Logo ROOT   6.19/01
Reference Guide
TMLPAnalyzer Class Reference

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. More...
 
void CheckNetwork ()
 Gives some information about the network in the terminal. More...
 
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. More...
 
void DrawDInputs ()
 Draws the distribution (on the test sample) of the impact on the network output of a small variation of each input. More...
 
void DrawNetwork (Int_t neuron, const char *signal, const char *bg)
 Draws the distribution of the neural network (using ith neuron). More...
 
TProfileDrawTruthDeviation (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. More...
 
TProfileDrawTruthDeviationInOut (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. More...
 
THStackDrawTruthDeviationInsOut (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. More...
 
THStackDrawTruthDeviations (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. More...
 
void GatherInformations ()
 Collect information about what is useful in the network. More...
 
TTreeGetIOTree () const
 
- Public Member Functions inherited from TObject
 TObject ()
 TObject constructor. More...
 
 TObject (const TObject &object)
 TObject copy ctor. More...
 
virtual ~TObject ()
 TObject destructor. More...
 
void AbstractMethod (const char *method) const
 Use this method to implement an "abstract" method that you don't want to leave purely abstract. More...
 
virtual void AppendPad (Option_t *option="")
 Append graphics object to current pad. More...
 
virtual void Browse (TBrowser *b)
 Browse object. May be overridden for another default action. More...
 
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. More...
 
virtual const char * ClassName () const
 Returns name of class to which the object belongs. More...
 
virtual void Clear (Option_t *="")
 
virtual TObjectClone (const char *newname="") const
 Make a clone of an object using the Streamer facility. More...
 
virtual Int_t Compare (const TObject *obj) const
 Compare abstract method. More...
 
virtual void Copy (TObject &object) const
 Copy this to obj. More...
 
virtual void Delete (Option_t *option="")
 Delete this object. More...
 
virtual Int_t DistancetoPrimitive (Int_t px, Int_t py)
 Computes distance from point (px,py) to the object. More...
 
virtual void Draw (Option_t *option="")
 Default Draw method for all objects. More...
 
virtual void DrawClass () const
 Draw class inheritance tree of the class to which this object belongs. More...
 
virtual TObjectDrawClone (Option_t *option="") const
 Draw a clone of this object in the current selected pad for instance with: gROOT->SetSelectedPad(gPad). More...
 
virtual void Dump () const
 Dump contents of object on stdout. More...
 
virtual void Error (const char *method, const char *msgfmt,...) const
 Issue error message. More...
 
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. More...
 
virtual void Execute (TMethod *method, TObjArray *params, Int_t *error=0)
 Execute method on this object with parameters stored in the TObjArray. More...
 
virtual void ExecuteEvent (Int_t event, Int_t px, Int_t py)
 Execute action corresponding to an event at (px,py). More...
 
virtual void Fatal (const char *method, const char *msgfmt,...) const
 Issue fatal error message. More...
 
virtual TObjectFindObject (const char *name) const
 Must be redefined in derived classes. More...
 
virtual TObjectFindObject (const TObject *obj) const
 Must be redefined in derived classes. More...
 
virtual Option_tGetDrawOption () const
 Get option used by the graphics system to draw this object. More...
 
virtual const char * GetIconName () const
 Returns mime type name of object. More...
 
virtual const char * GetName () const
 Returns name of object. More...
 
virtual char * GetObjectInfo (Int_t px, Int_t py) const
 Returns string containing info about the object at position (px,py). More...
 
virtual Option_tGetOption () const
 
virtual const char * GetTitle () const
 Returns title of object. More...
 
virtual UInt_t GetUniqueID () const
 Return the unique object id. More...
 
virtual Bool_t HandleTimer (TTimer *timer)
 Execute action in response of a timer timing out. More...
 
virtual ULong_t Hash () const
 Return hash value for this object. More...
 
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. More...
 
virtual void Info (const char *method, const char *msgfmt,...) const
 Issue info message. More...
 
virtual Bool_t InheritsFrom (const char *classname) const
 Returns kTRUE if object inherits from class "classname". More...
 
virtual Bool_t InheritsFrom (const TClass *cl) const
 Returns kTRUE if object inherits from TClass cl. More...
 
virtual void Inspect () const
 Dump contents of this object in a graphics canvas. More...
 
void InvertBit (UInt_t f)
 
virtual Bool_t IsEqual (const TObject *obj) const
 Default equal comparison (objects are equal if they have the same address in memory). More...
 
virtual Bool_t IsFolder () const
 Returns kTRUE in case object contains browsable objects (like containers or lists of other objects). More...
 
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. More...
 
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). More...
 
virtual Bool_t Notify ()
 This method must be overridden to handle object notification. More...
 
void Obsolete (const char *method, const char *asOfVers, const char *removedFromVers) const
 Use this method to declare a method obsolete. More...
 
void operator delete (void *ptr)
 Operator delete. More...
 
void operator delete[] (void *ptr)
 Operator delete []. More...
 
voidoperator new (size_t sz)
 
voidoperator new (size_t sz, void *vp)
 
voidoperator new[] (size_t sz)
 
voidoperator new[] (size_t sz, void *vp)
 
TObjectoperator= (const TObject &rhs)
 TObject assignment operator. More...
 
virtual void Paint (Option_t *option="")
 This method must be overridden if a class wants to paint itself. More...
 
virtual void Pop ()
 Pop on object drawn in a pad to the top of the display list. More...
 
virtual void Print (Option_t *option="") const
 This method must be overridden when a class wants to print itself. More...
 
virtual Int_t Read (const char *name)
 Read contents of object with specified name from the current directory. More...
 
virtual void RecursiveRemove (TObject *obj)
 Recursively remove this object from a list. More...
 
void ResetBit (UInt_t f)
 
virtual void SaveAs (const char *filename="", Option_t *option="") const
 Save this object in the file specified by filename. More...
 
virtual void SavePrimitive (std::ostream &out, Option_t *option="")
 Save a primitive as a C++ statement(s) on output stream "out". More...
 
void SetBit (UInt_t f, Bool_t set)
 Set or unset the user status bits as specified in f. More...
 
void SetBit (UInt_t f)
 
virtual void SetDrawOption (Option_t *option="")
 Set drawing option for object. More...
 
virtual void SetUniqueID (UInt_t uid)
 Set the unique object id. More...
 
virtual void SysError (const char *method, const char *msgfmt,...) const
 Issue system error message. More...
 
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. More...
 
virtual void Warning (const char *method, const char *msgfmt,...) const
 Issue warning message. More...
 
virtual Int_t Write (const char *name=0, Int_t option=0, Int_t bufsize=0)
 Write this object to the current directory. More...
 
virtual Int_t Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const
 Write this object to the current directory. More...
 

Protected Member Functions

const char * GetInputNeuronTitle (Int_t in)
 Returns the name of any neuron from the input layer. More...
 
Int_t GetLayers ()
 Returns the number of layers. More...
 
TString GetNeuronFormula (Int_t idx)
 Returns the formula used as input for neuron (idx) in the first layer. More...
 
Int_t GetNeurons (Int_t layer)
 Returns the number of neurons in given layer. More...
 
const char * GetOutputNeuronTitle (Int_t out)
 Returns the name of any neuron from the output layer. More...
 
- 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). More...
 
void MakeZombie ()
 

Private Attributes

TTreefAnalysisTree
 
TTreefIOTree
 
TMultiLayerPerceptronfNetwork
 

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 Long_t GetDtorOnly ()
 Return destructor only flag. More...
 
static Bool_t GetObjectStat ()
 Get status of object stat flag. More...
 
static void SetDtorOnly (void *obj)
 Set destructor only flag. More...
 
static void SetObjectStat (Bool_t stat)
 Turn on/off tracking of objects in the TObjectTable. More...
 

#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 46 of file TMLPAnalyzer.cxx.

Member Function Documentation

◆ CheckNetwork()

void TMLPAnalyzer::CheckNetwork ( )

Gives some information about the network in the terminal.

Definition at line 147 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 285 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 312 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 338 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 399 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 475 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 507 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 432 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 171 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 129 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 55 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 103 of file TMLPAnalyzer.cxx.

◆ GetNeurons()

Int_t TMLPAnalyzer::GetNeurons ( Int_t  layer)
protected

Returns the number of neurons in given layer.

Definition at line 64 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 138 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: