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.
Softmax Class Reference

#include <Softmax.h>

Inheritance diagram for Softmax:
Classifier MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

Public Member Functions

 Softmax (const bool useScaling=false, const Float learningRate=0.1, const Float minChange=1.0e-10, const UINT maxNumEpochs=1000)
 
 Softmax (const Softmax &rhs)
 
virtual ~Softmax (void)
 
Softmaxoperator= (const Softmax &rhs)
 
virtual bool deepCopyFrom (const Classifier *classifier)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool predict_ (VectorFloat &inputVector)
 
virtual bool clear ()
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::fstream &file)
 
Vector< SoftmaxModelgetModels () const
 
- Public Member Functions inherited from Classifier
 Classifier (void)
 
virtual ~Classifier (void)
 
bool copyBaseVariables (const Classifier *classifier)
 
virtual bool reset ()
 
std::string getClassifierType () const
 
bool getSupportsNullRejection () const
 
bool getNullRejectionEnabled () const
 
Float getNullRejectionCoeff () const
 
Float getMaximumLikelihood () const
 
Float getBestDistance () const
 
Float getPhase () const
 
virtual UINT getNumClasses () const
 
UINT getClassLabelIndexValue (UINT classLabel) const
 
UINT getPredictedClassLabel () const
 
VectorFloat getClassLikelihoods () const
 
VectorFloat getClassDistances () const
 
VectorFloat getNullRejectionThresholds () const
 
Vector< UINT > getClassLabels () const
 
Vector< MinMaxgetRanges () const
 
bool enableNullRejection (bool useNullRejection)
 
virtual bool setNullRejectionCoeff (Float nullRejectionCoeff)
 
virtual bool setNullRejectionThresholds (VectorFloat newRejectionThresholds)
 
virtual bool recomputeNullRejectionThresholds ()
 
bool getTimeseriesCompatible () const
 
ClassifiercreateNewInstance () const
 
ClassifierdeepCopy () const
 
const ClassifiergetClassifierPointer () const
 
const ClassifiergetBaseClassifier () 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_ (UnlabelledData &trainingData)
 
virtual bool train (MatrixFloat data)
 
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

bool trainSoftmaxModel (UINT classLabel, SoftmaxModel &model, ClassificationData &data)
 
bool loadLegacyModelFromFile (std::fstream &file)
 
- Protected Member Functions inherited from Classifier
bool saveBaseSettingsToFile (std::fstream &file) const
 
bool loadBaseSettingsFromFile (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

Vector< SoftmaxModelmodels
 
- Protected Attributes inherited from Classifier
std::string classifierType
 
bool supportsNullRejection
 
bool useNullRejection
 
UINT numClasses
 
UINT predictedClassLabel
 
UINT classifierMode
 
Float nullRejectionCoeff
 
Float maxLikelihood
 
Float bestDistance
 
Float phase
 
VectorFloat classLikelihoods
 
VectorFloat classDistances
 
VectorFloat nullRejectionThresholds
 
Vector< UINT > classLabels
 
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
 

Static Protected Attributes

static RegisterClassifierModule< SoftmaxregisterModule
 

Additional Inherited Members

- Public Types inherited from Classifier
typedef std::map< std::string, Classifier *(*)() > StringClassifierMap
 
- Public Types inherited from MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from Classifier
static ClassifiercreateInstanceFromString (std::string const &classifierType)
 
static Vector< std::string > getRegisteredClassifiers ()
 
- Static Public Member Functions inherited from GRTBase
static std::string getGRTVersion (bool returnRevision=true)
 
static std::string getGRTRevison ()
 
- Protected Types inherited from Classifier
enum  ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }
 
- Static Protected Member Functions inherited from Classifier
static StringClassifierMapgetMap ()
 

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 Softmax.h.

Constructor & Destructor Documentation

Softmax::Softmax ( const bool  useScaling = false,
const Float  learningRate = 0.1,
const Float  minChange = 1.0e-10,
const UINT  maxNumEpochs = 1000 
)

Default Constructor

Parameters
useScalingsets if the training and real-time data should be scaled between [0 1]. Default value = false
learningRatethe learningRate value used during the training phase. Default = 0.1
minChangesets the minimum change that must be achieved between two training epochs for the training to continue. Default = 1.0e-10
maxNumEpochssets the maximum number of iterations that can be run during the training phase. Default = 1000

Definition at line 28 of file Softmax.cpp.

Softmax::Softmax ( const Softmax rhs)

Defines the copy constructor.

Parameters
rhsthe instance from which all the data will be copied into this instance

Definition at line 43 of file Softmax.cpp.

Softmax::~Softmax ( void  )
virtual

Default Destructor

Definition at line 54 of file Softmax.cpp.

Member Function Documentation

bool Softmax::clear ( )
virtual

This overrides the clear function in the Classifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.

Returns
returns true if the module was cleared succesfully, false otherwise

Reimplemented from Classifier.

Definition at line 262 of file Softmax.cpp.

bool Softmax::deepCopyFrom ( const Classifier classifier)
virtual

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 Softmax instance) into this instance

Parameters
classifiera pointer to the Classifier Base Class, this should be pointing to another Softmax instance
Returns
returns true if the clone was successfull, false otherwise

Reimplemented from Classifier.

Definition at line 71 of file Softmax.cpp.

Vector< SoftmaxModel > Softmax::getModels ( ) const

Get the softmax models for each class. The Softmax class must be trained first.

Returns
returns a vector of softmax models, with each element representing the model for a specific class

Definition at line 388 of file Softmax.cpp.

bool Softmax::loadLegacyModelFromFile ( std::fstream &  file)
protected

Read the ranges if needed

Definition at line 392 of file Softmax.cpp.

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

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

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

Reimplemented from MLBase.

Definition at line 305 of file Softmax.cpp.

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

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

Parameters
rhsanother instance of a Softmax
Returns
returns a pointer to this instance of the Softmax

Definition at line 58 of file Softmax.cpp.

bool Softmax::predict_ ( VectorFloat inputVector)
virtual

This predicts the class of the inputVector. This overrides the predict function in the Classifier base class.

Parameters
inputVectorthe input vector to classify
Returns
returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 133 of file Softmax.cpp.

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

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

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

Reimplemented from MLBase.

Definition at line 273 of file Softmax.cpp.

bool Softmax::train_ ( ClassificationData trainingData)
virtual

This trains the Softmax model, using the labelled classification data. This overrides the train function in the Classifier base class.

Parameters
trainingDataa reference to the training data
Returns
returns true if the Softmax model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 89 of file Softmax.cpp.


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