#include <DecisionTreeThresholdNode.h>

Inheritance diagram for DecisionTreeThresholdNode:

Public Member Functions
	DecisionTreeThresholdNode ()

virtual	~DecisionTreeThresholdNode ()

virtual bool	predict (const VectorFloat &x)

virtual bool	clear ()

virtual bool	print () const

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

virtual Node *	deepCopyNode () const

DecisionTreeThresholdNode *	deepCopy () const

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 ()

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)

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	computeFeatureWeights (VectorFloat &weights) const

virtual bool	computeLeafNodeWeights (MatrixFloat &weights) 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

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	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 GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

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

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

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 42 of file DecisionTreeThresholdNode.h.

Constructor & Destructor Documentation

DecisionTreeThresholdNode::DecisionTreeThresholdNode ( )

Default Constructor. Sets all the pointers to NULL.

Definition at line 10 of file DecisionTreeThresholdNode.cpp.

DecisionTreeThresholdNode::~DecisionTreeThresholdNode ( )

virtual

Default Destructor. Cleans up any memory.

Definition at line 18 of file DecisionTreeThresholdNode.cpp.

Member Function Documentation

bool DecisionTreeThresholdNode::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 DecisionTreeNode.

Definition at line 29 of file DecisionTreeThresholdNode.cpp.

DecisionTreeThresholdNode * DecisionTreeThresholdNode::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 DecisionTreeThresholdNode, or NULL if the deep copy was not successful

Definition at line 110 of file DecisionTreeThresholdNode.cpp.

Node * DecisionTreeThresholdNode::deepCopyNode ( ) const

virtual

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 77 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 114 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::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

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 52 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 118 of file DecisionTreeThresholdNode.cpp.

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

protectedvirtual

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 304 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::predict ( const VectorFloat & x )

virtual

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

const VectorFloat &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 22 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::print ( ) const

virtual

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 40 of file DecisionTreeThresholdNode.cpp.

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

protectedvirtual

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 283 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 122 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