This utility class contains a set of tests usefull when developing a neural network.

It allows you to check for unneeded variables, and to control the network structure.

Definition at line 25 of file TMLPAnalyzer.h.

Public Member Functions
	TMLPAnalyzer (TMultiLayerPerceptron &net)

	TMLPAnalyzer (TMultiLayerPerceptron *net)

virtual	~TMLPAnalyzer ()
	Destructor.

void	CheckNetwork ()
	Gives some information about the network in the terminal.

void	DrawDInput (Int_t i)
	Draws the distribution (on the test sample) of the impact on the network output of a small variation of the ith input.

void	DrawDInputs ()
	Draws the distribution (on the test sample) of the impact on the network output of a small variation of each input.

void	DrawNetwork (Int_t neuron, const char signal, const char bg)
	Draws the distribution of the neural network (using ith neuron).

TProfile *	DrawTruthDeviation (Int_t outnode=0, Option_t *option="")
	Create a profile of the difference of the MLP output minus the true value for a given output node outnode, vs the true value for outnode, for all test data events.

TProfile *	DrawTruthDeviationInOut (Int_t innode, Int_t outnode=0, Option_t *option="")
	Creates a profile of the difference of the MLP output outnode minus the true value of outnode vs the input value innode, for all test data events.

THStack *	DrawTruthDeviationInsOut (Int_t outnode=0, Option_t *option="")
	Creates a profile of the difference of the MLP output outnode minus the true value of outnode vs the input value, stacked for all inputs, for all test data events.

THStack *	DrawTruthDeviations (Option_t *option="")
	Creates TProfiles of the difference of the MLP output minus the true value vs the true value, one for each output, filled with the test data events.

void	GatherInformations ()
	Collect information about what is useful in the network.

TTree *	GetIOTree () const

Public Member Functions inherited from TObject
	TObject ()
	TObject constructor.

	TObject (const TObject &object)
	TObject copy ctor.

virtual	~TObject ()
	TObject destructor.

void	AbstractMethod (const char *method) const
	Use this method to implement an "abstract" method that you don't want to leave purely abstract.

virtual void	AppendPad (Option_t *option="")
	Append graphics object to current pad.

virtual void	Browse (TBrowser *b)
	Browse object. May be overridden for another default action.

ULong_t	CheckedHash ()
	Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object.

virtual const char *	ClassName () const
	Returns name of class to which the object belongs.

virtual void	Clear (Option_t *="")

virtual TObject *	Clone (const char *newname="") const
	Make a clone of an object using the Streamer facility.

virtual Int_t	Compare (const TObject *obj) const
	Compare abstract method.

virtual void	Copy (TObject &object) const
	Copy this to obj.

virtual void	Delete (Option_t *option="")
	Delete this object.

virtual Int_t	DistancetoPrimitive (Int_t px, Int_t py)
	Computes distance from point (px,py) to the object.

virtual void	Draw (Option_t *option="")
	Default Draw method for all objects.

virtual void	DrawClass () const
	Draw class inheritance tree of the class to which this object belongs.

virtual TObject *	DrawClone (Option_t *option="") const
	Draw a clone of this object in the current selected pad for instance with: `gROOT->SetSelectedPad(gPad)`.

virtual void	Dump () const
	Dump contents of object on stdout.

virtual void	Error (const char method, const char msgfmt,...) const
	Issue error message.

virtual void	Execute (const char method, const char params, Int_t *error=0)
	Execute method on this object with the given parameter string, e.g.

virtual void	Execute (TMethod method, TObjArray params, Int_t *error=0)
	Execute method on this object with parameters stored in the TObjArray.

virtual void	ExecuteEvent (Int_t event, Int_t px, Int_t py)
	Execute action corresponding to an event at (px,py).

virtual void	Fatal (const char method, const char msgfmt,...) const
	Issue fatal error message.

virtual TObject *	FindObject (const char *name) const
	Must be redefined in derived classes.

virtual TObject *	FindObject (const TObject *obj) const
	Must be redefined in derived classes.

virtual Option_t *	GetDrawOption () const
	Get option used by the graphics system to draw this object.

virtual const char *	GetIconName () const
	Returns mime type name of object.

virtual const char *	GetName () const
	Returns name of object.

virtual char *	GetObjectInfo (Int_t px, Int_t py) const
	Returns string containing info about the object at position (px,py).

virtual Option_t *	GetOption () const

virtual const char *	GetTitle () const
	Returns title of object.

virtual UInt_t	GetUniqueID () const
	Return the unique object id.

virtual Bool_t	HandleTimer (TTimer *timer)
	Execute action in response of a timer timing out.

virtual ULong_t	Hash () const
	Return hash value for this object.

Bool_t	HasInconsistentHash () const
	Return true is the type of this object is known to have an inconsistent setup for Hash and RecursiveRemove (i.e.

virtual void	Info (const char method, const char msgfmt,...) const
	Issue info message.

virtual Bool_t	InheritsFrom (const char *classname) const
	Returns kTRUE if object inherits from class "classname".

virtual Bool_t	InheritsFrom (const TClass *cl) const
	Returns kTRUE if object inherits from TClass cl.

virtual void	Inspect () const
	Dump contents of this object in a graphics canvas.

void	InvertBit (UInt_t f)

Bool_t	IsDestructed () const
	IsDestructed.

virtual Bool_t	IsEqual (const TObject *obj) const
	Default equal comparison (objects are equal if they have the same address in memory).

virtual Bool_t	IsFolder () const
	Returns kTRUE in case object contains browsable objects (like containers or lists of other objects).

R__ALWAYS_INLINE Bool_t	IsOnHeap () const

virtual Bool_t	IsSortable () const

R__ALWAYS_INLINE Bool_t	IsZombie () const

virtual void	ls (Option_t *option="") const
	The ls function lists the contents of a class on stdout.

void	MayNotUse (const char *method) const
	Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary).

virtual Bool_t	Notify ()
	This method must be overridden to handle object notification.

void	Obsolete (const char method, const char asOfVers, const char *removedFromVers) const
	Use this method to declare a method obsolete.

void	operator delete (void *ptr)
	Operator delete.

void	operator delete[] (void *ptr)
	Operator delete [].

void *	operator new (size_t sz)

void *	operator new (size_t sz, void *vp)

void *	operator new[] (size_t sz)

void *	operator new[] (size_t sz, void *vp)

TObject &	operator= (const TObject &rhs)
	TObject assignment operator.

virtual void	Paint (Option_t *option="")
	This method must be overridden if a class wants to paint itself.

virtual void	Pop ()
	Pop on object drawn in a pad to the top of the display list.

virtual void	Print (Option_t *option="") const
	This method must be overridden when a class wants to print itself.

virtual Int_t	Read (const char *name)
	Read contents of object with specified name from the current directory.

virtual void	RecursiveRemove (TObject *obj)
	Recursively remove this object from a list.

void	ResetBit (UInt_t f)

virtual void	SaveAs (const char filename="", Option_t option="") const
	Save this object in the file specified by filename.

virtual void	SavePrimitive (std::ostream &out, Option_t *option="")
	Save a primitive as a C++ statement(s) on output stream "out".

void	SetBit (UInt_t f)

void	SetBit (UInt_t f, Bool_t set)
	Set or unset the user status bits as specified in f.

virtual void	SetDrawOption (Option_t *option="")
	Set drawing option for object.

virtual void	SetUniqueID (UInt_t uid)
	Set the unique object id.

virtual void	SysError (const char method, const char msgfmt,...) const
	Issue system error message.

R__ALWAYS_INLINE Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

virtual void	UseCurrentStyle ()
	Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked.

virtual void	Warning (const char method, const char msgfmt,...) const
	Issue warning message.

virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0)
	Write this object to the current directory.

virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const
	Write this object to the current directory.

Protected Member Functions
const char *	GetInputNeuronTitle (Int_t in)
	Returns the name of any neuron from the input layer.

Int_t	GetLayers ()
	Returns the number of layers.

TString	GetNeuronFormula (Int_t idx)
	Returns the formula used as input for neuron (idx) in the first layer.

Int_t	GetNeurons (Int_t layer)
	Returns the number of neurons in given layer.

const char *	GetOutputNeuronTitle (Int_t out)
	Returns the name of any neuron from the output layer.

Protected Member Functions inherited from TObject
virtual void	DoError (int level, const char location, const char fmt, va_list va) const
	Interface to ErrorHandler (protected).

void	MakeZombie ()

Private Attributes
TTree *	fAnalysisTree

TTree *	fIOTree

TMultiLayerPerceptron *	fNetwork

Additional Inherited Members
Public Types inherited from TObject
enum	{ kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 , kBitMask = 0x00ffffff }

enum	{ kSingleKey = BIT(0) , kOverwrite = BIT(1) , kWriteDelete = BIT(2) }

enum	EDeprecatedStatusBits { kObjInCanvas = BIT(3) }

enum	EStatusBits { kCanDelete = BIT(0) , kMustCleanup = BIT(3) , kIsReferenced = BIT(4) , kHasUUID = BIT(5) , kCannotPick = BIT(6) , kNoContextMenu = BIT(8) , kInvalidObject = BIT(13) }

Static Public Member Functions inherited from TObject
static Longptr_t	GetDtorOnly ()
	Return destructor only flag.

static Bool_t	GetObjectStat ()
	Get status of object stat flag.

static void	SetDtorOnly (void *obj)
	Set destructor only flag.

static void	SetObjectStat (Bool_t stat)
	Turn on/off tracking of objects in the TObjectTable.

Protected Types inherited from TObject
enum	{ kOnlyPrepStep = BIT(3) }

#include <TMLPAnalyzer.h>

Inheritance diagram for TMLPAnalyzer:

[legend]

Constructor & Destructor Documentation

◆ TMLPAnalyzer() [1/2]

TMLPAnalyzer::TMLPAnalyzer ( TMultiLayerPerceptron & net )

inline

Definition at line 40 of file TMLPAnalyzer.h.

◆ TMLPAnalyzer() [2/2]

TMLPAnalyzer::TMLPAnalyzer ( TMultiLayerPerceptron * net )

inline

Definition at line 42 of file TMLPAnalyzer.h.

◆ ~TMLPAnalyzer()

TMLPAnalyzer::~TMLPAnalyzer ( )

virtual

Destructor.

Definition at line 45 of file TMLPAnalyzer.cxx.

Member Function Documentation

◆ CheckNetwork()

void TMLPAnalyzer::CheckNetwork ( )

Gives some information about the network in the terminal.

Definition at line 146 of file TMLPAnalyzer.cxx.

◆ DrawDInput()

void TMLPAnalyzer::DrawDInput ( Int_t i )

Draws the distribution (on the test sample) of the impact on the network output of a small variation of the ith input.

Definition at line 284 of file TMLPAnalyzer.cxx.

◆ DrawDInputs()

void TMLPAnalyzer::DrawDInputs ( )

Draws the distribution (on the test sample) of the impact on the network output of a small variation of each input.

DrawDInputs() draws something that approximates the distribution of the derivative of the NN w.r.t. each input. That quantity is recognized as one of the measures to determine key quantities in the network.

What is done is to vary one input around its nominal value and to see how the NN changes. This is done for each entry in the sample and produces a distribution.

What you can learn from that is:

is variable a really useful, or is my network insensitive to it ?
is there any risk of big systematic ? Is the network extremely sensitive to small variations of any of my inputs ?

As you might understand, this is to be considered with care and can serve as input for an "educated guess" when optimizing the network.

Definition at line 311 of file TMLPAnalyzer.cxx.

◆ DrawNetwork()

void TMLPAnalyzer::DrawNetwork	(	Int_t	neuron,
		const char *	signal,
		const char *	bg
	)

Draws the distribution of the neural network (using ith neuron).

Two distributions are drawn, for events passing respectively the "signal" and "background" cuts. Only the test sample is used.

Definition at line 337 of file TMLPAnalyzer.cxx.

◆ DrawTruthDeviation()

TProfile * TMLPAnalyzer::DrawTruthDeviation	(	Int_t	outnode = `0`,
		Option_t *	option = `""`
	)

Create a profile of the difference of the MLP output minus the true value for a given output node outnode, vs the true value for outnode, for all test data events.

This method is mainly useful when doing regression analysis with the MLP (i.e. not classification, but continuous truth values). The resulting TProfile histogram is returned. It is not drawn if option "goff" is specified. Options are passed to TProfile::Draw

Definition at line 398 of file TMLPAnalyzer.cxx.

◆ DrawTruthDeviationInOut()

TProfile * TMLPAnalyzer::DrawTruthDeviationInOut	(	Int_t	innode,
		Int_t	outnode = `0`,
		Option_t *	option = `""`
	)

Creates a profile of the difference of the MLP output outnode minus the true value of outnode vs the input value innode, for all test data events.

The resulting TProfile histogram is returned. It is not drawn if option "goff" is specified. Options are passed to TProfile::Draw

Definition at line 474 of file TMLPAnalyzer.cxx.

◆ DrawTruthDeviationInsOut()

THStack * TMLPAnalyzer::DrawTruthDeviationInsOut	(	Int_t	outnode = `0`,
		Option_t *	option = `""`
	)

Creates a profile of the difference of the MLP output outnode minus the true value of outnode vs the input value, stacked for all inputs, for all test data events.

The returned THStack contains all the TProfiles. It is drawn unless the option "goff" is specified. Options are passed to TProfile::Draw.

Definition at line 506 of file TMLPAnalyzer.cxx.

◆ DrawTruthDeviations()

THStack * TMLPAnalyzer::DrawTruthDeviations ( Option_t * option = "" )

Creates TProfiles of the difference of the MLP output minus the true value vs the true value, one for each output, filled with the test data events.

This method is mainly useful when doing regression analysis with the MLP (i.e. not classification, but continuous truth values). The returned THStack contains all the TProfiles. It is drawn unless the option "goff" is specified. Options are passed to TProfile::Draw.

Definition at line 431 of file TMLPAnalyzer.cxx.

◆ GatherInformations()

void TMLPAnalyzer::GatherInformations ( )

Collect information about what is useful in the network.

This method has to be called first when analyzing a network. Fills the two analysis trees.

Definition at line 170 of file TMLPAnalyzer.cxx.

◆ GetInputNeuronTitle()

const char * TMLPAnalyzer::GetInputNeuronTitle ( Int_t in )

protected

Returns the name of any neuron from the input layer.

Definition at line 128 of file TMLPAnalyzer.cxx.

◆ GetIOTree()

TTree * TMLPAnalyzer::GetIOTree ( ) const

inline

Definition at line 56 of file TMLPAnalyzer.h.

◆ GetLayers()

Int_t TMLPAnalyzer::GetLayers ( )

protected

Returns the number of layers.

Definition at line 54 of file TMLPAnalyzer.cxx.

◆ GetNeuronFormula()

TString TMLPAnalyzer::GetNeuronFormula ( Int_t idx )

protected

Returns the formula used as input for neuron (idx) in the first layer.

Definition at line 102 of file TMLPAnalyzer.cxx.

◆ GetNeurons()

Int_t TMLPAnalyzer::GetNeurons ( Int_t layer )

protected

Returns the number of neurons in given layer.

Definition at line 63 of file TMLPAnalyzer.cxx.

◆ GetOutputNeuronTitle()

const char * TMLPAnalyzer::GetOutputNeuronTitle ( Int_t out )

protected

Returns the name of any neuron from the output layer.

Definition at line 137 of file TMLPAnalyzer.cxx.

Member Data Documentation

◆ fAnalysisTree

TTree* TMLPAnalyzer::fAnalysisTree

private

Definition at line 29 of file TMLPAnalyzer.h.

◆ fIOTree

TTree* TMLPAnalyzer::fIOTree

private

Definition at line 30 of file TMLPAnalyzer.h.

◆ fNetwork

TMultiLayerPerceptron* TMLPAnalyzer::fNetwork

private

Definition at line 28 of file TMLPAnalyzer.h.

Libraries for TMLPAnalyzer:

[legend]

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

math/mlp/inc/TMLPAnalyzer.h
math/mlp/src/TMLPAnalyzer.cxx

Public Member Functions

Protected Member Functions

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ TMLPAnalyzer() [1/2]

◆ TMLPAnalyzer() [2/2]

◆ ~TMLPAnalyzer()

Member Function Documentation

◆ CheckNetwork()

◆ DrawDInput()

◆ DrawDInputs()

◆ DrawNetwork()

◆ DrawTruthDeviation()

◆ DrawTruthDeviationInOut()

◆ DrawTruthDeviationInsOut()

◆ DrawTruthDeviations()

◆ GatherInformations()

◆ GetInputNeuronTitle()

◆ GetIOTree()

◆ GetLayers()

◆ GetNeuronFormula()

◆ GetNeurons()

◆ GetOutputNeuronTitle()

Member Data Documentation

◆ fAnalysisTree

◆ fIOTree

◆ fNetwork