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GestureRecognitionToolkit
Version: 0.2.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
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GestureRecognitionToolkit
Version: 0.2.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
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#include <Clusterer.h>
Public Types | |
| typedef std::map< std::string, Clusterer *(*)() > | StringClustererMap |
 Public Types inherited from MLBase | |
| enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
Public Member Functions | |
| Clusterer (void) | |
| virtual | ~Clusterer (void) |
| virtual bool | deepCopyFrom (const Clusterer *clusterer) |
| bool | copyBaseVariables (const Clusterer *clusterer) |
| virtual bool | train_ (MatrixFloat &trainingData) |
| virtual bool | train_ (ClassificationData &trainingData) |
| virtual bool | train_ (UnlabelledData &trainingData) |
| virtual bool | reset () |
| virtual bool | clear () |
| bool | getConverged () const |
| UINT | getNumClusters () const |
| UINT | getPredictedClusterLabel () const |
| Float | getMaximumLikelihood () const |
| Float | getBestDistance () const |
| VectorFloat | getClusterLikelihoods () const |
| VectorFloat | getClusterDistances () const |
| Vector< UINT > | getClusterLabels () const |
| std::string | getClustererType () const |
| bool | setNumClusters (const UINT numClusters) |
| Clusterer * | createNewInstance () const |
| Clusterer * | deepCopy () const |
| const Clusterer & | getBaseClusterer () const |
| Public Member Functions inherited from MLBase | |
| 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 (ClassificationDataStream trainingData) |
| virtual bool | train_ (ClassificationDataStream &trainingData) |
| virtual bool | train (UnlabelledData trainingData) |
| virtual bool | train (MatrixFloat data) |
| virtual bool | predict (VectorFloat inputVector) |
| virtual bool | predict_ (VectorFloat &inputVector) |
| virtual bool | predict (MatrixFloat inputMatrix) |
| virtual bool | predict_ (MatrixFloat &inputMatrix) |
| virtual bool | map (VectorFloat inputVector) |
| virtual bool | map_ (VectorFloat &inputVector) |
| virtual bool | print () const |
| virtual bool | save (const std::string filename) const |
| virtual bool | load (const std::string filename) |
| virtual bool | save (std::fstream &file) const |
| virtual bool | load (std::fstream &file) |
| GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(std::string filename) instead", virtual bool saveModelToFile(std::string filename) const ) | |
| GRT_DEPRECATED_MSG ("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const ) | |
| GRT_DEPRECATED_MSG ("loadModelFromFile(std::string filename) is deprecated, use load(std::string filename) instead", virtual bool loadModelFromFile(std::string filename)) | |
| GRT_DEPRECATED_MSG ("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file)) | |
| virtual bool | getModel (std::ostream &stream) const |
| Float | scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false) |
| virtual std::string | getModelAsString () const |
| DataType | getInputType () const |
| DataType | getOutputType () 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 |
| Float | getMinChange () const |
| Float | getLearningRate () const |
| Float | getRootMeanSquaredTrainingError () const |
| Float | getTotalSquaredTrainingError () const |
| Float | getValidationSetAccuracy () const |
| VectorFloat | getValidationSetPrecision () const |
| VectorFloat | getValidationSetRecall () 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 (const bool useScaling) |
| bool | setMaxNumEpochs (const UINT maxNumEpochs) |
| bool | setMinNumEpochs (const UINT minNumEpochs) |
| bool | setMinChange (const Float minChange) |
| bool | setLearningRate (const Float learningRate) |
| bool | setUseValidationSet (const bool useValidationSet) |
| bool | setValidationSetSize (const UINT validationSetSize) |
| bool | setRandomiseTrainingOrder (const bool randomiseTrainingOrder) |
| bool | setTrainingLoggingEnabled (const bool loggingEnabled) |
| 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 |
| 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) |
| GRTBase * | getGRTBasePointer () |
| const GRTBase * | getGRTBasePointer () const |
| Public Member Functions inherited from Observer< TrainingResult > | |
| virtual void | notify (const TrainingResult &data) |
| Public Member Functions inherited from Observer< TestInstanceResult > | |
| virtual void | notify (const TestInstanceResult &data) |
Static Public Member Functions | |
| static Clusterer * | createInstanceFromString (std::string const &ClustererType) |
| static Vector< std::string > | getRegisteredClusterers () |
| Static Public Member Functions inherited from GRTBase | |
| static std::string | getGRTVersion (bool returnRevision=true) |
| static std::string | getGRTRevison () |
Protected Member Functions | |
| bool | saveClustererSettingsToFile (std::fstream &file) const |
| bool | loadClustererSettingsFromFile (std::fstream &file) |
| Protected Member Functions inherited from MLBase | |
| bool | saveBaseSettingsToFile (std::fstream &file) const |
| bool | loadBaseSettingsFromFile (std::fstream &file) |
| Protected Member Functions inherited from GRTBase | |
| Float | SQR (const Float &x) const |
Static Protected Member Functions | |
| static StringClustererMap * | getMap () |
Protected Attributes | |
| std::string | clustererType |
| UINT | numClusters |
| Number of clusters in the model. | |
| UINT | predictedClusterLabel |
| Stores the predicted cluster label from the most recent predict( ) | |
| Float | maxLikelihood |
| Float | bestDistance |
| VectorFloat | clusterLikelihoods |
| VectorFloat | clusterDistances |
| Vector< UINT > | clusterLabels |
| bool | converged |
| Vector< MinMax > | ranges |
| Protected Attributes inherited from MLBase | |
| bool | trained |
| bool | useScaling |
| DataType | inputType |
| DataType | outputType |
| UINT | baseType |
| UINT | numInputDimensions |
| UINT | numOutputDimensions |
| UINT | numTrainingIterationsToConverge |
| UINT | minNumEpochs |
| UINT | maxNumEpochs |
| UINT | validationSetSize |
| Float | learningRate |
| Float | minChange |
| Float | rootMeanSquaredTrainingError |
| Float | totalSquaredTrainingError |
| Float | validationSetAccuracy |
| bool | useValidationSet |
| bool | randomiseTrainingOrder |
| VectorFloat | validationSetPrecision |
| VectorFloat | validationSetRecall |
| Random | random |
| std::vector< TrainingResult > | trainingResults |
| TrainingResultsObserverManager | trainingResultsObserverManager |
| TestResultsObserverManager | testResultsObserverManager |
| Protected Attributes inherited from GRTBase | |
| std::string | classType |
| DebugLog | debugLog |
| ErrorLog | errorLog |
| InfoLog | infoLog |
| TrainingLog | trainingLog |
| TestingLog | testingLog |
| WarningLog | warningLog |
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 38 of file Clusterer.h.
| typedef std::map< std::string, Clusterer*(*)() > Clusterer::StringClustererMap |
Defines a map between a string (which will contain the name of the Clusterer, such as KMeans) and a function returns a new instance of that Clusterer
Definition at line 190 of file Clusterer.h.
| Clusterer::Clusterer | ( | void | ) |
Default Clusterer Constructor
Definition at line 66 of file Clusterer.cpp.
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virtual |
Default Clusterer Destructor
Definition at line 80 of file Clusterer.cpp.
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virtual |
This function clears the Clusterer module, removing any trained model and setting all the base variables to their default values.
Reimplemented from MLBase.
Reimplemented in SelfOrganizingMap, HierarchicalClustering, ClusterTree, KMeans, and GaussianMixtureModels.
Definition at line 142 of file Clusterer.cpp.
| bool Clusterer::copyBaseVariables | ( | const Clusterer * | clusterer | ) |
This copies the Clusterer base class variables from the Clusterer pointer to this instance.
| clusterer | a pointer to a Clusterer from which the values will be copied to this instance |
Definition at line 87 of file Clusterer.cpp.
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static |
Creates a new Clusterer instance based on the input string (which should contain the name of a valid Clusterer such as KMeans).
| string | const &ClustererType: the name of the Clusterer |
Definition at line 29 of file Clusterer.cpp.
| Clusterer * Clusterer::createNewInstance | ( | ) | const |
Creates a new Clusterer instance based on the current clustererType string value.
Definition at line 37 of file Clusterer.cpp.
| Clusterer * Clusterer::deepCopy | ( | ) | const |
This creates a new Clusterer instance and deep copies the variables and models from this instance into the deep copy. The function will then return a pointer to the new instance. It is up to the user who calls this function to delete the dynamic instance when they are finished using it.
Definition at line 41 of file Clusterer.cpp.
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inlinevirtual |
This is the base deep copy function for the Clusterer modules. This function should be overwritten by the derived class. This deep copies the variables and models from the Clusterer pointer to this Clusterer instance.
| clusterer | a pointer to the Clusterer base class, this should be pointing to another instance of a matching derived class |
Reimplemented in SelfOrganizingMap, HierarchicalClustering, ClusterTree, KMeans, and GaussianMixtureModels.
Definition at line 58 of file Clusterer.h.
| const Clusterer & Clusterer::getBaseClusterer | ( | ) | const |
| Float Clusterer::getBestDistance | ( | ) | const |
Returns the current bestDistance value. The bestDistance value is computed during the prediction phase and is either the minimum or maximum distance, depending on the algorithm. This value will return 0 if a prediction has not been made.
Definition at line 244 of file Clusterer.cpp.
| VectorFloat Clusterer::getClusterDistances | ( | ) | const |
Gets a Vector of the cluster distances from the last prediction, this will be an N-dimensional Vector, where N is the number of clusters in the model. The exact form of these distances depends on the cluster algorithm.
Definition at line 252 of file Clusterer.cpp.
| std::string Clusterer::getClustererType | ( | ) | const |
Returns the classifeir type as a string.
Definition at line 260 of file Clusterer.cpp.
| Vector< UINT > Clusterer::getClusterLabels | ( | ) | const |
Gets a Vector of unsigned ints containing the label of each cluster, this will be an K-dimensional Vector, where K is the number of clusters in the model.
Definition at line 256 of file Clusterer.cpp.
| VectorFloat Clusterer::getClusterLikelihoods | ( | ) | const |
Gets a Vector of the cluster likelihoods from the last prediction, this will be an N-dimensional Vector, where N is the number of clusters in the model. The exact form of these likelihoods depends on the cluster algorithm.
Definition at line 248 of file Clusterer.cpp.
| bool Clusterer::getConverged | ( | ) | const |
Returns true if the training algorithm converged during the most recent training process. This function will return false if the model has not been trained.
Definition at line 230 of file Clusterer.cpp.
| Float Clusterer::getMaximumLikelihood | ( | ) | const |
Returns the current maximumLikelihood value. The maximumLikelihood value is computed during the prediction phase and is the likelihood of the most likely model. This value will return 0 if a prediction has not been made.
Definition at line 240 of file Clusterer.cpp.
| UINT Clusterer::getNumClusters | ( | ) | const |
Returns the number of clusters in the model.
Definition at line 235 of file Clusterer.cpp.
| UINT Clusterer::getPredictedClusterLabel | ( | ) | const |
Returns the predicted cluster label.
Definition at line 237 of file Clusterer.cpp.
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static |
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protected |
Loads the core clusterer settings from a file.
Definition at line 179 of file Clusterer.cpp.
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virtual |
This resets the Clusterer. This overrides the reset function in the MLBase base class.
Reimplemented from MLBase.
Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, and GaussianMixtureModels.
Definition at line 128 of file Clusterer.cpp.
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protected |
Saves the core clusterer settings to a file.
Definition at line 157 of file Clusterer.cpp.
| bool Clusterer::setNumClusters | ( | const UINT | numClusters | ) |
Sets the number of clusters that will be used the next time a model is trained. This will clear any previous model. The number of clusters must be greater than zero.
| numClusters | the number of clusters, must be greater than zero |
Definition at line 266 of file Clusterer.cpp.
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virtual |
This is the main interface for training the clusterer model.
| trainingData | a reference to the training data that will be used to train the ML model |
Reimplemented from MLBase.
Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, GaussianMixtureModels, and ClusterTree.
Definition at line 114 of file Clusterer.cpp.
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virtual |
Override the main ClassificationData train function to pass MatrixFloat data to the Clusterer train function.
| trainingData | a reference to the training data that will be used to train the ML model |
Reimplemented from MLBase.
Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, and GaussianMixtureModels.
Definition at line 118 of file Clusterer.cpp.
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virtual |
Override the main UnlabelledData train function to pass MatrixFloat data to the Clusterer train function.
| trainingData | a reference to the training data that will be used to train the ML model |
Reimplemented from MLBase.
Reimplemented in SelfOrganizingMap, HierarchicalClustering, KMeans, and GaussianMixtureModels.
Definition at line 123 of file Clusterer.cpp.
1.8.11 
