From 8b34a3b8e7481989ca751652c2633d63f3c61f7d Mon Sep 17 00:00:00 2001
From: Rene Brun <Rene.Brun@cern.ch>
Date: Mon, 27 Nov 2006 17:10:33 +0000
Subject: [PATCH] Fix coding conventions replacing enum NeuronType by
ENeuronType, etc
git-svn-id: http://root.cern.ch/svn/root/trunk@16966 27541ba8-7e3a-0410-8455-c3a389f83636
---
mlp/inc/TMultiLayerPerceptron.h | 28 ++++++++++++++--------------
mlp/inc/TNeuron.h | 10 +++++-----
mlp/src/TMultiLayerPerceptron.cxx | 14 +++++++-------
mlp/src/TNeuron.cxx | 6 +++---
tmva/src/MethodTMlpANN.cxx | 6 +++---
5 files changed, 32 insertions(+), 32 deletions(-)
diff --git a/mlp/inc/TMultiLayerPerceptron.h b/mlp/inc/TMultiLayerPerceptron.h
index 0c23a108e56..f5fbf34a3b5 100644
--- a/mlp/inc/TMultiLayerPerceptron.h
+++ b/mlp/inc/TMultiLayerPerceptron.h
@@ -1,4 +1,4 @@
-// @(#)root/mlp:$Name: $:$Id: TMultiLayerPerceptron.h,v 1.10 2006/01/09 15:47:30 brun Exp $
+// @(#)root/mlp:$Name: $:$Id: TMultiLayerPerceptron.h,v 1.11 2006/05/26 15:13:02 rdm Exp $
// Author: Christophe.Delaere@cern.ch 20/07/03
/*************************************************************************
@@ -59,31 +59,31 @@ class TMultiLayerPerceptron : public TObject {
friend class TMLPAnalyzer;
public:
- enum LearningMethod { kStochastic, kBatch, kSteepestDescent,
- kRibierePolak, kFletcherReeves, kBFGS };
- enum DataSet { kTraining, kTest };
+ enum ELearningMethod { kStochastic, kBatch, kSteepestDescent,
+ kRibierePolak, kFletcherReeves, kBFGS };
+ enum EDataSet { kTraining, kTest };
TMultiLayerPerceptron();
TMultiLayerPerceptron(const char* layout, TTree* data = 0,
const char* training = "Entry$%2==0",
const char* test = "",
- TNeuron::NeuronType type = TNeuron::kSigmoid,
+ TNeuron::ENeuronType type = TNeuron::kSigmoid,
const char* extF = "", const char* extD = "");
TMultiLayerPerceptron(const char* layout,
const char* weight, TTree* data = 0,
const char* training = "Entry$%2==0",
const char* test = "",
- TNeuron::NeuronType type = TNeuron::kSigmoid,
+ TNeuron::ENeuronType type = TNeuron::kSigmoid,
const char* extF = "", const char* extD = "");
TMultiLayerPerceptron(const char* layout, TTree* data,
TEventList* training,
TEventList* test,
- TNeuron::NeuronType type = TNeuron::kSigmoid,
+ TNeuron::ENeuronType type = TNeuron::kSigmoid,
const char* extF = "", const char* extD = "");
TMultiLayerPerceptron(const char* layout,
const char* weight, TTree* data,
TEventList* training,
TEventList* test,
- TNeuron::NeuronType type = TNeuron::kSigmoid,
+ TNeuron::ENeuronType type = TNeuron::kSigmoid,
const char* extF = "", const char* extD = "");
virtual ~TMultiLayerPerceptron();
void SetData(TTree*);
@@ -91,12 +91,12 @@ class TMultiLayerPerceptron : public TObject {
void SetTestDataSet(TEventList* test);
void SetTrainingDataSet(const char* train);
void SetTestDataSet(const char* test);
- void SetLearningMethod(TMultiLayerPerceptron::LearningMethod method);
+ void SetLearningMethod(TMultiLayerPerceptron::ELearningMethod method);
void SetEventWeight(const char*);
void Train(Int_t nEpoch, Option_t* option = "text");
Double_t Result(Int_t event, Int_t index = 0) const;
Double_t GetError(Int_t event) const;
- Double_t GetError(TMultiLayerPerceptron::DataSet set) const;
+ Double_t GetError(TMultiLayerPerceptron::EDataSet set) const;
void ComputeDEDw() const;
void Randomize() const;
void SetEta(Double_t eta);
@@ -112,7 +112,7 @@ class TMultiLayerPerceptron : public TObject {
inline Double_t GetTau() const { return fTau; }
inline Int_t GetReset() const { return fReset; }
inline TString GetStructure() const { return fStructure; }
- inline TNeuron::NeuronType GetType() const { return fType; }
+ inline TNeuron::ENeuronType GetType() const { return fType; }
void DrawResult(Int_t index = 0, Option_t* option = "test") const;
void DumpWeights(Option_t* filename = "-") const;
void LoadWeights(Option_t* filename = "");
@@ -155,13 +155,13 @@ class TMultiLayerPerceptron : public TObject {
TObjArray fSynapses; // Collection of all the synapses in the network
TString fStructure; // String containing the network structure
TString fWeight; // String containing the event weight
- TNeuron::NeuronType fType; // Type of hidden neurons
- TNeuron::NeuronType fOutType; // Type of output neurons
+ TNeuron::ENeuronType fType; // Type of hidden neurons
+ TNeuron::ENeuronType fOutType; // Type of output neurons
TString fextF; // String containing the function name
TString fextD; // String containing the derivative name
TEventList *fTraining; //! EventList defining the events in the training dataset
TEventList *fTest; //! EventList defining the events in the test dataset
- LearningMethod fLearningMethod; //! The Learning Method
+ ELearningMethod fLearningMethod; //! The Learning Method
TTreeFormula* fEventWeight; //! formula representing the event weight
TTreeFormulaManager* fManager; //! TTreeFormulaManager for the weight and neurons
Double_t fEta; //! Eta - used in stochastic minimisation - Default=0.1
diff --git a/mlp/inc/TNeuron.h b/mlp/inc/TNeuron.h
index bf6a8acecb2..dbfedc9a77e 100644
--- a/mlp/inc/TNeuron.h
+++ b/mlp/inc/TNeuron.h
@@ -1,4 +1,4 @@
-// @(#)root/mlp:$Name: $:$Id: TNeuron.h,v 1.9 2006/01/09 15:47:30 brun Exp $
+// @(#)root/mlp:$Name: $:$Id: TNeuron.h,v 1.10 2006/01/09 18:05:57 pcanal Exp $
// Author: Christophe.Delaere@cern.ch 20/07/03
/*************************************************************************
@@ -49,9 +49,9 @@ class TNeuron : public TNamed {
friend class TSynapse;
public:
- enum NeuronType { kOff, kLinear, kSigmoid, kTanh, kGauss, kSoftmax, kExternal };
+ enum ENeuronType { kOff, kLinear, kSigmoid, kTanh, kGauss, kSoftmax, kExternal };
- TNeuron(NeuronType type = kSigmoid,
+ TNeuron(ENeuronType type = kSigmoid,
const char* name = "", const char* title = "",
const char* extF = "", const char* extD = "" );
virtual ~ TNeuron() {}
@@ -66,7 +66,7 @@ class TNeuron : public TNamed {
Double_t GetTarget() const;
Double_t GetDeDw() const;
Double_t GetBranch() const;
- NeuronType GetType() const;
+ ENeuronType GetType() const;
void SetWeight(Double_t w);
inline Double_t GetWeight() const { return fWeight; }
void SetNormalisation(Double_t mean, Double_t RMS);
@@ -89,7 +89,7 @@ class TNeuron : public TNamed {
TObjArray flayer; // pointers to the current level in a network (neurons, not synapses)
Double_t fWeight; // weight used for computation
Double_t fNorm[2]; // normalisation to mean=0, RMS=1.
- NeuronType fType; // neuron type
+ ENeuronType fType; // neuron type
TFormula* fExtF; // function (external mode)
TFormula* fExtD; // derivative (external mode)
//buffers
diff --git a/mlp/src/TMultiLayerPerceptron.cxx b/mlp/src/TMultiLayerPerceptron.cxx
index 9f58c86beee..f5893188ead 100644
--- a/mlp/src/TMultiLayerPerceptron.cxx
+++ b/mlp/src/TMultiLayerPerceptron.cxx
@@ -1,4 +1,4 @@
-// @(#)root/mlp:$Name: $:$Id: TMultiLayerPerceptron.cxx,v 1.37 2006/05/26 15:13:02 rdm Exp $
+// @(#)root/mlp:$Name: $:$Id: TMultiLayerPerceptron.cxx,v 1.38 2006/10/08 15:28:01 brun Exp $
// Author: Christophe.Delaere@cern.ch 20/07/03
/*************************************************************************
@@ -306,7 +306,7 @@ TMultiLayerPerceptron::TMultiLayerPerceptron()
TMultiLayerPerceptron::TMultiLayerPerceptron(const char * layout, TTree * data,
TEventList * training,
TEventList * test,
- TNeuron::NeuronType type,
+ TNeuron::ENeuronType type,
const char* extF, const char* extD)
{
// The network is described by a simple string:
@@ -363,7 +363,7 @@ TMultiLayerPerceptron::TMultiLayerPerceptron(const char * layout,
const char * weight, TTree * data,
TEventList * training,
TEventList * test,
- TNeuron::NeuronType type,
+ TNeuron::ENeuronType type,
const char* extF, const char* extD)
{
// The network is described by a simple string:
@@ -418,7 +418,7 @@ TMultiLayerPerceptron::TMultiLayerPerceptron(const char * layout,
TMultiLayerPerceptron::TMultiLayerPerceptron(const char * layout, TTree * data,
const char * training,
const char * test,
- TNeuron::NeuronType type,
+ TNeuron::ENeuronType type,
const char* extF, const char* extD)
{
// The network is described by a simple string:
@@ -483,7 +483,7 @@ TMultiLayerPerceptron::TMultiLayerPerceptron(const char * layout,
const char * weight, TTree * data,
const char * training,
const char * test,
- TNeuron::NeuronType type,
+ TNeuron::ENeuronType type,
const char* extF, const char* extD)
{
// The network is described by a simple string:
@@ -635,7 +635,7 @@ void TMultiLayerPerceptron::SetTestDataSet(const char * test)
}
//______________________________________________________________________________
-void TMultiLayerPerceptron::SetLearningMethod(TMultiLayerPerceptron::LearningMethod method)
+void TMultiLayerPerceptron::SetLearningMethod(TMultiLayerPerceptron::ELearningMethod method)
{
// Sets the learning method.
// Available methods are: kStochastic, kBatch,
@@ -1007,7 +1007,7 @@ Double_t TMultiLayerPerceptron::GetError(Int_t event) const
}
//______________________________________________________________________________
-Double_t TMultiLayerPerceptron::GetError(TMultiLayerPerceptron::DataSet set) const
+Double_t TMultiLayerPerceptron::GetError(TMultiLayerPerceptron::EDataSet set) const
{
// Error on the whole dataset
TEventList *list =
diff --git a/mlp/src/TNeuron.cxx b/mlp/src/TNeuron.cxx
index 11ab7fecd84..b657b0ad5ad 100644
--- a/mlp/src/TNeuron.cxx
+++ b/mlp/src/TNeuron.cxx
@@ -1,4 +1,4 @@
-// @(#)root/mlp:$Name: $:$Id: TNeuron.cxx,v 1.19 2006/05/12 08:19:02 brun Exp $
+// @(#)root/mlp:$Name: $:$Id: TNeuron.cxx,v 1.20 2006/05/26 15:13:02 rdm Exp $
// Author: Christophe.Delaere@cern.ch 20/07/03
/*************************************************************************
@@ -45,7 +45,7 @@
ClassImp(TNeuron)
//______________________________________________________________________________
-TNeuron::TNeuron(TNeuron::NeuronType type /*= kSigmoid*/,
+TNeuron::TNeuron(TNeuron::ENeuronType type /*= kSigmoid*/,
const char* name /*= ""*/, const char* title /*= ""*/,
const char* extF /*= ""*/, const char* extD /*= ""*/ )
:TNamed(name, title)
@@ -855,7 +855,7 @@ void TNeuron::AddInLayer(TNeuron * nearP)
}
//______________________________________________________________________________
-TNeuron::NeuronType TNeuron::GetType() const
+TNeuron::ENeuronType TNeuron::GetType() const
{
// Returns the neuron type.
return fType;
diff --git a/tmva/src/MethodTMlpANN.cxx b/tmva/src/MethodTMlpANN.cxx
index 298c2fb8ab8..4be3c3ddd16 100644
--- a/tmva/src/MethodTMlpANN.cxx
+++ b/tmva/src/MethodTMlpANN.cxx
@@ -1,4 +1,4 @@
-// @(#)root/tmva $Id: MethodTMlpANN.cxx,v 1.34 2006/11/17 00:21:35 stelzer Exp $
+// @(#)root/tmva $Id: MethodTMlpANN.cxx,v 1.10 2006/11/20 15:35:28 brun Exp $
// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
@@ -59,8 +59,8 @@
// some additional TMlpANN options
const Bool_t EnforceNormalization__=kTRUE;
-const TMultiLayerPerceptron::LearningMethod LearningMethod__= TMultiLayerPerceptron::kStochastic;
-// const TMultiLayerPerceptron::LearningMethod LearningMethod__= TMultiLayerPerceptron::kBatch;
+const TMultiLayerPerceptron::ELearningMethod LearningMethod__= TMultiLayerPerceptron::kStochastic;
+// const TMultiLayerPerceptron::ELearningMethod LearningMethod__= TMultiLayerPerceptron::kBatch;
ClassImp(TMVA::MethodTMlpANN)
;
--
GitLab