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

Public Member Functions

 DecisionTreeClusterNode ()
 
virtual ~DecisionTreeClusterNode ()
 
virtual bool predict (const VectorDouble &x)
 
virtual bool clear ()
 
virtual bool print () const
 
virtual bool getModel (ostream &stream) const
 
virtual NodedeepCopyNode () const
 
DecisionTreeClusterNodedeepCopy () const
 
UINT getFeatureIndex () const
 
double getThreshold () const
 
bool set (const UINT nodeSize, const UINT featureIndex, const double threshold, const VectorDouble &classProbabilities)
 
- Public Member Functions inherited from GRT::DecisionTreeNode
 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)
 
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 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)
 
bool computeBestSpilt (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)
 
- Protected Member Functions inherited from GRT::DecisionTreeNode
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 featureIndex
 
double threshold
 
- Protected Attributes inherited from GRT::DecisionTreeNode
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< DecisionTreeClusterNoderegisterModule
 
- Static Protected Attributes inherited from GRT::DecisionTreeNode
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 43 of file DecisionTreeClusterNode.h.

Constructor & Destructor Documentation

DecisionTreeClusterNode::DecisionTreeClusterNode ( )

Default Constructor. Sets all the pointers to NULL.

Definition at line 9 of file DecisionTreeClusterNode.cpp.

DecisionTreeClusterNode::~DecisionTreeClusterNode ( )
virtual

Default Destructor. Cleans up any memory.

Definition at line 17 of file DecisionTreeClusterNode.cpp.

Member Function Documentation

bool DecisionTreeClusterNode::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::DecisionTreeNode.

Definition at line 28 of file DecisionTreeClusterNode.cpp.

DecisionTreeClusterNode * DecisionTreeClusterNode::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 DecisionTreeThresholdNode, or NULL if the deep copy was not successful

Definition at line 114 of file DecisionTreeClusterNode.cpp.

Node * DecisionTreeClusterNode::deepCopyNode ( ) const
virtual

This function returns a deep copy of the DecisionTreeThresholdNode 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 DecisionTreeClusterNode, or NULL if the deep copy was not successful

Reimplemented from GRT::DecisionTreeNode.

Definition at line 81 of file DecisionTreeClusterNode.cpp.

UINT DecisionTreeClusterNode::getFeatureIndex ( ) const

This function returns the featureIndex, this is index in the input data that the decision threshold is computed on.

Returns
returns the featureIndex

Definition at line 118 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::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::DecisionTreeNode.

Definition at line 51 of file DecisionTreeClusterNode.cpp.

double DecisionTreeClusterNode::getThreshold ( ) const

This function returns the threshold, this is the value used to compute the decision threshold.

Returns
returns the threshold

Definition at line 122 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::loadParametersFromFile ( fstream &  file)
protectedvirtual

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::DecisionTreeNode.

Definition at line 265 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::predict ( const VectorDouble &  x)
virtual

This function predicts if the input is greater than or equal to the nodes threshold. If the input is greater than or equal to the nodes threshold then this function will return true, otherwise it will return false.

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

Parameters
constVectorDouble &x: the input vector that will be used for the prediction
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 DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::print ( ) const
virtual

This functions prints the node data to std::out. It will recursively print all the child nodes.

Returns
returns true if the data was printed correctly, false otherwise

Reimplemented from GRT::Node.

Definition at line 39 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::saveParametersToFile ( fstream &  file) const
protectedvirtual

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::DecisionTreeNode.

Definition at line 244 of file DecisionTreeClusterNode.cpp.

bool DecisionTreeClusterNode::set ( const UINT  nodeSize,
const UINT  featureIndex,
const double  threshold,
const VectorDouble &  classProbabilities 
)

This function sets the Decision Tree Threshold Node.

Parameters
constUINT nodeSize: sets the node size, this is the number of training samples at that node
constUINT featureIndex: sets the index of the feature that should be used for the threshold spilt
constdouble threshold: set the threshold value used for the spilt
constVectorDouble &classProbabilities: the vector of class probabilities at this node
Returns
returns true if the node was set, false otherwise

Definition at line 126 of file DecisionTreeClusterNode.cpp.


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