Inheritance diagram for DecisionTreeNode:

Public Member Functions
	DecisionTreeNode (const std::string id="DecisionTreeNode")

	DecisionTreeNode (const DecisionTreeNode &rhs)=delete

virtual	~DecisionTreeNode ()

DecisionTreeNode &	operator= (const DecisionTreeNode &rhs)=delete

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

virtual bool	computeBestSplit (const UINT &trainingMode, const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError)

virtual bool	clear () override

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

virtual Node *	deepCopy () const override

UINT	getNodeSize () const

UINT	getNumClasses () const

VectorFloat	getClassProbabilities () const

bool	setLeafNode (const UINT nodeSize, const VectorFloat &classProbabilities)

bool	setNodeSize (const UINT nodeSize)

bool	setClassProbabilities (const VectorFloat &classProbabilities)

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

virtual	~Node ()

virtual bool	predict_ (VectorFloat &x) override

virtual bool	computeFeatureWeights (VectorFloat &weights) const

virtual bool	computeLeafNodeWeights (MatrixFloat &weights) const

virtual bool	print () 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)

Static Public Member Functions
static UINT	getClassLabelIndexValue (UINT classLabel, const Vector< UINT > &classLabels)

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 ()

Protected Member Functions
virtual bool	computeBestSplitBestIterativeSplit (const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError)

virtual bool	computeBestSplitBestRandomSplit (const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError)

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	nodeSize

VectorFloat	classProbabilities

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< DecisionTreeNode >	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 Protected Member Functions inherited from Node
static StringNodeMap *	getMap ()

Detailed Description

Definition at line 41 of file DecisionTreeNode.h.

Constructor & Destructor Documentation

DecisionTreeNode::DecisionTreeNode ( const std::string id = "DecisionTreeNode" )

Default Constructor. Sets all the pointers to NULL.

Definition at line 10 of file DecisionTreeNode.cpp.

DecisionTreeNode::DecisionTreeNode ( const DecisionTreeNode & rhs )

delete

Disable the copy constructor.

DecisionTreeNode::~DecisionTreeNode ( )

virtual

Default Destructor. Cleans up any memory.

Definition at line 14 of file DecisionTreeNode.cpp.

Member Function Documentation

bool DecisionTreeNode::clear ( )

overridevirtual

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.

Reimplemented in DecisionTreeClusterNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 82 of file DecisionTreeNode.cpp.

bool DecisionTreeNode::computeBestSplit	(	const UINT &	trainingMode,
		const UINT &	numSplittingSteps,
		const ClassificationData &	trainingData,
		const Vector< UINT > &	features,
		const Vector< UINT > &	classLabels,
		UINT &	featureIndex,
		Float &	minError
	)

virtual

This function calls the best spliting algorithm based on the current trainingMode.

This function will return true if the best spliting algorithm found a split, false otherwise.

Parameters

trainingMode	the training mode to use, this should be one of the
numSplittingSteps	sets the number of iterations that will be used to search for the best threshold
trainingData	the training data to use for the best split search
features	a Vector containing the indexs of the features that can be used for the search
classLabels	a Vector containing the class labels for the search
featureIndex	this will store the best feature index found during the search
minError	this will store the minimum error found during the search

Returns: returns true if the best spliting algorithm found a split, false otherwise

Definition at line 64 of file DecisionTreeNode.cpp.

Node * DecisionTreeNode::deepCopy ( ) const

overridevirtual

This function returns a deep copy of the DecisionTreeNode 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 DecisionTreeNode, or NULL if the deep copy was not successful

Reimplemented from Node.

Reimplemented in DecisionTreeClusterNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 118 of file DecisionTreeNode.cpp.

VectorFloat DecisionTreeNode::getClassProbabilities ( ) const

This function returns the class probabilities Vector.

Returns: returns the classProbabilities Vector

Definition at line 157 of file DecisionTreeNode.cpp.

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

overridevirtual

This function adds the current model to the formatted stream. This function should be overwritten by the derived class.

Parameters

stream a reference to the stream the model will be added to

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

Reimplemented from Node.

Reimplemented in DecisionTreeClusterNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 93 of file DecisionTreeNode.cpp.

UINT DecisionTreeNode::getNodeSize ( ) const

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 149 of file DecisionTreeNode.cpp.

UINT DecisionTreeNode::getNumClasses ( ) const

This function returns the number of classes in the class probabilities Vector.

Returns: returns the number of classes in the class probabilities Vector

Definition at line 153 of file DecisionTreeNode.cpp.

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

inlineoverrideprotectedvirtual

This loads the Decision Tree Node 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.

Reimplemented in DecisionTreeClusterNode, DecisionTreeTripleFeatureNode, and DecisionTreeThresholdNode.

Definition at line 217 of file DecisionTreeNode.h.

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

delete

Disable the equals operator.

bool DecisionTreeNode::predict_	(	VectorFloat &	x,
		VectorFloat &	classLikelihoods
	)

overridevirtual

This function recursively calls predict on the input vector x, when it reaches the leaf node it stores the class probability associated with that leaf node in the output vector y.

NOTE: This function should only be called after the decision tree model has been trained.

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 18 of file DecisionTreeNode.cpp.

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

inlineoverrideprotectedvirtual

This saves the DecisionTreeNode 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.

Reimplemented in DecisionTreeClusterNode, DecisionTreeTripleFeatureNode, and DecisionTreeThresholdNode.

Definition at line 188 of file DecisionTreeNode.h.

bool DecisionTreeNode::setClassProbabilities ( const VectorFloat & classProbabilities )

This function sets the Decision Tree Node class probabilities.

Parameters

classProbabilities the Vector of class probabilities at this node

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

Definition at line 173 of file DecisionTreeNode.cpp.

bool DecisionTreeNode::setLeafNode	(	const UINT	nodeSize,
		const VectorFloat &	classProbabilities
	)

This function sets the Decision Tree Node as a leaf node.

Parameters

nodeSize	sets the node size, this is the number of training samples at that node
classProbabilities	the Vector of class probabilities at this node

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

Definition at line 161 of file DecisionTreeNode.cpp.

bool DecisionTreeNode::setNodeSize ( const UINT nodeSize )

This function sets the Decision Tree Node nodeSize.

Parameters

nodeSize sets the node size, this is the number of training samples at that node

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

Definition at line 168 of file DecisionTreeNode.cpp.

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

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Static Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation