GestureRecognitionToolkit  Version: 0.1.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
KMeans Class Reference

#include <KMeans.h>

Inheritance diagram for KMeans:
Clusterer MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

Public Member Functions

 KMeans (const UINT numClusters=10, const UINT minNumEpochs=5, const UINT maxNumEpochs=1000, const Float minChange=1.0e-5, const bool computeTheta=true)
 
 KMeans (const KMeans &rhs)
 
virtual ~KMeans ()
 
KMeansoperator= (const KMeans &rhs)
 
virtual bool deepCopyFrom (const Clusterer *clusterer)
 
virtual bool reset ()
 
virtual bool clear ()
 
bool trainModel (MatrixFloat &data)
 
virtual bool train_ (MatrixFloat &data)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool train_ (UnlabelledData &trainingData)
 
virtual bool predict_ (VectorFloat &inputVector)
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::fstream &file)
 
Float getTheta ()
 
bool getModelTrained ()
 
const VectorFloatgetTrainingThetaLog () const
 
const MatrixFloatgetClusters () const
 
const Vector< UINT > & getClassLabelsVector () const
 
const Vector< UINT > & getClassCountVector () const
 
bool setComputeTheta (const bool computeTheta)
 
bool setClusters (const MatrixFloat &clusters)
 
- Public Member Functions inherited from Clusterer
 Clusterer (void)
 
virtual ~Clusterer (void)
 
bool copyBaseVariables (const Clusterer *clusterer)
 
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)
 
ClusterercreateNewInstance () const
 
ClustererdeepCopy () const
 
const ClusterergetBaseClusterer () 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 (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 saveModelToFile (std::string filename) const
 
virtual bool loadModelFromFile (std::string filename)
 
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)
 
MLBasegetMLBasePointer ()
 
const MLBasegetMLBasePointer () 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)
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () 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)
 

Protected Member Functions

UINT estep (const MatrixFloat &data)
 
void mstep (const MatrixFloat &data)
 
Float calculateTheta (const MatrixFloat &data)
 
Float SQR (const Float a)
 
- Protected Member Functions inherited from Clusterer
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
 

Protected Attributes

bool computeTheta
 
UINT numTrainingSamples
 Number of training examples.
 
UINT nchg
 Number of values changes.
 
Float finalTheta
 
MatrixFloat clusters
 
Vector< UINT > assign
 
Vector< UINT > count
 
VectorFloat thetaTracker
 
- Protected Attributes inherited from Clusterer
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< MinMaxranges
 
- 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
 

Additional Inherited Members

- Public Types inherited from Clusterer
typedef std::map< std::string, Clusterer *(*)() > StringClustererMap
 
- Public Types inherited from MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from Clusterer
static ClusterercreateInstanceFromString (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 ()
 
- Static Protected Member Functions inherited from Clusterer
static StringClustererMapgetMap ()
 

Detailed Description

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 41 of file KMeans.h.

Constructor & Destructor Documentation

KMeans::KMeans ( const UINT  numClusters = 10,
const UINT  minNumEpochs = 5,
const UINT  maxNumEpochs = 1000,
const Float  minChange = 1.0e-5,
const bool  computeTheta = true 
)

Default Constructor.

Definition at line 29 of file KMeans.cpp.

KMeans::KMeans ( const KMeans rhs)

Defines how the data from the rhs KMeans should be copied to this KMeans

Parameters
rhsanother instance of a KMeans

Definition at line 51 of file KMeans.cpp.

KMeans::~KMeans ( )
virtual

Default Destructor

Definition at line 77 of file KMeans.cpp.

Member Function Documentation

bool KMeans::clear ( )
virtual

This function clears the Clusterer module, removing any trained model and setting all the base variables to their default values.

Returns
returns true if the derived class was cleared succesfully, false otherwise

Reimplemented from Clusterer.

Definition at line 499 of file KMeans.cpp.

bool KMeans::deepCopyFrom ( const Clusterer clusterer)
virtual

This deep copies the variables and models from the Clusterer pointer to this KMeans instance. This overrides the base deep copy function for the Clusterer modules.

Parameters
clusterera pointer to the Clusterer base class, this should be pointing to another KMeans instance
Returns
returns true if the clone was successfull, false otherwise

Reimplemented from Clusterer.

Definition at line 100 of file KMeans.cpp.

bool KMeans::loadModelFromFile ( std::fstream &  file)
virtual

This loads a trained KMeans model from a file. This overrides the loadModelFromFile function in the base class.

Parameters
filea reference to the file the KMeans model will be loaded from
Returns
returns true if the model was loaded successfully, false otherwise

Reimplemented from MLBase.

Definition at line 445 of file KMeans.cpp.

KMeans & KMeans::operator= ( const KMeans rhs)

Defines how the data from the rhs KMeans should be copied to this KMeans

Parameters
rhsanother instance of a KMeans
Returns
returns a reference to this instance of the KMeans

Definition at line 80 of file KMeans.cpp.

bool KMeans::predict_ ( VectorFloat inputVector)
virtual

This is the main prediction interface for all reference VectorFloat data. It overrides the predict_ function in the ML base class.

Parameters
inputVectora reference to the input Vector for prediction
Returns
returns true if the prediction was completed succesfully, false otherwise (the base class always returns false)

Reimplemented from MLBase.

Definition at line 199 of file KMeans.cpp.

bool KMeans::reset ( )
virtual

This resets the Clusterer. This overrides the reset function in the MLBase base class.

Returns
returns true if the Clusterer was reset, false otherwise

Reimplemented from Clusterer.

Definition at line 486 of file KMeans.cpp.

bool KMeans::saveModelToFile ( std::fstream &  file) const
virtual

This saves the trained KMeans model to a file. This overrides the saveModelToFile function in the base class.

Parameters
filea reference to the file the KMeans model will be saved to
Returns
returns true if the model was saved successfully, false otherwise

Reimplemented from MLBase.

Definition at line 417 of file KMeans.cpp.

bool KMeans::setClusters ( const MatrixFloat clusters)

This function lets you set the models clusters. You can use this to initalize the cluster values for the training algorithm. If you do that, then you should call the trainModel to run the training algorithm so the cluster values do not get reset.

Parameters
constMatrixFloat &clusters: the initial cluster values that will be used to train the KMeans model
Returns
returns true if the model was loaded successfully, false otherwise

Definition at line 518 of file KMeans.cpp.

bool KMeans::train_ ( MatrixFloat data)
virtual

This is the main training interface for referenced MatrixFloat data. It overrides the train_ function in the ML base class.

Parameters
trainingDataa reference to the training data that will be used to train the ML model
Returns
returns true if the model was successfully trained, false otherwise

Reimplemented from Clusterer.

Definition at line 162 of file KMeans.cpp.

bool KMeans::train_ ( ClassificationData trainingData)
virtual

This is the main training interface for reference ClassificationData data. It overrides the train_ function in the ML base class.

Parameters
trainingDataa reference to the training data that will be used to train the ML model
Returns
returns true if the model was successfully trained, false otherwise

Reimplemented from Clusterer.

Definition at line 123 of file KMeans.cpp.

bool KMeans::train_ ( UnlabelledData trainingData)
virtual

This is the main training interface for reference UnlabelledData data. It overrides the train_ function in the ML base class.

Parameters
trainingDataa reference to the training data that will be used to train the ML model
Returns
returns true if the model was successfully trained, false otherwise

Reimplemented from Clusterer.

Definition at line 147 of file KMeans.cpp.

bool KMeans::trainModel ( MatrixFloat data)

This is the main training algorithm for training a KMeans model. You should only call this function if you have manually set the clusters, otherwise you should use any of the train or train_ in functions.

Parameters
trainingDatathe training data that will be used to train the ML model
Returns
returns true if the model was successfully trained, false otherwise

Definition at line 258 of file KMeans.cpp.


The documentation for this class was generated from the following files: