#include <ClusterTreeNode.h>

Inheritance diagram for ClusterTreeNode:

Public Member Functions
	ClusterTreeNode ()

virtual	~ClusterTreeNode ()

virtual bool	predict_ (VectorFloat &x) override

virtual bool	predict_ (VectorFloat &x, VectorFloat &y) override

virtual bool	clear () override

virtual bool	print () const override

virtual Node *	deepCopy () const override

ClusterTreeNode *	deepCopyTree () const

UINT	getNodeSize () const

UINT	getFeatureIndex () const

Float	getThreshold () const

UINT	getClusterLabel () const

bool	set (const UINT nodeSize, const UINT featureIndex, const Float threshold, const UINT clusterLabel)

Public Member Functions inherited from Node
	Node (const std::string id="Node")

virtual	~Node ()

virtual bool	computeFeatureWeights (VectorFloat &weights) const

virtual bool	computeLeafNodeWeights (MatrixFloat &weights) const

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

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

virtual bool	load (std::fstream &file) override

std::string	getNodeType () const

UINT	getDepth () const

UINT	getNodeID () const

UINT	getPredictedNodeID () const

UINT	getMaxDepth () const

bool	getIsLeafNode () const

bool	getHasParent () const

bool	getHasLeftChild () const

bool	getHasRightChild () const

Node *	getParent () const

Node *	getLeftChild () const

Node *	getRightChild () const

bool	initNode (Node *parent, const UINT depth, const UINT nodeID, const bool isLeafNode=false)

bool	setParent (Node *parent)

bool	setLeftChild (Node *leftChild)

bool	setRightChild (Node *rightChild)

bool	setDepth (const UINT depth)

bool	setNodeID (const UINT nodeID)

bool	setIsLeafNode (const bool isLeafNode)

Node *	createNewInstance () const

Public Member Functions inherited from MLBase
	MLBase (const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)

virtual	~MLBase (void)

bool	copyMLBaseVariables (const MLBase *mlBase)

virtual bool	train (ClassificationData trainingData)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	train (RegressionData trainingData)

virtual bool	train_ (RegressionData &trainingData)

virtual bool	train (RegressionData trainingData, RegressionData validationData)

virtual bool	train_ (RegressionData &trainingData, RegressionData &validationData)

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	reset ()

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(const std::string &filename) instead", virtual bool saveModelToFile(const 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(const std::string &filename) instead", virtual bool loadModelFromFile(const 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 std::string	getModelAsString () const

DataType	getInputType () const

DataType	getOutputType () const

BaseType	getType () const

UINT	getNumInputFeatures () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

UINT	getMinNumEpochs () const

UINT	getMaxNumEpochs () const

UINT	getBatchSize () const

UINT	getNumRestarts () const

UINT	getValidationSetSize () const

UINT	getNumTrainingIterationsToConverge () const

Float	getMinChange () const

Float	getLearningRate () const

Float	getRMSTrainingError () const

	GRT_DEPRECATED_MSG ("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )

Float	getTotalSquaredTrainingError () const

Float	getRMSValidationError () const

Float	getValidationSetAccuracy () const

VectorFloat	getValidationSetPrecision () const

VectorFloat	getValidationSetRecall () const

bool	getUseValidationSet () const

bool	getRandomiseTrainingOrder () const

bool	getTrained () const

	GRT_DEPRECATED_MSG ("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const )

bool	getConverged () const

bool	getScalingEnabled () const

bool	getIsBaseTypeClassifier () const

bool	getIsBaseTypeRegressifier () const

bool	getIsBaseTypeClusterer () const

bool	getTrainingLoggingEnabled () const

bool	getTestingLoggingEnabled () const

bool	enableScaling (const bool useScaling)

bool	setMaxNumEpochs (const UINT maxNumEpochs)

bool	setBatchSize (const UINT batchSize)

bool	setMinNumEpochs (const UINT minNumEpochs)

bool	setNumRestarts (const UINT numRestarts)

