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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 | |
RandomForests (const DecisionTreeNode &decisionTreeNode=DecisionTreeClusterNode(), const UINT forestSize=10, const UINT numRandomSplits=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const UINT trainingMode=DecisionTree::BEST_RANDOM_SPLIT, const bool removeFeaturesAtEachSpilt=true, const bool useScaling=false) | |
RandomForests (const RandomForests &rhs) | |
virtual | ~RandomForests (void) |
RandomForests & | operator= (const RandomForests &rhs) |
virtual bool | deepCopyFrom (const Classifier *classifier) |
virtual bool | train_ (ClassificationData &trainingData) |
virtual bool | predict_ (VectorDouble &inputVector) |
virtual bool | clear () |
virtual bool | print () const |
virtual bool | saveModelToFile (fstream &file) const |
virtual bool | loadModelFromFile (fstream &file) |
UINT | getForestSize () const |
UINT | getNumRandomSplits () const |
UINT | getMinNumSamplesPerNode () const |
UINT | getMaxDepth () const |
UINT | getTrainingMode () const |
const vector< DecisionTreeNode * > | getForest () const |
bool | getRemoveFeaturesAtEachSpilt () const |
DecisionTreeNode * | deepCopyDecisionTreeNode () const |
bool | setForestSize (const UINT forestSize) |
bool | setNumRandomSplits (const UINT numSplittingSteps) |
bool | setMinNumSamplesPerNode (const UINT minNumSamplesPerNode) |
bool | setMaxDepth (const UINT maxDepth) |
bool | setRemoveFeaturesAtEachSpilt (const bool removeFeaturesAtEachSpilt) |
bool | setTrainingMode (const UINT trainingMode) |
bool | setDecisionTreeNode (const DecisionTreeNode &node) |
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Classifier (void) | |
virtual | ~Classifier (void) |
bool | copyBaseVariables (const Classifier *classifier) |
virtual bool | reset () |
string | getClassifierType () const |
bool | getSupportsNullRejection () const |
bool | getNullRejectionEnabled () const |
double | getNullRejectionCoeff () const |
double | getMaximumLikelihood () const |
double | getBestDistance () const |
double | getPhase () const |
virtual UINT | getNumClasses () const |
UINT | getClassLabelIndexValue (UINT classLabel) const |
UINT | getPredictedClassLabel () const |
VectorDouble | getClassLikelihoods () const |
VectorDouble | getClassDistances () const |
VectorDouble | getNullRejectionThresholds () const |
vector< UINT > | getClassLabels () const |
vector< MinMax > | getRanges () const |
bool | enableNullRejection (bool useNullRejection) |
virtual bool | setNullRejectionCoeff (double nullRejectionCoeff) |
virtual bool | setNullRejectionThresholds (VectorDouble newRejectionThresholds) |
virtual bool | recomputeNullRejectionThresholds () |
bool | getTimeseriesCompatible () const |
Classifier * | createNewInstance () const |
Classifier * | deepCopy () const |
const Classifier * | getClassifierPointer () const |
const Classifier & | getBaseClassifier () const |
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MLBase (void) | |
virtual | ~MLBase (void) |
bool | copyMLBaseVariables (const MLBase *mlBase) |
virtual bool | train (ClassificationData trainingData) |
virtual bool | train (RegressionData 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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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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virtual void | notify (const TrainingResult &data) |
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virtual void | notify (const TestInstanceResult &data) |
Protected Attributes | |
UINT | forestSize |
UINT | numRandomSplits |
UINT | minNumSamplesPerNode |
UINT | maxDepth |
UINT | trainingMode |
bool | removeFeaturesAtEachSpilt |
DecisionTreeNode * | decisionTreeNode |
vector< DecisionTreeNode * > | forest |
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string | classifierType |
bool | supportsNullRejection |
bool | useNullRejection |
UINT | numClasses |
UINT | predictedClassLabel |
UINT | classifierMode |
double | nullRejectionCoeff |
double | maxLikelihood |
double | bestDistance |
double | phase |
VectorDouble | classLikelihoods |
VectorDouble | classDistances |
VectorDouble | nullRejectionThresholds |
vector< UINT > | classLabels |
vector< MinMax > | ranges |
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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 |
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string | classType |
DebugLog | debugLog |
ErrorLog | errorLog |
InfoLog | infoLog |
TrainingLog | trainingLog |
TestingLog | testingLog |
WarningLog | warningLog |
Additional Inherited Members | |
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typedef std::map< string, Classifier *(*)() > | StringClassifierMap |
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enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
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static Classifier * | createInstanceFromString (string const &classifierType) |
static vector< string > | getRegisteredClassifiers () |
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static string | getGRTVersion (bool returnRevision=true) |
static string | getGRTRevison () |
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enum | ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE } |
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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) |
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double | SQR (const double &x) const |
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static StringClassifierMap * | getMap () |
Definition at line 43 of file RandomForests.h.
