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::DecisionTreeNode Class Reference
Inheritance diagram for GRT::DecisionTreeNode:
GRT::Node GRT::GRTBase GRT::DecisionTreeClusterNode GRT::DecisionTreeThresholdNode GRT::DecisionTreeTripleFeatureNode

Public Member Functions

 DecisionTreeNode ()
 
virtual ~DecisionTreeNode ()
 
virtual bool predict (const VectorDouble &x, VectorDouble &classLikelihoods)
 
virtual bool computeBestSpilt (const UINT &trainingMode, const UINT &numSplittingSteps, const ClassificationData &trainingData, const vector< UINT > &features, const vector< UINT > &classLabels, UINT &featureIndex, double &minError)
 
virtual bool clear ()
 
virtual bool getModel (ostream &stream) const
 
virtual NodedeepCopyNode () const
 
DecisionTreeNodedeepCopy () const
 
UINT getNodeSize () const
 
UINT getNumClasses () const
 
VectorDouble getClassProbabilities () const
 
bool setLeafNode (const UINT nodeSize, const VectorDouble &classProbabilities)
 
bool setNodeSize (const UINT nodeSize)
 
bool setClassProbabilities (const VectorDouble &classProbabilities)
 
- Public Member Functions inherited from GRT::Node
 Node ()
 
virtual ~Node ()
 
virtual bool predict (const VectorDouble &x)
 
virtual bool print () const
 
virtual bool saveToFile (fstream &file) const
 
virtual bool loadFromFile (fstream &file)
 
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 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
 

Protected Member Functions

virtual bool computeBestSpiltBestIterativeSpilt (const UINT &numSplittingSteps, const ClassificationData &trainingData, const vector< UINT > &features, const vector< UINT > &classLabels, UINT &featureIndex, double &minError)
 
virtual bool computeBestSpiltBestRandomSpilt (const UINT &numSplittingSteps, const ClassificationData &trainingData, const vector< UINT > &features, const vector< UINT > &classLabels, UINT &featureIndex, double &minError)
 
virtual bool saveParametersToFile (fstream &file) const
 
virtual bool loadParametersFromFile (fstream &file)
 
UINT getClassLabelIndexValue (UINT classLabel, const vector< UINT > &classLabels) const
 
- Protected Member Functions inherited from GRT::GRTBase
double SQR (const double &x) const
 

Protected Attributes

UINT nodeSize
 
VectorDouble classProbabilities
 
- Protected Attributes inherited from GRT::Node
string nodeType
 
UINT depth
 
UINT nodeID
 
UINT predictedNodeID
 
bool isLeafNode
 
Nodeparent
 
NodeleftChild
 
NoderightChild
 
- Protected Attributes inherited from GRT::GRTBase
string classType
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
TrainingLog trainingLog
 
TestingLog testingLog
 
WarningLog warningLog
 

Static Protected Attributes

static RegisterNode< DecisionTreeNoderegisterModule
 

Additional Inherited Members

- Public Types inherited from GRT::Node
typedef std::map< string, Node *(*)() > StringNodeMap
 
- Static Public Member Functions inherited from GRT::Node
static NodecreateInstanceFromString (string const &nodeType)
 
- Static Public Member Functions inherited from GRT::GRTBase
static string getGRTVersion (bool returnRevision=true)
 
static string getGRTRevison ()
 
- Static Protected Member Functions inherited from GRT::Node
static StringNodeMapgetMap ()
 

Detailed Description

Definition at line 40 of file DecisionTreeNode.h.

Constructor & Destructor Documentation

GRT::DecisionTreeNode::DecisionTreeNode ( )

Default Constructor. Sets all the pointers to NULL.

Definition at line 9 of file DecisionTreeNode.cpp.

GRT::DecisionTreeNode::~DecisionTreeNode ( )
virtual

Default Destructor. Cleans up any memory.

Definition at line 17 of file DecisionTreeNode.cpp.

Member Function Documentation

bool GRT::DecisionTreeNode::clear ( )
virtual

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 GRT::Node.

Reimplemented in GRT::DecisionTreeClusterNode, GRT::DecisionTreeThresholdNode, and GRT::DecisionTreeTripleFeatureNode.

Definition at line 75 of file DecisionTreeNode.cpp.

bool GRT::DecisionTreeNode::computeBestSpilt ( const UINT &  trainingMode,
const UINT &  numSplittingSteps,
const ClassificationData trainingData,
const vector< UINT > &  features,
const vector< UINT > &  classLabels,
UINT &  featureIndex,
double &  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
constUINT &trainingMode: the training mode to use, this should be one of the
constUINT &numSplittingSteps: sets the number of iterations that will be used to search for the best threshold
constClassificationData &trainingData: the training data to use for the best split search
constconst vector< UINT > &features: a vector containing the indexs of the features that can be used for the search
constvector< UINT > &classLabels: a vector containing the class labels for the search
UINT&featureIndex: this will store the best feature index found during the search
double&minError: this 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 57 of file DecisionTreeNode.cpp.

DecisionTreeNode * GRT::DecisionTreeNode::deepCopy ( ) const

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

Definition at line 142 of file DecisionTreeNode.cpp.

Node * GRT::DecisionTreeNode::deepCopyNode ( ) const
virtual

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 GRT::Node.

Reimplemented in GRT::DecisionTreeClusterNode, GRT::DecisionTreeThresholdNode, and GRT::DecisionTreeTripleFeatureNode.

Definition at line 111 of file DecisionTreeNode.cpp.

VectorDouble GRT::DecisionTreeNode::getClassProbabilities ( ) const

This function returns the class probabilities vector.

Returns
returns the classProbabilities vector

Definition at line 154 of file DecisionTreeNode.cpp.

bool GRT::DecisionTreeNode::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::Node.

Reimplemented in GRT::DecisionTreeClusterNode, GRT::DecisionTreeThresholdNode, and GRT::DecisionTreeTripleFeatureNode.

Definition at line 86 of file DecisionTreeNode.cpp.

UINT GRT::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 146 of file DecisionTreeNode.cpp.

UINT GRT::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 150 of file DecisionTreeNode.cpp.

virtual bool GRT::DecisionTreeNode::loadParametersFromFile ( fstream &  file)
inlineprotectedvirtual

This loads the Decision Tree Node parameters from a file.

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

Reimplemented from GRT::Node.

Reimplemented in GRT::DecisionTreeTripleFeatureNode, GRT::DecisionTreeClusterNode, and GRT::DecisionTreeThresholdNode.

Definition at line 215 of file DecisionTreeNode.h.

bool GRT::DecisionTreeNode::predict ( const VectorDouble &  x,
VectorDouble &  classLikelihoods 
)
virtual

This function recursively predicts if the probability of the input vector. If this node is a leaf node, then the class likelihoods are equal to the class probabilities at the leaf node. If this node is not a leaf node, then this function will recursively call the predict function on either the left or right children until a leaf node is reached.

NOTE: The threshold, featureIndex and classProbabilities should be set first BEFORE this function is called. The threshold, featureIndex and classProbabilities can be set by training the node through the DecisionTree class.

Parameters
constVectorDouble &x: the input vector that will be used for the prediction
VectorDouble&classLikelihoods: a 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 GRT::Node.

Definition at line 21 of file DecisionTreeNode.cpp.

virtual bool GRT::DecisionTreeNode::saveParametersToFile ( fstream &  file) const
inlineprotectedvirtual

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

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

Reimplemented from GRT::Node.

Reimplemented in GRT::DecisionTreeTripleFeatureNode, GRT::DecisionTreeClusterNode, and GRT::DecisionTreeThresholdNode.

Definition at line 186 of file DecisionTreeNode.h.

bool GRT::DecisionTreeNode::setClassProbabilities ( const VectorDouble &  classProbabilities)

This function sets the Decision Tree Node class probabilities.

Parameters
constVectorDouble &classProbabilities: the vector of class probabilities at this node
Returns
returns true if the node was set classProbabilities, false otherwise

Definition at line 170 of file DecisionTreeNode.cpp.

bool GRT::DecisionTreeNode::setLeafNode ( const UINT  nodeSize,
const VectorDouble &  classProbabilities 
)

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

Parameters
constUINT nodeSize: sets the node size, this is the number of training samples at that node
constVectorDouble &classProbabilities: the vector of class probabilities at this node
Returns
returns true if the node was updated, false otherwise

Definition at line 158 of file DecisionTreeNode.cpp.

bool GRT::DecisionTreeNode::setNodeSize ( const UINT  nodeSize)

This function sets the Decision Tree Node nodeSize.

Parameters
constUINT nodeSize: sets 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 165 of file DecisionTreeNode.cpp.


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