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	setTestingLoggingEnabled (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 (const std::string &id="")

virtual	~GRTBase (void)

bool	copyGRTBaseVariables (const GRTBase *GRTBase)

	GRT_DEPRECATED_MSG ("getClassType is deprecated, use getId() instead!", std::string getClassType() const )

std::string	getId () 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)

bool	setDebugLoggingEnabled (const bool loggingEnabled)

GRTBase *	getGRTBasePointer ()

const GRTBase *	getGRTBasePointer () const

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

Float	SQR (const Float &x) 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
virtual bool	saveParametersToFile (std::fstream &file) const override

virtual bool	loadParametersFromFile (std::fstream &file) override

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

bool	loadBaseSettingsFromFile (std::fstream &file)

Protected Attributes
UINT	clusterLabel

UINT	nodeSize

UINT	featureIndex

Float	threshold

Protected Attributes inherited from Node
std::string	nodeType

UINT	depth

UINT	nodeID

UINT	predictedNodeID

bool	isLeafNode

Node *	parent

Node *	leftChild

Node *	rightChild

Protected Attributes inherited from MLBase
bool	trained

bool	useScaling

bool	converged

DataType	inputType

DataType	outputType

BaseType	baseType

UINT	numInputDimensions

UINT	numOutputDimensions

UINT	numTrainingIterationsToConverge

UINT	minNumEpochs

UINT	maxNumEpochs

UINT	batchSize

UINT	validationSetSize

UINT	numRestarts

Float	learningRate

Float	minChange

Float	rmsTrainingError

Float	rmsValidationError

Float	totalSquaredTrainingError

Float	validationSetAccuracy

bool	useValidationSet

bool	randomiseTrainingOrder

VectorFloat	validationSetPrecision

VectorFloat	validationSetRecall

Random	random

Vector< TrainingResult >	trainingResults

TrainingResultsObserverManager	trainingResultsObserverManager

TestResultsObserverManager	testResultsObserverManager

TrainingLog	trainingLog

TestingLog	testingLog

Protected Attributes inherited from GRTBase
std::string	classId
	Stores the name of the class (e.g., MinDist)

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

WarningLog	warningLog

Static Protected Attributes
static RegisterNode< ClusterTreeNode >	registerModule

Additional Inherited Members
Public Types inherited from Node
typedef std::map< std::string, Node ()() >	StringNodeMap

Public Types inherited from MLBase
enum	BaseType { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER, PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT }

Static Public Member Functions inherited from Node
static Node *	createInstanceFromString (std::string const &nodeType)

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

static std::string	getGRTRevison ()

Static Protected Member Functions inherited from Node
static StringNodeMap *	getMap ()

Detailed Description

GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Definition at line 36 of file ClusterTreeNode.h.

Constructor & Destructor Documentation

ClusterTreeNode::ClusterTreeNode ( )

inline

Default Constructor. Sets all the pointers to NULL.

Definition at line 41 of file ClusterTreeNode.h.

virtual ClusterTreeNode::~ClusterTreeNode ( )

inlinevirtual

Default Destructor. Cleans up any memory.

Definition at line 52 of file ClusterTreeNode.h.

Member Function Documentation

virtual bool ClusterTreeNode::clear ( )

inlineoverridevirtual

This functions cleans up any dynamic memory assigned by the node. It will recursively clear the memory for the left and right child nodes.

Returns: returns true of the node was cleared correctly, false otherwise

Reimplemented from Node.

Definition at line 112 of file ClusterTreeNode.h.

virtual Node* ClusterTreeNode::deepCopy ( ) const

inlineoverridevirtual

This function returns a deep copy of the ClusterTreeNode and all it's children. The user is responsible for managing the dynamic data that is returned from this function as a pointer.

Returns: returns a pointer to a deep copy of the ClusterTreeNode, or NULL if the deep copy was not successful

Reimplemented from Node.

Definition at line 158 of file ClusterTreeNode.h.

UINT ClusterTreeNode::getClusterLabel ( ) const

inline

This function returns the cluster label.

Returns: returns the cluster label.

Definition at line 226 of file ClusterTreeNode.h.

UINT ClusterTreeNode::getFeatureIndex ( ) const

inline

This function returns the featureIndex, this is index in the input data that the decision threshold is computed on.

Returns: returns the featureIndex

Definition at line 208 of file ClusterTreeNode.h.

UINT ClusterTreeNode::getNodeSize ( ) const

inline

This function returns the nodeSize, this is the number of training samples that reached the node during the training phase.

Returns: returns the nodeSize

Definition at line 199 of file ClusterTreeNode.h.

Float ClusterTreeNode::getThreshold ( ) const

inline

This function returns the threshold, this is the value used to compute the decision threshold.

Returns: returns the threshold

Definition at line 217 of file ClusterTreeNode.h.

virtual bool ClusterTreeNode::loadParametersFromFile ( std::fstream & file )

inlineoverrideprotectedvirtual

This loads the ClusterTreeNode parameters from a file.

Parameters

file	a reference to the file the parameters will be loaded from

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

Reimplemented from Node.

Definition at line 278 of file ClusterTreeNode.h.

virtual bool ClusterTreeNode::predict_ ( VectorFloat & x )

inlineoverridevirtual

This function predicts if the input is greater than or equal to the nodes threshold. If the input is greater than or equal to the nodes threshold then this function will return true, otherwise it will return false.

NOTE: The threshold and featureIndex should be set first BEFORE this function is called. The threshold and featureIndex can be set by training the node through the DecisionTree class.

Parameters

x	the input Vector that will be used for the prediction

Returns: returns true if the input is greater than or equal to the nodes threshold, false otherwise

Reimplemented from Node.

Definition at line 66 of file ClusterTreeNode.h.

virtual bool ClusterTreeNode::predict_	(	VectorFloat &	x,
		VectorFloat &	y
	)

inlineoverridevirtual

This function recursively predicts if the probability of the input Vector. If this node is a leaf node, then the class likelihoods are equal to the class probabilities at the leaf node. If this node is not a leaf node, then this function will recursively call the predict function on either the left or right children until a leaf node is reached.

NOTE: The threshold, featureIndex and classProbabilities should be set first BEFORE this function is called. The threshold, featureIndex and classProbabilities can be set by training the node through the DecisionTree class.

Parameters

x	the input Vector that will be used for the prediction
classLikelihoods	a reference to a Vector that will store the class probabilities

Returns: returns true if the input is greater than or equal to the nodes threshold, false otherwise

Reimplemented from Node.

Definition at line 84 of file ClusterTreeNode.h.

virtual bool ClusterTreeNode::print ( ) const

inlineoverridevirtual

This functions prints the node data to std::out. It will recursively print all the child nodes.

Returns: returns true if the data was printed correctly, false otherwise

Reimplemented from Node.

Definition at line 131 of file ClusterTreeNode.h.

virtual bool ClusterTreeNode::saveParametersToFile ( std::fstream & file ) const

inlineoverrideprotectedvirtual

This saves the ClusterTreeNode custom parameters to a file. It will be called automatically by the Node base class if the save function is called.

Parameters

file	a reference to the file the parameters will be saved to

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

Reimplemented from Node.

Definition at line 255 of file ClusterTreeNode.h.

bool ClusterTreeNode::set	(	const UINT	nodeSize,
		const UINT	featureIndex,
		const Float	threshold,
		const UINT	clusterLabel
	)

inline

This function sets the Cluster Tree Node.

Parameters

nodeSize	sets the node size, this is the number of training samples at that node
featureIndex	sets the index of the feature that should be used for the threshold spilt
threshold	set the threshold value used for the spilt
clusterLabel	the cluster label for this node

Returns: returns true if the node was set, false otherwise

Definition at line 239 of file ClusterTreeNode.h.

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

Public Member Functions

Protected Member Functions

Protected Attributes

Static Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation