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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.
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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 Node * | deepCopyNode () const |
DecisionTreeClusterNode * | deepCopy () const |
UINT | getFeatureIndex () const |
double | getThreshold () const |
bool | set (const UINT nodeSize, const UINT featureIndex, const double threshold, const VectorDouble &classProbabilities) |
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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) |
DecisionTreeNode * | deepCopy () 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) |
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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 |
Node * | getParent () const |
Node * | getLeftChild () const |
Node * | getRightChild () 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) |
Node * | createNewInstance () const |
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GRTBase (void) | |
virtual | ~GRTBase (void) |
bool | copyGRTBaseVariables (const GRTBase *GRTBase) |
string | getClassType () const |
string | getLastWarningMessage () const |
string | getLastErrorMessage () const |
string | getLastInfoMessage () const |
GRTBase * | getGRTBasePointer () |
const GRTBase * | getGRTBasePointer () 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) |
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UINT | getClassLabelIndexValue (UINT classLabel, const vector< UINT > &classLabels) const |
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double | SQR (const double &x) const |
Protected Attributes | |
UINT | featureIndex |
double | threshold |
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UINT | nodeSize |
VectorDouble | classProbabilities |
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string | nodeType |
UINT | depth |
UINT | nodeID |
UINT | predictedNodeID |
bool | isLeafNode |
Node * | parent |
Node * | leftChild |
Node * | rightChild |
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string | classType |
DebugLog | debugLog |
ErrorLog | errorLog |
InfoLog | infoLog |
TrainingLog | trainingLog |
TestingLog | testingLog |
WarningLog | warningLog |
Static Protected Attributes | |
static RegisterNode< DecisionTreeClusterNode > | registerModule |
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static RegisterNode< DecisionTreeNode > | registerModule |
Additional Inherited Members | |
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typedef std::map< string, Node *(*)() > | StringNodeMap |
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static Node * | createInstanceFromString (string const &nodeType) |
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static string | getGRTVersion (bool returnRevision=true) |
static string | getGRTRevison () |
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static StringNodeMap * | getMap () |
Definition at line 43 of file DecisionTreeClusterNode.h.
DecisionTreeClusterNode::DecisionTreeClusterNode | ( | ) |
Default Constructor. Sets all the pointers to NULL.
Definition at line 9 of file DecisionTreeClusterNode.cpp.
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virtual |
Default Destructor. Cleans up any memory.
Definition at line 17 of file DecisionTreeClusterNode.cpp.
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This functions cleans up any dynamic memory assigned by the node. It will recursively clear the memory for the left and right child nodes.
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.
Definition at line 114 of file DecisionTreeClusterNode.cpp.
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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.
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.
Definition at line 118 of file DecisionTreeClusterNode.cpp.
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virtual |
This function adds the current model to the formatted stream. This function should be overwritten by the derived class.
ostream | &file: a reference to the stream the model will be added to |
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.
Definition at line 122 of file DecisionTreeClusterNode.cpp.
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protectedvirtual |
This loads the Decision Tree Node parameters from a file.
fstream | &file: a reference to the file the parameters will be loaded from |
Reimplemented from GRT::DecisionTreeNode.
Definition at line 265 of file DecisionTreeClusterNode.cpp.
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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.
const | VectorDouble &x: the input vector that will be used for the prediction |
Reimplemented from GRT::Node.
Definition at line 21 of file DecisionTreeClusterNode.cpp.
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This functions prints the node data to std::out. It will recursively print all the child nodes.
Reimplemented from GRT::Node.
Definition at line 39 of file DecisionTreeClusterNode.cpp.
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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.
fstream | &file: a reference to the file the parameters will be saved to |
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 | ||
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This function sets the Decision Tree Threshold Node.
const | UINT nodeSize: sets the node size, this is the number of training samples at that node |
const | UINT featureIndex: sets the index of the feature that should be used for the threshold spilt |
const | double threshold: set the threshold value used for the spilt |
const | VectorDouble &classProbabilities: the vector of class probabilities at this node |
Definition at line 126 of file DecisionTreeClusterNode.cpp.