GRT::RandomForests::RandomForests | ( | const DecisionTreeNode & | decisionTreeNode = DecisionTreeClusterNode() , |
const UINT | forestSize = 10 , |
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const UINT | numRandomSplits = 100 , |
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const UINT | minNumSamplesPerNode = 5 , |
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const UINT | maxDepth = 10 , |
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const UINT | trainingMode = DecisionTree::BEST_RANDOM_SPLIT , |
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const bool | removeFeaturesAtEachSpilt = true , |
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const bool | useScaling = false |
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Default Constructor
const | DecisionTreeNode &decisionTreeNode: sets the type of decision tree node that will be used when training a new RandomForest model. Default: DecisionTreeClusterNode |
const | UINT forestSize: sets the number of decision trees that will be trained. Default value = 10 |
const | UINT numRandomSplits: sets the number of random spilts that will be used to search for the best spliting value for each node. Default value = 100 |
const | 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 |
const | UINT maxDepth: sets the maximum depth of the tree. Default value = 10 |
const | bool removeFeaturesAtEachSpilt: sets if features are removed at each stage in the tree |
const | bool useScaling: sets if the training and real-time data should be scaled between [0 1]. Default value = false |
Definition at line 28 of file RandomForests.cpp.
GRT::RandomForests::RandomForests | ( | const RandomForests & | rhs | ) |
Defines the copy constructor.
const | RandomForests &rhs: the instance from which all the data will be copied into this instance |
Definition at line 49 of file RandomForests.cpp.
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Default Destructor
Definition at line 61 of file RandomForests.cpp.
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This function clears the RandomForests module, removing any trained model and setting all the base variables to their default values.
Reimplemented from GRT::Classifier.
Definition at line 262 of file RandomForests.cpp.
DecisionTreeNode * GRT::RandomForests::deepCopyDecisionTreeNode | ( | ) | const |
Gets a pointer to the decision tree node. NULL will be returned if the decision tree node has not been set.
Definition at line 539 of file RandomForests.cpp.
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This is required for the Gesture Recognition Pipeline for when the pipeline.setClassifier(...) method is called. It clones the data from the Base Class Classifier pointer (which should be pointing to an RandomForests instance) into this instance
Classifier | *classifier: a pointer to the Classifier Base Class, this should be pointing to another RandomForests instance |
Reimplemented from GRT::Classifier.
Definition at line 105 of file RandomForests.cpp.
UINT GRT::RandomForests::getForestSize | ( | ) | const |
Gets the number of trees in the random forest.
Definition at line 511 of file RandomForests.cpp.
UINT GRT::RandomForests::getMaxDepth | ( | ) | const |
Gets the maximum depth of the tree.
Definition at line 523 of file RandomForests.cpp.
UINT GRT::RandomForests::getMinNumSamplesPerNode | ( | ) | const |
Gets the minimum number of samples that are allowed per node, if the number of samples at a node is below this value then the node will automatically become a leaf node.
Definition at line 519 of file RandomForests.cpp.
UINT GRT::RandomForests::getNumRandomSplits | ( | ) | const |
Gets the number of random splits that will be used to search for the best spliting value for each node.
Definition at line 515 of file RandomForests.cpp.
bool GRT::RandomForests::getRemoveFeaturesAtEachSpilt | ( | ) | const |
Gets if a feature is removed at each spilt so it can not be used again. If true then the best feature selected at each node will be removed so it can not be used in any children of that node. If false, then the feature that provides the best spilt at each node will be used, regardless of how many times it has been used again.
Definition at line 531 of file RandomForests.cpp.
UINT GRT::RandomForests::getTrainingMode | ( | ) | const |
Gets the training mode that will be used to train each DecisionTree in the forest.
Definition at line 527 of file RandomForests.cpp.
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This loads a trained RandomForests model from a file. This overrides the loadModelFromFile function in the Classifier base class.
fstream | &file: a reference to the file the RandomForests model will be loaded from |
Reimplemented from GRT::MLBase.
Definition at line 352 of file RandomForests.cpp.
RandomForests & GRT::RandomForests::operator= | ( | const RandomForests & | rhs | ) |
Defines how the data from the rhs RandomForests should be copied to this RandomForests
const | RandomForests &rhs: another instance of a RandomForests |
Definition at line 71 of file RandomForests.cpp.
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This predicts the class of the inputVector. This overrides the predict function in the Classifier base class.
VectorDouble | inputVector: the input vector to classify |
Reimplemented from GRT::MLBase.
Definition at line 205 of file RandomForests.cpp.
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This function will print the model and settings to the display log.
Reimplemented from GRT::MLBase.
Definition at line 280 of file RandomForests.cpp.
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This saves the trained RandomForests model to a file. This overrides the saveModelToFile function in the Classifier base class.
fstream | &file: a reference to the file the RandomForests model will be saved to |
Reimplemented from GRT::MLBase.
Definition at line 302 of file RandomForests.cpp.
bool GRT::RandomForests::setDecisionTreeNode | ( | const DecisionTreeNode & | node | ) |
Sets the decision tree node, this will be used as the starting node the next time the RandomForest model is trained.
Definition at line 597 of file RandomForests.cpp.
bool GRT::RandomForests::setForestSize | ( | const UINT | forestSize | ) |
Sets the number of trees in the forest. Changing this value will clear any previously trained model.
UINT | forestSize: sets the number of trees in the forest. |
Definition at line 548 of file RandomForests.cpp.
bool GRT::RandomForests::setMaxDepth | ( | const UINT | maxDepth | ) |
Sets the maximum depth of the tree, any node that reaches this depth will automatically become a leaf node. Value must be larger than zero.
UINT | maxDepth: the maximum depth of the tree |
Definition at line 573 of file RandomForests.cpp.
bool GRT::RandomForests::setMinNumSamplesPerNode | ( | const UINT | minNumSamplesPerNode | ) |
Sets the minimum number of samples that are allowed per node, if the number of samples at a node is below this value then the node will automatically become a leaf node. Value must be larger than zero.
UINT | minNumSamplesPerNode: the minimum number of samples that are allowed per node |
Definition at line 565 of file RandomForests.cpp.
bool GRT::RandomForests::setNumRandomSplits | ( | const UINT | numSplittingSteps | ) |
Sets the number of steps that will be used to search for the best spliting value for each node.
A higher value will increase the chances of building a better model, but will take longer to train the model. Value must be larger than zero.
UINT | numSplittingSteps: sets the number of steps that will be used to search for the best spliting value for each node. |
Definition at line 557 of file RandomForests.cpp.
bool GRT::RandomForests::setRemoveFeaturesAtEachSpilt | ( | const bool | removeFeaturesAtEachSpilt | ) |
Sets if a feature is removed at each spilt so it can not be used again. If true then the best feature selected at each node will be removed so it can not be used in any children of that node. If false, then the feature that provides the best spilt at each node will be used, regardless of how many times it has been used again.
bool | removeFeaturesAtEachSpilt: if true, then each feature is removed at each spilt so it can not be used again |
Definition at line 581 of file RandomForests.cpp.
bool GRT::RandomForests::setTrainingMode | ( | const UINT | trainingMode | ) |
Sets the training mode used to train each DecisionTree in the forest, this should be one of the DecisionTree::TrainingModes enums.
const | UINT trainingMode: the new trainingMode, this should be one of the DecisionTree::TrainingModes enums |
Definition at line 586 of file RandomForests.cpp.
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This trains the RandomForests model, using the labelled classification data. This overrides the train function in the Classifier base class.
ClassificationData | trainingData: a reference to the training data |
Reimplemented from GRT::MLBase.
Definition at line 147 of file RandomForests.cpp.