Inheritance diagram for Node:

Public Types
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 }

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

virtual	~Node ()

virtual bool	predict_ (VectorFloat &x) override

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

virtual bool	computeFeatureWeights (VectorFloat &weights) const

virtual bool	computeLeafNodeWeights (MatrixFloat &weights) const

virtual bool	clear () override

virtual bool	print () const override

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

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

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

virtual Node *	deepCopy () const

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 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	saveParametersToFile (std::fstream &file) const

virtual bool	loadParametersFromFile (std::fstream &file)

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

bool	loadBaseSettingsFromFile (std::fstream &file)

Static Protected Member Functions
static StringNodeMap *	getMap ()

Protected Attributes
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

Detailed Description

Definition at line 37 of file Node.h.

Member Typedef Documentation

typedef std::map< std::string, Node*(*)() > Node::StringNodeMap

Defines a map between a string (which will contain the name of the node, such as DecisionTreeNode) and a function returns a new instance of that node.

Definition at line 215 of file Node.h.

Constructor & Destructor Documentation

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

Default Constructor. Sets all the pointers to NULL.

Definition at line 43 of file Node.cpp.

Node::~Node ( )

virtual

Default Destructor. Cleans up any memory.

Definition at line 52 of file Node.cpp.

Member Function Documentation

bool Node::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 MLBase.

Reimplemented in ClusterTreeNode, RegressionTreeNode, DecisionTreeNode, DecisionTreeClusterNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 66 of file Node.cpp.

bool Node::computeFeatureWeights ( VectorFloat & weights ) const

virtual

This function recursively computes the weights of features used for classification nodes and stores the results in the weights vector. This function should be overwritten by the inheriting class.

Parameters

weights the input vector that will be used to store the weights

Returns: returns true if the weights were updated, false otherwise

Reimplemented in DecisionTreeClusterNode.

Definition at line 97 of file Node.cpp.

bool Node::computeLeafNodeWeights ( MatrixFloat & weights ) const

virtual

This function recursively computes the weights of features used for classification nodes and stores the results in the weights vector. This function should be overwritten by the inheriting class.

Parameters

weights the input matrix that will be used to store the weights, rows represent classes, columns represent features

Returns: returns true if the weights were updated, false otherwise

Reimplemented in DecisionTreeClusterNode.

Definition at line 101 of file Node.cpp.

Node * Node::createInstanceFromString ( std::string const & nodeType )

static

Creates a new classifier instance based on the input string (which should contain the name of a valid classifier such as ANBC).

Parameters

string const &classifierType: the name of the classifier

Returns: Classifier*: a pointer to the new instance of the classifier

Definition at line 29 of file Node.cpp.

Node * Node::createNewInstance ( ) const

Creates a new classifier instance based on the current classifierType string value.

Returns: Classifier*: a pointer to the new instance of the classifier

Definition at line 39 of file Node.cpp.

Node * Node::deepCopy ( ) const

virtual

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

Reimplemented in RegressionTreeNode, ClusterTreeNode, DecisionTreeClusterNode, DecisionTreeNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 272 of file Node.cpp.

UINT Node::getDepth ( ) const

This function returns the depth of the node. The depth is the level in the tree at which the node is located, the root node has a depth of 0.

Returns: returns the depth of the node in the tree

Definition at line 304 of file Node.cpp.

bool Node::getHasLeftChild ( ) const

This function returns true if this node has a leftChild, false otherwise.

Returns: returns true if this node has a leftChild, false otherwise

Definition at line 347 of file Node.cpp.

bool Node::getHasParent ( ) const

This function returns true if this node has a parent, false otherwise.

Returns: returns true if this node has a parent, false otherwise

Definition at line 343 of file Node.cpp.

bool Node::getHasRightChild ( ) const

This function returns true if this node has a rightChild, false otherwise.

Returns: returns true if this node has a rightChild, false otherwise

Definition at line 351 of file Node.cpp.

bool Node::getIsLeafNode ( ) const

This function returns true if this node is a leaf node, false otherwise.

Returns: returns true if this node is a leaf node, false otherwise

Definition at line 339 of file Node.cpp.

bool Node::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 MLBase.

Reimplemented in DecisionTreeClusterNode, DecisionTreeNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 116 of file Node.cpp.

UINT Node::getNodeID ( ) const

This function returns the nodeID, this is a unique ID that represents this node within a Tree.

Returns: returns the nodeID of this node

Definition at line 308 of file Node.cpp.

std::string Node::getNodeType ( ) const

This function returns the node type, this is the type of node defined by the class that inherits from the Node base class.

Returns: returns the nodeType

Definition at line 300 of file Node.cpp.

UINT Node::getPredictedNodeID ( ) const

This function returns the predictedNodeID, this is ID of the leaf node that was reached during the last prediction call

Returns: returns the predictedNodeID

Definition at line 312 of file Node.cpp.

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

overridevirtual

This loads the Node from a file.

Parameters

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

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

Reimplemented from MLBase.

Definition at line 178 of file Node.cpp.

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

inlineprotectedvirtual

This loads the custom parameters to from file. This can be used by any class that inherits from the Node class to load the custom parameters from that class from a file by overridding this function.

Parameters

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

Returns: returns true if the parameters were loaded successfully, false otherwise

Reimplemented in ClusterTreeNode, RegressionTreeNode, DecisionTreeNode, DecisionTreeClusterNode, DecisionTreeTripleFeatureNode, and DecisionTreeThresholdNode.

Definition at line 255 of file Node.h.

bool Node::predict_ ( VectorFloat & x )

overridevirtual

This function predicts if the input is greater than or equal to the nodes threshold. This function should be overwritten by the inheriting 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 MLBase.

Reimplemented in RegressionTreeNode, DecisionTreeClusterNode, DecisionTreeThresholdNode, ClusterTreeNode, and DecisionTreeTripleFeatureNode.

Definition at line 56 of file Node.cpp.

bool Node::predict_	(	VectorFloat &	x,
		VectorFloat &	y
	)

virtual

This function recursively predicts if the probability of the input vector. This function should be overwritten by the inheriting class.

Parameters

x	the input vector that will be used for the prediction
y	a reference to a vector that will store the results

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

Reimplemented in RegressionTreeNode, ClusterTreeNode, and DecisionTreeNode.

Definition at line 61 of file Node.cpp.

bool Node::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 MLBase.

Reimplemented in ClusterTreeNode, RegressionTreeNode, DecisionTreeClusterNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 105 of file Node.cpp.

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

overridevirtual

This saves the Node to a file.

Parameters

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

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

Reimplemented from MLBase.

Definition at line 136 of file Node.cpp.

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

inlineprotectedvirtual

This saves the custom parameters to a file. This can be used by any class that inherits from the Node class to save the custom parameters from that class to a file by overridding this function.

Parameters

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

Returns: returns true if the parameters were saved successfully, false otherwise

Reimplemented in ClusterTreeNode, RegressionTreeNode, DecisionTreeNode, DecisionTreeClusterNode, DecisionTreeTripleFeatureNode, and DecisionTreeThresholdNode.

Definition at line 246 of file Node.h.

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation