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.
MLP Class Reference

#include <MLP.h>

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

Public Types

enum  TrainingModes { ONLINE_GRADIENT_DESCENT }
 
- 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 }
 

Public Member Functions

 MLP ()
 
 MLP (const MLP &rhs)
 
virtual ~MLP ()
 
MLPoperator= (const MLP &rhs)
 
virtual bool deepCopyFrom (const Regressifier *regressifier)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool train_ (RegressionData &trainingData)
 
virtual bool predict_ (VectorFloat &inputVector)
 
virtual bool clear ()
 
virtual bool print () const
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::fstream &file)
 
UINT getNumClasses () const
 
bool init (const UINT numInputNeurons, const UINT numHiddenNeurons, const UINT numOutputNeurons)
 
bool init (const UINT numInputNeurons, const UINT numHiddenNeurons, const UINT numOutputNeurons, const UINT inputLayerActivationFunction, const UINT hiddenLayerActivationFunction, const UINT outputLayerActivationFunction)
 
void printNetwork () const
 
bool checkForNAN () const
 
std::string activationFunctionToString (const UINT activationFunction) const
 
UINT activationFunctionFromString (const std::string activationName) const
 
bool validateActivationFunction (const UINT avactivationFunction) const
 
UINT getNumInputNeurons () const
 
UINT getNumHiddenNeurons () const
 
UINT getNumOutputNeurons () const
 
UINT getInputLayerActivationFunction () const
 
UINT getHiddenLayerActivationFunction () const
 
UINT getOutputLayerActivationFunction () const
 
UINT getNumRandomTrainingIterations () const
 
Float getTrainingRate () const
 
Float getMomentum () const
 
Float getGamma () const
 
Float getTrainingError () const
 
bool getClassificationModeActive () const
 
bool getRegressionModeActive () const
 
Vector< NeurongetInputLayer () const
 
Vector< NeurongetHiddenLayer () const
 
Vector< NeurongetOutputLayer () const
 
Vector< VectorFloatgetTrainingLog () const
 
bool getNullRejectionEnabled () const
 
Float getNullRejectionCoeff () const
 
Float getNullRejectionThreshold () const
 
Float getMaximumLikelihood () const
 
VectorFloat getClassLikelihoods () const
 
VectorFloat getClassDistances () const
 
UINT getPredictedClassLabel () const
 
bool setInputLayerActivationFunction (const UINT activationFunction)
 
bool setHiddenLayerActivationFunction (const UINT activationFunction)
 
bool setOutputLayerActivationFunction (const UINT activationFunction)
 
bool setTrainingRate (const Float trainingRate)
 
bool setMomentum (const Float momentum)
 
bool setGamma (const Float gamma)
 
bool setNumRandomTrainingIterations (const UINT numRandomTrainingIterations)
 
bool setNullRejection (const bool useNullRejection)
 
bool setNullRejectionCoeff (const Float nullRejectionCoeff)
 
- Public Member Functions inherited from Regressifier
 Regressifier (void)
 
virtual ~Regressifier (void)
 
bool copyBaseVariables (const Regressifier *regressifier)
 
virtual bool reset ()
 
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 (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 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 isNAN (const Float v) const
 
bool trainModel (RegressionData &trainingData)
 
bool trainOnlineGradientDescentClassification (const RegressionData &trainingData, const RegressionData &validationData)
 
bool trainOnlineGradientDescentRegression (const RegressionData &trainingData, const RegressionData &validationData)
 
bool loadLegacyModelFromFile (std::fstream &file)
 
Float back_prop (const VectorFloat &inputVector, const VectorFloat &targetVector, const Float alpha, const Float beta)
 
VectorFloat feedforward (VectorFloat data)
 
void feedforward (const VectorFloat &data, VectorFloat &inputNeuronsOuput, VectorFloat &hiddenNeuronsOutput, VectorFloat &outputNeuronsOutput)
 
- 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

UINT numInputNeurons
 
UINT numHiddenNeurons
 
UINT numOutputNeurons
 
UINT inputLayerActivationFunction
 
UINT hiddenLayerActivationFunction
 
UINT outputLayerActivationFunction
 
UINT numRandomTrainingIterations
 
UINT trainingMode
 
Float momentum
 
Float gamma
 
Float trainingError
 
bool initialized
 
Random random
 
Vector< NeuroninputLayer
 
Vector< NeuronhiddenLayer
 
Vector< NeuronoutputLayer
 
Vector< VectorFloattrainingErrorLog
 
bool classificationModeActive
 
bool useNullRejection
 
UINT predictedClassLabel
 
Float nullRejectionThreshold
 
Float nullRejectionCoeff
 
Float maxLikelihood
 
VectorFloat classLikelihoods
 
VectorFloat inputNeuronsOuput
 
VectorFloat hiddenNeuronsOutput
 
VectorFloat outputNeuronsOutput
 
VectorFloat deltaO
 
VectorFloat deltaH
 
- 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< MLPregisterModule
 

Additional Inherited Members

- 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 41 of file MLP.h.

Constructor & Destructor Documentation

MLP::MLP ( )

Default Constructor

Definition at line 31 of file MLP.cpp.

MLP::MLP ( const MLP rhs)

Copy Constructor

Parameters
rhsanother instance of a MLP that will be copied to this instance

Definition at line 60 of file MLP.cpp.

MLP::~MLP ( )
virtual

Default Destructor

Definition at line 71 of file MLP.cpp.

Member Function Documentation

UINT MLP::activationFunctionFromString ( const std::string  activationName) const

Gets the activation function value from a string.

Parameters
activationNamethe activation function as a string
Returns
returns an UINT activation function

Definition at line 1616 of file MLP.cpp.

std::string MLP::activationFunctionToString ( const UINT  activationFunction) const

Gets a string representation of the activation function

Parameters
activationFunctionthe activation function you want to convert to a string
Returns
returns a string representation of the activation function, returns UNKNOWN if the activation function is invalid

Definition at line 1595 of file MLP.cpp.

Float MLP::back_prop ( const VectorFloat inputVector,
const VectorFloat targetVector,
const Float  alpha,
const Float  beta 
)
protected

Performs one round of back propagation, using the training example and target Vector

Parameters
inputVectorthe input Vector to use for back propagation
targetVectorthe target Vector to use for back propagation
alphathe training rate
betathe momentum
Returns
returns the squared error for the current training example

Definition at line 851 of file MLP.cpp.

bool MLP::checkForNAN ( ) const

Checks if there are any NAN values in any of the layers.

Returns
returns true if there are any NAN values in any of the layers, false otherwise

Definition at line 1037 of file MLP.cpp.

bool MLP::clear ( )
virtual

Clears any previous model or settings.

Returns
returns true if the MLP was cleared, false otherwise

Reimplemented from Regressifier.

Definition at line 296 of file MLP.cpp.

bool MLP::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 MLP instance) into this instance

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

Reimplemented from Regressifier.

Definition at line 111 of file MLP.cpp.

VectorFloat MLP::feedforward ( VectorFloat  data)
protected

Performs the feedforward step using the current model and the input training example.

Parameters
datathe input Vector to use for the feedforward
Returns
returns a new Vector of the results from the feedforward step

Definition at line 939 of file MLP.cpp.

void MLP::feedforward ( const VectorFloat data,
VectorFloat inputNeuronsOuput,
VectorFloat hiddenNeuronsOutput,
VectorFloat outputNeuronsOutput 
)
protected

Performs the feedforward step for back propagation, using the input data

Parameters
datathe input Vector to use for the feedforward
inputNeuronsOuputthe results of the input layer
hiddenNeuronsOutputthe results of the hidden layer
outputNeuronsOutputthe results of the output layer

Definition at line 980 of file MLP.cpp.

VectorFloat MLP::getClassDistances ( ) const

Gets a Vector of the class distances from the last prediction, this will be an N-dimensional Vector, where N is the number of classes in the model.

Returns
returns a Vector of the class distances from the last prediction, an empty Vector will be returned if the model has not been trained

Definition at line 1584 of file MLP.cpp.

bool MLP::getClassificationModeActive ( ) const

Returns true if the MLP is in classification mode.

Returns
returns true if the MLP is in classification mode, false otherwise

Definition at line 1538 of file MLP.cpp.

VectorFloat MLP::getClassLikelihoods ( ) const

Gets a Vector of the class likelihoods from the last prediction, this will be an N-dimensional Vector, where N is the number of classes in the model.

Returns
returns a Vector of the class likelihoods from the last prediction, an empty Vector will be returned if the model has not been trained

Definition at line 1579 of file MLP.cpp.

Float MLP::getGamma ( ) const

Gets the gamma value. This controls the gamma parameter for the neurons.

Returns
returns the gamma value for the neurons

Definition at line 1530 of file MLP.cpp.

Vector< Neuron > MLP::getHiddenLayer ( ) const

Returns the neurons for the hidden layer.

Returns
returns a Vector of neurons for the hidden layer

Definition at line 1550 of file MLP.cpp.

UINT MLP::getHiddenLayerActivationFunction ( ) const

Gets the hidden layer activation function.

Returns
returns the hidden layer activation function

Definition at line 1510 of file MLP.cpp.

Vector< Neuron > MLP::getInputLayer ( ) const

Returns the neurons for the input layer.

Returns
returns a Vector of neurons for the input layer

Definition at line 1546 of file MLP.cpp.

UINT MLP::getInputLayerActivationFunction ( ) const

Gets the input layer activation function.

Returns
returns the input layer activation function

Definition at line 1506 of file MLP.cpp.

Float MLP::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 is only relevant if the MLP is in classification mode. This value will return 0 if a prediction has not been made.

Returns
returns the current maximumLikelihood value

Definition at line 1574 of file MLP.cpp.

Float MLP::getMomentum ( ) const

Gets the momentum rate. This should be a value between [0 1]

Returns
returns the momentum

Definition at line 1526 of file MLP.cpp.

Float MLP::getNullRejectionCoeff ( ) const

Returns the current null rejection coefficient value. The null rejection coefficient is a multipler controlling the null rejection threshold for each class. This is only relevant if the MLP is in classification mode.

Returns
returns the current null rejection coefficient

Definition at line 1566 of file MLP.cpp.

bool MLP::getNullRejectionEnabled ( ) const

Returns true if the MLP should use null rejection during classification. This is only relevant if the MLP is in classification mode.

Returns
returns true if the null rejection is enabled, false otherwise

Definition at line 1562 of file MLP.cpp.

Float MLP::getNullRejectionThreshold ( ) const

Returns the current null rejection threshold value. The null rejection threshold is value controlling if a classification result should be rejected or accepted. This is only relevant if the MLP is in classification mode.

Returns
returns the current null rejection threshold

Definition at line 1570 of file MLP.cpp.

UINT MLP::getNumClasses ( ) const

Returns the number of classes in the MLP model if the MLP is in classification mode. The number of classes in the model is the same as the number of output neurons. If the MLP is in regression mode then it will return 0.

Returns
returns the number of classes in the MLP model if the MLP is in classification mode

Definition at line 1488 of file MLP.cpp.

UINT MLP::getNumHiddenNeurons ( ) const

Gets the number of hidden neurons.

Returns
returns the number of hidden neurons

Definition at line 1498 of file MLP.cpp.

UINT MLP::getNumInputNeurons ( ) const

Gets the number of input neurons.

Returns
returns the number of input neurons

Definition at line 1494 of file MLP.cpp.

UINT MLP::getNumOutputNeurons ( ) const

Gets the number of output neurons.

Returns
returns the number of output neurons

Definition at line 1502 of file MLP.cpp.

UINT MLP::getNumRandomTrainingIterations ( ) const

Gets the number of random training iterations that should be performed during the training phase. The MLP back propagation algorithm starts with random values, and the accuracy of a trained model can depend on which random values the algorithm started with. The GRT MLP algorithm therefore trains a number of models and picks the best one. This value therefore represents the number of random training iterations that should be used.

Returns
returns the number of random training iterations that should be performed during the training phase

Definition at line 1518 of file MLP.cpp.

Vector< Neuron > MLP::getOutputLayer ( ) const

Returns the neurons for the output layer.

Returns
returns a Vector of neurons for the output layer

Definition at line 1554 of file MLP.cpp.

UINT MLP::getOutputLayerActivationFunction ( ) const

Gets the output layer activation function.

Returns
returns the output layer activation function

Definition at line 1514 of file MLP.cpp.

UINT MLP::getPredictedClassLabel ( ) const

Gets the predicted class label from the last prediction. This is only relevant if the MLP is in classification mode.

Returns
returns the label of the last predicted class, a value of 0 will be returned if the model has not been trained

Definition at line 1590 of file MLP.cpp.

bool MLP::getRegressionModeActive ( ) const

Returns true if the MLP is in regression mode.

Returns
returns true if the MLP is in regression mode, false otherwise

Definition at line 1542 of file MLP.cpp.

