#include <TANNModeler.h>

Inheritance diagram for URANIE::Modeler::TANNModeler:

Collaboration diagram for URANIE::Modeler::TANNModeler:

Public Types
enum	EProblem { kRegression, kClassification }

enum	ENorm { kCR, kMinusOneOne, kZeroOne }

Public Member Functions
Constructor and Destructor
	TANNModeler (URANIE::DataServer::TDataServer tds, TString architecture, Double_t dratio=0.80, Option_t option="")
	Constructor with a dataserver. More...

virtual	~TANNModeler ()
	Default destructor. More...

Setter and Getter function
Int_t	getNInput ()
	Gets the number of input. More...

Int_t	getNHidden ()
	Gets the number of hidden neurons. More...

Int_t	getNOutput ()
	Gets the number of output. More...

Int_t	getNAPP ()
	Gets the number of pure training patterns ("APPrentissage") More...

Int_t	getNTest ()
	Gets the number of pure training patterns ("APPrentissage") More...

void	setWeights (ColumnVector weights)
	Sets the weigts. More...

ColumnVector	getWeights ()

Double_t	getWeight (Int_t ind)
	Returns the weight givent by the index. More...

Double_t	getMinValue (Int_t ind)

Double_t	getMaxValue (Int_t ind)

Double_t *	getdval ()

void	setValue (Int_t ind, Int_t &ncrt, Double_t *xy, Bool_t normalize=true)

void	setDataSet (URANIE::DataServer::TPatternsEventList *tel)

void	setMix (Bool_t bmix=kTRUE)

void	setFcnTol (Double_t dprec)

Double_t	getFcnTol ()

void	setNormalization (ENorm n)
	Sets the normalisation. More...

ENorm	getNormalization ()
	Get the normalisation. More...

EProblem	getProblem ()
	Get the problem type. More...

void	setSeed (Int_t ind=0)
	Init the seed. More...

Int_t	getSeed ()
	Returns the seed value. More...

const char *	getInputName (Int_t i)
	Return the name of input attributes indexed by i. More...

const char *	getOutputName ()
	Get the attribute name of the output. More...

Training
void	init ()

void	randomizeWeights ()
	randomize the weights More...

void	train (Int_t niter=10, Int_t nInit=10, Option_t *option="text", Bool_t useGPU=false)

Double_t	getEQM (Option_t *option="")
	Gets the EQM. More...

Double_t	getWeightDecay ()
	Get the weight decay parameter. More...

void	saveIfBetter (const ColumnVector &weights, Double_t eqm_app, Double_t eqm_tst)
	Save a vector of weight if the RMS on the test database is better than what we currently have. More...

Exports function
The exports function to save an Artificial Neural Networks in an external format C/C++ Fortran PMML The user fonction is the exportFunction method. tann->exportFunction("c++", "uranie_ann_flowrate.C","ANNflowrate"); tann->exportFunction("fortran", "uranie_ann_flowrate.f","ANNflowrate"); tann->exportFunction("pmml", sPMMLFile, sANNName,"new"); Todo: Add different format in the exportFunction method tann->exportFunction("C++,fortran,pmml", "uranie_ann_flowrate","ANNflowrate");
void	exportModelCplusplus (const char file="", const char name="", Option_t *option="") const
	The only user interface method. More...

void	exportModelCplusplus (std::ofstream *sourcefile) const

void	exportModelPMML (const char file="", const char name="", Option_t *option="") const
	Export the TANNModeler object in a PMML File. More...

void	exportModelFortran (const char file, const char name, Option_t *option="")
	Export the ANN model in a Fortran file. More...

void	exportModelFortran (std::ofstream *sourcefile) const

void	exportModelPython (std::ofstream *sourcefile) const
	Export the model in Python langage in a file (not yet implemented) More...

Printing Log
virtual void	printLog (Option_t *option="")

Public Member Functions inherited from URANIE::Modeler::TModeler
	TModeler (URANIE::DataServer::TDataServer tds, TString architecture, Option_t option="")
	Constructor with a dataserver. More...

	TModeler (URANIE::DataServer::TDataServer tds, const char varexpinput, const char varexpoutput, Option_t option="")
	Default constructor with input and output attributes. More...

virtual	~TModeler ()
	Default destructor. More...

TVectorD	getParameters ()
	Return the parameter vector of the model. More...

Double_t	getR2 () const
	Return the R2 quality. More...

const char *	getInputName (Int_t i) const
	Return the name of input attributes. More...

const char *	getOutputName (Int_t i=0)
	Return the name of output attribute. More...

Int_t	getNInput ()
	Gets the number of input. More...

Bool_t	getIntercept ()
	Gets the boolean attribut _bIntercept. More...

void	setDrawProgressBar (Bool_t bbool=kTRUE)
	Set the "draw progress bar" flag. More...

Bool_t	getDrawProgressBar ()
	Get the "draw progress bar" flag. More...

void	train (Option_t *option="text")

virtual void	exportFunction (const char lang, const char file="", const char name="", Option_t option="")
	Export the model in an external file with a specified langage. More...

void	setLog ()

void	unsetLog ()

void	changeLog ()

Bool_t	getLog ()

Public Attributes
Bool_t	_blog
	Boolean for edit the log. More...

URANIE::DataServer::TDataServer *	_tds

UMLP *	_UMLP
	Pointeur vers un TDS. More...

Public Attributes inherited from URANIE::Modeler::TModeler
Bool_t	_blog
	Boolean for edit the log. More...

Bool_t	_bdrawProgressBar
	Boolean to know if the progress bar has to be drawn. More...

Bool_t	_bStoreYHat
	Boolean to specify if we add the {} attribute in the TDS [default kTRUE]. More...

URANIE::DataServer::TDataServer *	_tds

Int_t	_nS
	Pointer to a TDS. More...

TString	_sInputAttributes
	The string of input attributes (separated by the character ":". More...

TString	_sOutputAttributes
	The string of output attributes (separated by the character ":". More...

TString	_sIntermediateArchitecture
	The intermediate (between the first and last character ",") information. More...

TList *	_listOfInputAttributes
	The list of input Leaf. More...

TList *	_listOfOutputAttributes
	The list of output Leaf. More...

Int_t	_nX
	The number of input attributes. More...

Int_t	_nY
	The number of output attributes. More...

TString	_sFileName
	The name of the file when export the model without extension. More...

TString	_sFunctionName
	The name of the function when export the model. More...

TVectorD	_parameterValues
	The list of parameters. More...

Double_t	_dr2
	The R2 quality. More...

Bool_t	_bIntercept
	For certain models, add the intercept (Default is TRUE) More...

Protected Attributes
Int_t	_nSeed

Private Attributes
Int_t	_nInput
	! Seed for the random weight initialization More...

Int_t	_nHidden
	! The number of inputs More...

Int_t	_nHiddenLayer
	! The number of hidden neurons More...

Int_t	_nOutput
	! the number of hidden layer More...

Int_t	_nNeurons
	! The number of outputs More...

Int_t	_nApp
	! The number of neurons More...

Int_t	_nTest
	! Number of samples in the training set dedicated to back-propagation More...

Double_t	_dLearn
	! Number of samples in the training set dedicated to validation More...

Double_t	_dTest
	! The learning quality More...

Double_t	_dWeightDecay
	! The test quality More...

URANIE::DataServer::TPatternsEventList *	_elAll
	! The weight decay parameter - when used, favorize a smoother ANN More...

Bool_t	_bMix
	! The mix list of patterns More...

TString	_sArchi
	! True if data set must be mixed More...

TString	_sInput
	! The architecture, e.g. "x:y:z,3,yhat" specifying inputs, number of hidden neurons, and output More...

TString	_sHidden

TString	_sOutput

Int_t	_nWeight

ColumnVector	_vecWeights
	! The number of weights More...

TempSolution *	_tmpSolution

ColumnVector	_vecMinValues

ColumnVector	_vecMaxValues
	! Min values, used for normalization More...

TList *	_listOfAttributes
	! Max values, used for normalization More...

Parameters of algorithm
Double_t	_dFcnTol

ENorm	_nNormType
	! The function tolerance for the trust region algorithm More...

EProblem	_nProlemType
	! The normalized for input and output More...

Double_t *	_dval
	! The type of problem (Regression, Classification) More...

Static Private Attributes
static const int	OPTIM_MAX_ITER = 10000

static const int	OPTIM_MAX_FEVAL = 50000

Member Enumeration Documentation

◆ ENorm

enum URANIE::Modeler::TANNModeler::ENorm

Enumerator
kCR
kMinusOneOne
kZeroOne

◆ EProblem

enum URANIE::Modeler::TANNModeler::EProblem

Enumerator
kRegression
kClassification

Constructor & Destructor Documentation

◆ TANNModeler()

URANIE::Modeler::TANNModeler::TANNModeler	(	URANIE::DataServer::TDataServer *	tds,
		TString	architecture,
		Double_t	dratio = `0.80`,
		Option_t *	option = `""`
	)

Constructor with a dataserver.

◆ ~TANNModeler()

virtual URANIE::Modeler::TANNModeler::~TANNModeler ( )

virtual

Default destructor.

Member Function Documentation

◆ exportModelCplusplus() [1/2]

void URANIE::Modeler::TANNModeler::exportModelCplusplus	(	const char *	file = `""`,
		const char *	name = `""`,
		Option_t *	option = `""`
	)		const

The only user interface method.

Parameters

lang	(const char*) the keyword for the external format [c++\|fortran\|pmml]
file	(const char*) the filename. If empty, it is the name of the TDS with the good extension.[""]
name	(const char*) name of the function.
option	(const char) option parameter. NEW* : create a new PMML file UPDATE : Write the current ANN in the PMML file and if already exist an ANN in the PMML file, erase it. BACKUP : Write the current ANN in the PMML file and if already exist ANNs in the PMML file with modelname "name_I", rename the old with "name_I+1"Export the ANN model in a ROOT macro file

◆ exportModelCplusplus() [2/2]

void URANIE::Modeler::TANNModeler::exportModelCplusplus ( std::ofstream * sourcefile ) const

virtual

Todo:: Write this method as a TModeler method

Implements URANIE::Modeler::TModeler.

◆ exportModelFortran() [1/2]

void URANIE::Modeler::TANNModeler::exportModelFortran	(	const char *	file,
		const char *	name,
		Option_t *	option = `""`
	)

Export the ANN model in a Fortran file.

◆ exportModelFortran() [2/2]

void URANIE::Modeler::TANNModeler::exportModelFortran ( std::ofstream * sourcefile ) const

virtual

Todo:: Write this method as a TModeler method

Implements URANIE::Modeler::TModeler.

◆ exportModelPMML()

void URANIE::Modeler::TANNModeler::exportModelPMML	(	const char *	file = `""`,
		const char *	name = `""`,
		Option_t *	option = `""`
	)		const

virtual

Export the TANNModeler object in a PMML File.

Implements URANIE::Modeler::TModeler.

◆ exportModelPython()

void URANIE::Modeler::TANNModeler::exportModelPython ( std::ofstream * sourcefile ) const

inlinevirtual

Export the model in Python langage in a file (not yet implemented)

Warning: Don't use this method. Use the main method TModeler::exportFunction.

Parameters

sourcefile (ofstream *) the pointer to the file to export the modeler.

Implements URANIE::Modeler::TModeler.

◆ getdval()

Double_t* URANIE::Modeler::TANNModeler::getdval ( )

inline

Referenced by ClassImp().

◆ getEQM()

Double_t URANIE::Modeler::TANNModeler::getEQM ( Option_t * option = "" )

Gets the EQM.

Parameters

option ["learn|test"] give the Mean Square Error (MSE ) on the learn database ("learn") or on the test database ("test").

◆ getFcnTol()

Double_t URANIE::Modeler::TANNModeler::getFcnTol ( )

inline

◆ getInputName()

const char* URANIE::Modeler::TANNModeler::getInputName ( Int_t i )

inline

Return the name of input attributes indexed by i.

◆ getMaxValue()

Double_t URANIE::Modeler::TANNModeler::getMaxValue ( Int_t ind )

inline

Referenced by ClassImp().

◆ getMinValue()

Double_t URANIE::Modeler::TANNModeler::getMinValue ( Int_t ind )

inline

Referenced by ClassImp().

◆ getNAPP()

Int_t URANIE::Modeler::TANNModeler::getNAPP ( )

inline

Gets the number of pure training patterns ("APPrentissage")

Referenced by ClassImp().

◆ getNHidden()

Int_t URANIE::Modeler::TANNModeler::getNHidden ( )

inline

Gets the number of hidden neurons.

Referenced by ClassImp().

◆ getNInput()

Int_t URANIE::Modeler::TANNModeler::getNInput ( )

inline

Gets the number of input.

Referenced by ClassImp().

◆ getNormalization()

ENorm URANIE::Modeler::TANNModeler::getNormalization ( )

inline

Get the normalisation.

Referenced by ClassImp().

◆ getNOutput()

Int_t URANIE::Modeler::TANNModeler::getNOutput ( )

inline

Gets the number of output.

Referenced by ClassImp().

◆ getNTest()

Int_t URANIE::Modeler::TANNModeler::getNTest ( )

inline

Gets the number of pure training patterns ("APPrentissage")

Referenced by ClassImp().

◆ getOutputName()

const char* URANIE::Modeler::TANNModeler::getOutputName ( )

inline

Get the attribute name of the output.

◆ getProblem()

EProblem URANIE::Modeler::TANNModeler::getProblem ( )

inline

Get the problem type.

Referenced by ClassImp().

◆ getSeed()

Int_t URANIE::Modeler::TANNModeler::getSeed ( )

inline

Returns the seed value.

◆ getWeight()

Double_t URANIE::Modeler::TANNModeler::getWeight ( Int_t ind )

inline

Returns the weight givent by the index.

the index begins to 0 and ends to nW-1

◆ getWeightDecay()

Double_t URANIE::Modeler::TANNModeler::getWeightDecay ( )

inline

Get the weight decay parameter.

Referenced by ClassImp().

◆ getWeights()

ColumnVector URANIE::Modeler::TANNModeler::getWeights ( )

inline

Referenced by ClassImp().

◆ init()

void URANIE::Modeler::TANNModeler::init ( )

◆ printLog()

virtual void URANIE::Modeler::TANNModeler::printLog ( Option_t * option = "" )

virtual

Reimplemented from URANIE::Modeler::TModeler.

◆ randomizeWeights()

void URANIE::Modeler::TANNModeler::randomizeWeights ( )

randomize the weights

◆ saveIfBetter()

void URANIE::Modeler::TANNModeler::saveIfBetter	(	const ColumnVector &	weights,
		Double_t	eqm_app,
		Double_t	eqm_tst
	)

Save a vector of weight if the RMS on the test database is better than what we currently have.

Referenced by ClassImp().

◆ setDataSet()

void URANIE::Modeler::TANNModeler::setDataSet ( URANIE::DataServer::TPatternsEventList * tel )

inline

◆ setFcnTol()

void URANIE::Modeler::TANNModeler::setFcnTol ( Double_t dprec )

inline

◆ setMix()

void URANIE::Modeler::TANNModeler::setMix ( Bool_t bmix = kTRUE )

inline

◆ setNormalization()

void URANIE::Modeler::TANNModeler::setNormalization ( ENorm n )

Sets the normalisation.

◆ setSeed()

void URANIE::Modeler::TANNModeler::setSeed ( Int_t ind = 0 )

Init the seed.

If the value is zero, the seed is initialized with the hour in milliseconds

Parameters

ind	(Int_t) the seed value

◆ setValue()

void URANIE::Modeler::TANNModeler::setValue	(	Int_t	ind,
		Int_t &	ncrt,
		Double_t *	xy,
		Bool_t	normalize = `true`
	)

Referenced by ClassImp().

◆ setWeights()

void URANIE::Modeler::TANNModeler::setWeights ( ColumnVector weights )

inline

Sets the weigts.

◆ train()

void URANIE::Modeler::TANNModeler::train	(	Int_t	niter = `10`,
		Int_t	nInit = `10`,
		Option_t *	option = `"text"`,
		Bool_t	useGPU = `false`
	)

Launch the training of the ANN. This potentially uses the GPU if available.

Parameters

niter	number of times (a random permutation of) the test base will be presented for training
nInit	number of times the ANN is trained (from random start weights) with a given permutation of the test database

Member Data Documentation

◆ _blog

Bool_t URANIE::Modeler::TANNModeler::_blog

Boolean for edit the log.

◆ _bMix

Bool_t URANIE::Modeler::TANNModeler::_bMix

private

! The mix list of patterns

◆ _dFcnTol

Double_t URANIE::Modeler::TANNModeler::_dFcnTol

private

◆ _dLearn

Double_t URANIE::Modeler::TANNModeler::_dLearn

private

! Number of samples in the training set dedicated to validation

◆ _dTest

Double_t URANIE::Modeler::TANNModeler::_dTest

private

! The learning quality

◆ _dval

Double_t* URANIE::Modeler::TANNModeler::_dval

private

! The type of problem (Regression, Classification)

Temporary array used during training

◆ _dWeightDecay

Double_t URANIE::Modeler::TANNModeler::_dWeightDecay

private

! The test quality

◆ _elAll

URANIE::DataServer::TPatternsEventList* URANIE::Modeler::TANNModeler::_elAll

private

! The weight decay parameter - when used, favorize a smoother ANN

◆ _listOfAttributes

TList* URANIE::Modeler::TANNModeler::_listOfAttributes

private

! Max values, used for normalization

◆ _nApp

Int_t URANIE::Modeler::TANNModeler::_nApp

private

! The number of neurons

◆ _nHidden

Int_t URANIE::Modeler::TANNModeler::_nHidden

private

! The number of inputs

◆ _nHiddenLayer

Int_t URANIE::Modeler::TANNModeler::_nHiddenLayer

private

! The number of hidden neurons

◆ _nInput

Int_t URANIE::Modeler::TANNModeler::_nInput

private

! Seed for the random weight initialization

◆ _nNeurons

Int_t URANIE::Modeler::TANNModeler::_nNeurons

private

! The number of outputs

◆ _nNormType

ENorm URANIE::Modeler::TANNModeler::_nNormType

private

! The function tolerance for the trust region algorithm

◆ _nOutput

Int_t URANIE::Modeler::TANNModeler::_nOutput

private

! the number of hidden layer

◆ _nProlemType

EProblem URANIE::Modeler::TANNModeler::_nProlemType

private

! The normalized for input and output

◆ _nSeed

Int_t URANIE::Modeler::TANNModeler::_nSeed

protected

◆ _nTest

Int_t URANIE::Modeler::TANNModeler::_nTest

private

! Number of samples in the training set dedicated to back-propagation

◆ _nWeight

Int_t URANIE::Modeler::TANNModeler::_nWeight

private

◆ _sArchi

TString URANIE::Modeler::TANNModeler::_sArchi

private

! True if data set must be mixed

◆ _sHidden

TString URANIE::Modeler::TANNModeler::_sHidden

private

◆ _sInput

TString URANIE::Modeler::TANNModeler::_sInput

private

! The architecture, e.g. "x:y:z,3,yhat" specifying inputs, number of hidden neurons, and output

◆ _sOutput

TString URANIE::Modeler::TANNModeler::_sOutput

private

◆ _tds

URANIE::DataServer::TDataServer* URANIE::Modeler::TANNModeler::_tds

◆ _tmpSolution

TempSolution* URANIE::Modeler::TANNModeler::_tmpSolution

private

◆ _UMLP

UMLP* URANIE::Modeler::TANNModeler::_UMLP

Pointeur vers un TDS.

Referenced by ClassImp().

◆ _vecMaxValues

ColumnVector URANIE::Modeler::TANNModeler::_vecMaxValues

private

! Min values, used for normalization

◆ _vecMinValues

ColumnVector URANIE::Modeler::TANNModeler::_vecMinValues

private

◆ _vecWeights

ColumnVector URANIE::Modeler::TANNModeler::_vecWeights

private

! The number of weights

! The vector of weights, stored like this: [a][b(1) b(2) b(3).... b(_nInput+2)][c(1) c(2) c(3).... c(_nInput+2)][ ... ] a: scalar value giving the weight of the output biais (link betw. single output and biais neuron in hidden layer) b, c, d, ...: _nHidden blocks of (_nInput+2) scalars representing: b(1): weight link between second hidden neuron and output (c(1) for the 3rd hidden neuron, d(1) for the 4th, etc ...) b(2): weight between biais input neuron and second hidden neuron b(2+i): weight between i-th (real) input neuron and second hidden neuron See impl. of UMPL::evaluateInput().

◆ OPTIM_MAX_FEVAL

const int URANIE::Modeler::TANNModeler::OPTIM_MAX_FEVAL = 50000

staticprivate

◆ OPTIM_MAX_ITER

const int URANIE::Modeler::TANNModeler::OPTIM_MAX_ITER = 10000

staticprivate

Documentation / Manuel développeur

Modules disponibles

Public Types

Public Member Functions

Public Attributes

Protected Attributes

Private Attributes

Static Private Attributes

Member Enumeration Documentation

◆ ENorm

◆ EProblem

Constructor & Destructor Documentation

◆ TANNModeler()

◆ ~TANNModeler()

Member Function Documentation

◆ exportModelCplusplus() [1/2]

◆ exportModelCplusplus() [2/2]

◆ exportModelFortran() [1/2]

◆ exportModelFortran() [2/2]

◆ exportModelPMML()

◆ exportModelPython()

◆ getdval()

◆ getEQM()

◆ getFcnTol()

◆ getInputName()

◆ getMaxValue()

◆ getMinValue()

◆ getNAPP()

◆ getNHidden()

◆ getNInput()

◆ getNormalization()

◆ getNOutput()

◆ getNTest()

◆ getOutputName()

◆ getProblem()

◆ getSeed()

◆ getWeight()

◆ getWeightDecay()

◆ getWeights()

◆ init()

◆ printLog()

◆ randomizeWeights()

◆ saveIfBetter()

◆ setDataSet()

◆ setFcnTol()

◆ setMix()

◆ setNormalization()

◆ setSeed()

◆ setValue()

◆ setWeights()

◆ train()

Member Data Documentation

◆ _blog

◆ _bMix

◆ _dFcnTol

◆ _dLearn

◆ _dTest

◆ _dval

◆ _dWeightDecay

◆ _elAll

◆ _listOfAttributes

◆ _nApp

◆ _nHidden

◆ _nHiddenLayer

◆ _nInput

◆ _nNeurons

◆ _nNormType

◆ _nOutput

◆ _nProlemType

◆ _nSeed

◆ _nTest

◆ _nWeight

◆ _sArchi

◆ _sHidden

◆ _sInput

◆ _sOutput

◆ _tds

◆ _tmpSolution

◆ _UMLP

◆ _vecMaxValues