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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 Types | |
enum | NetworkTypology { RANDOM_NETWORK =0 } |
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typedef std::map< string, Clusterer *(*)() > | StringClustererMap |
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enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
Public Member Functions | |
SelfOrganizingMap (const UINT networkSize=20, const UINT networkTypology=RANDOM_NETWORK, const UINT maxNumEpochs=1000, const double alphaStart=0.8, const double alphaEnd=0.1) | |
SelfOrganizingMap (const SelfOrganizingMap &rhs) | |
virtual | ~SelfOrganizingMap () |
SelfOrganizingMap & | operator= (const SelfOrganizingMap &rhs) |
virtual bool | deepCopyFrom (const Clusterer *clusterer) |
virtual bool | reset () |
virtual bool | clear () |
virtual bool | train_ (MatrixDouble &data) |
virtual bool | train_ (ClassificationData &trainingData) |
virtual bool | train_ (UnlabelledData &trainingData) |
virtual bool | map_ (VectorDouble &x) |
virtual bool | saveModelToFile (fstream &file) const |
virtual bool | loadModelFromFile (fstream &file) |
bool | validateNetworkTypology (const UINT networkTypology) |
UINT | getNetworkSize () const |
double | getAlphaStart () const |
double | getAlphaEnd () const |
VectorDouble | getMappedData () const |
vector< GaussNeuron > | getNeurons () const |
const vector< GaussNeuron > & | getNeuronsRef () const |
MatrixDouble | getNetworkWeights () const |
bool | setNetworkSize (const UINT networkSize) |
bool | setNetworkTypology (const UINT networkTypology) |
bool | setAlphaStart (const double alphaStart) |
bool | setAlphaEnd (const double alphaEnd) |
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Clusterer (void) | |
virtual | ~Clusterer (void) |
bool | copyBaseVariables (const Clusterer *Clusterer) |
bool | getConverged () const |
UINT | getNumClusters () const |
UINT | getPredictedClusterLabel () const |
double | getMaximumLikelihood () const |
double | getBestDistance () const |
VectorDouble | getClusterLikelihoods () const |
VectorDouble | getClusterDistances () const |
vector< UINT > | getClusterLabels () const |
string | getClustererType () const |
bool | setNumClusters (const UINT numClusters) |
Clusterer * | createNewInstance () const |
Clusterer * | deepCopy () const |
const Clusterer & | getBaseClusterer () 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 (MatrixDouble data) |
virtual bool | predict (VectorDouble inputVector) |
virtual bool | predict_ (VectorDouble &inputVector) |
virtual bool | predict (MatrixDouble inputMatrix) |
virtual bool | predict_ (MatrixDouble &inputMatrix) |
virtual bool | map (VectorDouble inputVector) |
virtual bool | print () const |
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 | networkTypology |
double | alphaStart |
double | alphaEnd |
VectorDouble | mappedData |
vector< GaussNeuron > | neurons |
MatrixDouble | networkWeights |
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string | clustererType |
UINT | numClusters |
Number of clusters in the model. | |
UINT | predictedClusterLabel |
Stores the predicted cluster label from the most recent predict( ) | |
double | maxLikelihood |
double | bestDistance |
VectorDouble | clusterLikelihoods |
VectorDouble | clusterDistances |
vector< UINT > | clusterLabels |
bool | converged |
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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static Clusterer * | createInstanceFromString (string const &ClustererType) |
static vector< string > | getRegisteredClusterers () |
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static string | getGRTVersion (bool returnRevision=true) |
static string | getGRTRevison () |
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bool | saveClustererSettingsToFile (fstream &file) const |
bool | loadClustererSettingsFromFile (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 StringClustererMap * | getMap () |
Definition at line 197 of file SelfOrganizingMap.h.
GRT::SelfOrganizingMap::SelfOrganizingMap | ( | const UINT | networkSize = 20 , |
const UINT | networkTypology = RANDOM_NETWORK , |
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const UINT | maxNumEpochs = 1000 , |
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const double | alphaStart = 0.8 , |
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const double | alphaEnd = 0.1 |
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Default Constructor.
Definition at line 30 of file SelfOrganizingMap.cpp.
GRT::SelfOrganizingMap::SelfOrganizingMap | ( | const SelfOrganizingMap & | rhs | ) |
Defines how the data from the rhs SelfOrganizingMap should be copied to this SelfOrganizingMap
const | SelfOrganizingMap &rhs: another instance of a SelfOrganizingMap |
Definition at line 46 of file SelfOrganizingMap.cpp.
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Default Destructor.
Definition at line 66 of file SelfOrganizingMap.cpp.
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This function clears the Clusterer module, removing any trained model and setting all the base variables to their default values.
Reimplemented from GRT::Clusterer.
Definition at line 112 of file SelfOrganizingMap.cpp.
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This deep copies the variables and models from the Clusterer pointer to this SelfOrganizingMap instance. This overrides the base deep copy function for the Clusterer modules.
const | Clusterer *clusterer: a pointer to the Clusterer base class, this should be pointing to another SelfOrganizingMap instance |
Reimplemented from GRT::Clusterer.
Definition at line 85 of file SelfOrganizingMap.cpp.
UINT GRT::SelfOrganizingMap::getNetworkSize | ( | ) | const |
This function returns the size of the SOM network. This is the same as the number of clusters in the network.
const | UINT networkTypology: the network typology you want to test |
Definition at line 442 of file SelfOrganizingMap.cpp.
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This loads a trained SOM model from a file. This overrides the loadModelFromFile function in the base class.
fstream | &file: a reference to the file the SOM model will be loaded from |
Reimplemented from GRT::MLBase.
Definition at line 363 of file SelfOrganizingMap.cpp.
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This function maps the input vector x by reference through the self organizing map. The function will return true if the mapping was successful. The mapped data can then be accessed via the getMappedData function. You need to train the SOM model before you can use this function. Because the data is mapped by reference, the x input data might be modified by the map (if it has to scale the input data for example).
VectorDouble | &inputVector: the input vector for mapping |
Reimplemented from GRT::MLBase.
Definition at line 300 of file SelfOrganizingMap.cpp.
SelfOrganizingMap & GRT::SelfOrganizingMap::operator= | ( | const SelfOrganizingMap & | rhs | ) |
Defines how the data from the rhs SelfOrganizingMap should be copied to this SelfOrganizingMap
const | SelfOrganizingMap &rhs: another instance of a SelfOrganizingMap |
Definition at line 70 of file SelfOrganizingMap.cpp.
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This resets the Clusterer. This overrides the reset function in the MLBase base class.
Reimplemented from GRT::Clusterer.
Definition at line 104 of file SelfOrganizingMap.cpp.
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This saves the trained SOM model to a file. This overrides the saveModelToFile function in the base class.
fstream | &file: a reference to the file the SOM model will be saved to |
Reimplemented from GRT::MLBase.
Definition at line 322 of file SelfOrganizingMap.cpp.
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This is the main training interface for referenced MatrixDouble data. It overrides the train_ function in the ML base class. This function runs the main training algorithm and is called by all the other train and train_ functions.
MatrixDouble | &trainingData: a reference to the training data that will be used to train the ML model |
Reimplemented from GRT::Clusterer.
Definition at line 124 of file SelfOrganizingMap.cpp.
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This is the main training interface for reference ClassificationData data. It overrides the train_ function in the ML base class.
ClassificationData | &trainingData: a reference to the training data that will be used to train the ML model |
Reimplemented from GRT::Clusterer.
Definition at line 290 of file SelfOrganizingMap.cpp.
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This is the main training interface for reference UnlabelledData data. It overrides the trainInplace function in the ML base class.
UnlabelledData | &trainingData: a reference to the training data that will be used to train the ML model |
Reimplemented from GRT::Clusterer.
Definition at line 295 of file SelfOrganizingMap.cpp.
bool GRT::SelfOrganizingMap::validateNetworkTypology | ( | const UINT | networkTypology | ) |
This function validates the network typology to ensure it is one of the NetworkTypology enums.
const | UINT networkTypology: the network typology you want to test |
Definition at line 434 of file SelfOrganizingMap.cpp.