Inheritance diagram for DecisionTreeThresholdNode:

Public Member Functions
	DecisionTreeThresholdNode ()

virtual	~DecisionTreeThresholdNode ()

virtual bool	predict_ (VectorFloat &x) override

virtual bool	clear () override

virtual bool	print () const override

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

virtual Node *	deepCopy () const override

UINT	getFeatureIndex () const

Float	getThreshold () const

bool	set (const UINT nodeSize, const UINT featureIndex, const Float threshold, const VectorFloat &classProbabilities)

Public Member Functions inherited from DecisionTreeNode
	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)

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	computeFeatureWeights (VectorFloat &weights) const

virtual bool	computeLeafNodeWeights (MatrixFloat &weights) const

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	computeBestSplitBestIterativeSplit (const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError) override

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

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	featureIndex

Float	threshold

Protected Attributes inherited from DecisionTreeNode
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< DecisionTreeThresholdNode >	registerModule

Static Protected Attributes inherited from DecisionTreeNode
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 Public Member Functions inherited from DecisionTreeNode
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 ()

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

Detailed Description

Definition at line 43 of file DecisionTreeThresholdNode.h.

Constructor & Destructor Documentation

DecisionTreeThresholdNode::DecisionTreeThresholdNode ( )

Default Constructor. Sets all the pointers to NULL.

Parameters

trainingMode sets the training algorithm used during the training of the node

Definition at line 10 of file DecisionTreeThresholdNode.cpp.

DecisionTreeThresholdNode::~DecisionTreeThresholdNode ( )

virtual

Default Destructor. Cleans up any memory.

Definition at line 14 of file DecisionTreeThresholdNode.cpp.

Member Function Documentation

bool DecisionTreeThresholdNode::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 DecisionTreeNode.

Definition at line 25 of file DecisionTreeThresholdNode.cpp.

Node * DecisionTreeThresholdNode::deepCopy ( ) const

overridevirtual

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

Reimplemented from DecisionTreeNode.

Definition at line 73 of file DecisionTreeThresholdNode.cpp.

UINT DecisionTreeThresholdNode::getFeatureIndex ( ) const

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 106 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::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

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

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

Reimplemented from DecisionTreeNode.

Definition at line 48 of file DecisionTreeThresholdNode.cpp.

Float DecisionTreeThresholdNode::getThreshold ( ) const

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

Returns: returns the threshold

Definition at line 110 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::loadParametersFromFile ( std::fstream & file )

overrideprotectedvirtual

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 DecisionTreeNode.

Definition at line 298 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::predict_ ( VectorFloat & x )

overridevirtual

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 18 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::print ( ) const

overridevirtual

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 36 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::saveParametersToFile ( std::fstream & file ) const

overrideprotectedvirtual

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 DecisionTreeNode.

Definition at line 277 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::set	(	const UINT	nodeSize,
		const UINT	featureIndex,
		const Float	threshold,
		const VectorFloat &	classProbabilities
	)

This function sets the Decision Tree Threshold 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
classProbabilities	the vector of class probabilities at this node

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

Definition at line 114 of file DecisionTreeThresholdNode.cpp.

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