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::RegressionTreeNode Class Reference
Inheritance diagram for GRT::RegressionTreeNode:
GRT::Node GRT::GRTBase

Public Member Functions

 RegressionTreeNode ()
 
virtual ~RegressionTreeNode ()
 
virtual bool predict (const VectorDouble &x)
 
virtual bool predict (const VectorDouble &x, VectorDouble &y)
 
virtual bool clear ()
 
virtual bool print () const
 
virtual NodedeepCopyNode () const
 
RegressionTreeNodedeepCopyTree () const
 
bool set (const UINT nodeSize, const UINT featureIndex, const double threshold, const VectorDouble &regressionData)
 
- Public Member Functions inherited from GRT::Node
 Node ()
 
virtual ~Node ()
 
virtual bool getModel (ostream &stream) 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 saveParametersToFile (fstream &file) const
 
virtual bool loadParametersFromFile (fstream &file)
 
- Protected Member Functions inherited from GRT::GRTBase
double SQR (const double &x) const
 

Protected Attributes

UINT nodeSize
 
UINT featureIndex
 
double threshold
 
VectorDouble regressionData
 
- 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< RegressionTreeNoderegisterModule
 

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 37 of file RegressionTreeNode.h.

Constructor & Destructor Documentation

GRT::RegressionTreeNode::RegressionTreeNode ( )
inline

Default Constructor. Sets all the pointers to NULL.

Definition at line 42 of file RegressionTreeNode.h.

virtual GRT::RegressionTreeNode::~RegressionTreeNode ( )
inlinevirtual

Default Destructor. Cleans up any memory.

Definition at line 53 of file RegressionTreeNode.h.

Member Function Documentation

virtual bool GRT::RegressionTreeNode::clear ( )
inlinevirtual

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.

Definition at line 112 of file RegressionTreeNode.h.

virtual Node* GRT::RegressionTreeNode::deepCopyNode ( ) const
inlinevirtual

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.

Definition at line 162 of file RegressionTreeNode.h.

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

This loads the ClusterTreeNode 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.

Definition at line 251 of file RegressionTreeNode.h.

virtual bool GRT::RegressionTreeNode::predict ( const VectorDouble &  x)
inlinevirtual

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 67 of file RegressionTreeNode.h.

virtual bool GRT::RegressionTreeNode::predict ( const VectorDouble &  x,
VectorDouble &  y 
)
inlinevirtual

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 85 of file RegressionTreeNode.h.

virtual bool GRT::RegressionTreeNode::print ( ) const
inlinevirtual

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 131 of file RegressionTreeNode.h.

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

This saves the ClusterTreeNode 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.

Definition at line 223 of file RegressionTreeNode.h.

bool GRT::RegressionTreeNode::set ( const UINT  nodeSize,
const UINT  featureIndex,
const double  threshold,
const VectorDouble &  regressionData 
)
inline

This function sets the Decision Tree 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 &regressionData: the regression data at this node
Returns
returns true if the node was set, false otherwise

Definition at line 207 of file RegressionTreeNode.h.


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