GestureRecognitionToolkit  Version: 1.0 Revision: 04-03-15
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
GRT::RandomForests Class Reference
Inheritance diagram for GRT::RandomForests:
GRT::Classifier GRT::MLBase GRT::GRTBase GRT::Observer< TrainingResult > GRT::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)
 
 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 (fstream &file) const
 
virtual bool loadModelFromFile (fstream &file)
 
UINT getForestSize () const
 
UINT getNumRandomSplits () const
 
UINT getMinNumSamplesPerNode () const
 
UINT getMaxDepth () const
 
UINT getTrainingMode () const
 
const vector< DecisionTreeNode * > getForest () const
 
bool getRemoveFeaturesAtEachSpilt () const
 
DecisionTreeNodedeepCopyDecisionTreeNode () 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)
 
- Public Member Functions inherited from GRT::Classifier
 Classifier (void)
 
virtual ~Classifier (void)
 
bool copyBaseVariables (const Classifier *classifier)
 
virtual bool reset ()
 
string getClassifierType () const
 
bool getSupportsNullRejection () const
 
bool getNullRejectionEnabled () const
 
double getNullRejectionCoeff () const
 
double getMaximumLikelihood () const
 
double getBestDistance () const
 
double getPhase () const
 
virtual UINT getNumClasses () const
 
UINT getClassLabelIndexValue (UINT classLabel) const
 
UINT getPredictedClassLabel () const
 
VectorDouble getClassLikelihoods () const
 
VectorDouble getClassDistances () const
 
VectorDouble getNullRejectionThresholds () const
 
vector< UINT > getClassLabels () const
 
vector< MinMaxgetRanges () const
 
bool enableNullRejection (bool useNullRejection)
 
virtual bool setNullRejectionCoeff (double nullRejectionCoeff)
 
virtual bool setNullRejectionThresholds (VectorDouble newRejectionThresholds)
 
virtual bool recomputeNullRejectionThresholds ()
 
bool getTimeseriesCompatible () const
 
ClassifiercreateNewInstance () const
 
ClassifierdeepCopy () const
 
const ClassifiergetClassifierPointer () const
 
const ClassifiergetBaseClassifier () const
 
- Public Member Functions inherited from GRT::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 (TimeSeriesClassificationDataStream trainingData)
 
virtual bool train_ (TimeSeriesClassificationDataStream &trainingData)
 
virtual bool train (UnlabelledData trainingData)
 
virtual bool train_ (UnlabelledData &trainingData)
 
virtual bool train (MatrixDouble data)
 
virtual bool train_ (MatrixDouble &data)
 
virtual bool predict (VectorDouble inputVector)
 
virtual bool predict (MatrixDouble inputMatrix)
 
virtual bool predict_ (MatrixDouble &inputMatrix)
 
virtual bool map (VectorDouble inputVector)
 
virtual bool map_ (VectorDouble &inputVector)
 
virtual bool save (const string filename) const
 
virtual bool load (const string filename)
 
virtual bool saveModelToFile (string filename) const
 
virtual bool loadModelFromFile (string filename)
 
virtual bool getModel (ostream &stream) const
 
double scale (const double &x, const double &minSource, const double &maxSource, const double &minTarget, const double &maxTarget, const bool constrain=false)
 
virtual string getModelAsString () 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
 
double getMinChange () const
 
double getLearningRate () const
 
double getRootMeanSquaredTrainingError () const
 
double getTotalSquaredTrainingError () 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 (bool useScaling)
 
bool setMaxNumEpochs (const UINT maxNumEpochs)
 
bool setMinNumEpochs (const UINT minNumEpochs)
 
bool setMinChange (const double minChange)
 
bool setLearningRate (double learningRate)
 
bool setUseValidationSet (const bool useValidationSet)
 
bool setValidationSetSize (const UINT validationSetSize)
 
bool setRandomiseTrainingOrder (const bool randomiseTrainingOrder)
 
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< TrainingResultgetTrainingResults () const
 
- Public Member Functions inherited from GRT::GRTBase
 GRTBase (void)
 
virtual ~GRTBase (void)
 
bool copyGRTBaseVariables (const GRTBase *GRTBase)
 
string getClassType () const
 
string getLastWarningMessage () const
 
string getLastErrorMessage () const
 
string getLastInfoMessage () const
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () const
 
- Public Member Functions inherited from GRT::Observer< TrainingResult >
virtual void notify (const TrainingResult &data)
 
- Public Member Functions inherited from GRT::Observer< TestInstanceResult >
virtual void notify (const TestInstanceResult &data)
 

Protected Attributes

UINT forestSize
 
UINT numRandomSplits
 
UINT minNumSamplesPerNode
 
UINT maxDepth
 
UINT trainingMode
 
bool removeFeaturesAtEachSpilt
 
DecisionTreeNodedecisionTreeNode
 
vector< DecisionTreeNode * > forest
 
- Protected Attributes inherited from GRT::Classifier
string classifierType
 
bool supportsNullRejection
 
bool useNullRejection
 
UINT numClasses
 
UINT predictedClassLabel
 
UINT classifierMode
 
double nullRejectionCoeff
 
double maxLikelihood
 
double bestDistance
 
double phase
 
VectorDouble classLikelihoods
 
VectorDouble classDistances
 
VectorDouble nullRejectionThresholds
 
vector< UINT > classLabels
 
vector< MinMaxranges
 
- Protected Attributes inherited from GRT::MLBase
bool trained
 
bool useScaling
 
UINT baseType
 
UINT numInputDimensions
 
UINT numOutputDimensions
 
UINT numTrainingIterationsToConverge
 
UINT minNumEpochs
 
UINT maxNumEpochs
 
UINT validationSetSize
 
double learningRate
 
double minChange
 
double rootMeanSquaredTrainingError
 
double totalSquaredTrainingError
 
bool useValidationSet
 
bool randomiseTrainingOrder
 
Random random
 
vector< TrainingResulttrainingResults
 
TrainingResultsObserverManager trainingResultsObserverManager
 
TestResultsObserverManager testResultsObserverManager
 
- Protected Attributes inherited from GRT::GRTBase
string classType
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
TrainingLog trainingLog
 
TestingLog testingLog
 
WarningLog warningLog
 

Additional Inherited Members

- Public Types inherited from GRT::Classifier
typedef std::map< string, Classifier *(*)() > StringClassifierMap
 
- Public Types inherited from GRT::MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from GRT::Classifier
static ClassifiercreateInstanceFromString (string const &classifierType)
 
static vector< string > getRegisteredClassifiers ()
 
- Static Public Member Functions inherited from GRT::GRTBase
static string getGRTVersion (bool returnRevision=true)
 
static string getGRTRevison ()
 
- Protected Types inherited from GRT::Classifier
enum  ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }
 
- Protected Member Functions inherited from GRT::Classifier
bool saveBaseSettingsToFile (fstream &file) const
 
bool loadBaseSettingsFromFile (fstream &file)
 
- Protected Member Functions inherited from GRT::MLBase
bool saveBaseSettingsToFile (fstream &file) const
 
bool loadBaseSettingsFromFile (fstream &file)
 
- Protected Member Functions inherited from GRT::GRTBase
double SQR (const double &x) const
 
- Static Protected Member Functions inherited from GRT::Classifier
static StringClassifierMapgetMap ()
 

Detailed Description

Definition at line 43 of file RandomForests.h.

Constructor & Destructor Documentation

GRT::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 
)

Default Constructor

Parameters
constDecisionTreeNode &decisionTreeNode: sets the type of decision tree node that will be used when training a new RandomForest model. Default: DecisionTreeClusterNode
constUINT forestSize: sets the number of decision trees that will be trained. Default value = 10
constUINT numRandomSplits: sets the number of random spilts that will be used to search for the best spliting value for each node. Default value = 100
constUINT 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
constUINT maxDepth: sets the maximum depth of the tree. Default value = 10
constbool removeFeaturesAtEachSpilt: sets if features are removed at each stage in the tree
constbool useScaling: sets if the training and real-time data should be scaled between [0 1]. Default value = false

Definition at line 28 of file RandomForests.cpp.

GRT::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 49 of file RandomForests.cpp.

GRT::RandomForests::~RandomForests ( void  )
virtual

Default Destructor

Definition at line 61 of file RandomForests.cpp.

Member Function Documentation

bool GRT::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 GRT::Classifier.

Definition at line 262 of file RandomForests.cpp.

DecisionTreeNode * GRT::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 539 of file RandomForests.cpp.

bool GRT::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*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 GRT::Classifier.

Definition at line 105 of file RandomForests.cpp.

UINT GRT::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 511 of file RandomForests.cpp.

UINT GRT::RandomForests::getMaxDepth ( ) const

Gets the maximum depth of the tree.

Returns
returns the maximum depth of the tree

Definition at line 523 of file RandomForests.cpp.

UINT GRT::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 519 of file RandomForests.cpp.

UINT GRT::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 515 of file RandomForests.cpp.

bool GRT::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 531 of file RandomForests.cpp.

UINT GRT::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 527 of file RandomForests.cpp.

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

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

Parameters
fstream&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 GRT::MLBase.

Definition at line 352 of file RandomForests.cpp.

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

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

Parameters
constRandomForests &rhs: another instance of a RandomForests
Returns
returns a pointer to this instance of the RandomForests

Definition at line 71 of file RandomForests.cpp.

bool GRT::RandomForests::predict_ ( VectorDouble &  inputVector)
virtual

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

Parameters
VectorDoubleinputVector: the input vector to classify
Returns
returns true if the prediction was performed, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 205 of file RandomForests.cpp.

bool GRT::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 GRT::MLBase.

Definition at line 280 of file RandomForests.cpp.

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

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

Parameters
fstream&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 GRT::MLBase.

Definition at line 302 of file RandomForests.cpp.

bool GRT::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 597 of file RandomForests.cpp.

bool GRT::RandomForests::setForestSize ( const UINT  forestSize)

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

Parameters
UINTforestSize: sets the number of trees in the forest.
Returns
returns true if the parameter was set, false otherwise

Definition at line 548 of file RandomForests.cpp.

bool GRT::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
UINTmaxDepth: the maximum depth of the tree
Returns
returns true if the parameter was set, false otherwise

Definition at line 573 of file RandomForests.cpp.

bool GRT::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
UINTminNumSamplesPerNode: the minimum number of samples that are allowed per node
Returns
returns true if the parameter was set, false otherwise

Definition at line 565 of file RandomForests.cpp.

bool GRT::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
UINTnumSplittingSteps: 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 557 of file RandomForests.cpp.

bool GRT::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
boolremoveFeaturesAtEachSpilt: 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 581 of file RandomForests.cpp.

bool GRT::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
constUINT 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 586 of file RandomForests.cpp.

bool GRT::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
ClassificationDatatrainingData: a reference to the training data
Returns
returns true if the RandomForests model was trained, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 147 of file RandomForests.cpp.


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