
idxIndex of the data file. Negative value means no index.

Reimplemented from DirectGraphicalModels::CBaseRandomModel.

Definition at line 48 of file TrainNodeKNN.cpp.

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◆ loadFile()

void DirectGraphicalModels::CTrainNodeKNN::loadFile ( FILE *  pFile)
inlineprotectedvirtual

Loads the random model from the file.

Allows to re-use the class.

Parameters
pFilePointer to the file, opened for reading.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 68 of file TrainNodeKNN.h.

◆ reset()

void DirectGraphicalModels::CTrainNodeKNN::reset ( void  )
virtual

Resets class variables.

Allows to re-use the class.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 36 of file TrainNodeKNN.cpp.

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◆ save()

void DirectGraphicalModels::CTrainNodeKNN::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
pathPath to the destination folder.
nameName of data file. If empty, will be generated automatically from the class name.
idxIndex of the destination file. Negative value means no index.

Reimplemented from DirectGraphicalModels::CBaseRandomModel.

Definition at line 42 of file TrainNodeKNN.cpp.

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◆ saveFile()

void DirectGraphicalModels::CTrainNodeKNN::saveFile ( FILE *  pFile) const
inlineprotectedvirtual

Saves the random model into the file.

Allows to re-use the class.

Parameters
pFilePointer to the file, opened for writing.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 67 of file TrainNodeKNN.h.

◆ train()

void DirectGraphicalModels::CTrainNodeKNN::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
doCleanFlag indicating if the memory, keeping the trining data should be released after training

Reimplemented from DirectGraphicalModels::CTrainNode.

Definition at line 59 of file TrainNodeKNN.cpp.

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Member Data Documentation

◆ m_pSamplesAcc

CSamplesAccumulator* DirectGraphicalModels::CTrainNodeKNN::m_pSamplesAcc
protected

Samples Accumulator.

Definition at line 74 of file TrainNodeKNN.h.

◆ m_pTree

CKDTree* DirectGraphicalModels::CTrainNodeKNN::m_pTree
protected

k-D Tree

Definition at line 73 of file TrainNodeKNN.h.


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