#include <DecisionTreeNode.h>

Inheritance diagram for DecisionTreeNode:

Public Member Functions
	DecisionTreeNode ()

virtual	~DecisionTreeNode ()

virtual bool	predict (const VectorFloat &x, VectorFloat &classLikelihoods)

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

virtual bool	clear ()

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

virtual Node *	deepCopyNode () const

DecisionTreeNode *	deepCopy () const

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 ()

virtual	~Node ()

virtual bool	predict (const VectorFloat &x)

virtual bool	computeFeatureWeights (VectorFloat &weights) const

virtual bool	computeLeafNodeWeights (MatrixFloat &weights) const

virtual bool	print () const

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

virtual bool	load (std::fstream &file)

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 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

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

virtual bool	computeBestSpiltBestRandomSpilt (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

virtual bool	loadParametersFromFile (std::fstream &file)

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

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 GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

WarningLog	warningLog

Static Protected Attributes
static RegisterNode< DecisionTreeNode >	registerModule

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

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 40 of file DecisionTreeNode.h.

Constructor & Destructor Documentation

DecisionTreeNode::DecisionTreeNode ( )

Default Constructor. Sets all the pointers to NULL.

Definition at line 10 of file DecisionTreeNode.cpp.

DecisionTreeNode::~DecisionTreeNode ( )

virtual

Default Destructor. Cleans up any memory.

Definition at line 18 of file DecisionTreeNode.cpp.

Member Function Documentation

bool DecisionTreeNode::clear ( )

virtual

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 76 of file DecisionTreeNode.cpp.

bool DecisionTreeNode::computeBestSpilt	(	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 58 of file DecisionTreeNode.cpp.

DecisionTreeNode * DecisionTreeNode::deepCopy ( ) const

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

Definition at line 143 of file DecisionTreeNode.cpp.

Node * DecisionTreeNode::deepCopyNode ( ) const

virtual

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 112 of file DecisionTreeNode.cpp.

VectorFloat DecisionTreeNode::getClassProbabilities ( ) const

This function returns the class probabilities Vector.

Returns: returns the classProbabilities Vector

Definition at line 155 of file DecisionTreeNode.cpp.

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

virtual

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 87 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 147 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 151 of file DecisionTreeNode.cpp.

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

inlineprotectedvirtual

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.

bool DecisionTreeNode::predict	(	const VectorFloat &	x,
		VectorFloat &	classLikelihoods
	)

virtual

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 22 of file DecisionTreeNode.cpp.

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

inlineprotectedvirtual

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 171 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 159 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 166 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