Represents a Feature Vector of a single example, labeled or unlabeled. More...

#include <FeatureVector.h>

Inheritance diagram for KKMLL::FeatureVector:

Public Types
typedef FeatureVector *	FeatureVectorPtr

typedef float	FVFloat

Public Member Functions
	FeatureVector (kkint32 _numOfFeatures)

	FeatureVector (const FeatureVector &_example)

virtual	~FeatureVector ()

void	AddFeatureData (kkint32 _featureNum, float _featureData)

void	BreakTie (float _breakTie)
	Update the BreakTie value. More...

float	BreakTie () const
	The difference in probability between the two most likely classes. More...

const KKStr &	ClassName () const
	Name of class that this example is assigned to. More...

virtual FeatureVectorPtr	Duplicate () const

void	ExampleFileName (const KKStr &_exampleFileName)
	Name of source of feature vector, ex: file name of image that the feature vector was computed from. More...

const KKStr &	ExampleFileName () const
	Name of file that this FeatureVector was computed from. More...

void	FeatureData (kkint32 _featureNum, float _featureValue)
	Assign a value to a specific feature number for the feature vector. More...

float	FeatureData (kkint32 featureNum) const

const float *	FeatureData () const
	Returns as a pointer to the feature data itself. More...

float *	FeatureDataAlter ()
	Same as 'FeatureData() except you can modify the data. More...

const float *	FeatureDataConst () const

bool	FeatureDataValid ()

virtual kkint32	MemoryConsumedEstimated () const

void	MissingData (bool _missingData)
	True indicates that not all the feature data was present when this example was loaded from a data file. More...

bool	MissingData () const
	True indicates that one or more features were missing. More...

void	MLClass (MLClassPtr _mlClass)
	Assign a class to this example. More...

MLClassPtr	MLClass () const
	Class that is example is assigned to. More...

const KKStr &	MLClassName () const
	Name of class that this example is assigned to. More...

kkint32	NumOfFeatures () const
	Number of features in this FeatureVector. More...

bool	operator== (FeatureVector &other_example) const

void	OrigSize (float _origSize)
	The value of Feature[0] before normalization. More...

float	OrigSize () const
	The value of Feature[0] before normalization. More...

void	PredictedClass (MLClassPtr _predictedClass)

MLClassPtr	PredictedClass () const

const KKStr &	PredictedClassName () const

void	Probability (float _probability)
	Assign a prediction probability to this example. More...

float	Probability () const
	The probability assigned by classifier to the predicted class. More...

void	ResetNumOfFeatures (kkint32 newNumOfFeatures)

float	TotalOfFeatureData () const
	Returns the total of all Feature Attributes for this feature vector. More...

void	TrainWeight (float _trainWeight)
	Assign a specific example a higher weight for training purposes. More...

float	TrainWeight () const

void	Validated (bool _validated)
	Indicated whether an expert has validated the class assignment. More...

bool	Validated () const

void	Version (kkint16 _version)

kkint16	Version () const

Protected Member Functions
void	AllocateFeatureDataArray ()
	Used by container classes such as 'FeatureVectorList'. This way they can determine real underlying class. More...

Protected Attributes
float *	featureData

kkint32	numOfFeatures

Detailed Description

Represents a Feature Vector of a single example, labeled or unlabeled.

Author: Kurt Kramer

Used for the representation of a Single example. You create an instance of this object for each single feature vector. You can subclass from this Class to make a specialized FeatureVector as in the PostLarvaeFV class. Besides keeping track of feature data this class will also track other fields such as ExampleFileName which should indicate where the FeatureVector was derived from, probability, breakTie, and others.

See also: FeatureVectorList; PostLarvaeFV; FeatureFileIO

Used for the representation of a Single example. You create an instance of this object for each single feature vector. You can subclass from this Class to make a specialized FeatureVector as in the PostLarvaeFV class. Besides keeping track of feature data this class will also track other fields such as ExampleFileName which should indicate where the FeatureVector was derived from, probability, breakTie, and others.

See also: FeatureVectorList; PostLarvaeFV; FeatureFileIO

Definition at line 59 of file FeatureVector.h.

Member Typedef Documentation

typedef FeatureVector* KKMLL::FeatureVector::FeatureVectorPtr

Definition at line 63 of file FeatureVector.h.

typedef float KKMLL::FeatureVector::FVFloat

Definition at line 62 of file FeatureVector.h.

Constructor & Destructor Documentation

FeatureVector::FeatureVector ( kkint32 _numOfFeatures )

Definition at line 30 of file FeatureVector.cpp.

References AllocateFeatureDataArray(), featureData, KKB::KKStr::KKStr(), and numOfFeatures.

Referenced by KKMLL::GrayScaleImagesFVProducer::ComputeFeatureVector(), KKMLL::FeatureEncoder::CreateEncodedFeatureVector(), KKMLL::FeatureEncoder2::EncodeAExample(), KKMLL::FeatureEncoder::EncodeAExample(), KKMLL::GrayScaleImagesFV::GrayScaleImagesFV(), KKMLL::FeatureFileIODstWeb::LoadFile(), KKMLL::FeatureFileIOColumn::LoadFile(), KKMLL::FeatureFileIOUCI::LoadFile(), KKMLL::FeatureFileIOC45::LoadFile(), and KKMLL::FactoryFVProducer::ManufacturFeatureVector().

                                                     :
         featureData      (NULL),
         numOfFeatures    (_numOfFeatures),
         breakTie         (0.0f),
         mlClass          (NULL),
         exampleFileName  (),
         missingData      (false),
         origSize         (0.0f),
         predictedClass   (NULL),
         probability      (-1.0),
         trainWeight      (1.0f),
         validated        (false),
         version          (-1)
 {
   AllocateFeatureDataArray ();
 }

FeatureVector::FeatureVector ( const FeatureVector & _example )

Definition at line 50 of file FeatureVector.cpp.

References AllocateFeatureDataArray(), featureData, KKB::KKStr::KKStr(), and numOfFeatures.

Referenced by Duplicate(), KKMLL::FeatureVectorList::DuplicateListAndContents(), KKMLL::FeatureVectorList::ExtractExamplesForHierarchyLevel(), KKMLL::GrayScaleImagesFV::GrayScaleImagesFV(), and KKMLL::NormalizationParms::ToNormalized().

                                                            :
   featureData      (NULL),
   numOfFeatures    (_example.numOfFeatures),
   breakTie         (_example.breakTie),
   mlClass          (_example.mlClass),
   exampleFileName  (_example.exampleFileName),
   missingData      (false),
   origSize         (_example.origSize),
   predictedClass   (_example.predictedClass),
   probability      (_example.probability),
   trainWeight      (_example.trainWeight),
   validated        (_example.validated),
   version          (-1)
 {
   if  (_example.featureData)
   {
     AllocateFeatureDataArray ();
     for  (kkint32 x = 0; x < numOfFeatures; x++)
       featureData[x] = _example.featureData[x];
   }
 }

FeatureVector::~FeatureVector ( )

virtual

Definition at line 74 of file FeatureVector.cpp.

References featureData.

 {
   delete[] featureData;  featureData = NULL;
 }

Member Function Documentation

void FeatureVector::AddFeatureData	(	kkint32	_featureNum,
		float	_featureData
	)

Parameters

_featureNum	Indicates which feature number to update.
_featureData	New value to assign to '_featureNum'.

Definition at line 216 of file FeatureVector.cpp.

References KKB::KKStr::Concat(), featureData, KKB::KKException::KKException(), KKB::KKStr::KKStr(), and numOfFeatures.

Referenced by KKMLL::FeatureEncoder::CreateEncodedFeatureVector(), KKMLL::FeatureEncoder2::EncodeAExample(), KKMLL::FeatureEncoder::EncodeAExample(), KKMLL::FeatureFileIODstWeb::LoadFile(), KKMLL::FeatureFileIOC45::LoadFile(), and KKMLL::FeatureVectorList::ReSyncSymbolicData().

 {
   if  ((_featureNum < 0)  ||  (_featureNum >= numOfFeatures))
   {
     KKStr errMsg (128);
     errMsg << "FeatureVector::AddFeatureData   ***ERROR***   FeatureNum[" << _featureNum << "] exceeds maximum allowed Feature Number["<< numOfFeatures << "].";
     cerr << endl << errMsg << endl << endl;
     throw KKException (errMsg);
   }
 
   featureData[_featureNum] = _featureData;
 }  /* AddFeatureData */

void FeatureVector::AllocateFeatureDataArray ( )

protected

Used by container classes such as 'FeatureVectorList'. This way they can determine real underlying class.

Definition at line 129 of file FeatureVector.cpp.

References featureData, and numOfFeatures.

Referenced by FeatureVector().

 {
   if  (featureData)
     delete  featureData;
 
   featureData = new float [numOfFeatures];
 
   kkint32  x;
 
   for  (x = 0; x < numOfFeatures; x++)
     featureData[x] = 0;
 }  /* AllocateFeatureDataArray */

void KKMLL::FeatureVector::BreakTie ( float _breakTie )

inline

Update the BreakTie value.

Definition at line 73 of file FeatureVector.h.

float KKMLL::FeatureVector::BreakTie ( ) const

inline

The difference in probability between the two most likely classes.

Definition at line 112 of file FeatureVector.h.

Referenced by KKMLL::FeatureVectorList::BreakTieComparison::operator()(), and KKMLL::FeatureVectorList::BreakTieComparisonReversed::operator()().

const KKStr & FeatureVector::ClassName ( ) const

Name of class that this example is assigned to.

Definition at line 192 of file FeatureVector.cpp.

References KKB::KKStr::Concat(), and KKMLL::MLClass::Name().

Referenced by KKMLL::FeatureFileIORoberts::SaveFile(), KKMLL::FeatureFileIOUCI::SaveFile(), KKMLL::FeatureFileIOC45::SaveFile(), and KKMLL::FeatureFileIOSparse::SaveFile().

 {
   static  const
   KKStr  imageFeaturesClassNameInvalid = "*INVALID*";
 
   if  (!mlClass)
   {
     cerr << endl
          << "FeatureVector::ClassName   *** ERROR ***" << endl
          << endl
          << "               Attempt to get ClassName,  But not pointing to valid MLClass Object" << endl
          << endl;
     return  imageFeaturesClassNameInvalid;
   }
   else
   {
     return  mlClass->Name ();
   }
 }  /* ClassName */

FeatureVectorPtr FeatureVector::Duplicate ( ) const

virtual

Reimplemented in KKMLL::GrayScaleImagesFV.

Definition at line 94 of file FeatureVector.cpp.

References FeatureVector().

 {
   return new FeatureVector (*this);
 }

void KKMLL::FeatureVector::ExampleFileName ( const KKStr & _exampleFileName )

inline

Name of source of feature vector, ex: file name of image that the feature vector was computed from.

Definition at line 75 of file FeatureVector.h.

References KKB::KKStr::operator=().

Referenced by KKMLL::FeatureFileIO::FeatureDataReSink(), KKMLL::FeatureFileIODstWeb::LoadFile(), KKMLL::FeatureFileIOColumn::LoadFile(), KKMLL::FeatureFileIOUCI::LoadFile(), KKMLL::FeatureFileIOC45::LoadFile(), and KKMLL::FeatureFileIO::LoadInSubDirectoryTree().

const KKStr& KKMLL::FeatureVector::ExampleFileName ( ) const

inline

Name of file that this FeatureVector was computed from.

Definition at line 116 of file FeatureVector.h.

void FeatureVector::FeatureData	(	kkint32	_featureNum,
		float	_featureValue
	)

Assign a value to a specific feature number for the feature vector.

This method will validate that '_featureNum' is not out of range (0 - 'numOfFeatures'). This will prevent the caller from corrupting memory.

Parameters

[in]	_featureNum	Feature Number to assign '_featureValue' to.
[in]	_featureValue	Value to assign to feature '_featureNum'.

Definition at line 161 of file FeatureVector.cpp.

References featureData, and NumOfFeatures().

Referenced by KKMLL::GrayScaleImagesFVList::RecalcFeatureValuesFromImagesInDirTree().

 {
   if  ((_featureNum < 0)  ||  (_featureNum >= NumOfFeatures ()))
   {
     cerr << endl
          << endl
          << "*** ERROR ***   FeatureVector::FeatureData(" << _featureNum << ")  Index out of bounds,    no value set." << endl
          << endl;
     return;
   }
 
   featureData[_featureNum] = _featureValue;
 }  /* FeatureData */

float FeatureVector::FeatureData ( kkint32 featureNum ) const

Returns: The value of 'featureNum'

Definition at line 145 of file FeatureVector.cpp.

References featureData, and NumOfFeatures().

Referenced by KKMLL::FeatureVectorList::CalcStatsForFeatureNum(), KKMLL::GrayScaleImagesFVList::RecalcFeatureValuesFromImagesInDirTree(), KKMLL::FeatureVectorList::ReSyncSymbolicData(), KKMLL::FeatureFileIORoberts::SaveFile(), KKMLL::FeatureFileIOC45::SaveFile(), KKMLL::FeatureFileIOSparse::SaveFile(), and SVM289_MFS::Svm_Model::WriteXML().

 {
   if  ((featureNum < 0)  ||  (featureNum >= NumOfFeatures ()))
   {
     cerr << endl
          << "*** ERROR ***   FeatureVector::FeatureData(" << featureNum << ")  Index out of bounds." << endl
          << endl;
     return 0.0f;
   }
 
   return featureData[featureNum];
 }  /* FeatureData */

const float* KKMLL::FeatureVector::FeatureData ( ) const

inline

Returns as a pointer to the feature data itself.

Definition at line 128 of file FeatureVector.h.

References featureData.

Referenced by SVM289_MFS::Kernel::DotStatic(), SVM289_BFS::Kernel::DotStatic(), KKMLL::FeatureEncoder2::EncodeAExample(), KKMLL::FeatureEncoder::EncodeAExample(), SVM289_MFS::Kernel::k_function(), and SVM289_BFS::Kernel::k_function().

float* KKMLL::FeatureVector::FeatureDataAlter ( )

inline

Same as 'FeatureData() except you can modify the data.

Definition at line 129 of file FeatureVector.h.

References featureData.

Referenced by KKMLL::GrayScaleImagesFVProducer::ComputeFeatureVector(), KKMLL::NormalizationParms::NormalizeAExample(), and KKMLL::NormalizationParms::ToNormalized().

const float* KKMLL::FeatureVector::FeatureDataConst ( ) const

inline

Definition at line 131 of file FeatureVector.h.

References featureData.

Referenced by KKMLL::ImageFeaturesNodeKey::CompareTwoExamples(), and KKMLL::FeatureVectorList::ExtractMeanFeatureValues().

131 {return featureData;}

KKMLL::FeatureVector::featureData

float * featureData

Definition: FeatureVector.h:150

bool FeatureVector::FeatureDataValid ( )

Definition at line 234 of file FeatureVector.cpp.

References featureData, KKB::FloatMax, KKB::FloatMin, and numOfFeatures.

 {
   kkint32  featureNum;
 
   for  (featureNum = 0;  featureNum < numOfFeatures;  featureNum++)
   {
     if  ((featureData[featureNum] == KKB::FloatMin)  ||  (featureData[featureNum] == KKB::FloatMax))
       return  false;
   }
 
   return true;
 }  /* FeatureDataValid */

kkint32 FeatureVector::MemoryConsumedEstimated ( ) const

virtual

Definition at line 81 of file FeatureVector.cpp.

References featureData, KKB::KKStr::MemoryConsumedEstimated(), and numOfFeatures.

Referenced by KKMLL::FeatureVectorList::MemoryConsumedEstimated().

 {
   kkint32  memoryConsumedEstimated = sizeof (FeatureVector)
     +  exampleFileName.MemoryConsumedEstimated ();
 
   if  (featureData)
     memoryConsumedEstimated += sizeof (float) * numOfFeatures;
   
   return  memoryConsumedEstimated;
 }  /* MemoryConsumedEstimated */

void KKMLL::FeatureVector::MissingData ( bool _missingData )

inline

True indicates that not all the feature data was present when this example was loaded from a data file.

Definition at line 76 of file FeatureVector.h.

Referenced by KKMLL::FeatureFileIOC45::LoadFile().

bool KKMLL::FeatureVector::MissingData ( ) const

inline

True indicates that one or more features were missing.

Definition at line 117 of file FeatureVector.h.

Referenced by KKMLL::FeatureVectorList::MissingData().

void KKMLL::FeatureVector::MLClass ( MLClassPtr _mlClass )

inline

Assign a class to this example.

Definition at line 74 of file FeatureVector.h.

Referenced by KKMLL::Classifier2::ClassifyAExample(), KKMLL::GrayScaleImagesFVProducer::ComputeFeatureVector(), KKMLL::FeatureEncoder::CreateEncodedFeatureVector(), KKMLL::FeatureEncoder2::EncodeAExample(), KKMLL::FeatureEncoder::EncodeAExample(), KKMLL::FeatureEncoder2::EncodedFeatureVectorList(), KKMLL::FeatureVectorList::ExtractExamplesForHierarchyLevel(), KKMLL::FeatureFileIO::FeatureDataReSink(), KKMLL::FeatureFileIODstWeb::LoadFile(), KKMLL::FeatureFileIOColumn::LoadFile(), KKMLL::FeatureFileIOUCI::LoadFile(), and KKMLL::FeatureFileIOC45::LoadFile().

MLClassPtr KKMLL::FeatureVector::MLClass ( ) const

inline

Class that is example is assigned to.

Definition at line 114 of file FeatureVector.h.

Referenced by KKMLL::Classifier2::ClassifyAExample(), KKMLL::FeatureEncoder::CreateEncodedFeatureVector(), KKMLL::FeatureEncoder2::EncodeAExample(), KKMLL::FeatureEncoder::EncodeAExample(), KKMLL::FeatureEncoder2::EncodedFeatureVectorList(), KKMLL::FeatureEncoder::EncodeIntoSparseMatrix(), KKMLL::FeatureVectorList::ExtractExamplesForAGivenClass(), KKMLL::FeatureVectorList::ExtractExamplesForHierarchyLevel(), KKMLL::FeatureVectorList::ExtractListOfClasses(), KKMLL::FeatureFileIO::FeatureDataReSink(), KKMLL::FeatureVectorList::GetClassStatistics(), KKMLL::FeatureVectorList::ClassNameComparrison::operator()(), KKMLL::FeatureVectorList::ClassNameComparrisonReversed::operator()(), and KKMLL::GrayScaleImagesFVList::RecalcFeatureValuesFromImagesInDirTree().

const KKStr & FeatureVector::MLClassName ( ) const

Name of class that this example is assigned to.

Definition at line 262 of file FeatureVector.cpp.

References KKB::KKStr::Concat(), KKB::KKStr::EmptyStr(), and KKMLL::MLClass::Name().

 {
   if  (mlClass)
     return mlClass->Name ();
   else
     return KKStr::EmptyStr ();
 }  /* MLClassName */

kkint32 KKMLL::FeatureVector::NumOfFeatures ( ) const

inline

Number of features in this FeatureVector.

Definition at line 118 of file FeatureVector.h.

References numOfFeatures.

Referenced by KKMLL::ImageFeaturesNodeKey::CompareTwoExamples(), FeatureData(), KKMLL::FeatureVectorList::PushOnBack(), KKMLL::FeatureVectorList::PushOnFront(), TotalOfFeatureData(), and SVM289_MFS::Svm_Model::WriteXML().

bool FeatureVector::operator== ( FeatureVector & other_example ) const

Definition at line 273 of file FeatureVector.cpp.

References featureData, and numOfFeatures.

 {
   if (numOfFeatures != other_image.numOfFeatures)
     return false;
 
   for (kkint32 i = 0; i < numOfFeatures; i++)
   {
     if (featureData[i] != other_image.featureData[i])
     {
       return false;
     }
   }
 
   return true;
 }  /* operator== */

void KKMLL::FeatureVector::OrigSize ( float _origSize )

inline

The value of Feature[0] before normalization.

Definition at line 77 of file FeatureVector.h.

Referenced by KKMLL::GrayScaleImagesFVProducer::ComputeFeatureVector(), and KKMLL::GrayScaleImagesFVList::RecalcFeatureValuesFromImagesInDirTree().

float KKMLL::FeatureVector::OrigSize ( ) const

inline

The value of Feature[0] before normalization.

Definition at line 119 of file FeatureVector.h.

Referenced by KKMLL::FeatureVectorList::ExtractExamplesForAGivenClass(), and KKMLL::GrayScaleImagesFVList::RecalcFeatureValuesFromImagesInDirTree().

void KKMLL::FeatureVector::PredictedClass ( MLClassPtr _predictedClass )

inline

Definition at line 78 of file FeatureVector.h.

Referenced by KKMLL::FeatureEncoder2::EncodeAExample(), and KKMLL::FeatureEncoder::EncodeAExample().

78 {predictedClass = _predictedClass;}

MLClassPtr KKMLL::FeatureVector::PredictedClass ( ) const

inline

Definition at line 120 of file FeatureVector.h.

Referenced by KKMLL::FeatureEncoder2::EncodeAExample(), and KKMLL::FeatureEncoder::EncodeAExample().

120 {return predictedClass;}

const KKStr & FeatureVector::PredictedClassName ( ) const

Definition at line 251 of file FeatureVector.cpp.

References KKB::KKStr::Concat(), KKB::KKStr::EmptyStr(), and KKMLL::MLClass::Name().

 {
   if  (predictedClass)
     return  predictedClass->Name ();
   else
     return  KKStr::EmptyStr ();
 }  /* PredictedClasseName */

void KKMLL::FeatureVector::Probability ( float _probability )

inline

Assign a prediction probability to this example.

Definition at line 79 of file FeatureVector.h.

float KKMLL::FeatureVector::Probability ( ) const

inline

The probability assigned by classifier to the predicted class.

Definition at line 122 of file FeatureVector.h.

Referenced by KKMLL::FeatureVectorList::ProbabilityComparison::operator()(), and KKMLL::FeatureVectorList::ProbabilityComparisonReversed::operator()().

void FeatureVector::ResetNumOfFeatures ( kkint32 newNumOfFeatures )

Used to reallocate memory for feature data.

Definition at line 102 of file FeatureVector.cpp.

References KKB::KKStr::Concat(), featureData, KKB::KKException::KKException(), KKB::KKStr::KKStr(), and numOfFeatures.

Referenced by KKMLL::FeatureVectorList::ResetFileDesc(), and KKMLL::FeatureVectorList::ResetNumOfFeaturs().

 {
   if  (newNumOfFeatures < 1)  
   {
     KKStr errMsg (128);
     errMsg << "FeatureVector::ResetNumOfFeatures   ***ERROR***   NewNumOfFeatures[" << newNumOfFeatures << "] is invalid.";
     cerr << endl << errMsg << endl << endl;
     throw KKException (errMsg);
   }
 
   kkint32  x;
   float*  newFeatureData = new float[newNumOfFeatures];
   for  (x = 0;  x < newNumOfFeatures;  x++)
   {
     if  (x < numOfFeatures)
       newFeatureData[x] = featureData[x];
     else
       newFeatureData[x] = 0.0f;
   }
 
   delete  featureData;
   featureData   = newFeatureData;
   numOfFeatures = newNumOfFeatures;
 }  /* ResetNumOfFeatures */

float FeatureVector::TotalOfFeatureData ( ) const

Returns the total of all Feature Attributes for this feature vector.

Definition at line 180 of file FeatureVector.cpp.

References featureData, and NumOfFeatures().

 {
   float  totalOfFeatureData = 0.0f;
   for  (int x = 0;  x < NumOfFeatures ();  ++x)
     totalOfFeatureData += featureData[x];
   return  totalOfFeatureData;
 }

void KKMLL::FeatureVector::TrainWeight ( float _trainWeight )

inline

Assign a specific example a higher weight for training purposes.

The SVM will multiply the cost parameter by this amount when training the classifier for this specific example.

Definition at line 105 of file FeatureVector.h.

Referenced by KKMLL::FeatureEncoder2::EncodeAExample(), and KKMLL::FeatureEncoder::EncodeAExample().

105 {trainWeight = _trainWeight;}

float KKMLL::FeatureVector::TrainWeight ( ) const

inline

Definition at line 123 of file FeatureVector.h.

Referenced by KKMLL::FeatureEncoder2::EncodeAExample(), KKMLL::FeatureEncoder::EncodeAExample(), and KKMLL::FeatureEncoder::EncodeIntoSparseMatrix().

123 {return trainWeight;}

void KKMLL::FeatureVector::Validated ( bool _validated )

inline

Indicated whether an expert has validated the class assignment.

Definition at line 109 of file FeatureVector.h.

109 {validated = _validated;}

bool KKMLL::FeatureVector::Validated ( ) const

inline

Definition at line 124 of file FeatureVector.h.

124 {return validated;}

void KKMLL::FeatureVector::Version ( kkint16 _version )

inline

Definition at line 80 of file FeatureVector.h.

Referenced by KKMLL::GrayScaleImagesFVProducer::ComputeFeatureVector(), and KKMLL::GrayScaleImagesFVList::RecalcFeatureValuesFromImagesInDirTree().

80 {version = _version;}

kkint16 KKMLL::FeatureVector::Version ( ) const

inline

Definition at line 125 of file FeatureVector.h.

Referenced by KKMLL::GrayScaleImagesFVList::RecalcFeatureValuesFromImagesInDirTree().

125 {return version;}

Member Data Documentation

float* KKMLL::FeatureVector::featureData

protected

Definition at line 150 of file FeatureVector.h.

Referenced by AddFeatureData(), AllocateFeatureDataArray(), FeatureData(), FeatureDataAlter(), FeatureDataConst(), FeatureDataValid(), FeatureVector(), MemoryConsumedEstimated(), operator==(), ResetNumOfFeatures(), TotalOfFeatureData(), and ~FeatureVector().

kkint32 KKMLL::FeatureVector::numOfFeatures

protected

Definition at line 151 of file FeatureVector.h.

Referenced by AddFeatureData(), AllocateFeatureDataArray(), FeatureDataValid(), FeatureVector(), MemoryConsumedEstimated(), NumOfFeatures(), operator==(), and ResetNumOfFeatures().

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

C:/Users/Kurt/GitHub/KSquareLibraries/KKMachineLearning/FeatureVector.h
C:/Users/Kurt/GitHub/KSquareLibraries/KKMachineLearning/FeatureVector.cpp

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation