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.
LinearRegression Class Reference

#include <LinearRegression.h>

Inheritance diagram for LinearRegression:
Regressifier MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

Public Member Functions

 LinearRegression (bool useScaling=false)
 
virtual ~LinearRegression (void)
 
LinearRegressionoperator= (const LinearRegression &rhs)
 
virtual bool deepCopyFrom (const Regressifier *regressifier)
 
virtual bool train_ (RegressionData &trainingData)
 
virtual bool predict_ (VectorFloat &inputVector)
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::fstream &file)
 
UINT getMaxNumIterations () const
 
bool setMaxNumIterations (const UINT maxNumIterations)
 
- Public Member Functions inherited from Regressifier
 Regressifier (void)
 
virtual ~Regressifier (void)
 
bool copyBaseVariables (const Regressifier *regressifier)
 
virtual bool reset ()
 
virtual bool clear ()
 
std::string getRegressifierType () const
 
VectorFloat getRegressionData () const
 
Vector< MinMaxgetInputRanges () const
 
Vector< MinMaxgetOutputRanges () const
 
RegressifiercreateNewInstance () const
 
RegressifierdeepCopy () const
 
const RegressifiergetBaseRegressifier () 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_ (ClassificationData &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 loadLegacyModelFromFile (std::fstream &file)
 
- Protected Member Functions inherited from Regressifier
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

Float w0
 
VectorFloat w
 
- Protected Attributes inherited from Regressifier
std::string regressifierType
 
VectorFloat regressionData
 
Vector< MinMaxinputVectorRanges
 
Vector< MinMaxtargetVectorRanges
 
- 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 RegisterRegressifierModule< LinearRegressionregisterModule
 

Additional Inherited Members

- Public Types inherited from Regressifier
typedef std::map< std::string, Regressifier *(*)() > StringRegressifierMap
 
- Public Types inherited from MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from Regressifier
static RegressifiercreateInstanceFromString (const std::string &regressifierType)
 
static Vector< std::string > getRegisteredRegressifiers ()
 
- Static Public Member Functions inherited from GRTBase
static std::string getGRTVersion (bool returnRevision=true)
 
static std::string getGRTRevison ()
 
- Static Protected Member Functions inherited from Regressifier
static StringRegressifierMapgetMap ()
 

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 40 of file LinearRegression.h.

Constructor & Destructor Documentation

LinearRegression::LinearRegression ( bool  useScaling = false)

Default Constructor

Parameters
useScalingsets if the training and real-time data should be scaled between [0 1]. Default value = false

Definition at line 28 of file LinearRegression.cpp.

LinearRegression::~LinearRegression ( void  )
virtual

Default Destructor

Definition at line 42 of file LinearRegression.cpp.

Member Function Documentation

bool LinearRegression::deepCopyFrom ( const Regressifier regressifier)
virtual

This is required for the Gesture Recognition Pipeline for when the pipeline.setRegressifier(...) method is called. It clones the data from the Base Class Regressifier pointer (which should be pointing to an Logistic Regression instance) into this instance

Parameters
regressifiera pointer to the Regressifier Base Class, this should be pointing to another Logistic Regression instance
Returns
returns true if the clone was successfull, false otherwise

Reimplemented from Regressifier.

Definition at line 57 of file LinearRegression.cpp.

UINT LinearRegression::getMaxNumIterations ( ) const

Gets the current maxNumIterations value, this is the maximum number of iterations that can be run during the training phase.

Returns
returns the maxNumIterations value

Definition at line 316 of file LinearRegression.cpp.

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

Read the ranges if needed

Definition at line 320 of file LinearRegression.cpp.

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

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

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

Reimplemented from MLBase.

Definition at line 259 of file LinearRegression.cpp.

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

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

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

Definition at line 46 of file LinearRegression.cpp.

bool LinearRegression::predict_ ( VectorFloat inputVector)
virtual

This performs the regression by mapping the inputVector using the current Logistic Regression model. This overrides the predict function in the Regressifier base class.

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

Reimplemented from MLBase.

Definition at line 196 of file LinearRegression.cpp.

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

This saves the trained Logistic Regression model to a file. This overrides the saveModelToFile function in the ML base class.

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

Reimplemented from MLBase.

Definition at line 230 of file LinearRegression.cpp.

bool LinearRegression::setMaxNumIterations ( const UINT  maxNumIterations)

Sets the maximum number of iterations that can be run during the training phase. The maxNumIterations value must be greater than zero.

Parameters
maxNumIterationsthe maximum number of iterations value, must be greater than zero
Returns
returns true if the value was updated successfully, false otherwise

Definition at line 312 of file LinearRegression.cpp.

bool LinearRegression::train_ ( RegressionData trainingData)
virtual

This trains the Logistic Regression model, using the labelled regression data. This overrides the train function in the Regression base class.

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

Reimplemented from MLBase.

Definition at line 74 of file LinearRegression.cpp.


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