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::DecisionTree Class Reference
Inheritance diagram for GRT::DecisionTree:
GRT::Tree GRT::Classifier GRT::GRTBase GRT::MLBase GRT::GRTBase GRT::Observer< TrainingResult > GRT::Observer< TestInstanceResult >

Public Member Functions

 DecisionTree (const DecisionTreeNode &decisionTreeNode=DecisionTreeClusterNode(), const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT, const UINT numSplittingSteps=100, const bool useScaling=false)
 
 DecisionTree (const DecisionTree &rhs)
 
virtual ~DecisionTree (void)
 
DecisionTreeoperator= (const DecisionTree &rhs)
 
virtual bool deepCopyFrom (const Classifier *classifier)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool predict_ (VectorDouble &inputVector)
 
virtual bool clear ()
 
virtual bool recomputeNullRejectionThresholds ()
 
virtual bool saveModelToFile (fstream &file) const
 
virtual bool loadModelFromFile (fstream &file)
 
virtual bool getModel (ostream &stream) const
 
DecisionTreeNodedeepCopyTree () const
 
const DecisionTreeNodegetTree () const
 
DecisionTreeNodedeepCopyDecisionTreeNode () const
 
bool setDecisionTreeNode (const DecisionTreeNode &node)
 
- 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 print () 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::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)
 
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 print () const
 
virtual bool save (const string filename) const
 
virtual bool load (const string filename)
 
virtual bool saveModelToFile (string filename) const
 
virtual bool loadModelFromFile (string filename)
 
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

bool loadLegacyModelFromFile_v1 (fstream &file)
 
bool loadLegacyModelFromFile_v2 (fstream &file)
 
bool loadLegacyModelFromFile_v3 (fstream &file)
 
DecisionTreeNodebuildTree (ClassificationData &trainingData, DecisionTreeNode *parent, vector< UINT > features, const vector< UINT > &classLabels, UINT nodeID)
 
double getNodeDistance (const VectorDouble &x, const UINT nodeID)
 
double getNodeDistance (const VectorDouble &x, const VectorDouble &y)
 
- Protected Member Functions inherited from GRT::GRTBase
double SQR (const double &x) const
 
- 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 Attributes

DecisionTreeNodedecisionTreeNode
 
std::map< UINT, VectorDouble > nodeClusters
 
VectorDouble classClusterMean
 
VectorDouble classClusterStdDev
 
- 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::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
 

Static Protected Attributes

static RegisterClassifierModule< DecisionTreeregisterModule
 

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::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::GRTBase
static string getGRTVersion (bool returnRevision=true)
 
static string getGRTRevison ()
 
- Static Public Member Functions inherited from GRT::Classifier
static ClassifiercreateInstanceFromString (string const &classifierType)
 
static vector< string > getRegisteredClassifiers ()
 
- Protected Types inherited from GRT::Classifier
enum  ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }
 
- Static Protected Member Functions inherited from GRT::Classifier
static StringClassifierMapgetMap ()
 

Detailed Description

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  removeFeaturesAtEachSpilt = false,
const UINT  trainingMode = BEST_ITERATIVE_SPILT,
const UINT  numSplittingSteps = 100,
const bool  useScaling = false 
)

Default Constructor

Parameters
constDecisionTreeNode &decisionTreeNode: sets the type of decision tree node that will be used when training a new decision tree model. Default: DecisionTreeClusterNode
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
UINTnumSplittingSteps: sets the number of steps that will be used to search for the best spliting value for each node. Default value = 100
booluseScaling: sets if the training and real-time data should be scaled between [0 1]. Default value = false

Definition at line 28 of file DecisionTree.cpp.

DecisionTree::DecisionTree ( const DecisionTree rhs)

Defines the copy constructor.

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

Definition at line 51 of file DecisionTree.cpp.

DecisionTree::~DecisionTree ( void  )
virtual

Default Destructor

Definition at line 64 of file DecisionTree.cpp.

Member Function Documentation

bool DecisionTree::clear ( )
virtual

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 GRT::Tree.

Definition at line 322 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 662 of file DecisionTree.cpp.

bool DecisionTree::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 DecisionTree instance) into this instance

Parameters
Classifier*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 GRT::Classifier.

Definition at line 104 of file DecisionTree.cpp.

DecisionTreeNode * DecisionTree::deepCopyTree ( ) const
virtual

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

Reimplemented from GRT::Tree.

Definition at line 653 of file DecisionTree.cpp.

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

This function adds the current model to the formatted stream. This function should be overwritten by the derived class.

Parameters
ostream&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 GRT::Tree.

Definition at line 645 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 671 of file DecisionTree.cpp.

bool DecisionTree::loadLegacyModelFromFile_v1 ( fstream &  file)
protected

Read the ranges if needed

Definition at line 809 of file DecisionTree.cpp.

bool DecisionTree::loadModelFromFile ( fstream &  file)
virtual

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

Parameters
fstream&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 GRT::MLBase.

Definition at line 445 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
constDecisionTree &rhs: another instance of a DecisionTree
Returns
returns a pointer to this instance of the DecisionTree

Definition at line 74 of file DecisionTree.cpp.

bool DecisionTree::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 246 of file DecisionTree.cpp.

bool DecisionTree::recomputeNullRejectionThresholds ( )
virtual

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 GRT::Classifier.

Definition at line 342 of file DecisionTree.cpp.

bool DecisionTree::saveModelToFile ( fstream &  file) const
virtual

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

Parameters
fstream&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 GRT::MLBase.

Definition at line 364 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

Definition at line 675 of file DecisionTree.cpp.

bool DecisionTree::train_ ( ClassificationData trainingData)
virtual

This trains the DecisionTree 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 DecisionTree model was trained, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 140 of file DecisionTree.cpp.


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