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::RegressionTree Class Reference
Inheritance diagram for GRT::RegressionTree:
GRT::Tree GRT::Regressifier GRT::GRTBase GRT::MLBase GRT::GRTBase GRT::Observer< TrainingResult > GRT::Observer< TestInstanceResult >

Public Member Functions

 RegressionTree (const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT, const bool useScaling=false, const double minRMSErrorPerNode=0.01)
 
 RegressionTree (const RegressionTree &rhs)
 
virtual ~RegressionTree (void)
 
RegressionTreeoperator= (const RegressionTree &rhs)
 
virtual bool deepCopyFrom (const Regressifier *regressifier)
 
virtual bool train_ (RegressionData &trainingData)
 
virtual bool predict_ (VectorDouble &inputVector)
 
virtual bool clear ()
 
virtual bool print () const
 
virtual bool saveModelToFile (fstream &file) const
 
virtual bool loadModelFromFile (fstream &file)
 
RegressionTreeNodedeepCopyTree () const
 
const RegressionTreeNodegetTree () const
 
double getMinRMSErrorPerNode () const
 
bool setMinRMSErrorPerNode (const double minRMSErrorPerNode)
 
- Public Member Functions inherited from GRT::Tree
 Tree (const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT)
 
virtual ~Tree (void)
 
virtual bool getModel (ostream &stream) const
 
const NodegetTree () const
 
UINT getTrainingMode () const
 
UINT getNumSplittingSteps () const
 
UINT getMinNumSamplesPerNode () const
 
UINT getMaxDepth () const
 
UINT getPredictedNodeID () const
 
bool getRemoveFeaturesAtEachSpilt () const
 
bool setTrainingMode (const UINT trainingMode)
 
bool setNumSplittingSteps (const UINT numSplittingSteps)
 
bool setMinNumSamplesPerNode (const UINT minNumSamplesPerNode)
 
bool setMaxDepth (const UINT maxDepth)
 
bool setRemoveFeaturesAtEachSpilt (const bool removeFeaturesAtEachSpilt)
 
- 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::Regressifier
 Regressifier (void)
 
virtual ~Regressifier (void)
 
bool copyBaseVariables (const Regressifier *regressifier)
 
virtual bool reset ()
 
string getRegressifierType () const
 
VectorDouble getRegressionData () const
 
vector< MinMaxgetInputRanges () const
 
vector< MinMaxgetOutputRanges () const
 
RegressifiercreateNewInstance () const
 
RegressifierdeepCopy () const
 
const RegressifiergetBaseRegressifier () 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_ (ClassificationData &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::Observer< TrainingResult >
virtual void notify (const TrainingResult &data)
 
- Public Member Functions inherited from GRT::Observer< TestInstanceResult >
virtual void notify (const TestInstanceResult &data)
 

Protected Member Functions

RegressionTreeNodebuildTree (const RegressionData &trainingData, RegressionTreeNode *parent, vector< UINT > features, UINT nodeID)
 
bool computeBestSpilt (const RegressionData &trainingData, const vector< UINT > &features, UINT &featureIndex, double &threshold, double &minError)
 
bool computeBestSpiltBestIterativeSpilt (const RegressionData &trainingData, const vector< UINT > &features, UINT &featureIndex, double &threshold, double &minError)
 
bool computeNodeRegressionData (const RegressionData &trainingData, VectorDouble &regressionData)
 
- Protected Member Functions inherited from GRT::GRTBase
double SQR (const double &x) const
 
- Protected Member Functions inherited from GRT::Regressifier
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 Attributes

double minRMSErrorPerNode
 <Tell the compiler we are using the base class predict method to stop hidden virtual function warnings
 
- Protected Attributes inherited from GRT::Tree
UINT trainingMode
 
UINT numSplittingSteps
 
UINT minNumSamplesPerNode
 
UINT maxDepth
 
bool removeFeaturesAtEachSpilt
 
Nodetree
 
- Protected Attributes inherited from GRT::GRTBase
string classType
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
TrainingLog trainingLog
 
TestingLog testingLog
 
WarningLog warningLog
 
- Protected Attributes inherited from GRT::Regressifier
string regressifierType
 
VectorDouble regressionData
 
vector< MinMaxinputVectorRanges
 
vector< MinMaxtargetVectorRanges
 
- 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
 

Static Protected Attributes

static RegisterRegressifierModule< RegressionTreeregisterModule
 

Additional Inherited Members

- Public Types inherited from GRT::Tree
enum  TrainingMode { BEST_ITERATIVE_SPILT =0, BEST_RANDOM_SPLIT, NUM_TRAINING_MODES }
 
- Public Types inherited from GRT::Regressifier
typedef std::map< string, Regressifier *(*)() > StringRegressifierMap
 
- Public Types inherited from GRT::MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from GRT::GRTBase
static string getGRTVersion (bool returnRevision=true)
 
static string getGRTRevison ()
 
- Static Public Member Functions inherited from GRT::Regressifier
static RegressifiercreateInstanceFromString (string const &regressifierType)
 
static vector< string > getRegisteredRegressifiers ()
 
- Static Protected Member Functions inherited from GRT::Regressifier
static StringRegressifierMapgetMap ()
 

Detailed Description

Definition at line 40 of file RegressionTree.h.

Constructor & Destructor Documentation

GRT::RegressionTree::RegressionTree ( const UINT  numSplittingSteps = 100,
const UINT  minNumSamplesPerNode = 5,
const UINT  maxDepth = 10,
const bool  removeFeaturesAtEachSpilt = false,
const UINT  trainingMode = BEST_ITERATIVE_SPILT,
const bool  useScaling = false,
const double  minRMSErrorPerNode = 0.01 
)

Default Constructor

Parameters
UINTnumSplittingSteps: sets the number of steps that will be used to search for the best spliting value for each node. Default value = 100
UINTminNumSamplesPerNode: 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
UINTmaxDepth: sets the maximum depth of the tree. Default value = 10
boolremoveFeaturesAtEachSpilt: sets if a feature is removed at each spilt so it can not be used again. Default value = false
UINTtrainingMode: sets the training mode, this should be one of the TrainingMode enums. Default value = BEST_ITERATIVE_SPILT
booluseScaling: sets if the training and real-time data should be scaled between [0 1]. Default value = false
constdouble minRMSErrorPerNode: sets the minimum RMS error that allowed per node, if the RMS error is below that, the node will become a leafNode. Default value = 0.01

Definition at line 31 of file RegressionTree.cpp.

GRT::RegressionTree::RegressionTree ( const RegressionTree rhs)

Defines the copy constructor.

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

Definition at line 50 of file RegressionTree.cpp.

GRT::RegressionTree::~RegressionTree ( void  )
virtual

Default Destructor

Definition at line 61 of file RegressionTree.cpp.

Member Function Documentation

bool GRT::RegressionTree::clear ( )
virtual

This overrides the clear function in the Regressifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.

Returns
returns true if the module was cleared succesfully, false otherwise

Reimplemented from GRT::Tree.

Definition at line 196 of file RegressionTree.cpp.

bool GRT::RegressionTree::deepCopyFrom ( const Regressifier regressifier)
virtual

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

Parameters
Regressifier*regressifier: a pointer to the Regressifier Base Class, this should be pointing to another RegressionTree instance
Returns
returns true if the clone was successfull, false otherwise

Reimplemented from GRT::Regressifier.

Definition at line 89 of file RegressionTree.cpp.

RegressionTreeNode * GRT::RegressionTree::deepCopyTree ( ) const
virtual

Deep copies the regression tree, returning a pointer to the new regression tree. The user is in charge of cleaning up the memory so must delete the pointer when they no longer need it. NULL will be returned if the tree could not be copied.

Returns
returns a pointer to a deep copy of the regression tree

Reimplemented from GRT::Tree.

Definition at line 345 of file RegressionTree.cpp.

double GRT::RegressionTree::getMinRMSErrorPerNode ( ) const

Gets the minimum root mean squared error value that needs to be exceeded for the tree to continue growing at a specific node. If the RMS error is below this value then the node will be made into a leaf node.

Returns
returns the minimum RMS error per node

Definition at line 358 of file RegressionTree.cpp.

const RegressionTreeNode * GRT::RegressionTree::getTree ( ) const

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

Returns
returns a const pointer to the regression tree

Definition at line 354 of file RegressionTree.cpp.

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

This loads a trained RegressionTree model from a file. This overrides the loadModelFromFile function in the Regressifier base class.

Parameters
fstream&file: a reference to the file the RegressionTree model will be loaded from
Returns
returns true if the model was loaded successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 251 of file RegressionTree.cpp.

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

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

Parameters
constRegressionTree &rhs: another instance of a RegressionTree
Returns
returns a pointer to this instance of the RegressionTree

Definition at line 66 of file RegressionTree.cpp.

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

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

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

Reimplemented from GRT::MLBase.

Definition at line 165 of file RegressionTree.cpp.

bool GRT::RegressionTree::print ( ) const
virtual

Prints the tree to std::cout.

Returns
returns true if the model was printed

Reimplemented from GRT::Tree.

Definition at line 210 of file RegressionTree.cpp.

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

This saves the trained RegressionTree model to a file. This overrides the saveModelToFile function in the Regressifier base class.

Parameters
fstream&file: a reference to the file the RegressionTree model will be saved to
Returns
returns true if the model was saved successfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 216 of file RegressionTree.cpp.

bool GRT::RegressionTree::setMinRMSErrorPerNode ( const double  minRMSErrorPerNode)

Sets the minimum RMS error that needs to be exceeded for the tree to continue growing at a specific node.

Returns
returns true if the parameter was updated

Definition at line 362 of file RegressionTree.cpp.

bool GRT::RegressionTree::train_ ( RegressionData trainingData)
virtual

This trains the RegressionTree model, using the labelled regression data. This overrides the train function in the Regressifier base class.

Parameters
RegressionDatatrainingData: a reference to the training data
Returns
returns true if the RegressionTree model was trained, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 118 of file RegressionTree.cpp.


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