Float MLP::getTrainingError ( ) const

Gets training error from the last round of training. If the MLP is in classification mode, the training error will be the classification accuracy. If the MLP is in regression mode then the training error will be the RMS error.

Returns
returns the training error from the last round of training

Definition at line 1534 of file MLP.cpp.

Vector< VectorFloat > MLP::getTrainingLog ( ) const

Returns a Vector of VectorFloat representing the training log for each random round of training. The outer Vector represents each round and the inner Vector represents each epoch in that round.

Returns
returns a Vector of MinMax values representing the ranges of the output layer

Definition at line 1558 of file MLP.cpp.

Float MLP::getTrainingRate ( ) const

Gets the training rate. This should be a value between [0 1]

Returns
returns the training rate

Definition at line 1522 of file MLP.cpp.

bool MLP::init ( const UINT  numInputNeurons,
const UINT  numHiddenNeurons,
const UINT  numOutputNeurons 
)

Initializes the MLP for training. This should be called before the MLP is trained. The number of input neurons should match the number of input dimensions in your training data. The number of output neurons should match the number of target dimensions in your training data. The number of hidden units should be chosen by the user, a common rule of thumb is to set this as a value somewhere between the number of input neurons and the number of output neurons. Initializaling the MLP will clear any previous model or settings.

This function calls the other init function below, passing in the layer activation functions.

Parameters
numInputNeuronsthe number of input neurons (should match the number of input dimensions in your training data)
numHiddenNeuronsthe number of hidden neurons
numOutputNeuronsthe number of output neurons (should match the number of target dimensions in your training data)
Returns
returns true if the MLP was initialized, false otherwise

Definition at line 222 of file MLP.cpp.

bool MLP::init ( const UINT  numInputNeurons,
const UINT  numHiddenNeurons,
const UINT  numOutputNeurons,
const UINT  inputLayerActivationFunction,
const UINT  hiddenLayerActivationFunction,
const UINT  outputLayerActivationFunction 
)

Initializes the MLP for training. This should be called before the MLP is trained. The number of input neurons should match the number of input dimensions in your training data. The number of output neurons should match the number of target dimensions in your training data. The number of hidden units should be chosen by the user, a common rule of thumb is to set this as a value somewhere between the number of input neurons and the number of output neurons. The activation functions should be one of the Neuron ActivationFunctions enums. Initializaling the MLP will clear any previous model or settings.

Parameters
numInputNeuronsthe number of input neurons (should match the number of input dimensions in your training data)
numHiddenNeuronsthe number of hidden neurons
numOutputNeuronsthe number of output neurons (should match the number of target dimensions in your training data)
inputLayerActivationFunctionthe activation function to use for the input layer
hiddenLayerActivationFunctionthe activation function to use for the input layer
outputLayerActivationFunctionthe activation function to use for the input layer
Returns
returns true if the MLP was initialized, false otherwise

Definition at line 226 of file MLP.cpp.

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

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

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

Reimplemented from MLBase.

Definition at line 1146 of file MLP.cpp.

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

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

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

Definition at line 75 of file MLP.cpp.

bool MLP::predict_ ( VectorFloat inputVector)
virtual

This function either predicts the class of the input Vector (if the MLP is in Classification Mode), or it performs regression using the MLP model.

Parameters
inputVectorthe input Vector to classify or perform regression on
Returns
returns true if the prediction/regression was performed, false otherwise

Reimplemented from MLBase.

Definition at line 163 of file MLP.cpp.

bool MLP::print ( ) const
virtual

This function will print the model and settings to the display log.

Returns
returns true if the model was printed succesfully, false otherwise

Reimplemented from MLBase.

Definition at line 312 of file MLP.cpp.

void MLP::printNetwork ( ) const

Prints the current MLP weights and coefficents to std out. This function is depreciated, you should now use print() instead.

Definition at line 1005 of file MLP.cpp.

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

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

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

Reimplemented from MLBase.

Definition at line 1072 of file MLP.cpp.

bool MLP::setGamma ( const Float  gamma)

Sets the gamma parameter for the Neurons. Gamma must be greater than zero. If the MLP instance has been initialized then this function will also call the init function to reinitialize the instance.

Parameters
gammathe gamma value for each Neuron, gamma must be greater than zero
Returns
returns true if the value was updated successfully, false otherwise

Definition at line 1698 of file MLP.cpp.

bool MLP::setHiddenLayerActivationFunction ( const UINT  activationFunction)

This function sets the activation function for all the Neurons in the hidden layer. If the MLP instance has been initialized then this function will also call the init function to reinitialize the instance.

Parameters
activationFunctionthe activation function for the hidden layer, this should be one of the Neuron ActivationFunctions enums
Returns
returns true if the hidden layer activation function was set successfully, false otherwise

Definition at line 1655 of file MLP.cpp.

bool MLP::setInputLayerActivationFunction ( const UINT  activationFunction)

This function sets the activation function for all the Neurons in the input layer. If the MLP instance has been initialized then this function will also call the init function to reinitialize the instance.

Parameters
activationFunctionthe activation function for the input layer, this should be one of the Neuron ActivationFunctions enums
Returns
returns true if the input layer activation function was set successfully, false otherwise

Definition at line 1639 of file MLP.cpp.

bool MLP::setMomentum ( const Float  momentum)

Sets the momentum parameter. This is used to update the weights at each step of the stochastic gradient descent. The momentum parameter is normally set between [0.1 0.9], with 0.5 being a common value.

Parameters
momentumthe momentum value used during the training phase, must be greater than zero
Returns
returns true if the value was updated successfully, false otherwise

Definition at line 1690 of file MLP.cpp.

bool MLP::setNullRejection ( const bool  useNullRejection)

Sets if null rejection should be used for the real-time prediction. This is only used if the MLP is in classificationMode.

Parameters
useNullRejectionif true then null rejection will be used
Returns
returns true if the value was updated successfully, false otherwise

Definition at line 1721 of file MLP.cpp.

bool MLP::setNullRejectionCoeff ( const Float  nullRejectionCoeff)

This function lets you manually control the null rejection threshold. Any class with a prediction value less than the null rejection threshold will be rejected, setting the predicted class label to 0.

This is only used if the MLP is in classificationMode.

Parameters
nullRejectionCoeffthe new null rejection threshold
Returns
returns true if the value was updated successfully, false otherwise

Definition at line 1726 of file MLP.cpp.

bool MLP::setNumRandomTrainingIterations ( const UINT  numRandomTrainingIterations)

Sets number of times the MLP model should be trained to find the best model. This value must be greater than zero.

Setting this value to a high number (i.e. 100) will most likely give you a better model, however it will take much longer to train the overall model. Setting this value to a low number (i.e. 5) will make the training process much faster, but you might not get the best model.

Parameters
numRandomTrainingIterationsthe number of times you want to randomly train the MLP model to search for the best results
Returns
returns true if the value was updated successfully, false otherwise

Definition at line 1713 of file MLP.cpp.

bool MLP::setOutputLayerActivationFunction ( const UINT  activationFunction)

This function sets the activation function for all the Neurons in the output layer. If the MLP instance has been initialized then this function will also call the init function to reinitialize the instance.

Parameters
activationFunctionthe activation function for the output layer, this should be one of the Neuron ActivationFunctions enums
Returns
returns true if the output layer activation function was set successfully, false otherwise

Definition at line 1671 of file MLP.cpp.

bool MLP::setTrainingRate ( const Float  trainingRate)

Sets the training rate, which controls the learning rate parameter. This is used to update the weights at each step of the stochastic gradient descent. The learningRate value must be greater than zero, a value of 0.1 normally works well. If you find the MLP fails to train with this value then try setting the learning rate to a smaller value (for example 0.01).

Parameters
trainingRatethe learningRate value used during the training phase, must be greater than zero
Returns
returns true if the value was updated successfully, false otherwise

Definition at line 1686 of file MLP.cpp.

bool MLP::train_ ( ClassificationData trainingData)
virtual

This trains the MLP model, using the labelled classification data. Calling this function sets the MLP into Classification Model.

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

Reimplemented from MLBase.

Definition at line 129 of file MLP.cpp.

bool MLP::train_ ( RegressionData trainingData)
virtual

This trains the MLP model, using the labelled regression data. Calling this function sets the MLP into Regression Model.

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

Reimplemented from MLBase.

Definition at line 154 of file MLP.cpp.

bool MLP::validateActivationFunction ( const UINT  avactivationFunction) const

Validates if the activationFunction is valid.

Parameters
activationFunctionthe activation function you want to valid
Returns
returns true if the activation function is valid, false otherwise

Definition at line 1634 of file MLP.cpp.


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