RegressionTree Class Reference

This class implements a basic Regression Tree. More...

#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 Tree::TrainingMode trainingMode=Tree::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) override
virtual bool	train_ (RegressionData &trainingData) override
virtual bool	predict_ (VectorFloat &inputVector) override
virtual bool	clear () override
virtual bool	print () const override
virtual bool	save (std::fstream &file) const override
virtual bool	load (std::fstream &file) override
RegressionTreeNode *	deepCopyTree () const
const RegressionTreeNode *	getTree () const
Float	getMinRMSErrorPerNode () const
Tree::TrainingMode	getTrainingMode () const
UINT	getNumSplittingSteps () const
UINT	getMinNumSamplesPerNode () const
UINT	getMaxDepth () const
UINT	getPredictedNodeID () const
bool	getRemoveFeaturesAtEachSpilt () const
bool	setTrainingMode (const Tree::TrainingMode trainingMode)
bool	setNumSplittingSteps (const UINT numSplittingSteps)
bool	setMinNumSamplesPerNode (const UINT minNumSamplesPerNode)
bool	setMaxDepth (const UINT maxDepth)
bool	setRemoveFeaturesAtEachSpilt (const bool removeFeaturesAtEachSpilt)
bool	setMinRMSErrorPerNode (const Float minRMSErrorPerNode)
Public Member Functions inherited from Regressifier
	Regressifier (const std::string &id="")
virtual	~Regressifier (void)
bool	copyBaseVariables (const Regressifier *regressifier)
virtual bool	reset () override
VectorFloat	getRegressionData () const
Vector< MinMax >	getInputRanges () const
Vector< MinMax >	getOutputRanges () const
Regressifier *	deepCopy () const
const Regressifier &	getBaseRegressifier () const
Regressifier *	create () const
	GRT_DEPRECATED_MSG ("createNewInstance is deprecated, use create() instead.", Regressifier *createNewInstance() const )
	GRT_DEPRECATED_MSG ("createInstanceFromString(id) is deprecated, use create(id) instead.", static Regressifier *createInstanceFromString(const std::string &id))
	GRT_DEPRECATED_MSG ("getRegressifierType is deprecated, use getId() instead", std::string getRegressifierType() const )
Public Member Functions inherited from MLBase
	MLBase (const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)
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 (RegressionData trainingData, RegressionData validationData)
virtual bool	train_ (RegressionData &trainingData, RegressionData &validationData)
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)
	GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(const std::string &filename) instead", virtual bool saveModelToFile(const std::string &filename) const )
	GRT_DEPRECATED_MSG ("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const )
	GRT_DEPRECATED_MSG ("loadModelFromFile(std::string filename) is deprecated, use load(const std::string &filename) instead", virtual bool loadModelFromFile(const std::string &filename))
	GRT_DEPRECATED_MSG ("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file))
virtual bool	getModel (std::ostream &stream) const
virtual std::string	getModelAsString () const
DataType	getInputType () const
DataType	getOutputType () const
BaseType	getType () const
UINT	getNumInputFeatures () const
UINT	getNumInputDimensions () const
UINT	getNumOutputDimensions () const
UINT	getMinNumEpochs () const
UINT	getMaxNumEpochs () const
UINT	getBatchSize () const
UINT	getNumRestarts () const
UINT	getValidationSetSize () const
UINT	getNumTrainingIterationsToConverge () const
Float	getMinChange () const
Float	getLearningRate () const
Float	getRMSTrainingError () const
	GRT_DEPRECATED_MSG ("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )
Float	getTotalSquaredTrainingError () const
Float	getRMSValidationError () const
Float	getValidationSetAccuracy () const
VectorFloat	getValidationSetPrecision () const
VectorFloat	getValidationSetRecall () const
bool	getUseValidationSet () const
bool	getRandomiseTrainingOrder () const
bool	getTrained () const
	GRT_DEPRECATED_MSG ("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const )
bool	getConverged () const
bool	getScalingEnabled () const
bool	getIsBaseTypeClassifier () const
bool	getIsBaseTypeRegressifier () const
bool	getIsBaseTypeClusterer () const
bool	getTrainingLoggingEnabled () const
bool	getTestingLoggingEnabled () const
bool	enableScaling (const bool useScaling)
bool	setMaxNumEpochs (const UINT maxNumEpochs)
bool	setBatchSize (const UINT batchSize)
bool	setMinNumEpochs (const UINT minNumEpochs)
bool	setNumRestarts (const UINT numRestarts)
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	setTestingLoggingEnabled (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 GRTBase
	GRTBase (const std::string &id="")
virtual	~GRTBase (void)
bool	copyGRTBaseVariables (const GRTBase *GRTBase)
	GRT_DEPRECATED_MSG ("getClassType is deprecated, use getId() instead!", std::string getClassType() const )
std::string	getId () 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)
bool	setDebugLoggingEnabled (const bool loggingEnabled)
GRTBase *	getGRTBasePointer ()
const GRTBase *	getGRTBasePointer () const
Float	scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)
Float	SQR (const Float &x) 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)

Static Public Member Functions
static std::string	getId ()
Static Public Member Functions inherited from Regressifier
static Vector< std::string >	getRegisteredRegressifiers ()
static Regressifier *	create (const std::string &id)
Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)
static std::string	getGRTRevison ()

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 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
Node *	tree
	<Tell the compiler we are using the base class predict method to stop hidden virtual function warnings
UINT	minNumSamplesPerNode
UINT	maxDepth
UINT	numSplittingSteps
bool	removeFeaturesAtEachSpilt
Tree::TrainingMode	trainingMode
Float	minRMSErrorPerNode
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
bool	converged
DataType	inputType
DataType	outputType
BaseType	baseType
UINT	numInputDimensions
UINT	numOutputDimensions
UINT	numTrainingIterationsToConverge
UINT	minNumEpochs
UINT	maxNumEpochs
UINT	batchSize
UINT	validationSetSize
UINT	numRestarts
Float	learningRate
Float	minChange
Float	rmsTrainingError
Float	rmsValidationError
Float	totalSquaredTrainingError
Float	validationSetAccuracy
bool	useValidationSet
bool	randomiseTrainingOrder
VectorFloat	validationSetPrecision
VectorFloat	validationSetRecall
Random	random
Vector< TrainingResult >	trainingResults
TrainingResultsObserverManager	trainingResultsObserverManager
TestResultsObserverManager	testResultsObserverManager
TrainingLog	trainingLog
TestingLog	testingLog
Protected Attributes inherited from GRTBase
std::string	classId
	Stores the name of the class (e.g., MinDist)
DebugLog	debugLog
ErrorLog	errorLog
InfoLog	infoLog
WarningLog	warningLog

Additional Inherited Members
Public Types inherited from Regressifier
typedef std::map< std::string, Regressifier *(*)() >	StringRegressifierMap
Public Types inherited from MLBase
enum	BaseType {   BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER,   PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT }
Static Protected Member Functions inherited from Regressifier
static StringRegressifierMap *	getMap ()

Detailed Description

This class implements a basic Regression Tree.

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.

Remarks

This algorithm is still under development.

Definition at line 39 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 Tree::TrainingMode	trainingMode = Tree::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 36 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 48 of file RegressionTree.cpp.

RegressionTree::~RegressionTree ( void  )

virtual

Default Destructor

Definition at line 54 of file RegressionTree.cpp.

Member Function Documentation

bool RegressionTree::clear ( )

overridevirtual

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 Regressifier.

Definition at line 189 of file RegressionTree.cpp.

bool RegressionTree::deepCopyFrom ( const Regressifier *  regressifier )

overridevirtual

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 82 of file RegressionTree.cpp.

RegressionTreeNode * RegressionTree::deepCopyTree

(

)

const

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

Definition at line 340 of file RegressionTree.cpp.

std::string RegressionTree::getId ( )

static

Gets a string that represents the RegressionTree class.

Returns

returns a string containing the ID of this class

Definition at line 31 of file RegressionTree.cpp.

UINT RegressionTree::getMaxDepth

(

)

const

Gets the maximum depth of the tree.

Returns

returns the maximum depth of the tree

Definition at line 369 of file RegressionTree.cpp.

UINT RegressionTree::getMinNumSamplesPerNode

(

)

const

Gets the minimum number of samples that are allowed per node, if the number of samples at a node is below this value then the node will automatically become a leaf node.

Returns

returns the minimum number of samples that are allowed per node

Definition at line 365 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 353 of file RegressionTree.cpp.

UINT RegressionTree::getNumSplittingSteps

(

)

const

Gets the number of steps that will be used to search for the best spliting value for each node.

If the trainingMode is set to BEST_ITERATIVE_SPILT, then the numSplittingSteps controls how many iterative steps there will be per feature. If the trainingMode is set to BEST_RANDOM_SPLIT, then the numSplittingSteps controls how many random searches there will be per feature.

Returns

returns the number of steps that will be used to search for the best spliting value for each node

Definition at line 361 of file RegressionTree.cpp.

UINT RegressionTree::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, this will be zero if the tree does not exist or predict has not been called

Definition at line 373 of file RegressionTree.cpp.

bool RegressionTree::getRemoveFeaturesAtEachSpilt

(

)

const

Gets if a feature is removed at each spilt so it can not be used again.

Returns

returns true if a feature is removed at each spilt so it can not be used again, false otherwise

Definition at line 382 of file RegressionTree.cpp.

Tree::TrainingMode RegressionTree::getTrainingMode

(

)

const

Gets the current training mode. This will be one of the TrainingModes enums.

Returns

returns the training mode

Definition at line 357 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 349 of file RegressionTree.cpp.

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

overridevirtual

This loads a trained RegressionTree model from a file. This overrides the load 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 244 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 59 of file RegressionTree.cpp.

bool RegressionTree::predict_ ( VectorFloat &  inputVector )

overridevirtual

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 158 of file RegressionTree.cpp.

bool RegressionTree::print ( ) const

overridevirtual

Prints the tree to std::cout.

Returns

returns true if the model was printed

Reimplemented from MLBase.

Definition at line 203 of file RegressionTree.cpp.

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

overridevirtual

This saves the trained RegressionTree model to a file. This overrides the save 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 209 of file RegressionTree.cpp.

bool RegressionTree::setMaxDepth

(

const UINT

maxDepth

)

Sets the maximum depth of the tree, any node that reaches this depth will automatically become a leaf node. Value must be larger than zero.

Parameters

maxDepth

the maximum depth of the tree

Returns

returns true if the parameter was set, false otherwise

Definition at line 413 of file RegressionTree.cpp.

bool RegressionTree::setMinNumSamplesPerNode

(

const UINT

minNumSamplesPerNode

)

Sets the minimum number of samples that are allowed per node, if the number of samples at a node is below this value then the node will automatically become a leaf node. Value must be larger than zero.

Parameters

minNumSamplesPerNode

the minimum number of samples that are allowed per node

Returns

returns true if the parameter was set, false otherwise

Definition at line 404 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 427 of file RegressionTree.cpp.

bool RegressionTree::setNumSplittingSteps

(

const UINT

numSplittingSteps

)

Sets the number of steps that will be used to search for the best spliting value for each node.

If the trainingMode is set to BEST_ITERATIVE_SPILT, then the numSplittingSteps controls how many iterative steps there will be per feature. If the trainingMode is set to BEST_RANDOM_SPLIT, then the numSplittingSteps controls how many random searches there will be per feature.

A higher value will increase the chances of building a better model, but will take longer to train the model. Value must be larger than zero.

Parameters

numSplittingSteps

sets the number of steps that will be used to search for the best spliting value for each node.

Returns

returns true if the parameter was set, false otherwise

Definition at line 395 of file RegressionTree.cpp.

bool RegressionTree::setRemoveFeaturesAtEachSpilt

(

const bool

removeFeaturesAtEachSpilt

)

Sets if a feature is removed at each spilt so it can not be used again. If true then the best feature selected at each node will be removed so it can not be used in any children of that node. If false, then the feature that provides the best spilt at each node will be used, regardless of how many times it has been used again.

Parameters

removeFeaturesAtEachSpilt

if true, then each feature is removed at each spilt so it can not be used again

Returns

returns true if the parameter was set, false otherwise

Definition at line 422 of file RegressionTree.cpp.

bool RegressionTree::setTrainingMode

(

const Tree::TrainingMode

trainingMode

)

Sets the training mode, this should be one of the TrainingModes enums.

Parameters

trainingMode

the new trainingMode, this should be one of the TrainingModes enums

Returns

returns true if the trainingMode was set successfully, false otherwise

Definition at line 386 of file RegressionTree.cpp.

bool RegressionTree::train_ ( RegressionData &  trainingData )

overridevirtual

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 111 of file RegressionTree.cpp.

---

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

• RegressionTree.h
• RegressionTree.cpp