DecisionTreeClusterNode Class Reference

#include <DecisionTreeClusterNode.h>

Inheritance diagram for DecisionTreeClusterNode:

Public Member Functions
	DecisionTreeClusterNode ()
virtual	~DecisionTreeClusterNode ()
virtual bool	predict (const VectorFloat &x)
virtual bool	clear ()
virtual bool	print () const
virtual bool	computeFeatureWeights (VectorFloat &weights) const
virtual bool	computeLeafNodeWeights (MatrixFloat &weights) const
virtual bool	getModel (std::ostream &stream) const
virtual Node *	deepCopyNode () const
DecisionTreeClusterNode *	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	saveToFile (std::fstream &file) const
virtual bool	loadFromFile (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)
bool	computeBestSpilt (const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError)
bool	computeError (const ClassificationData &trainingData, MatrixFloat &data, const Vector< UINT > &classLabels, Vector< MinMax > ranges, Vector< UINT > groupIndex, const UINT featureIndex, Float &threshold, Float &error)
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< DecisionTreeClusterNode >	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 43 of file DecisionTreeClusterNode.h.

Constructor & Destructor Documentation

DecisionTreeClusterNode::DecisionTreeClusterNode

(

)

Default Constructor. Sets all the pointers to NULL.

Definition at line 9 of file DecisionTreeClusterNode.cpp.

DecisionTreeClusterNode::~DecisionTreeClusterNode ( )

virtual

Default Destructor. Cleans up any memory.

Definition at line 17 of file DecisionTreeClusterNode.cpp.

Member Function Documentation

bool DecisionTreeClusterNode::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 28 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::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.

Parameters

weights

the input Vector that will be used to store the weights

Returns

returns true if the weights were updated, false otherwise

Reimplemented from Node.

Definition at line 51 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::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.

Parameters

weights

the input Vector that will be used to store the weights

Returns

returns true if the weights were updated, false otherwise

Reimplemented from Node.

Definition at line 74 of file DecisionTreeClusterNode.cpp.

DecisionTreeClusterNode * DecisionTreeClusterNode::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 175 of file DecisionTreeClusterNode.cpp.

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

Reimplemented from DecisionTreeNode.

Definition at line 142 of file DecisionTreeClusterNode.cpp.

UINT DecisionTreeClusterNode::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 179 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::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 112 of file DecisionTreeClusterNode.cpp.

Float DecisionTreeClusterNode::getThreshold

(

)

const

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

Returns

returns the threshold

Definition at line 183 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::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 412 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::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

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 21 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::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 39 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::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 saveToFile 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 391 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::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 187 of file DecisionTreeClusterNode.cpp.

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

• DecisionTreeClusterNode.h
• DecisionTreeClusterNode.cpp