Inheritance diagram for KNN:

Public Types
enum	DistanceMethods { EUCLIDEAN_DISTANCE =0, COSINE_DISTANCE, MANHATTAN_DISTANCE }

Public Types inherited from Classifier
enum	ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }

typedef std::map< std::string, Classifier ()() >	StringClassifierMap

Public Types inherited from MLBase
enum	BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }

Public Member Functions
	KNN (UINT K=10, bool useScaling=false, bool useNullRejection=false, Float nullRejectionCoeff=10.0, bool searchForBestKValue=false, UINT minKSearchValue=1, UINT maxKSearchValue=10)

	KNN (const KNN &rhs)

virtual	~KNN (void)

KNN &	operator= (const KNN &rhs)

virtual bool	deepCopyFrom (const Classifier *classifier)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	clear ()

virtual bool	save (std::fstream &file) const

virtual bool	load (std::fstream &file)

virtual bool	recomputeNullRejectionThresholds ()

UINT	getK ()

UINT	getDistanceMethod ()

bool	setK (UINT K)

bool	setMinKSearchValue (UINT minKSearchValue)

bool	setMaxKSearchValue (UINT maxKSearchValue)

bool	enableBestKValueSearch (bool searchForBestKValue)

bool	setNullRejectionCoeff (Float nullRejectionCoeff)

bool	setDistanceMethod (UINT distanceMethod)

Public Member Functions inherited from Classifier
	Classifier (void)

virtual	~Classifier (void)

bool	copyBaseVariables (const Classifier *classifier)

virtual bool	reset ()

std::string	getClassifierType () const

bool	getSupportsNullRejection () const

bool	getNullRejectionEnabled () const

Float	getNullRejectionCoeff () const

Float	getMaximumLikelihood () const

Float	getBestDistance () const

Float	getPhase () const

virtual UINT	getNumClasses () const

UINT	getClassLabelIndexValue (UINT classLabel) const

UINT	getPredictedClassLabel () const

VectorFloat	getClassLikelihoods () const

VectorFloat	getClassDistances () const

VectorFloat	getNullRejectionThresholds () const

Vector< UINT >	getClassLabels () const

Vector< MinMax >	getRanges () const

bool	enableNullRejection (bool useNullRejection)

virtual bool	setNullRejectionThresholds (VectorFloat newRejectionThresholds)

bool	getTimeseriesCompatible () const

Classifier *	createNewInstance () const

Classifier *	deepCopy () const

const Classifier *	getClassifierPointer () const

const Classifier &	getBaseClassifier () const

Public Member Functions inherited from MLBase
	MLBase (void)

virtual	~MLBase (void)

bool	copyMLBaseVariables (const MLBase *mlBase)

virtual bool	train (ClassificationData trainingData)

virtual bool	train (RegressionData trainingData)

virtual bool	train_ (RegressionData &trainingData)

virtual bool	train (TimeSeriesClassificationData trainingData)

virtual bool	train_ (TimeSeriesClassificationData &trainingData)

virtual bool	train (ClassificationDataStream trainingData)

virtual bool	train_ (ClassificationDataStream &trainingData)

virtual bool	train (UnlabelledData trainingData)

virtual bool	train_ (UnlabelledData &trainingData)

virtual bool	train (MatrixFloat data)

virtual bool	train_ (MatrixFloat &data)

virtual bool	predict (VectorFloat inputVector)

virtual bool	predict (MatrixFloat inputMatrix)

virtual bool	predict_ (MatrixFloat &inputMatrix)

virtual bool	map (VectorFloat inputVector)

virtual bool	map_ (VectorFloat &inputVector)

virtual bool	print () const

virtual bool	save (const std::string filename) const

virtual bool	load (const std::string filename)

	GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(std::string filename) instead", virtual bool saveModelToFile(std::string filename) const )

	GRT_DEPRECATED_MSG ("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const )

	GRT_DEPRECATED_MSG ("loadModelFromFile(std::string filename) is deprecated, use load(std::string filename) instead", virtual bool loadModelFromFile(std::string filename))

	GRT_DEPRECATED_MSG ("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file))

virtual bool	getModel (std::ostream &stream) const

Float	scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)

virtual std::string	getModelAsString () const

DataType	getInputType () const

DataType	getOutputType () const

UINT	getBaseType () const

UINT	getNumInputFeatures () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

UINT	getMinNumEpochs () const

UINT	getMaxNumEpochs () const

UINT	getValidationSetSize () const

UINT	getNumTrainingIterationsToConverge () const

Float	getMinChange () const

Float	getLearningRate () const

Float	getRootMeanSquaredTrainingError () const

Float	getTotalSquaredTrainingError () const

Float	getValidationSetAccuracy () const

VectorFloat	getValidationSetPrecision () const

VectorFloat	getValidationSetRecall () const

bool	getUseValidationSet () const

bool	getRandomiseTrainingOrder () const

bool	getTrained () const

bool	getModelTrained () const

bool	getScalingEnabled () const

bool	getIsBaseTypeClassifier () const

bool	getIsBaseTypeRegressifier () const

bool	getIsBaseTypeClusterer () const

bool	enableScaling (const bool useScaling)

bool	setMaxNumEpochs (const UINT maxNumEpochs)

bool	setMinNumEpochs (const UINT minNumEpochs)

bool	setMinChange (const Float minChange)

bool	setLearningRate (const Float learningRate)

bool	setUseValidationSet (const bool useValidationSet)

bool	setValidationSetSize (const UINT validationSetSize)

bool	setRandomiseTrainingOrder (const bool randomiseTrainingOrder)

bool	setTrainingLoggingEnabled (const bool loggingEnabled)

bool	registerTrainingResultsObserver (Observer< TrainingResult > &observer)

bool	registerTestResultsObserver (Observer< TestInstanceResult > &observer)

bool	removeTrainingResultsObserver (const Observer< TrainingResult > &observer)

bool	removeTestResultsObserver (const Observer< TestInstanceResult > &observer)

bool	removeAllTrainingObservers ()

bool	removeAllTestObservers ()

bool	notifyTrainingResultsObservers (const TrainingResult &data)

bool	notifyTestResultsObservers (const TestInstanceResult &data)

MLBase *	getMLBasePointer ()

const MLBase *	getMLBasePointer () const

Vector< TrainingResult >	getTrainingResults () const

Public Member Functions inherited from GRTBase
	GRTBase (void)

virtual	~GRTBase (void)

bool	copyGRTBaseVariables (const GRTBase *GRTBase)

std::string	getClassType () const

std::string	getLastWarningMessage () const

std::string	getLastErrorMessage () const

std::string	getLastInfoMessage () const

bool	setInfoLoggingEnabled (const bool loggingEnabled)

bool	setWarningLoggingEnabled (const bool loggingEnabled)

bool	setErrorLoggingEnabled (const bool loggingEnabled)

GRTBase *	getGRTBasePointer ()

const GRTBase *	getGRTBasePointer () const

Public Member Functions inherited from Observer< TrainingResult >
virtual void	notify (const TrainingResult &data)

Public Member Functions inherited from Observer< TestInstanceResult >
virtual void	notify (const TestInstanceResult &data)

Protected Member Functions
bool	train_ (const ClassificationData &trainingData, const UINT K)

bool	predict (const VectorFloat &inputVector, const UINT K)

bool	loadLegacyModelFromFile (std::fstream &file)

Float	computeEuclideanDistance (const VectorFloat &a, const VectorFloat &b)

Float	computeCosineDistance (const VectorFloat &a, const VectorFloat &b)

Float	computeManhattanDistance (const VectorFloat &a, const VectorFloat &b)

Protected Member Functions inherited from Classifier
bool	saveBaseSettingsToFile (std::fstream &file) const

bool	loadBaseSettingsFromFile (std::fstream &file)

Protected Member Functions inherited from MLBase
bool	saveBaseSettingsToFile (std::fstream &file) const

bool	loadBaseSettingsFromFile (std::fstream &file)

Protected Member Functions inherited from GRTBase
Float	SQR (const Float &x) const

Protected Attributes
UINT	K

UINT	distanceMethod
	The number of neighbours to search for

bool	searchForBestKValue
	The distance method used to compute the distance between each data point

UINT	minKSearchValue
	Sets if the best K value should be searched for or if the model should be trained with K

UINT	maxKSearchValue
	The minimum K value to start the search from

ClassificationData	trainingData
	The maximum K value to end the search at

VectorFloat	trainingMu
	Holds the trainingData to perform the predictions

VectorFloat	trainingSigma
	Holds the average max-class distance of the training data for each of classes

Protected Attributes inherited from Classifier
std::string	classifierType

bool	supportsNullRejection

bool	useNullRejection

UINT	numClasses

UINT	predictedClassLabel

UINT	classifierMode

Float	nullRejectionCoeff

Float	maxLikelihood

Float	bestDistance

Float	phase

VectorFloat	classLikelihoods

VectorFloat	classDistances

VectorFloat	nullRejectionThresholds

Vector< UINT >	classLabels

Vector< MinMax >	ranges

Protected Attributes inherited from MLBase
bool	trained

bool	useScaling

DataType	inputType

DataType	outputType

UINT	baseType

UINT	numInputDimensions

UINT	numOutputDimensions

UINT	numTrainingIterationsToConverge

UINT	minNumEpochs

UINT	maxNumEpochs

UINT	validationSetSize

Float	learningRate

Float	minChange

Float	rootMeanSquaredTrainingError

Float	totalSquaredTrainingError

Float	validationSetAccuracy

bool	useValidationSet

bool	randomiseTrainingOrder

VectorFloat	validationSetPrecision

VectorFloat	validationSetRecall

Random	random

std::vector< TrainingResult >	trainingResults

TrainingResultsObserverManager	trainingResultsObserverManager

TestResultsObserverManager	testResultsObserverManager

Protected Attributes inherited from GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

WarningLog	warningLog

Static Protected Attributes
static RegisterClassifierModule< KNN >	registerModule
	Holds the stddev of the max-class distance of the training data for each of classes

Additional Inherited Members
Static Public Member Functions inherited from Classifier
static Classifier *	createInstanceFromString (std::string const &classifierType)

static Vector< std::string >	getRegisteredClassifiers ()

Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)

static std::string	getGRTRevison ()

Static Protected Member Functions inherited from Classifier
static StringClassifierMap *	getMap ()

Detailed Description

Definition at line 51 of file KNN.h.

Constructor & Destructor Documentation

KNN::KNN	(	UINT	K = `10`,
		bool	useScaling = `false`,
		bool	useNullRejection = `false`,
		Float	nullRejectionCoeff = `10.0`,
		bool	searchForBestKValue = `false`,
		UINT	minKSearchValue = `1`,
		UINT	maxKSearchValue = `10`
	)

Default Constructor

Parameters

K	the number of neigbors the algorithm will us to perform a classification. Default value is K = 10
useScaling	sets if the training and prediction data should be scaled to a specific range. Default value is useScaling = false
useNullRejection	sets if null rejection will be used for the realtime prediction. If useNullRejection is set to true then the predictedClassLabel will be set to 0 (which is the default null label) if the distance between the inputVector and the top K datum is greater than the null rejection threshold for the top predicted class. The null rejection threshold is computed for each class during the training phase. Default value is useNullRejection = false
nullRejectionCoeff	sets the null rejection coefficient, this is a multipler controlling the null rejection threshold for each class. This will only be used if the useNullRejection parameter is set to true. Default value is nullRejectionCoeff = 10.0
searchForBestKValue	sets if the training algorithm will search for the best K value. Default value is searchForBestKValue = false
minKSearchValue	sets the minimum K value to use when searching for the best K value. Default value is minKSearchValue = 1
maxKSearchValue	sets the maximum K value to use when searching for the best K value. Default value is maxKSearchValue = 1

KNN::KNN ( const KNN & rhs )

Defines the copy constructor.

Parameters

const KNN &rhs: the instance from which all the data will be copied into this instance

Definition at line 49 of file KNN.cpp.

KNN::~KNN ( void )

virtual

Default Destructor

Definition at line 60 of file KNN.cpp.

Member Function Documentation

bool KNN::clear ( )

virtual

This overrides the clear function in the Classifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.

Returns: returns true if the module was cleared succesfully, false otherwise

Reimplemented from Classifier.

Definition at line 464 of file KNN.cpp.

bool KNN::deepCopyFrom ( const Classifier * classifier )

virtual

This is required for the Gesture Recognition Pipeline for when the pipeline.setClassifier method is called. It clones the data from the Base Class Classifier pointer (which should be pointing to a KNN instance) into this instance

Parameters

classifier a pointer to the Classifier Base Class, this should be pointing to another KNN instance

Returns: returns true if the clone was successfull, false otherwise

Reimplemented from Classifier.

