Inheritance diagram for DecisionTree:

Public Member Functions
	DecisionTree (const DecisionTreeNode &decisionTreeNode=DecisionTreeClusterNode(), const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSplit=false, const Tree::TrainingMode trainingMode=Tree::TrainingMode::BEST_ITERATIVE_SPILT, const UINT numSplittingSteps=100, const bool useScaling=false)

	DecisionTree (const DecisionTree &rhs)

virtual	~DecisionTree (void)

DecisionTree &	operator= (const DecisionTree &rhs)

virtual bool	deepCopyFrom (const Classifier *classifier) override

virtual bool	train_ (ClassificationData &trainingData) override

virtual bool	predict_ (VectorFloat &inputVector) override

virtual bool	clear () override

virtual bool	recomputeNullRejectionThresholds () override

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

virtual bool	load (std::fstream &file) override

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

DecisionTreeNode *	deepCopyTree () const

DecisionTreeNode *	deepCopyDecisionTreeNode () const

const DecisionTreeNode *	getTree () const

Tree::TrainingMode	getTrainingMode () const

UINT	getNumSplittingSteps () const

UINT	getMinNumSamplesPerNode () const

UINT	getMaxDepth () const

UINT	getPredictedNodeID () const

bool	getRemoveFeaturesAtEachSplit () const

bool	setTrainingMode (const Tree::TrainingMode trainingMode)

bool	setNumSplittingSteps (const UINT numSplittingSteps)

bool	setMinNumSamplesPerNode (const UINT minNumSamplesPerNode)

bool	setMaxDepth (const UINT maxDepth)

bool	setRemoveFeaturesAtEachSplit (const bool removeFeaturesAtEachSplit)

	GRT_DEPRECATED_MSG ("setRemoveFeaturesAtEachSpilt(const bool removeFeaturesAtEachSpilt) is deprecated, use setRemoveFeaturesAtEachSplit(const bool removeFeaturesAtEachSplit) instead", bool setRemoveFeaturesAtEachSpilt(const bool removeFeaturesAtEachSpilt))

bool	setDecisionTreeNode (const DecisionTreeNode &node)

Public Member Functions inherited from Classifier
	Classifier (const std::string &classifierId="")

virtual	~Classifier (void)

bool	copyBaseVariables (const Classifier *classifier)

virtual bool	reset ()

virtual bool	computeAccuracy (const ClassificationData &data, Float &accuracy)

std::string	getClassifierType () const

bool	getSupportsNullRejection () const

bool	getNullRejectionEnabled () const

Float	getNullRejectionCoeff () const

Float	getMaximumLikelihood () const

Float	getBestDistance () const

Float	getPhase () const

Float	getTrainingSetAccuracy () const

virtual UINT	getNumClasses () const

UINT	getClassLabelIndexValue (const UINT classLabel) const

UINT	getPredictedClassLabel () const

VectorFloat	getClassLikelihoods () const

VectorFloat	getClassDistances () const

VectorFloat	getNullRejectionThresholds () const

Vector< UINT >	getClassLabels () const

Vector< MinMax >	getRanges () const

bool	enableNullRejection (const bool useNullRejection)

virtual bool	setNullRejectionCoeff (const Float nullRejectionCoeff)

virtual bool	setNullRejectionThresholds (const VectorFloat &newRejectionThresholds)

bool	getTimeseriesCompatible () const

Classifier *	create () const

	GRT_DEPRECATED_MSG ("createNewInstance is deprecated, use create instead.", Classifier *createNewInstance() const )

	GRT_DEPRECATED_MSG ("createInstanceFromString is deprecated, use create instead.", static Classifier *createInstanceFromString(const std::string &id))

Classifier *	deepCopy () const

const Classifier *	getClassifierPointer () const

const Classifier &	getBaseClassifier () 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 (RegressionData 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	print () const

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 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 Classifier
static Classifier *	create (const std::string &id)

static Vector< std::string >	getRegisteredClassifiers ()

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

static std::string	getGRTRevison ()

Protected Member Functions
bool	loadLegacyModelFromFile_v1 (std::fstream &file)

bool	loadLegacyModelFromFile_v2 (std::fstream &file)

bool	loadLegacyModelFromFile_v3 (std::fstream &file)

bool	trainTree (ClassificationData trainingData, const ClassificationData &trainingDataCopy, const ClassificationData &validationData, Vector< UINT > features)

DecisionTreeNode *	buildTree (ClassificationData &trainingData, DecisionTreeNode *parent, Vector< UINT > features, const Vector< UINT > &classLabels, UINT nodeID)

Float	getNodeDistance (const VectorFloat &x, const UINT nodeID)

Float	getNodeDistance (const VectorFloat &x, const VectorFloat &y)

Protected Member Functions inherited from Classifier
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
DecisionTreeNode *	decisionTreeNode

std::map< UINT, VectorFloat >	nodeClusters

VectorFloat	classClusterMean

VectorFloat	classClusterStdDev

DecisionTreeNode *	tree

UINT	minNumSamplesPerNode

UINT	maxDepth

UINT	numSplittingSteps

bool	removeFeaturesAtEachSplit

Tree::TrainingMode	trainingMode

Protected Attributes inherited from Classifier
bool	supportsNullRejection

bool	useNullRejection

UINT	numClasses

UINT	predictedClassLabel

UINT	classifierMode

Float	nullRejectionCoeff

Float	maxLikelihood

Float	bestDistance

Float	phase

Float	trainingSetAccuracy

VectorFloat	classLikelihoods

VectorFloat	classDistances

VectorFloat	nullRejectionThresholds

Vector< UINT >	classLabels

Vector< MinMax >	ranges

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 Classifier
enum	ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }

typedef std::map< std::string, Classifier ()() >	StringClassifierMap

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 Classifier
static StringClassifierMap *	getMap ()

Detailed Description

Examples:: ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp.

Definition at line 47 of file DecisionTree.h.

Constructor & Destructor Documentation

DecisionTree::DecisionTree	(	const DecisionTreeNode &	decisionTreeNode = `DecisionTreeClusterNode()`,
		const UINT	minNumSamplesPerNode = `5`,
		const UINT	maxDepth = `10`,
		const bool	removeFeaturesAtEachSplit = `false`,
		const Tree::TrainingMode	trainingMode = `Tree::TrainingMode::BEST_ITERATIVE_SPILT`,
		const UINT	numSplittingSteps = `100`,
		const bool	useScaling = `false`
	)

Default Constructor

Parameters

decisionTreeNode	sets the type of decision tree node that will be used when training a new decision tree model. Default: DecisionTreeClusterNode
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
removeFeaturesAtEachSplit	sets if a feature is removed at each split 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
numSplittingSteps	sets the number of steps that will be used to search for the best spliting value for each node. Default value = 100
useScaling	sets if the training and real-time data should be scaled between [0 1]. Default value = false

Definition at line 33 of file DecisionTree.cpp.

DecisionTree::DecisionTree ( const DecisionTree & 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 DecisionTree.cpp.

DecisionTree::~DecisionTree ( void )

virtual

Default Destructor

Definition at line 56 of file DecisionTree.cpp.

Member Function Documentation

bool DecisionTree::clear ( )

overridevirtual

This overrides the clear function in the Classifier 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 Classifier.

Definition at line 499 of file DecisionTree.cpp.

DecisionTreeNode * DecisionTree::deepCopyDecisionTreeNode ( ) const

Gets a pointer to the decision tree node. NULL will be returned if the decision tree node has not been set.

Returns: returns a pointer to a deep copy of the decision tree node

Definition at line 842 of file DecisionTree.cpp.

bool DecisionTree::deepCopyFrom ( const Classifier * classifier )

overridevirtual

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

Parameters

classifier a pointer to the Classifier Base Class, this should be pointing to another DecisionTree instance

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

Reimplemented from Classifier.

Definition at line 96 of file DecisionTree.cpp.

DecisionTreeNode * DecisionTree::deepCopyTree ( ) const

Deep copies the decision tree, returning a pointer to the new decision 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 decision tree

Definition at line 833 of file DecisionTree.cpp.

std::string DecisionTree::getId ( )

static

Gets a string that represents the DecisionTree class.

Returns: returns a string containing the ID of this class

Definition at line 28 of file DecisionTree.cpp.

UINT DecisionTree::getMaxDepth ( ) const

Gets the maximum depth of the tree.

Returns: returns the maximum depth of the tree

Definition at line 1022 of file DecisionTree.cpp.

UINT DecisionTree::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 1018 of file DecisionTree.cpp.

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

overridevirtual

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 MLBase.

Definition at line 825 of file DecisionTree.cpp.

UINT DecisionTree::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 1014 of file DecisionTree.cpp.

UINT DecisionTree::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 1026 of file DecisionTree.cpp.

bool DecisionTree::getRemoveFeaturesAtEachSplit ( ) const

Gets if a feature is removed at each split 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 1035 of file DecisionTree.cpp.

Tree::TrainingMode DecisionTree::getTrainingMode ( ) const

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

Returns: returns the training mode

Definition at line 1010 of file DecisionTree.cpp.

const DecisionTreeNode * DecisionTree::getTree ( ) const

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

Returns: returns a const pointer to the decision tree

Definition at line 851 of file DecisionTree.cpp.

DecisionTree::GRT_DEPRECATED_MSG	(	"setRemoveFeaturesAtEachSpilt(const bool removeFeaturesAtEachSpilt) is	deprecated,
		use setRemoveFeaturesAtEachSplit(const bool removeFeaturesAtEachSplit) instead"	,
		bool	setRemoveFeaturesAtEachSpiltconst bool removeFeaturesAtEachSpilt
	)

Deprecated:: use setRemoveFeaturesAtEachSplit(const bool removeFeaturesAtEachSplit)

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

overridevirtual

This loads a trained DecisionTree model from a file. This overrides the load function in the Classifier base class.

Parameters

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

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

Reimplemented from MLBase.

Examples:: ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp.

Definition at line 622 of file DecisionTree.cpp.

bool DecisionTree::loadLegacyModelFromFile_v1 ( std::fstream & file )

protected

Read the ranges if needed

Definition at line 1084 of file DecisionTree.cpp.

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

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

Parameters

rhs	another instance of a DecisionTree

Returns: returns a pointer to this instance of the DecisionTree

Definition at line 66 of file DecisionTree.cpp.

bool DecisionTree::predict_ ( VectorFloat & inputVector )

overridevirtual

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

Parameters

inputVector the input Vector to classify

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

Reimplemented from MLBase.

Definition at line 423 of file DecisionTree.cpp.

bool DecisionTree::recomputeNullRejectionThresholds ( )

overridevirtual

This recomputes the null rejection thresholds for each of the classes in the DecisionTree model. The DecisionTree model needs to be trained first before this function can be called.

Returns: returns true if the null rejection thresholds were updated successfully, false otherwise

Reimplemented from Classifier.

Definition at line 519 of file DecisionTree.cpp.

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

overridevirtual

This saves the trained DecisionTree model to a file. This overrides the save function in the Classifier base class.

Parameters

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

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

Reimplemented from MLBase.

Examples:: ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp.

Definition at line 541 of file DecisionTree.cpp.

bool DecisionTree::setDecisionTreeNode ( const DecisionTreeNode & node )

Sets the decision tree node, this will be used as the starting node the next time the DecisionTree model is trained.

Returns: returns true if the decision tree node was updated, false otherwise

Examples:: ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp.

Definition at line 855 of file DecisionTree.cpp.

bool DecisionTree::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

Examples:: ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp.

Definition at line 1066 of file DecisionTree.cpp.

bool DecisionTree::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

Examples:: ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp.

Definition at line 1057 of file DecisionTree.cpp.

bool DecisionTree::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

Examples:: ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp.

Definition at line 1048 of file DecisionTree.cpp.

bool DecisionTree::setRemoveFeaturesAtEachSplit ( const bool removeFeaturesAtEachSplit )

Sets if a feature is removed at each split 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

removeFeaturesAtEachSplit 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 1075 of file DecisionTree.cpp.

bool DecisionTree::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 1039 of file DecisionTree.cpp.

bool DecisionTree::train_ ( ClassificationData & trainingData )

overridevirtual

This trains the DecisionTree model, using the labelled classification data. This overrides the train function in the Classifier base class.

Parameters

trainingData a reference to the training data

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

Reimplemented from MLBase.

Definition at line 132 of file DecisionTree.cpp.

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

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation