#include <RandomForests.h>

Inheritance diagram for RandomForests:

Public Member Functions
	RandomForests (const DecisionTreeNode &decisionTreeNode=DecisionTreeClusterNode(), const UINT forestSize=10, const UINT numRandomSplits=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const UINT trainingMode=DecisionTree::BEST_RANDOM_SPLIT, const bool removeFeaturesAtEachSpilt=true, const bool useScaling=false, const Float bootstrappedDatasetWeight=0.8)

	RandomForests (const RandomForests &rhs)

virtual	~RandomForests (void)

RandomForests &	operator= (const RandomForests &rhs)

virtual bool	deepCopyFrom (const Classifier *classifier)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	predict_ (VectorDouble &inputVector)

virtual bool	clear ()

virtual bool	print () const

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

virtual bool	load (std::fstream &file)

bool	combineModels (const RandomForests &forest)

UINT	getForestSize () const

UINT	getNumRandomSplits () const

UINT	getMinNumSamplesPerNode () const

UINT	getMaxDepth () const

UINT	getTrainingMode () const

const Vector< DecisionTreeNode * > &	getForest () const

bool	getRemoveFeaturesAtEachSpilt () const

Float	getBootstrappedDatasetWeight () const

DecisionTreeNode *	getTree (const UINT index) const

DecisionTreeNode *	deepCopyDecisionTreeNode () const

VectorDouble	getFeatureWeights (const bool normWeights=true) const

MatrixDouble	getLeafNodeFeatureWeights (const bool normWeights=true) const

bool	setForestSize (const UINT forestSize)

bool	setNumRandomSplits (const UINT numSplittingSteps)

bool	setMinNumSamplesPerNode (const UINT minNumSamplesPerNode)

bool	setMaxDepth (const UINT maxDepth)

bool	setRemoveFeaturesAtEachSpilt (const bool removeFeaturesAtEachSpilt)

bool	setTrainingMode (const UINT trainingMode)

bool	setDecisionTreeNode (const DecisionTreeNode &node)

bool	setBootstrappedDatasetWeight (const Float bootstrappedDatasetWeight)

Public Member Functions inherited from Classifier
	Classifier (void)

virtual	~Classifier (void)

bool	copyBaseVariables (const Classifier *classifier)

virtual bool	reset ()

std::string	getClassifierType () const

bool	getSupportsNullRejection () const

bool	getNullRejectionEnabled () const

Float	getNullRejectionCoeff () const

Float	getMaximumLikelihood () const

Float	getBestDistance () const

Float	getPhase () const

virtual UINT	getNumClasses () const

UINT	getClassLabelIndexValue (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 (bool useNullRejection)

virtual bool	setNullRejectionCoeff (Float nullRejectionCoeff)

virtual bool	setNullRejectionThresholds (VectorFloat newRejectionThresholds)

virtual bool	recomputeNullRejectionThresholds ()

bool	getTimeseriesCompatible () const

Classifier *	createNewInstance () const

Classifier *	deepCopy () const

const Classifier *	getClassifierPointer () const

const Classifier &	getBaseClassifier () 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 (RegressionData 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)

	GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(std::string filename) instead", virtual bool saveModelToFile(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(std::string filename) instead", virtual bool loadModelFromFile(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

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 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 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 *	createInstanceFromString (std::string const &classifierType)

static Vector< std::string >	getRegisteredClassifiers ()

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

static std::string	getGRTRevison ()

Protected Attributes
UINT	forestSize

UINT	numRandomSplits

UINT	minNumSamplesPerNode

UINT	maxDepth

UINT	trainingMode

bool	removeFeaturesAtEachSpilt

Float	bootstrappedDatasetWeight

DecisionTreeNode *	decisionTreeNode

Vector< DecisionTreeNode * >	forest

Protected Attributes inherited from Classifier
std::string	classifierType

bool	supportsNullRejection

bool	useNullRejection

UINT	numClasses

UINT	predictedClassLabel

UINT	classifierMode

Float	nullRejectionCoeff

Float	maxLikelihood

Float	bestDistance

Float	phase

VectorFloat	classLikelihoods

VectorFloat	classDistances

VectorFloat	nullRejectionThresholds

Vector< UINT >	classLabels

Vector< MinMax >	ranges

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

Protected Attributes inherited from GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

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	BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }

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

Static Protected Member Functions inherited from Classifier
static StringClassifierMap *	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 42 of file RandomForests.h.

