GestureRecognitionToolkit  Version: 0.2.5
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
DecisionTreeNode Class Reference
Inheritance diagram for DecisionTreeNode:
Node MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult > DecisionTreeClusterNode DecisionTreeThresholdNode DecisionTreeTripleFeatureNode

Public Member Functions

 DecisionTreeNode (const std::string id="DecisionTreeNode")
 
 DecisionTreeNode (const DecisionTreeNode &rhs)=delete
 
virtual ~DecisionTreeNode ()
 
DecisionTreeNodeoperator= (const DecisionTreeNode &rhs)=delete
 
virtual bool predict_ (VectorFloat &x, VectorFloat &classLikelihoods) override
 
virtual bool computeBestSplit (const UINT &trainingMode, const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError)
 
virtual bool clear () override
 
virtual bool getModel (std::ostream &stream) const override
 
virtual NodedeepCopy () const override
 
UINT getNodeSize () const
 
UINT getNumClasses () const
 
VectorFloat getClassProbabilities () const
 
bool setLeafNode (const UINT nodeSize, const VectorFloat &classProbabilities)
 
bool setNodeSize (const UINT nodeSize)
 
bool setClassProbabilities (const VectorFloat &classProbabilities)
 
- Public Member Functions inherited from Node
 Node (const std::string id="Node")
 
virtual ~Node ()
 
virtual bool predict_ (VectorFloat &x) override
 
virtual bool computeFeatureWeights (VectorFloat &weights) const
 
virtual bool computeLeafNodeWeights (MatrixFloat &weights) const
 
virtual bool print () const override
 
virtual bool save (std::fstream &file) const override
 
virtual bool load (std::fstream &file) override
 
std::string getNodeType () const
 
UINT getDepth () const
 
UINT getNodeID () const
 
UINT getPredictedNodeID () const
 
UINT getMaxDepth () const
 
bool getIsLeafNode () const
 
bool getHasParent () const
 
bool getHasLeftChild () const
 
bool getHasRightChild () const
 
NodegetParent () const
 
NodegetLeftChild () const
 
NodegetRightChild () const
 
bool initNode (Node *parent, const UINT depth, const UINT nodeID, const bool isLeafNode=false)
 
bool setParent (Node *parent)
 
bool setLeftChild (Node *leftChild)
 
bool setRightChild (Node *rightChild)
 
bool setDepth (const UINT depth)
 
bool setNodeID (const UINT nodeID)
 
bool setIsLeafNode (const bool isLeafNode)
 
NodecreateNewInstance () const
 
- Public Member Functions inherited from MLBase
 MLBase (const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)
 
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_ (RegressionData &trainingData)
 
virtual bool train (RegressionData trainingData, RegressionData validationData)
 
virtual bool train_ (RegressionData &trainingData, RegressionData &validationData)
 
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 reset ()
 
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(const std::string &filename) instead", virtual bool saveModelToFile(const 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(const std::string &filename) instead", virtual bool loadModelFromFile(const 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 std::string getModelAsString () const
 
DataType getInputType () const
 
DataType getOutputType () const
 
BaseType getType () const
 
UINT getNumInputFeatures () const
 
UINT getNumInputDimensions () const
 
UINT getNumOutputDimensions () const
 
UINT getMinNumEpochs () const
 
UINT getMaxNumEpochs () const
 
UINT getBatchSize () const
 
UINT getNumRestarts () const
 
UINT getValidationSetSize () const
 
UINT getNumTrainingIterationsToConverge () const
 
Float getMinChange () const
 
Float getLearningRate () const
 
Float getRMSTrainingError () const
 
 GRT_DEPRECATED_MSG ("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )
 
Float getTotalSquaredTrainingError () const
 
Float getRMSValidationError () const
 
Float getValidationSetAccuracy () const
 
VectorFloat getValidationSetPrecision () const
 
VectorFloat getValidationSetRecall () const
 
bool getUseValidationSet () const
 
bool getRandomiseTrainingOrder () const
 
bool getTrained () const
 
 GRT_DEPRECATED_MSG ("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const )
 
bool getConverged () const
 
bool getScalingEnabled () const
 
bool getIsBaseTypeClassifier () const
 
bool getIsBaseTypeRegressifier () const
 
bool getIsBaseTypeClusterer () const
 
bool getTrainingLoggingEnabled () const
 
bool getTestingLoggingEnabled () const
 
bool enableScaling (const bool useScaling)
 
bool setMaxNumEpochs (const UINT maxNumEpochs)
 
bool setBatchSize (const UINT batchSize)
 
bool setMinNumEpochs (const UINT minNumEpochs)
 
bool setNumRestarts (const UINT numRestarts)
 
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 setTestingLoggingEnabled (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< TrainingResultgetTrainingResults () const
 
- Public Member Functions inherited from GRTBase
 GRTBase (const std::string &id="")
 
virtual ~GRTBase (void)
 
bool copyGRTBaseVariables (const GRTBase *GRTBase)
 
 GRT_DEPRECATED_MSG ("getClassType is deprecated, use getId() instead!", std::string getClassType() const )
 
std::string getId () 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)
 
bool setDebugLoggingEnabled (const bool loggingEnabled)
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () const
 
Float scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)
 
Float SQR (const Float &x) 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 UINT getClassLabelIndexValue (UINT classLabel, const Vector< UINT > &classLabels)
 
- Static Public Member Functions inherited from Node
static NodecreateInstanceFromString (std::string const &nodeType)
 
- Static Public Member Functions inherited from GRTBase
static std::string getGRTVersion (bool returnRevision=true)
 
static std::string getGRTRevison ()
 

Protected Member Functions

virtual bool computeBestSplitBestIterativeSplit (const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError)
 
virtual bool computeBestSplitBestRandomSplit (const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError)
 
virtual bool saveParametersToFile (std::fstream &file) const override
 
virtual bool loadParametersFromFile (std::fstream &file) override
 
- Protected Member Functions inherited from MLBase
bool saveBaseSettingsToFile (std::fstream &file) const
 
bool loadBaseSettingsFromFile (std::fstream &file)
 

Protected Attributes

UINT nodeSize
 
VectorFloat classProbabilities
 
- Protected Attributes inherited from Node
std::string nodeType
 
UINT depth
 
UINT nodeID
 
UINT predictedNodeID
 
bool isLeafNode
 
Nodeparent
 
NodeleftChild
 
NoderightChild
 
- Protected Attributes inherited from MLBase
bool trained
 
bool useScaling
 
bool converged
 
DataType inputType
 
DataType outputType
 
BaseType baseType
 
UINT numInputDimensions
 
UINT numOutputDimensions
 
UINT numTrainingIterationsToConverge
 
UINT minNumEpochs
 
UINT maxNumEpochs
 
UINT batchSize
 
UINT validationSetSize
 
UINT numRestarts
 
Float learningRate
 
Float minChange
 
Float rmsTrainingError
 
Float rmsValidationError
 
Float totalSquaredTrainingError
 
Float validationSetAccuracy
 
bool useValidationSet
 
bool randomiseTrainingOrder
 
VectorFloat validationSetPrecision
 
VectorFloat validationSetRecall
 
Random random
 
Vector< TrainingResulttrainingResults
 
TrainingResultsObserverManager trainingResultsObserverManager
 
TestResultsObserverManager testResultsObserverManager
 
TrainingLog trainingLog
 
TestingLog testingLog
 
- Protected Attributes inherited from GRTBase
std::string classId
 Stores the name of the class (e.g., MinDist)
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
WarningLog warningLog
 

Static Protected Attributes

static RegisterNode< DecisionTreeNoderegisterModule
 

Additional Inherited Members

- Public Types inherited from Node
typedef std::map< std::string, Node *(*)() > StringNodeMap
 
- Public Types inherited from MLBase
enum  BaseType {
  BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER,
  PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT
}
 
- Static Protected Member Functions inherited from Node
static StringNodeMapgetMap ()
 

Detailed Description

Definition at line 41 of file DecisionTreeNode.h.

Constructor & Destructor Documentation

DecisionTreeNode::DecisionTreeNode ( const std::string  id = "DecisionTreeNode")

Default Constructor. Sets all the pointers to NULL.

Definition at line 10 of file DecisionTreeNode.cpp.

DecisionTreeNode::DecisionTreeNode ( const DecisionTreeNode rhs)
delete

Disable the copy constructor.

DecisionTreeNode::~DecisionTreeNode ( )
virtual

Default Destructor. Cleans up any memory.

Definition at line 14 of file DecisionTreeNode.cpp.

Member Function Documentation

bool DecisionTreeNode::clear ( )
overridevirtual

This functions cleans up any dynamic memory assigned by the node. It will recursively clear the memory for the left and right child nodes.

Returns
returns true of the node was cleared correctly, false otherwise

Reimplemented from Node.

Reimplemented in DecisionTreeClusterNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 82 of file DecisionTreeNode.cpp.

bool DecisionTreeNode::computeBestSplit ( const UINT &  trainingMode,
const UINT &  numSplittingSteps,
const ClassificationData trainingData,
const Vector< UINT > &  features,
const Vector< UINT > &  classLabels,
UINT &  featureIndex,
Float &  minError 
)
virtual

This function calls the best spliting algorithm based on the current trainingMode.

This function will return true if the best spliting algorithm found a split, false otherwise.

Parameters
trainingModethe training mode to use, this should be one of the
numSplittingStepssets the number of iterations that will be used to search for the best threshold
trainingDatathe training data to use for the best split search
featuresa Vector containing the indexs of the features that can be used for the search
classLabelsa Vector containing the class labels for the search
featureIndexthis will store the best feature index found during the search
minErrorthis will store the minimum error found during the search
Returns
returns true if the best spliting algorithm found a split, false otherwise

Definition at line 64 of file DecisionTreeNode.cpp.

Node * DecisionTreeNode::deepCopy ( ) const
overridevirtual

This function returns a deep copy of the DecisionTreeNode and all it's children. The user is responsible for managing the dynamic data that is returned from this function as a pointer.

Returns
returns a pointer to a deep copy of the DecisionTreeNode, or NULL if the deep copy was not successful

Reimplemented from Node.

Reimplemented in DecisionTreeClusterNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 118 of file DecisionTreeNode.cpp.

VectorFloat DecisionTreeNode::getClassProbabilities ( ) const

This function returns the class probabilities Vector.

Returns
returns the classProbabilities Vector

Definition at line 157 of file DecisionTreeNode.cpp.

bool DecisionTreeNode::getModel ( std::ostream &  stream) const
overridevirtual

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

Parameters
streama reference to the stream the model will be added to
Returns
returns true if the model was added successfully, false otherwise

Reimplemented from Node.

Reimplemented in DecisionTreeClusterNode, DecisionTreeThresholdNode, and DecisionTreeTripleFeatureNode.

Definition at line 93 of file DecisionTreeNode.cpp.

UINT DecisionTreeNode::getNodeSize ( ) const

This function returns the nodeSize, this is the number of training samples that reached the node during the training phase.

Returns
returns the nodeSize

Definition at line 149 of file DecisionTreeNode.cpp.

UINT DecisionTreeNode::getNumClasses ( ) const

This function returns the number of classes in the class probabilities Vector.

Returns
returns the number of classes in the class probabilities Vector

Definition at line 153 of file DecisionTreeNode.cpp.

virtual bool DecisionTreeNode::loadParametersFromFile ( std::fstream &  file)
inlineoverrideprotectedvirtual

This loads the Decision Tree Node parameters from a file.

Parameters
filea reference to the file the parameters will be loaded from
Returns
returns true if the model was loaded successfully, false otherwise

Reimplemented from Node.

Reimplemented in DecisionTreeClusterNode, DecisionTreeTripleFeatureNode, and DecisionTreeThresholdNode.

Definition at line 217 of file DecisionTreeNode.h.

DecisionTreeNode& DecisionTreeNode::operator= ( const DecisionTreeNode rhs)
delete

Disable the equals operator.

bool DecisionTreeNode::predict_ ( VectorFloat x,
VectorFloat classLikelihoods 
)
overridevirtual

This function recursively calls predict on the input vector x, when it reaches the leaf node it stores the class probability associated with that leaf node in the output vector y.

NOTE: This function should only be called after the decision tree model has been trained.

Parameters
xthe input Vector that will be used for the prediction
classLikelihoodsa reference to a Vector that will store the class probabilities
Returns
returns true if the input is greater than or equal to the nodes threshold, false otherwise

Reimplemented from Node.

Definition at line 18 of file DecisionTreeNode.cpp.

virtual bool DecisionTreeNode::saveParametersToFile ( std::fstream &  file) const
inlineoverrideprotectedvirtual

This saves the DecisionTreeNode custom parameters to a file. It will be called automatically by the Node base class if the save function is called.

Parameters
filea reference to the file the parameters will be saved to
Returns
returns true if the model was saved successfully, false otherwise

Reimplemented from Node.

Reimplemented in DecisionTreeClusterNode, DecisionTreeTripleFeatureNode, and DecisionTreeThresholdNode.

Definition at line 188 of file DecisionTreeNode.h.

bool DecisionTreeNode::setClassProbabilities ( const VectorFloat classProbabilities)

This function sets the Decision Tree Node class probabilities.

Parameters
classProbabilitiesthe Vector of class probabilities at this node
Returns
returns true if the node was set classProbabilities, false otherwise

Definition at line 173 of file DecisionTreeNode.cpp.

bool DecisionTreeNode::setLeafNode ( const UINT  nodeSize,
const VectorFloat classProbabilities 
)

This function sets the Decision Tree Node as a leaf node.

Parameters
nodeSizesets the node size, this is the number of training samples at that node
classProbabilitiesthe Vector of class probabilities at this node
Returns
returns true if the node was updated, false otherwise

Definition at line 161 of file DecisionTreeNode.cpp.

bool DecisionTreeNode::setNodeSize ( const UINT  nodeSize)

This function sets the Decision Tree Node nodeSize.

Parameters
nodeSizesets the node size, this is the number of training samples at that node
Returns
returns true if the node size was set, false otherwise

Definition at line 168 of file DecisionTreeNode.cpp.


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