GestureRecognitionToolkit  Version: 0.1.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
RandomForests Class Reference

#include <RandomForests.h>

Inheritance diagram for RandomForests:
Classifier MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

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)
 
RandomForestsoperator= (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 saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (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
 
DecisionTreeNodegetTree (const UINT index) const
 
DecisionTreeNodedeepCopyDecisionTreeNode () 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< MinMaxgetRanges () const
 
bool enableNullRejection (bool useNullRejection)
 
virtual bool setNullRejectionCoeff (Float nullRejectionCoeff)
 
virtual bool setNullRejectionThresholds (VectorFloat newRejectionThresholds)
 
virtual bool recomputeNullRejectionThresholds ()
 
bool getTimeseriesCompatible () const
 
ClassifiercreateNewInstance () const
 
ClassifierdeepCopy () const
 
const ClassifiergetClassifierPointer () const
 
const ClassifiergetBaseClassifier () 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)
 
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)
 
MLBasegetMLBasePointer ()
 
const MLBasegetMLBasePointer () 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)
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () 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 Attributes

UINT forestSize
 
UINT numRandomSplits
 
UINT minNumSamplesPerNode
 
UINT maxDepth
 
UINT trainingMode
 
bool removeFeaturesAtEachSpilt
 
Float bootstrappedDatasetWeight
 
DecisionTreeNodedecisionTreeNode
 
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< MinMaxranges
 
- 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
typedef std::map< std::string, Classifier *(*)() > StringClassifierMap
 
- Public Types inherited from MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from Classifier
static ClassifiercreateInstanceFromString (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 Types inherited from Classifier
enum  ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }
 
- 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 StringClassifierMapgetMap ()
 

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
decisionTreeNodesets the type of decision tree node that will be used when training a new RandomForest model. Default: DecisionTreeClusterNode
forestSizesets the number of decision trees that will be trained. Default value = 10
numRandomSplitssets the number of random spilts that will be used to search for the best spliting value for each node. Default value = 100
minNumSamplesPerNodesets 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
maxDepthsets the maximum depth of the tree. Default value = 10
removeFeaturesAtEachSpiltsets if features are removed at each stage in the tree
useScalingsets if the training and real-time data should be scaled between [0 1]. Default value = false
bootstrappedDatasetWeightsets 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 28 of file RandomForests.cpp.

RandomForests::RandomForests ( const RandomForests rhs)

Defines the copy constructor.

Parameters
constRandomForests &rhs: the instance from which all the data will be copied into this instance

Definition at line 52 of file RandomForests.cpp.

RandomForests::~RandomForests ( void  )
virtual

Default Destructor

Definition at line 64 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 329 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
forestanother random forest instance that will be merged with this instance
Returns
returns true if the model was combined successfully, false otherwise

Definition at line 579 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 643 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
classifiera 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 109 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 635 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
normWeightsif 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 659 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 639 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 611 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
normWeightsif 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 685 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 623 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 619 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 615 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 631 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 627 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 652 of file RandomForests.cpp.

bool RandomForests::loadModelFromFile ( std::fstream &  file)
virtual

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

Parameters
filea 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 419 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
rhsanother instance of a RandomForests
Returns
returns a pointer to this instance of the RandomForests

Definition at line 74 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
inputVectorthe input vector to classify
Returns
returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 271 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 347 of file RandomForests.cpp.

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

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

Parameters
filea 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 369 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 777 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 766 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
forestSizesets the number of trees in the forest.
Returns
returns true if the parameter was set, false otherwise

Definition at line 717 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
maxDepththe maximum depth of the tree
Returns
returns true if the parameter was set, false otherwise

Definition at line 742 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
minNumSamplesPerNodethe minimum number of samples that are allowed per node
Returns
returns true if the parameter was set, false otherwise

Definition at line 734 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
numSplittingStepssets 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 726 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
removeFeaturesAtEachSpiltif 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 750 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
trainingModethe 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 755 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
trainingDataa reference to the training data
Returns
returns true if the RandomForests model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 153 of file RandomForests.cpp.


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