Constructor & Destructor Documentation

RandomForests::RandomForests	(	const DecisionTreeNode &	decisionTreeNode = `DecisionTreeClusterNode()`,
		const UINT	forestSize = `10`,
		const UINT	numRandomSplits = `100`,
		const UINT	minNumSamplesPerNode = `5`,
		const UINT	maxDepth = `10`,
		const UINT	trainingMode = `DecisionTree::BEST_RANDOM_SPLIT`,
		const bool	removeFeaturesAtEachSpilt = `true`,
		const bool	useScaling = `false`,
		const Float	bootstrappedDatasetWeight = `0.8`
	)

Default Constructor

Parameters

decisionTreeNode	sets the type of decision tree node that will be used when training a new RandomForest model. Default: DecisionTreeClusterNode
forestSize	sets the number of decision trees that will be trained. Default value = 10
numRandomSplits	sets the number of random spilts 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 features are removed at each stage in the tree
useScaling	sets if the training and real-time data should be scaled between [0 1]. Default value = false
bootstrappedDatasetWeight	sets the size of the bootstrapped dataset used to train a tree, the number of bootstrapped samples will be M*bootstrappedDatasetWeight, where M is the number of samples in the original training dataset

Definition at line 33 of file RandomForests.cpp.

RandomForests::RandomForests ( const RandomForests & rhs )

Defines the copy constructor.

Parameters

const RandomForests &rhs: the instance from which all the data will be copied into this instance

Definition at line 57 of file RandomForests.cpp.

RandomForests::~RandomForests ( void )

virtual

Default Destructor

Definition at line 69 of file RandomForests.cpp.

Member Function Documentation

bool RandomForests::clear ( )

virtual

This function clears the RandomForests module, removing any trained model and setting all the base variables to their default values.

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

Reimplemented from Classifier.

Definition at line 333 of file RandomForests.cpp.

bool RandomForests::combineModels ( const RandomForests & forest )

This function enables multiple random forest models to be merged together. The model in forest will be combined with this instance. For example, if this instance has 10 trees, and the other forest has 15 trees, the resulting model will have 25 trees. Both forests must be trained and have the same number of inputs.

Parameters

forest another random forest instance that will be merged with this instance

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

Definition at line 583 of file RandomForests.cpp.

DecisionTreeNode * RandomForests::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 647 of file RandomForests.cpp.

bool RandomForests::deepCopyFrom ( const Classifier * classifier )

virtual

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 RandomForests instance) into this instance

Parameters

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

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

Reimplemented from Classifier.

Definition at line 114 of file RandomForests.cpp.

Float RandomForests::getBootstrappedDatasetWeight ( ) const

Gets bootstrapped dataset weight, this controls the size of the bootstrapped dataset used to train each tree in the forest. The number of bootstrapped samples will be M*bootstrappedDatasetWeight, where M is the number of samples in the original training dataset.

Returns: returns the bootstrappedDatasetWeight parameter

Definition at line 639 of file RandomForests.cpp.

VectorDouble RandomForests::getFeatureWeights ( const bool normWeights = true ) const

Returns a vector of weights reflecting the importance of each feature in the random forest model. The size of the vector will match the number of inputs (i.e. features) to the classifier. The value in each element in the vector represents the weight (i.e. importance) of the corresponding feature. A higher value represents a higher weight.

The vector will be empty if the model has not been trained.

Note: This method only works with DecisionTreeNodes that support the getFeatureWeight() function.

Parameters

normWeights if true, the weights will be normalized so they sum to 1.0

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

Definition at line 663 of file RandomForests.cpp.

const Vector< DecisionTreeNode * > & RandomForests::getForest ( ) const

Gets a vector of DecisionTreeNodes pointers that represent the trees in the forest.

Returns: returns a vector of DecisionTreeNodes

Definition at line 643 of file RandomForests.cpp.

UINT RandomForests::getForestSize ( ) const

Gets the number of trees in the random forest.

Returns: returns the number of trees in the random forest

Definition at line 615 of file RandomForests.cpp.

std::string RandomForests::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 RandomForests.cpp.

