#include <RegressionTree.h>

Inheritance diagram for RegressionTree:

Public Member Functions
	RegressionTree (const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT, const bool useScaling=false, const Float minRMSErrorPerNode=0.01)

	RegressionTree (const RegressionTree &rhs)

virtual	~RegressionTree (void)

RegressionTree &	operator= (const RegressionTree &rhs)

virtual bool	deepCopyFrom (const Regressifier *regressifier)

virtual bool	train_ (RegressionData &trainingData)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	clear ()

virtual bool	print () const

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

virtual bool	loadModelFromFile (std::fstream &file)

RegressionTreeNode *	deepCopyTree () const

const RegressionTreeNode *	getTree () const

Float	getMinRMSErrorPerNode () const

bool	setMinRMSErrorPerNode (const Float minRMSErrorPerNode)

Public Member Functions inherited from Tree
	Tree (const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT)

virtual	~Tree (void)

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

const Node *	getTree () const

UINT	getTrainingMode () const

UINT	getNumSplittingSteps () const

UINT	getMinNumSamplesPerNode () const

UINT	getMaxDepth () const

UINT	getPredictedNodeID () const

bool	getRemoveFeaturesAtEachSpilt () const

bool	setTrainingMode (const UINT trainingMode)

bool	setNumSplittingSteps (const UINT numSplittingSteps)

bool	setMinNumSamplesPerNode (const UINT minNumSamplesPerNode)

bool	setMaxDepth (const UINT maxDepth)

bool	setRemoveFeaturesAtEachSpilt (const bool removeFeaturesAtEachSpilt)

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

Public Member Functions inherited from Regressifier
	Regressifier (void)

virtual	~Regressifier (void)

bool	copyBaseVariables (const Regressifier *regressifier)

virtual bool	reset ()

std::string	getRegressifierType () const

VectorFloat	getRegressionData () const

Vector< MinMax >	getInputRanges () const

Vector< MinMax >	getOutputRanges () const

Regressifier *	createNewInstance () const

Regressifier *	deepCopy () const

const Regressifier &	getBaseRegressifier () const

Public Member Functions inherited from MLBase
	MLBase (void)

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 (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	save (const std::string filename) const

virtual bool	load (const std::string filename)

virtual bool	saveModelToFile (std::string filename) const

virtual bool	loadModelFromFile (std::string filename)

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

Float	scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)

virtual std::string	getModelAsString () const

DataType	getInputType () const

DataType	getOutputType () const

UINT	getBaseType () const

UINT	getNumInputFeatures () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

UINT	getMinNumEpochs () const

UINT	getMaxNumEpochs () const

UINT	getValidationSetSize () const

UINT	getNumTrainingIterationsToConverge () const

Float	getMinChange () const

Float	getLearningRate () const

Float	getRootMeanSquaredTrainingError () const

Float	getTotalSquaredTrainingError () const

Float	getValidationSetAccuracy () const

VectorFloat	getValidationSetPrecision () const

VectorFloat	getValidationSetRecall () const

bool	getUseValidationSet () const

bool	getRandomiseTrainingOrder () const

bool	getTrained () const

bool	getModelTrained () const

bool	getScalingEnabled () const

bool	getIsBaseTypeClassifier () const

bool	getIsBaseTypeRegressifier () const

bool	getIsBaseTypeClusterer () const

bool	enableScaling (const bool useScaling)

bool	setMaxNumEpochs (const UINT maxNumEpochs)

bool	setMinNumEpochs (const UINT minNumEpochs)

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	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 Observer< TrainingResult >
virtual void	notify (const TrainingResult &data)

Public Member Functions inherited from Observer< TestInstanceResult >
virtual void	notify (const TestInstanceResult &data)

Protected Member Functions
RegressionTreeNode *	buildTree (const RegressionData &trainingData, RegressionTreeNode *parent, Vector< UINT > features, UINT nodeID)

bool	computeBestSpilt (const RegressionData &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError)

bool	computeBestSpiltBestIterativeSpilt (const RegressionData &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError)

bool	computeNodeRegressionData (const RegressionData &trainingData, VectorFloat &regressionData)

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

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

bool	loadBaseSettingsFromFile (std::fstream &file)

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

bool	loadBaseSettingsFromFile (std::fstream &file)

Protected Attributes
Float	minRMSErrorPerNode
	<Tell the compiler we are using the base class predict method to stop hidden virtual function warnings

Protected Attributes inherited from Tree
UINT	trainingMode

UINT	numSplittingSteps

UINT	minNumSamplesPerNode

UINT	maxDepth

bool	removeFeaturesAtEachSpilt

Node *	tree

Protected Attributes inherited from GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

WarningLog	warningLog

Protected Attributes inherited from Regressifier
std::string	regressifierType

VectorFloat	regressionData

Vector< MinMax >	inputVectorRanges

Vector< MinMax >	targetVectorRanges

Protected Attributes inherited from MLBase
bool	trained

bool	useScaling

DataType	inputType

DataType	outputType

UINT	baseType

UINT	numInputDimensions

UINT	numOutputDimensions

UINT	numTrainingIterationsToConverge

UINT	minNumEpochs

UINT	maxNumEpochs

UINT	validationSetSize

Float	learningRate

Float	minChange

Float	rootMeanSquaredTrainingError

Float	totalSquaredTrainingError

Float	validationSetAccuracy

bool	useValidationSet

bool	randomiseTrainingOrder

VectorFloat	validationSetPrecision

VectorFloat	validationSetRecall

Random	random

std::vector< TrainingResult >	trainingResults

TrainingResultsObserverManager	trainingResultsObserverManager

TestResultsObserverManager	testResultsObserverManager

Static Protected Attributes
static RegisterRegressifierModule< RegressionTree >	registerModule

Additional Inherited Members
Public Types inherited from Tree
enum	TrainingMode { BEST_ITERATIVE_SPILT =0, BEST_RANDOM_SPLIT, NUM_TRAINING_MODES }

Public Types inherited from Regressifier
typedef std::map< std::string, Regressifier ()() >	StringRegressifierMap

Public Types inherited from MLBase
enum	BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }

Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)

static std::string	getGRTRevison ()

Static Public Member Functions inherited from Regressifier
static Regressifier *	createInstanceFromString (const std::string &regressifierType)

static Vector< std::string >	getRegisteredRegressifiers ()

Static Protected Member Functions inherited from Regressifier
static StringRegressifierMap *	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 RegressionTree.h.

Constructor & Destructor Documentation

RegressionTree::RegressionTree	(	const UINT	numSplittingSteps = `100`,
		const UINT	minNumSamplesPerNode = `5`,
		const UINT	maxDepth = `10`,
		const bool	removeFeaturesAtEachSpilt = `false`,
		const UINT	trainingMode = `BEST_ITERATIVE_SPILT`,
		const bool	useScaling = `false`,
		const Float	minRMSErrorPerNode = `0.01`
	)

Default Constructor

Parameters

numSplittingSteps	sets the number of steps that will be used to search for the best spliting value for each node. Default value = 100
minNumSamplesPerNode	sets the minimum number of samples that are allowed per node, if the number of samples is below that, the node will become a leafNode. Default value = 5
maxDepth	sets the maximum depth of the tree. Default value = 10
removeFeaturesAtEachSpilt	sets if a feature is removed at each spilt so it can not be used again. Default value = false
trainingMode	sets the training mode, this should be one of the TrainingMode enums. Default value = BEST_ITERATIVE_SPILT
useScaling	sets if the training and real-time data should be scaled between [0 1]. Default value = false
minRMSErrorPerNode	sets the minimum RMS error that allowed per node, if the RMS error is below that, the node will become a leafNode. Default value = 0.01

Definition at line 31 of file RegressionTree.cpp.

RegressionTree::RegressionTree ( const RegressionTree & rhs )

Defines the copy constructor.

Parameters

rhs	the instance from which all the data will be copied into this instance

Definition at line 50 of file RegressionTree.cpp.

RegressionTree::~RegressionTree ( void )

virtual

Default Destructor

Definition at line 61 of file RegressionTree.cpp.

Member Function Documentation

bool RegressionTree::clear ( )

virtual

This overrides the clear function in the Regressifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.

Returns: returns true if the module was cleared succesfully, false otherwise

Reimplemented from Tree.

Definition at line 196 of file RegressionTree.cpp.

bool RegressionTree::deepCopyFrom ( const Regressifier * regressifier )

virtual

This is required for the Gesture Recognition Pipeline for when the pipeline.setRegressifier(...) method is called. It clones the data from the Base Class Regressifier pointer (which should be pointing to an RegressionTree instance) into this instance

Parameters

regressifier a pointer to the Regressifier Base Class, this should be pointing to another RegressionTree instance

Returns: returns true if the clone was successfull, false otherwise

Reimplemented from Regressifier.

Definition at line 89 of file RegressionTree.cpp.

RegressionTreeNode * RegressionTree::deepCopyTree ( ) const

virtual

Deep copies the regression tree, returning a pointer to the new regression tree. The user is in charge of cleaning up the memory so must delete the pointer when they no longer need it. NULL will be returned if the tree could not be copied.

Returns: returns a pointer to a deep copy of the regression tree

Reimplemented from Tree.

Definition at line 345 of file RegressionTree.cpp.

Float RegressionTree::getMinRMSErrorPerNode ( ) const

Gets the minimum root mean squared error value that needs to be exceeded for the tree to continue growing at a specific node. If the RMS error is below this value then the node will be made into a leaf node.

Returns: returns the minimum RMS error per node

Definition at line 358 of file RegressionTree.cpp.

const RegressionTreeNode * RegressionTree::getTree ( ) const

Gets a pointer to the regression tree. NULL will be returned if the decision tree model has not be trained.

Returns: returns a const pointer to the regression tree

Definition at line 354 of file RegressionTree.cpp.

bool RegressionTree::loadModelFromFile ( std::fstream & file )

virtual

This loads a trained RegressionTree model from a file. This overrides the loadModelFromFile function in the Regressifier base class.

Parameters

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

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

Reimplemented from MLBase.

Definition at line 251 of file RegressionTree.cpp.

RegressionTree & RegressionTree::operator= ( const RegressionTree & rhs )

Defines how the data from the rhs RegressionTree should be copied to this RegressionTree

Parameters

rhs	another instance of a RegressionTree

Returns: returns a pointer to this instance of the RegressionTree

Definition at line 66 of file RegressionTree.cpp.

bool RegressionTree::predict_ ( VectorFloat & inputVector )

virtual

This predicts the class of the inputVector. This overrides the predict function in the Regressifier base class.

Parameters

inputVector the input Vector to predict

Returns: returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 165 of file RegressionTree.cpp.

bool RegressionTree::print ( ) const

virtual

Prints the tree to std::cout.

Returns: returns true if the model was printed

Reimplemented from Tree.

Definition at line 210 of file RegressionTree.cpp.

bool RegressionTree::saveModelToFile ( std::fstream & file ) const

virtual

This saves the trained RegressionTree model to a file. This overrides the saveModelToFile function in the Regressifier base class.

Parameters

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

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

Reimplemented from MLBase.

Definition at line 216 of file RegressionTree.cpp.

bool RegressionTree::setMinRMSErrorPerNode ( const Float minRMSErrorPerNode )

Sets the minimum RMS error that needs to be exceeded for the tree to continue growing at a specific node.

Parameters

minRMSErrorPerNode sets the minRMSErrorPerNode parameter

Returns: returns true if the parameter was updated

Definition at line 362 of file RegressionTree.cpp.

bool RegressionTree::train_ ( RegressionData & trainingData )

virtual

This trains the RegressionTree model, using the labelled regression data. This overrides the train function in the Regressifier base class.

Parameters

trainingData a reference to the training data

Returns: returns true if the RegressionTree model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 118 of file RegressionTree.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