Inheritance diagram for Node:

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

Public Member Functions
	Node ()

virtual	~Node ()

virtual bool	predict (const VectorFloat &x)

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

virtual bool	computeFeatureWeights (VectorFloat &weights) const

virtual bool	computeLeafNodeWeights (MatrixFloat &weights) const

virtual bool	clear ()

virtual bool	print () const

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

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

virtual bool	load (std::fstream &file)

virtual Node *	deepCopyNode () 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 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 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 GRTBase
Float	SQR (const Float &x) const

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 GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

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 ( )

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 56 of file Node.cpp.

Member Function Documentation

bool Node::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 in ClusterTreeNode, RegressionTreeNode, DecisionTreeNode, DecisionTreeClusterNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 70 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 101 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 105 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::deepCopyNode ( ) 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 276 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 308 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 351 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 347 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 355 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 343 of file Node.cpp.

bool Node::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 in DecisionTreeClusterNode, DecisionTreeNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 120 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 312 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 304 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 316 of file Node.cpp.

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

virtual

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

Definition at line 182 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 250 of file Node.h.

bool Node::predict ( const VectorFloat & x )

virtual

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

Definition at line 60 of file Node.cpp.

bool Node::predict	(	const 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 65 of file Node.cpp.

bool Node::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 in ClusterTreeNode, RegressionTreeNode, DecisionTreeClusterNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 109 of file Node.cpp.

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

virtual

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

Definition at line 140 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 241 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