GestureRecognitionToolkit  Version: 0.1.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
DecisionTreeThresholdNode Class Reference

#include <DecisionTreeThresholdNode.h>

Inheritance diagram for DecisionTreeThresholdNode:
DecisionTreeNode Node GRTBase

Public Member Functions

 DecisionTreeThresholdNode ()
 
virtual ~DecisionTreeThresholdNode ()
 
virtual bool predict (const VectorFloat &x)
 
virtual bool clear ()
 
virtual bool print () const
 
virtual bool getModel (std::ostream &stream) const
 
virtual NodedeepCopyNode () const
 
DecisionTreeThresholdNodedeepCopy () const
 
UINT getFeatureIndex () const
 
Float getThreshold () const
 
bool set (const UINT nodeSize, const UINT featureIndex, const Float threshold, const VectorFloat &classProbabilities)
 
- Public Member Functions inherited from DecisionTreeNode
 DecisionTreeNode ()
 
virtual ~DecisionTreeNode ()
 
virtual bool predict (const VectorFloat &x, VectorFloat &classLikelihoods)
 
virtual bool computeBestSpilt (const UINT &trainingMode, const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError)
 
DecisionTreeNodedeepCopy () const
 
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 ()
 
virtual ~Node ()
 
virtual bool computeFeatureWeights (VectorFloat &weights) const
 
virtual bool computeLeafNodeWeights (MatrixFloat &weights) const
 
virtual bool saveToFile (std::fstream &file) const
 
virtual bool loadFromFile (std::fstream &file)
 
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 GRTBase
 GRTBase (void)
 
virtual ~GRTBase (void)
 
bool copyGRTBaseVariables (const GRTBase *GRTBase)
 
std::string getClassType () 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)
 
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, Float &minError)
 
virtual bool computeBestSpiltBestRandomSpilt (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
 
virtual bool loadParametersFromFile (std::fstream &file)
 
- Protected Member Functions inherited from GRTBase
Float SQR (const Float &x) const
 

Protected Attributes

UINT featureIndex
 
Float threshold
 
- Protected Attributes inherited from DecisionTreeNode
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 GRTBase
std::string classType
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
TrainingLog trainingLog
 
TestingLog testingLog
 
WarningLog warningLog
 

Static Protected Attributes

static RegisterNode< DecisionTreeThresholdNoderegisterModule
 
- Static Protected Attributes inherited from DecisionTreeNode
static RegisterNode< DecisionTreeNoderegisterModule
 

Additional Inherited Members

- Public Types inherited from Node
typedef std::map< std::string, Node *(*)() > StringNodeMap
 
- Static Public Member Functions inherited from DecisionTreeNode
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 ()
 
- Static Protected Member Functions inherited from Node
static StringNodeMapgetMap ()
 

Detailed Description

GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Definition at line 42 of file DecisionTreeThresholdNode.h.

Constructor & Destructor Documentation

DecisionTreeThresholdNode::DecisionTreeThresholdNode ( )

Default Constructor. Sets all the pointers to NULL.

Definition at line 9 of file DecisionTreeThresholdNode.cpp.

DecisionTreeThresholdNode::~DecisionTreeThresholdNode ( )
virtual

Default Destructor. Cleans up any memory.

Definition at line 17 of file DecisionTreeThresholdNode.cpp.

Member Function Documentation

bool DecisionTreeThresholdNode::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 DecisionTreeNode.

Definition at line 28 of file DecisionTreeThresholdNode.cpp.

DecisionTreeThresholdNode * DecisionTreeThresholdNode::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 109 of file DecisionTreeThresholdNode.cpp.

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

Reimplemented from DecisionTreeNode.

Definition at line 76 of file DecisionTreeThresholdNode.cpp.

UINT DecisionTreeThresholdNode::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 113 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::getModel ( std::ostream &  stream) const
virtual

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

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

Reimplemented from DecisionTreeNode.

Definition at line 51 of file DecisionTreeThresholdNode.cpp.

Float DecisionTreeThresholdNode::getThreshold ( ) const

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

Returns
returns the threshold

Definition at line 117 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::loadParametersFromFile ( std::fstream &  file)
protectedvirtual

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 DecisionTreeNode.

Definition at line 303 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::predict ( const VectorFloat 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
constVectorFloat &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 Node.

Definition at line 21 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::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 Node.

Definition at line 39 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::saveParametersToFile ( std::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
filea reference to the file the parameters will be saved to
Returns
returns true if the model was saved successfully, false otherwise

Reimplemented from DecisionTreeNode.

Definition at line 282 of file DecisionTreeThresholdNode.cpp.

bool DecisionTreeThresholdNode::set ( const UINT  nodeSize,
const UINT  featureIndex,
const Float  threshold,
const VectorFloat classProbabilities 
)

This function sets the Decision Tree Threshold Node.

Parameters
nodeSizesets the node size, this is the number of training samples at that node
featureIndexsets the index of the feature that should be used for the threshold spilt
thresholdset the threshold value used for the spilt
classProbabilitiesthe vector of class probabilities at this node
Returns
returns true if the node was set, false otherwise

Definition at line 121 of file DecisionTreeThresholdNode.cpp.


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