MatrixDouble RandomForests::getLeafNodeFeatureWeights ( const bool normWeights = true ) const

Returns a vector of weights reflecting the importance of each feature in the random forest model. The size of the vector will match the number of inputs (i.e. features) to the classifier. The value in each element in the vector represents the weight (i.e. importance) of the corresponding feature. A higher value represents a higher weight.

The vector will be empty if the model has not been trained.

Note: This method only works with DecisionTreeNodes that support the getFeatureWeight() function.

Parameters

normWeights if true, the weights will be normalized so they sum to 1.0

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

Definition at line 689 of file RandomForests.cpp.

UINT RandomForests::getMaxDepth ( ) const

Gets the maximum depth of the tree.

Returns: returns the maximum depth of the tree

Definition at line 627 of file RandomForests.cpp.

UINT RandomForests::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 623 of file RandomForests.cpp.

UINT RandomForests::getNumRandomSplits ( ) const

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

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

Definition at line 619 of file RandomForests.cpp.

bool RandomForests::getRemoveFeaturesAtEachSpilt ( ) const

Gets 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.

Returns: returns the removeFeaturesAtEachSpilt parameter

Definition at line 635 of file RandomForests.cpp.

UINT RandomForests::getTrainingMode ( ) const

Gets the training mode that will be used to train each DecisionTree in the forest.

Returns: returns the trainingMode

Definition at line 631 of file RandomForests.cpp.

DecisionTreeNode * RandomForests::getTree ( const UINT index ) const

Gets a pointer to the tree at the specific index in the forest. NULL will be returned if the model has not been trained or the index is invalid.

Returns: returns a pointer to the tree at the specific index

Definition at line 656 of file RandomForests.cpp.

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

virtual

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

Parameters

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

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

Reimplemented from MLBase.

Definition at line 423 of file RandomForests.cpp.

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

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

Parameters

rhs	another instance of a RandomForests

Returns: returns a pointer to this instance of the RandomForests

Definition at line 79 of file RandomForests.cpp.

bool RandomForests::predict_ ( VectorDouble & inputVector )

virtual

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 275 of file RandomForests.cpp.

bool RandomForests::print ( ) const

virtual

This function will print the model and settings to the display log.

Returns: returns true if the model was printed succesfully, false otherwise

Reimplemented from MLBase.

Definition at line 351 of file RandomForests.cpp.

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

virtual

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

Parameters

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

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

Reimplemented from MLBase.

Definition at line 373 of file RandomForests.cpp.

bool RandomForests::setBootstrappedDatasetWeight ( const Float bootstrappedDatasetWeight )

Sets the size of the bootstrapped dataset used to train a tree. The number of bootstrapped samples will be M*bootstrappedDatasetWeight, where M is the number of samples in the original training dataset. The weight should be in the range [> 0.0 <= 1.0]

Returns: returns true if the parameter was updated, false otherwise

Definition at line 781 of file RandomForests.cpp.

bool RandomForests::setDecisionTreeNode ( const DecisionTreeNode & node )

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

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

Definition at line 770 of file RandomForests.cpp.

bool RandomForests::setForestSize ( const UINT forestSize )

Sets the number of trees in the forest. Changing this value will clear any previously trained model.

Parameters

forestSize sets the number of trees in the forest.

Returns: returns true if the parameter was set, false otherwise

Definition at line 721 of file RandomForests.cpp.

bool RandomForests::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 746 of file RandomForests.cpp.

bool RandomForests::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 738 of file RandomForests.cpp.

bool RandomForests::setNumRandomSplits ( const UINT numSplittingSteps )

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

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 730 of file RandomForests.cpp.

bool RandomForests::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 754 of file RandomForests.cpp.

bool RandomForests::setTrainingMode ( const UINT trainingMode )

Sets the training mode used to train each DecisionTree in the forest, this should be one of the DecisionTree::TrainingModes enums.

Parameters

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

Returns: returns true if the trainingMode was set successfully, false otherwise

Definition at line 759 of file RandomForests.cpp.

bool RandomForests::train_ ( ClassificationData & trainingData )

virtual

This trains the RandomForests 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 RandomForests model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 158 of file RandomForests.cpp.

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

Public Member Functions

Static Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation