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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 | |
RegressionTree (const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT, const bool useScaling=false, const double minRMSErrorPerNode=0.01) | |
RegressionTree (const RegressionTree &rhs) | |
virtual | ~RegressionTree (void) |
RegressionTree & | operator= (const RegressionTree &rhs) |
virtual bool | deepCopyFrom (const Regressifier *regressifier) |
virtual bool | train_ (RegressionData &trainingData) |
virtual bool | predict_ (VectorDouble &inputVector) |
virtual bool | clear () |
virtual bool | print () const |
virtual bool | saveModelToFile (fstream &file) const |
virtual bool | loadModelFromFile (fstream &file) |
RegressionTreeNode * | deepCopyTree () const |
const RegressionTreeNode * | getTree () const |
double | getMinRMSErrorPerNode () const |
bool | setMinRMSErrorPerNode (const double minRMSErrorPerNode) |
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Tree (const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT) | |
virtual | ~Tree (void) |
virtual bool | getModel (ostream &stream) const |
const Node * | getTree () const |
UINT | getTrainingMode () const |
UINT | getNumSplittingSteps () const |
UINT | getMinNumSamplesPerNode () const |
UINT | getMaxDepth () const |
UINT | getPredictedNodeID () const |
bool | getRemoveFeaturesAtEachSpilt () const |
bool | setTrainingMode (const UINT trainingMode) |
bool | setNumSplittingSteps (const UINT numSplittingSteps) |
bool | setMinNumSamplesPerNode (const UINT minNumSamplesPerNode) |
bool | setMaxDepth (const UINT maxDepth) |
bool | setRemoveFeaturesAtEachSpilt (const bool removeFeaturesAtEachSpilt) |
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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 |
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Regressifier (void) | |
virtual | ~Regressifier (void) |
bool | copyBaseVariables (const Regressifier *regressifier) |
virtual bool | reset () |
string | getRegressifierType () const |
VectorDouble | getRegressionData () const |
vector< MinMax > | getInputRanges () const |
vector< MinMax > | getOutputRanges () const |
Regressifier * | createNewInstance () const |
Regressifier * | deepCopy () const |
const Regressifier & | getBaseRegressifier () const |
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MLBase (void) | |
virtual | ~MLBase (void) |
bool | copyMLBaseVariables (const MLBase *mlBase) |
virtual bool | train (ClassificationData trainingData) |
virtual bool | train_ (ClassificationData &trainingData) |
virtual bool | train (RegressionData trainingData) |
virtual bool | train (TimeSeriesClassificationData trainingData) |
virtual bool | train_ (TimeSeriesClassificationData &trainingData) |
virtual bool | train (TimeSeriesClassificationDataStream trainingData) |
virtual bool | train_ (TimeSeriesClassificationDataStream &trainingData) |
virtual bool | train (UnlabelledData trainingData) |
virtual bool | train_ (UnlabelledData &trainingData) |
virtual bool | train (MatrixDouble data) |
virtual bool | train_ (MatrixDouble &data) |
virtual bool | predict (VectorDouble inputVector) |
virtual bool | predict (MatrixDouble inputMatrix) |
virtual bool | predict_ (MatrixDouble &inputMatrix) |
virtual bool | map (VectorDouble inputVector) |
virtual bool | map_ (VectorDouble &inputVector) |
virtual bool | save (const string filename) const |
virtual bool | load (const string filename) |
virtual bool | saveModelToFile (string filename) const |
virtual bool | loadModelFromFile (string filename) |
virtual bool | getModel (ostream &stream) const |
double | scale (const double &x, const double &minSource, const double &maxSource, const double &minTarget, const double &maxTarget, const bool constrain=false) |
virtual string | getModelAsString () const |
UINT | getBaseType () const |
UINT | getNumInputFeatures () const |
UINT | getNumInputDimensions () const |
UINT | getNumOutputDimensions () const |
UINT | getMinNumEpochs () const |
UINT | getMaxNumEpochs () const |
UINT | getValidationSetSize () const |
UINT | getNumTrainingIterationsToConverge () const |
double | getMinChange () const |
double | getLearningRate () const |
double | getRootMeanSquaredTrainingError () const |
double | getTotalSquaredTrainingError () const |
bool | getUseValidationSet () const |
bool | getRandomiseTrainingOrder () const |
bool | getTrained () const |
bool | getModelTrained () const |
bool | getScalingEnabled () const |
bool | getIsBaseTypeClassifier () const |
bool | getIsBaseTypeRegressifier () const |
bool | getIsBaseTypeClusterer () const |
bool | enableScaling (bool useScaling) |
bool | setMaxNumEpochs (const UINT maxNumEpochs) |
bool | setMinNumEpochs (const UINT minNumEpochs) |
bool | setMinChange (const double minChange) |
bool | setLearningRate (double learningRate) |
bool | setUseValidationSet (const bool useValidationSet) |
bool | setValidationSetSize (const UINT validationSetSize) |
bool | setRandomiseTrainingOrder (const bool randomiseTrainingOrder) |
bool | registerTrainingResultsObserver (Observer< TrainingResult > &observer) |
bool | registerTestResultsObserver (Observer< TestInstanceResult > &observer) |
bool | removeTrainingResultsObserver (const Observer< TrainingResult > &observer) |
bool | removeTestResultsObserver (const Observer< TestInstanceResult > &observer) |
bool | removeAllTrainingObservers () |
bool | removeAllTestObservers () |
bool | notifyTrainingResultsObservers (const TrainingResult &data) |
bool | notifyTestResultsObservers (const TestInstanceResult &data) |
MLBase * | getMLBasePointer () |
const MLBase * | getMLBasePointer () const |
vector< TrainingResult > | getTrainingResults () const |
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virtual void | notify (const TrainingResult &data) |
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virtual void | notify (const TestInstanceResult &data) |
Protected Member Functions | |
RegressionTreeNode * | buildTree (const RegressionData &trainingData, RegressionTreeNode *parent, vector< UINT > features, UINT nodeID) |
bool | computeBestSpilt (const RegressionData &trainingData, const vector< UINT > &features, UINT &featureIndex, double &threshold, double &minError) |
bool | computeBestSpiltBestIterativeSpilt (const RegressionData &trainingData, const vector< UINT > &features, UINT &featureIndex, double &threshold, double &minError) |
bool | computeNodeRegressionData (const RegressionData &trainingData, VectorDouble ®ressionData) |
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double | SQR (const double &x) const |
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bool | saveBaseSettingsToFile (fstream &file) const |
bool | loadBaseSettingsFromFile (fstream &file) |
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bool | saveBaseSettingsToFile (fstream &file) const |
bool | loadBaseSettingsFromFile (fstream &file) |
Protected Attributes | |
double | minRMSErrorPerNode |
<Tell the compiler we are using the base class predict method to stop hidden virtual function warnings | |
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UINT | trainingMode |
UINT | numSplittingSteps |
UINT | minNumSamplesPerNode |
UINT | maxDepth |
bool | removeFeaturesAtEachSpilt |
Node * | tree |
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string | classType |
DebugLog | debugLog |
ErrorLog | errorLog |
InfoLog | infoLog |
TrainingLog | trainingLog |
TestingLog | testingLog |
WarningLog | warningLog |
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string | regressifierType |
VectorDouble | regressionData |
vector< MinMax > | inputVectorRanges |
vector< MinMax > | targetVectorRanges |
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bool | trained |
bool | useScaling |
UINT | baseType |
UINT | numInputDimensions |
UINT | numOutputDimensions |
UINT | numTrainingIterationsToConverge |
UINT | minNumEpochs |
UINT | maxNumEpochs |
UINT | validationSetSize |
double | learningRate |
double | minChange |
double | rootMeanSquaredTrainingError |
double | totalSquaredTrainingError |
bool | useValidationSet |
bool | randomiseTrainingOrder |
Random | random |
vector< TrainingResult > | trainingResults |
TrainingResultsObserverManager | trainingResultsObserverManager |
TestResultsObserverManager | testResultsObserverManager |
Static Protected Attributes | |
static RegisterRegressifierModule< RegressionTree > | registerModule |
Additional Inherited Members | |
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enum | TrainingMode { BEST_ITERATIVE_SPILT =0, BEST_RANDOM_SPLIT, NUM_TRAINING_MODES } |
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typedef std::map< string, Regressifier *(*)() > | StringRegressifierMap |
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enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
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static string | getGRTVersion (bool returnRevision=true) |
static string | getGRTRevison () |
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static Regressifier * | createInstanceFromString (string const ®ressifierType) |
static vector< string > | getRegisteredRegressifiers () |
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static StringRegressifierMap * | getMap () |
Definition at line 40 of file RegressionTree.h.
GRT::RegressionTree::RegressionTree | ( | const UINT | numSplittingSteps = 100 , |
const UINT | minNumSamplesPerNode = 5 , |
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const UINT | maxDepth = 10 , |
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const bool | removeFeaturesAtEachSpilt = false , |
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const UINT | trainingMode = BEST_ITERATIVE_SPILT , |
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const bool | useScaling = false , |
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const double | minRMSErrorPerNode = 0.01 |
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Default Constructor
UINT | numSplittingSteps: sets the number of steps that will be used to search for the best spliting value for each node. Default value = 100 |
UINT | minNumSamplesPerNode: sets the minimum number of samples that are allowed per node, if the number of samples is below that, the node will become a leafNode. Default value = 5 |
UINT | maxDepth: sets the maximum depth of the tree. Default value = 10 |
bool | removeFeaturesAtEachSpilt: sets if a feature is removed at each spilt so it can not be used again. Default value = false |
UINT | trainingMode: sets the training mode, this should be one of the TrainingMode enums. Default value = BEST_ITERATIVE_SPILT |
bool | useScaling: sets if the training and real-time data should be scaled between [0 1]. Default value = false |
const | double minRMSErrorPerNode: sets the minimum RMS error that allowed per node, if the RMS error is below that, the node will become a leafNode. Default value = 0.01 |
Definition at line 31 of file RegressionTree.cpp.
GRT::RegressionTree::RegressionTree | ( | const RegressionTree & | rhs | ) |
Defines the copy constructor.
const | RegressionTree &rhs: the instance from which all the data will be copied into this instance |
Definition at line 50 of file RegressionTree.cpp.
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Default Destructor
Definition at line 61 of file RegressionTree.cpp.
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This overrides the clear function in the Regressifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.
Reimplemented from GRT::Tree.
Definition at line 196 of file RegressionTree.cpp.
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This is required for the Gesture Recognition Pipeline for when the pipeline.setRegressifier(...) method is called. It clones the data from the Base Class Regressifier pointer (which should be pointing to an RegressionTree instance) into this instance
Regressifier | *regressifier: a pointer to the Regressifier Base Class, this should be pointing to another RegressionTree instance |
Reimplemented from GRT::Regressifier.
Definition at line 89 of file RegressionTree.cpp.
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Deep copies the regression tree, returning a pointer to the new regression tree. The user is in charge of cleaning up the memory so must delete the pointer when they no longer need it. NULL will be returned if the tree could not be copied.
Reimplemented from GRT::Tree.
Definition at line 345 of file RegressionTree.cpp.
double GRT::RegressionTree::getMinRMSErrorPerNode | ( | ) | const |
Gets the minimum root mean squared error value that needs to be exceeded for the tree to continue growing at a specific node. If the RMS error is below this value then the node will be made into a leaf node.
Definition at line 358 of file RegressionTree.cpp.
const RegressionTreeNode * GRT::RegressionTree::getTree | ( | ) | const |
Gets a pointer to the regression tree. NULL will be returned if the decision tree model has not be trained.
Definition at line 354 of file RegressionTree.cpp.
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This loads a trained RegressionTree model from a file. This overrides the loadModelFromFile function in the Regressifier base class.
fstream | &file: a reference to the file the RegressionTree model will be loaded from |
Reimplemented from GRT::MLBase.
Definition at line 251 of file RegressionTree.cpp.
RegressionTree & GRT::RegressionTree::operator= | ( | const RegressionTree & | rhs | ) |
Defines how the data from the rhs RegressionTree should be copied to this RegressionTree
const | RegressionTree &rhs: another instance of a RegressionTree |
Definition at line 66 of file RegressionTree.cpp.
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This predicts the class of the inputVector. This overrides the predict function in the Regressifier base class.
VectorDouble | inputVector: the input vector to predict |
Reimplemented from GRT::MLBase.
Definition at line 165 of file RegressionTree.cpp.
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Prints the tree to std::cout.
Reimplemented from GRT::Tree.
Definition at line 210 of file RegressionTree.cpp.
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This saves the trained RegressionTree model to a file. This overrides the saveModelToFile function in the Regressifier base class.
fstream | &file: a reference to the file the RegressionTree model will be saved to |
Reimplemented from GRT::MLBase.
Definition at line 216 of file RegressionTree.cpp.
bool GRT::RegressionTree::setMinRMSErrorPerNode | ( | const double | minRMSErrorPerNode | ) |
Sets the minimum RMS error that needs to be exceeded for the tree to continue growing at a specific node.
Definition at line 362 of file RegressionTree.cpp.
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This trains the RegressionTree model, using the labelled regression data. This overrides the train function in the Regressifier base class.
RegressionData | trainingData: a reference to the training data |
Reimplemented from GRT::MLBase.
Definition at line 118 of file RegressionTree.cpp.