Definition at line 82 of file KNN.cpp.

bool KNN::enableBestKValueSearch ( bool searchForBestKValue )

Sets if the best K value should be searched for. If true then the best K value will be searched during the training phase. If false then the KNN algorithm will be trained with the K value set by the user.

Returns: returns true if the searchForBestKValue was set successfully, false otherwise

Definition at line 701 of file KNN.cpp.

UINT KNN::getDistanceMethod ( )

inline

Returns the current distance method being used to compute the neighest neighbours. See the enum DistanceMethods.

Returns: returns the current distance method being used to compute the neighest neighbours

Definition at line 162 of file KNN.h.

UINT KNN::getK ( )

inline

Gets the K nearest neighbours that will be searched for by the algorithm during prediction

Returns: returns the K nearest neighbours that will be searched for by the algorithm during prediction

Definition at line 154 of file KNN.h.

bool KNN::load ( std::fstream & file )

virtual

This loads a trained KNN model from a file. This overrides the load function in the Classifier base class.

Parameters

file	a reference to the file the KNN model will be loaded from

Returns: returns true if the model was loaded successfully, false otherwise

Reimplemented from MLBase.

Definition at line 530 of file KNN.cpp.

bool KNN::loadLegacyModelFromFile ( std::fstream & file )

protected

Read the ranges if needed

Definition at line 759 of file KNN.cpp.

KNN & KNN::operator= ( const KNN & rhs )

Defines how the data from the rhs KNN should be copied to this KNN

Parameters

rhs	another instance of a KNN

Returns: returns a pointer to this instance of the KNN

Definition at line 64 of file KNN.cpp.

bool KNN::predict_ ( VectorFloat & inputVector )

virtual

This predicts the class of the inputVector. This overrides the predict function in the Classifier base class.

Parameters

inputVector the input vector to classify

Returns: returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 315 of file KNN.cpp.

bool KNN::recomputeNullRejectionThresholds ( )

virtual

This recomputes the null rejection thresholds for each of the classes in the KNN model. This will be called automatically if the setGamma(Float gamma) function is called. The KNN model needs to be trained first before this function can be called.

Returns: returns true if the null rejection thresholds were updated successfully, false otherwise

Reimplemented from Classifier.

Definition at line 664 of file KNN.cpp.

bool KNN::save ( std::fstream & file ) const

virtual

This saves the trained KNN model to a file. This overrides the save function in the Classifier base class.

Parameters

file	a reference to the file the KNN model will be saved to

Returns: returns true if the model was saved successfully, false otherwise

Reimplemented from MLBase.

Definition at line 477 of file KNN.cpp.

bool KNN::setDistanceMethod ( UINT distanceMethod )

Sets the current distance method being used to compute the neighest neighbours. This should be called prior to training a KNN model. See the enum DistanceMethods for a list of possible distance methods.

Returns: returns true if the distance method was updated successfully, false otherwise

Definition at line 715 of file KNN.cpp.

bool KNN::setK ( UINT K )

Sets the K nearest neighbours that will be searched for by the algorithm during prediction. This function should be called prior to running the training algorithm.

Returns: returns true if the K was set successfully, false otherwise

Definition at line 683 of file KNN.cpp.

bool KNN::setMaxKSearchValue ( UINT maxKSearchValue )

Sets the maximum K value to use when searching for the best K value. This value should be greater than the minKSearchValue.

Returns: returns true if the maxKSearchValue was set successfully, false otherwise

Definition at line 696 of file KNN.cpp.

bool KNN::setMinKSearchValue ( UINT minKSearchValue )

Sets the minimum K value to use when searching for the best K value. This value should be less than the maxKSearchValue.

Returns: returns true if the minimumKValue was set successfully, false otherwise

Definition at line 691 of file KNN.cpp.

bool KNN::setNullRejectionCoeff ( Float nullRejectionCoeff )

virtual

Sets the nullRejectionCoeff parameter. The nullRejectionCoeff parameter is a multipler controlling the null rejection threshold for each class. This function will also recompute the null rejection thresholds.

Returns: returns true if the nullRejectionCoeff parameter was updated successfully, false otherwise

Reimplemented from Classifier.

Definition at line 706 of file KNN.cpp.

bool KNN::train_ ( ClassificationData & trainingData )

virtual

This trains the KNN model, using the labelled classification data. This overrides the train function in the Classifier base class.

Parameters

trainingData a reference to the training data

Returns: returns true if the KNN model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 105 of file KNN.cpp.

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

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Static Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation