From 9e3861ce62e68dbfbcf354b58c11f29f552bdbdc Mon Sep 17 00:00:00 2001
From: Fons Rademakers <Fons.Rademakers@cern.ch>
Date: Mon, 4 Feb 2013 18:36:25 +0000
Subject: [PATCH] fix warnings reported by the latest Apple LLVM 4.2 clang++
(based on llvm 3.2svn). Mostly were: warning: private field 'fOneDim' is not
used [-Wunused-private-field]
git-svn-id: http://root.cern.ch/svn/root/trunk@48467 27541ba8-7e3a-0410-8455-c3a389f83636
---
graf2d/asimage/src/libAfterImage/afterbase.c | 3 +-
hist/hbook/src/THbookFile.cxx | 6 +-
main/src/h2root.cxx | 2 +-
math/foam/inc/TFoamSampler.h | 2 +-
math/foam/src/TFoamSampler.cxx | 108 ++++++------
math/mathmore/inc/Math/GSLNLSMinimizer.h | 163 +++++++++---------
.../inc/Minuit2/FumiliStandardChi2FCN.h | 84 +++++----
.../FumiliStandardMaximumLikelihoodFCN.h | 50 +++---
8 files changed, 203 insertions(+), 215 deletions(-)
diff --git a/graf2d/asimage/src/libAfterImage/afterbase.c b/graf2d/asimage/src/libAfterImage/afterbase.c
index b9dc251f69e..d6581bc6a64 100644
--- a/graf2d/asimage/src/libAfterImage/afterbase.c
+++ b/graf2d/asimage/src/libAfterImage/afterbase.c
@@ -705,7 +705,8 @@ double asim_parse_math(const char* str, char** endptr, double size) {
num = asim_parse_math(str + 1, &ptr, size);
else if (*str == '$')
{
- for (ptr = (char*)str + 1 ; *ptr && !isspace(*ptr) && *ptr != '+' && *ptr != '-' && *ptr != '*' && *ptr != '!' && *ptr != '/' && *ptr != ')' ; ptr++);
+ for (ptr = (char*)str + 1 ; *ptr && !isspace(*ptr) && *ptr != '+' && *ptr != '-' && *ptr != '*' && *ptr != '!' && *ptr != '/' && *ptr != ')' ; ptr++)
+ ;
num = asim_asxml_var_nget((char*)str + 1, ptr - (str + 1));
}else
num = strtod(str, &ptr);
diff --git a/hist/hbook/src/THbookFile.cxx b/hist/hbook/src/THbookFile.cxx
index e374e44b4ee..d0bf42652a2 100644
--- a/hist/hbook/src/THbookFile.cxx
+++ b/hist/hbook/src/THbookFile.cxx
@@ -284,7 +284,7 @@ THbookFile::THbookFile(const char *fname, Int_t lrecl)
char topdir[20];
snprintf(topdir,19,"lun%d",fLun);
- Int_t ier;
+ Int_t ier = 0;
#ifndef WIN32
hropen(fLun,PASSCHAR(topdir),PASSCHAR(fname),PASSCHAR("p"),lrecl,ier,strlen(topdir),strlen(fname),1);
#else
@@ -885,7 +885,7 @@ TObject *THbookFile::ConvertProfile(Int_t id)
const Int_t kCON1 = 9;
Int_t i;
- Float_t x;
+ Float_t x = 0.0;
Float_t y = 0.5*(ymin+ymax);
for (i=1;i<=ncx;i++) {
Int_t n = Int_t(q[ln+i]);
@@ -974,7 +974,7 @@ TObject *THbookFile::Convert2D(Int_t id)
Int_t lw = lq[lcont];
if (lw) h2->Sumw2();
- Float_t x,y;
+ Float_t x = 0.0, y = 0.0;
for (Int_t j=0;j<=ncy+1;j++) {
for (Int_t i=0;i<=ncx+1;i++) {
hijxy(id,i,j,x,y);
diff --git a/main/src/h2root.cxx b/main/src/h2root.cxx
index bf3c8f33af1..c8a9b7b696d 100644
--- a/main/src/h2root.cxx
+++ b/main/src/h2root.cxx
@@ -521,7 +521,7 @@ void convert_2d(Int_t id)
Int_t lw = lq[lcont];
if (lw) h2->Sumw2();
- Float_t x,y;
+ Float_t x = 0.0, y = 0.0;
for (Int_t j=0;j<=ncy+1;j++) {
for (Int_t i=0;i<=ncx+1;i++) {
hijxy(id,i,j,x,y);
diff --git a/math/foam/inc/TFoamSampler.h b/math/foam/inc/TFoamSampler.h
index 409cc29e17a..7b30c327587 100644
--- a/math/foam/inc/TFoamSampler.h
+++ b/math/foam/inc/TFoamSampler.h
@@ -123,7 +123,7 @@ protected:
private:
// private member
- bool fOneDim; // flag to indicate if the function is 1 dimension
+// bool fOneDim; // flag to indicate if the function is 1 dimension
// bool fHasMode; // flag to indicate if a mode is set
// bool fHasArea; // flag to indicate if a area is set
// double fMode; // mode of dist
diff --git a/math/foam/src/TFoamSampler.cxx b/math/foam/src/TFoamSampler.cxx
index f7265897fe1..5f1c11dad7e 100644
--- a/math/foam/src/TFoamSampler.cxx
+++ b/math/foam/src/TFoamSampler.cxx
@@ -26,13 +26,13 @@
#include <cassert>
#include <cmath>
-class FoamDistribution : public TFoamIntegrand {
+class FoamDistribution : public TFoamIntegrand {
public:
- FoamDistribution(const ROOT::Math::IMultiGenFunction & f, const ROOT::Fit::DataRange & range) :
- fFunc(f),
- fX(std::vector<double>(f.NDim() ) ),
+ FoamDistribution(const ROOT::Math::IMultiGenFunction & f, const ROOT::Fit::DataRange & range) :
+ fFunc(f),
+ fX(std::vector<double>(f.NDim() ) ),
fMinX(std::vector<double>(f.NDim() ) ),
fDeltaX(std::vector<double>(f.NDim() ) )
{
@@ -41,33 +41,33 @@ public:
for (unsigned int i = 0; i < range.NDim(); ++i) {
if (range.Size(i) == 0)
Error("FoamDistribution","Range is not set for coordinate dim %d",i);
- else if (range.Size(i)>1)
+ else if (range.Size(i)>1)
Warning("FoamDistribution","Using only first range in coordinate dim %d",i);
- std::pair<double,double> r = range(i);
+ std::pair<double,double> r = range(i);
fMinX[i] = r.first;
- fDeltaX[i] = r.second - r.first;
+ fDeltaX[i] = r.second - r.first;
}
- }
+ }
// in principle function does not need to be cloned
virtual double Density(int ndim, double * x) {
assert(ndim == (int) fFunc.NDim() );
for (int i = 0; i < ndim; ++i)
- fX[i] = fMinX[i] + x[i] * fDeltaX[i];
+ fX[i] = fMinX[i] + x[i] * fDeltaX[i];
return (fFunc)(&fX[0]);
}
double MinX(unsigned int i) { return fMinX[i]; }
double DeltaX(unsigned int i) { return fDeltaX[i]; }
-
+
private:
const ROOT::Math::IMultiGenFunction & fFunc;
- std::vector<double> fX;
- std::vector<double> fMinX;
- std::vector<double> fDeltaX;
+ std::vector<double> fX;
+ std::vector<double> fMinX;
+ std::vector<double> fDeltaX;
};
@@ -80,12 +80,12 @@ ClassImp(TFoamSampler)
/**
TFoamSampler class
class implementing the ROOT::Math::DistSampler interface using FOAM
- for sampling arbitrary distributions.
+ for sampling arbitrary distributions.
*/
-TFoamSampler::TFoamSampler() : ROOT::Math::DistSampler(),
- fOneDim(false),
+TFoamSampler::TFoamSampler() : ROOT::Math::DistSampler(),
+// fOneDim(false)
// fDiscrete(false),
// fHasMode(false), fHasArea(false),
// fMode(0), fArea(0),
@@ -96,20 +96,20 @@ TFoamSampler::TFoamSampler() : ROOT::Math::DistSampler(),
TFoamSampler::~TFoamSampler() {
assert(fFoam != 0);
- delete fFoam;
- if (fFoamDist) delete fFoamDist;
+ delete fFoam;
+ if (fFoamDist) delete fFoamDist;
}
-bool TFoamSampler::Init(const char *) {
+bool TFoamSampler::Init(const char *) {
// initialize using default options
ROOT::Math::DistSamplerOptions opt(0);
- ROOT::Math::IOptions * foamOpt = ROOT::Math::DistSamplerOptions::FindDefault("Foam");
- if (foamOpt) opt.SetExtraOptions(*foamOpt);
+ ROOT::Math::IOptions * foamOpt = ROOT::Math::DistSamplerOptions::FindDefault("Foam");
+ if (foamOpt) opt.SetExtraOptions(*foamOpt);
return Init(opt);
}
-bool TFoamSampler::Init(const ROOT::Math::DistSamplerOptions & opt) {
+bool TFoamSampler::Init(const ROOT::Math::DistSamplerOptions & opt) {
// initialize foam classes using the given algorithm
assert (fFoam != 0 );
if (NDim() == 0) {
@@ -117,24 +117,24 @@ bool TFoamSampler::Init(const ROOT::Math::DistSamplerOptions & opt) {
return false;
}
- // initialize the foam
+ // initialize the foam
fFoam->SetkDim(NDim() );
- // initialize random number
+ // initialize random number
if (!GetRandom()) SetRandom(gRandom);
- // create TFoamIntegrand class
- if (fFoamDist) delete fFoamDist;
+ // create TFoamIntegrand class
+ if (fFoamDist) delete fFoamDist;
fFoamDist = new FoamDistribution(ParentPdf(),PdfRange());
fFoam->SetRho(fFoamDist);
// set print level
fFoam->SetChat(opt.PrintLevel());
- // get extra options
- ROOT::Math::IOptions * fopt = opt.ExtraOptions();
- if (fopt) {
- int nval = 0;
+ // get extra options
+ ROOT::Math::IOptions * fopt = opt.ExtraOptions();
+ if (fopt) {
+ int nval = 0;
double fval = 0;
if (fopt->GetIntValue("nCells", nval) ) fFoam->SetnCells(nval);
if (fopt->GetIntValue("nCell1D", nval) && NDim() ==1) fFoam->SetnCells(nval);
@@ -154,55 +154,55 @@ bool TFoamSampler::Init(const ROOT::Math::DistSamplerOptions & opt) {
fFoam->Initialize();
return true;
-
+
}
-void TFoamSampler::SetFunction(TF1 * pdf) {
- // set function from a TF1 pointer
+void TFoamSampler::SetFunction(TF1 * pdf) {
+ // set function from a TF1 pointer
SetFunction<TF1>(*pdf, pdf->GetNdim());
-}
+}
-void TFoamSampler::SetRandom(TRandom * r) {
+void TFoamSampler::SetRandom(TRandom * r) {
// set random generator (must be called before Init to have effect)
- fFoam->SetPseRan(r);
-}
+ fFoam->SetPseRan(r);
+}
-void TFoamSampler::SetSeed(unsigned int seed) {
+void TFoamSampler::SetSeed(unsigned int seed) {
// set random generator seed (must be called before Init to have effect)
TRandom * r = fFoam->GetPseRan();
if (r) r->SetSeed(seed);
-}
+}
-TRandom * TFoamSampler::GetRandom() {
- // get random generator used
- return fFoam->GetPseRan();
-}
+TRandom * TFoamSampler::GetRandom() {
+ // get random generator used
+ return fFoam->GetPseRan();
+}
-// double TFoamSampler::Sample1D() {
+// double TFoamSampler::Sample1D() {
// // sample 1D distributions
-// return (fDiscrete) ? (double) fFoam->SampleDiscr() : fFoam->Sample();
+// return (fDiscrete) ? (double) fFoam->SampleDiscr() : fFoam->Sample();
// }
-bool TFoamSampler::Sample(double * x) {
+bool TFoamSampler::Sample(double * x) {
// sample multi-dim distributions
fFoam->MakeEvent();
fFoam->GetMCvect(x);
- // adjust for the range
- for (unsigned int i = 0; i < NDim(); ++i)
+ // adjust for the range
+ for (unsigned int i = 0; i < NDim(); ++i)
x[i] = ( (FoamDistribution*)fFoamDist)->MinX(i) + ( ( (FoamDistribution*) fFoamDist)->DeltaX(i))*x[i];
- return true;
-}
+ return true;
+}
bool TFoamSampler::SampleBin(double prob, double & value, double *error) {
// sample a bin according to Poisson statistics
- TRandom * r = GetRandom();
- if (!r) return false;
- value = r->Poisson(prob);
+ TRandom * r = GetRandom();
+ if (!r) return false;
+ value = r->Poisson(prob);
if (error) *error = std::sqrt(value);
- return true;
+ return true;
}
diff --git a/math/mathmore/inc/Math/GSLNLSMinimizer.h b/math/mathmore/inc/Math/GSLNLSMinimizer.h
index 6e5abb1d24f..9fa2ed2ff8e 100644
--- a/math/mathmore/inc/Math/GSLNLSMinimizer.h
+++ b/math/mathmore/inc/Math/GSLNLSMinimizer.h
@@ -55,88 +55,88 @@
#include <map>
#include <string>
-namespace ROOT {
+namespace ROOT {
- namespace Math {
+ namespace Math {
- class GSLMultiFit;
+ class GSLMultiFit;
//________________________________________________________________________________
-/**
- LSResidualFunc class description.
+/**
+ LSResidualFunc class description.
Internal class used for accessing the residuals of the Least Square function
- and their derivates which are estimated numerically using GSL numerical derivation.
- The class contains a pointer to the fit method function and an index specifying
+ and their derivates which are estimated numerically using GSL numerical derivation.
+ The class contains a pointer to the fit method function and an index specifying
the i-th residual and wraps it in a multi-dim gradient function interface
- ROOT::Math::IGradientFunctionMultiDim.
+ ROOT::Math::IGradientFunctionMultiDim.
The class is used by ROOT::Math::GSLNLSMinimizer (GSL non linear least square fitter)
@ingroup MultiMin
*/
-class LSResidualFunc : public IMultiGradFunction {
-public:
+class LSResidualFunc : public IMultiGradFunction {
+public:
- //default ctor (required by CINT)
+ //default ctor (required by CINT)
LSResidualFunc() : fIndex(0), fChi2(0)
{}
- LSResidualFunc(const ROOT::Math::FitMethodFunction & func, unsigned int i) :
- fIndex(i),
- fChi2(&func),
+ LSResidualFunc(const ROOT::Math::FitMethodFunction & func, unsigned int i) :
+ fIndex(i),
+ fChi2(&func),
fX2(std::vector<double>(func.NDim() ) )
{}
// copy ctor
LSResidualFunc(const LSResidualFunc & rhs) :
- IMultiGenFunction(),
- IMultiGradFunction()
- {
+ IMultiGenFunction(),
+ IMultiGradFunction()
+ {
operator=(rhs);
- }
+ }
// assignment
- LSResidualFunc & operator= (const LSResidualFunc & rhs)
- {
+ LSResidualFunc & operator= (const LSResidualFunc & rhs)
+ {
fIndex = rhs.fIndex;
- fChi2 = rhs.fChi2;
+ fChi2 = rhs.fChi2;
fX2 = rhs.fX2;
return *this;
- }
+ }
- IMultiGenFunction * Clone() const {
- return new LSResidualFunc(*fChi2,fIndex);
+ IMultiGenFunction * Clone() const {
+ return new LSResidualFunc(*fChi2,fIndex);
}
unsigned int NDim() const { return fChi2->NDim(); }
- void Gradient( const double * x, double * g) const {
- double f0 = 0;
+ void Gradient( const double * x, double * g) const {
+ double f0 = 0;
FdF(x,f0,g);
}
- void FdF (const double * x, double & f, double * g) const {
- unsigned int n = NDim();
+ void FdF (const double * x, double & f, double * g) const {
+ unsigned int n = NDim();
std::copy(x,x+n,fX2.begin());
const double kEps = 1.0E-4;
- f = DoEval(x);
- for (unsigned int i = 0; i < n; ++i) {
+ f = DoEval(x);
+ for (unsigned int i = 0; i < n; ++i) {
fX2[i] += kEps;
g[i] = ( DoEval(&fX2.front()) - f )/kEps;
fX2[i] = x[i];
}
- }
-
+ }
-private:
- double DoEval (const double * x) const {
+private:
+
+ double DoEval (const double * x) const {
return fChi2->DataElement(x, fIndex);
}
-
- double DoDerivative(const double * x, unsigned int icoord) const {
+
+ double DoDerivative(const double * x, unsigned int icoord) const {
//return ROOT::Math::Derivator::Eval(*this, x, icoord, 1E-8);
std::copy(x,x+NDim(),fX2.begin());
const double kEps = 1.0E-4;
@@ -144,117 +144,114 @@ private:
return ( DoEval(&fX2.front()) - DoEval(x) )/kEps;
}
- unsigned int fIndex;
- const ROOT::Math::FitMethodFunction * fChi2;
+ unsigned int fIndex;
+ const ROOT::Math::FitMethodFunction * fChi2;
mutable std::vector<double> fX2; // cached vector
};
//_____________________________________________________________________________________________________
-/**
+/**
GSLNLSMinimizer class for Non Linear Least Square fitting
- It Uses the Levemberg-Marquardt algorithm from
+ It Uses the Levemberg-Marquardt algorithm from
<A HREF="http://www.gnu.org/software/gsl/manual/html_node/Nonlinear-Least_002dSquares-Fitting.html">
GSL Non Linear Least Square fitting</A>.
@ingroup MultiMin
-*/
+*/
class GSLNLSMinimizer : public ROOT::Math::BasicMinimizer {
-public:
+public:
- /**
+ /**
Default constructor
- */
- GSLNLSMinimizer (int type = 0);
+ */
+ GSLNLSMinimizer (int type = 0);
- /**
+ /**
Destructor (no operations)
- */
- ~GSLNLSMinimizer ();
+ */
+ ~GSLNLSMinimizer ();
private:
// usually copying is non trivial, so we make this unaccessible
- /**
+ /**
Copy constructor
- */
- GSLNLSMinimizer(const GSLNLSMinimizer &) : ROOT::Math::BasicMinimizer() {}
+ */
+ GSLNLSMinimizer(const GSLNLSMinimizer &) : ROOT::Math::BasicMinimizer() {}
- /**
+ /**
Assignment operator
- */
+ */
GSLNLSMinimizer & operator = (const GSLNLSMinimizer & rhs) {
if (this == &rhs) return *this; // time saving self-test
return *this;
}
-public:
+public:
/// set the function to minimize
- virtual void SetFunction(const ROOT::Math::IMultiGenFunction & func);
+ virtual void SetFunction(const ROOT::Math::IMultiGenFunction & func);
/// set gradient the function to minimize
- virtual void SetFunction(const ROOT::Math::IMultiGradFunction & func);
+ virtual void SetFunction(const ROOT::Math::IMultiGradFunction & func);
+
-
/// method to perform the minimization
- virtual bool Minimize();
+ virtual bool Minimize();
/// return expected distance reached from the minimum
virtual double Edm() const { return fEdm; } // not impl. }
- /// return pointer to gradient values at the minimum
- virtual const double * MinGradient() const;
+ /// return pointer to gradient values at the minimum
+ virtual const double * MinGradient() const;
- /// number of function calls to reach the minimum
- virtual unsigned int NCalls() const { return (fChi2Func) ? fChi2Func->NCalls() : 0; }
+ /// number of function calls to reach the minimum
+ virtual unsigned int NCalls() const { return (fChi2Func) ? fChi2Func->NCalls() : 0; }
- /// number of free variables (real dimension of the problem)
- /// this is <= Function().NDim() which is the total
-// virtual unsigned int NFree() const { return fNFree; }
+ /// number of free variables (real dimension of the problem)
+ /// this is <= Function().NDim() which is the total
+// virtual unsigned int NFree() const { return fNFree; }
/// minimizer provides error and error matrix
- virtual bool ProvidesError() const { return true; }
+ virtual bool ProvidesError() const { return true; }
- /// return errors at the minimum
+ /// return errors at the minimum
virtual const double * Errors() const { return (fErrors.size() > 0) ? &fErrors.front() : 0; }
-// {
-// static std::vector<double> err;
+// {
+// static std::vector<double> err;
// err.resize(fDim);
-// return &err.front();
+// return &err.front();
// }
- /** return covariance matrices elements
+ /** return covariance matrices elements
if the variable is fixed the matrix is zero
The ordering of the variables is the same as in errors
- */
+ */
virtual double CovMatrix(unsigned int , unsigned int ) const;
/// return covariance matrix status
virtual int CovMatrixStatus() const;
-protected:
+protected:
-private:
-
+private:
- unsigned int fNFree; // dimension of the internal function to be minimized
+ unsigned int fNFree; // dimension of the internal function to be minimized
unsigned int fSize; // number of fit points (residuals)
-
- ROOT::Math::GSLMultiFit * fGSLMultiFit; // pointer to GSL multi fit solver
+ ROOT::Math::GSLMultiFit * fGSLMultiFit; // pointer to GSL multi fit solver
const ROOT::Math::FitMethodFunction * fChi2Func; // pointer to Least square function
-
- double fMinVal; // minimum function value
+
double fEdm; // edm value
double fLSTolerance; // Line Search Tolerance
- std::vector<double> fValues;
+ std::vector<double> fValues;
std::vector<double> fErrors;
- std::vector<double> fCovMatrix; // cov matrix (stored as cov[ i * dim + j]
+ std::vector<double> fCovMatrix; // cov matrix (stored as cov[ i * dim + j]
std::vector<double> fSteps;
std::vector<std::string> fNames;
std::vector<LSResidualFunc> fResiduals; //! transient Vector of the residual functions
@@ -263,7 +260,7 @@ private:
std::map< unsigned int, std::pair<double, double> > fBounds; // map specifying the bound using as key the parameter index
-};
+};
} // end namespace Math
diff --git a/math/minuit2/inc/Minuit2/FumiliStandardChi2FCN.h b/math/minuit2/inc/Minuit2/FumiliStandardChi2FCN.h
index 1909ff4f058..fbf7fb10d12 100644
--- a/math/minuit2/inc/Minuit2/FumiliStandardChi2FCN.h
+++ b/math/minuit2/inc/Minuit2/FumiliStandardChi2FCN.h
@@ -1,5 +1,5 @@
// @(#)root/minuit2:$Id$
-// Authors: M. Winkler, F. James, L. Moneta, A. Zsenei 2003-2005
+// Authors: M. Winkler, F. James, L. Moneta, A. Zsenei 2003-2005
/**********************************************************************
* *
@@ -10,7 +10,7 @@
#ifndef ROOT_Minuit2_FumiliStandardChi2FCN
#define ROOT_Minuit2_FumiliStandardChi2FCN
-
+
#include "Minuit2/FumiliChi2FCN.h"
#include "Minuit2/ParametricFunction.h"
#include <assert.h>
@@ -26,8 +26,8 @@ namespace ROOT {
/**
Class implementing the standard chi square function, which
-is the sum of the squares of the figures-of-merit calculated for each measurement
-point, the individual figures-of-merit being: (the Value predicted by the
+is the sum of the squares of the figures-of-merit calculated for each measurement
+point, the individual figures-of-merit being: (the Value predicted by the
model-measured Value)/standard deviation.
@author Andras Zsenei and Lorenzo Moneta, Creation date: 31 Aug 2004
@@ -46,9 +46,9 @@ public:
/**
-
+
Constructor which initializes chi square function for one-dimensional model function
-
+
@param modelFCN the model function used for describing the data.
@param meas vector containing the measured values.
@@ -56,7 +56,7 @@ public:
@param pos vector containing the x values corresponding to the
measurements
- @param mvar vector containing the variances corresponding to each
+ @param mvar vector containing the variances corresponding to each
measurement (where the variance equals the standard deviation squared).
If the variances are zero, a Value of 1 is used (as it is done in ROOT/PAW)
@@ -64,36 +64,36 @@ public:
FumiliStandardChi2FCN(const ParametricFunction& modelFCN, const std::vector<double>& meas,
const std::vector<double>& pos,
- const std::vector<double>& mvar) :
- fErrorDef(1.) { //this->fModelFCN = &modelFunction;
- this->SetModelFunction(modelFCN);
+ const std::vector<double>& mvar)
+ { //this->fModelFCN = &modelFunction;
+ this->SetModelFunction(modelFCN);
assert(meas.size() == pos.size());
assert(meas.size() == mvar.size());
fMeasurements = meas;
- std::vector<double> x(1);
- unsigned int n = mvar.size();
+ std::vector<double> x(1);
+ unsigned int n = mvar.size();
fPositions.reserve( n);
// correct for variance == 0
fInvErrors.resize(n);
- for (unsigned int i = 0; i < n; ++i)
- {
+ for (unsigned int i = 0; i < n; ++i)
+ {
x[0] = pos[i];
fPositions.push_back(x);
// PAW/ROOT hack : use 1 for 0 entries bins
- if (mvar[i] == 0)
- fInvErrors[i] = 1;
- else
- fInvErrors[i] = 1.0/std::sqrt(mvar[i]);
+ if (mvar[i] == 0)
+ fInvErrors[i] = 1;
+ else
+ fInvErrors[i] = 1.0/std::sqrt(mvar[i]);
}
}
/**
-
+
Constructor which initializes the multi-dimensional model function.
-
+
@param modelFCN the model function used for describing the data.
@param meas vector containing the measured values.
@@ -101,7 +101,7 @@ public:
@param pos vector containing the x values corresponding to the
measurements
- @param mvar vector containing the variances corresponding to each
+ @param mvar vector containing the variances corresponding to each
measurement (where the variance equals the standard deviation squared).
If the variances are zero, a Value of 1 is used (as it is done in ROOT/PAW)
@@ -109,24 +109,24 @@ public:
FumiliStandardChi2FCN(const ParametricFunction& modelFCN, const std::vector<double>& meas,
const std::vector<std::vector<double> >& pos,
- const std::vector<double>& mvar) :
- fErrorDef(1.) { //this->fModelFCN = &modelFunction;
- this->SetModelFunction(modelFCN);
+ const std::vector<double>& mvar)
+ { //this->fModelFCN = &modelFunction;
+ this->SetModelFunction(modelFCN);
assert(meas.size() == pos.size());
assert(meas.size() == mvar.size());
fMeasurements = meas;
fPositions = pos;
// correct for variance == 0
- unsigned int n = mvar.size();
+ unsigned int n = mvar.size();
fInvErrors.resize(n);
- for (unsigned int i = 0; i < n; ++i)
- {
+ for (unsigned int i = 0; i < n; ++i)
+ {
// PAW/ROOT hack : use 1 for 0 entries bins
- if (mvar[i] == 0)
- fInvErrors[i] = 1;
- else
- fInvErrors[i] = 1.0/std::sqrt(mvar[i]);
+ if (mvar[i] == 0)
+ fInvErrors[i] = 1;
+ else
+ fInvErrors[i] = 1.0/std::sqrt(mvar[i]);
}
}
@@ -142,16 +142,16 @@ public:
/**
- Evaluates the model function for the different measurement points and
+ Evaluates the model function for the different measurement points and
the Parameter values supplied, calculates a figure-of-merit for each
measurement and returns a vector containing the result of this
- evaluation. The figure-of-merit is (Value predicted by the model
+ evaluation. The figure-of-merit is (Value predicted by the model
function-measured Value)/standard deviation.
@param par vector of Parameter values to feed to the model function.
- @return A vector containing the figures-of-merit for the model function evaluated
- for each set of measurements.
+ @return A vector containing the figures-of-merit for the model function evaluated
+ for each set of measurements.
\todo What to do when the variances are 0???!! (right now just pushes back 0...)
@@ -176,7 +176,7 @@ public:
/**
- Accessor to the number of measurements used for calculating
+ Accessor to the number of measurements used for calculating
the chi-square.
@return the number of measurements.
@@ -187,16 +187,16 @@ public:
/**
-
+
Evaluate function Value, Gradient and Hessian using Fumili approximation, for values of parameters p
- The resul is cached inside and is return from the FumiliFCNBase::Value , FumiliFCNBase::Gradient and
- FumiliFCNBase::Hessian methods
+ The resul is cached inside and is return from the FumiliFCNBase::Value , FumiliFCNBase::Gradient and
+ FumiliFCNBase::Hessian methods
@param par vector of parameters
**/
- virtual void EvaluateAll( const std::vector<double> & par );
+ virtual void EvaluateAll( const std::vector<double> & par );
private:
@@ -206,10 +206,6 @@ public:
// support multi dim coordinates
std::vector<std::vector<double> > fPositions;
std::vector<double> fInvErrors;
- double fErrorDef;
-
-
-
};
} // namespace Minuit2
diff --git a/math/minuit2/inc/Minuit2/FumiliStandardMaximumLikelihoodFCN.h b/math/minuit2/inc/Minuit2/FumiliStandardMaximumLikelihoodFCN.h
index ab6ea60604c..4bd2c68373c 100644
--- a/math/minuit2/inc/Minuit2/FumiliStandardMaximumLikelihoodFCN.h
+++ b/math/minuit2/inc/Minuit2/FumiliStandardMaximumLikelihoodFCN.h
@@ -1,5 +1,5 @@
// @(#)root/minuit2:$Id$
-// Authors: M. Winkler, F. James, L. Moneta, A. Zsenei 2003-2005
+// Authors: M. Winkler, F. James, L. Moneta, A. Zsenei 2003-2005
/**********************************************************************
* *
@@ -24,7 +24,7 @@ namespace ROOT {
/**
-Class implementing the Elements member function for the standard
+Class implementing the Elements member function for the standard
maximum likelihood method.
@author Andras Zsenei and Lorenzo Moneta, Creation date: 4 Sep 2004
@@ -41,9 +41,9 @@ public:
/**
-
+
Constructor which initializes the measurement points for the one dimensional model function.
-
+
@param modelFCN the model function used for describing the data.
@param pos vector containing the x values corresponding to the
@@ -51,26 +51,25 @@ public:
*/
- FumiliStandardMaximumLikelihoodFCN(const ParametricFunction& modelFCN,
- const std::vector<double>& pos) :
- fErrorDef(0.5)
+ FumiliStandardMaximumLikelihoodFCN(const ParametricFunction& modelFCN,
+ const std::vector<double>& pos)
{
- this->SetModelFunction(modelFCN);
- unsigned int n = pos.size();
+ this->SetModelFunction(modelFCN);
+ unsigned int n = pos.size();
fPositions.reserve( n );
std::vector<double> x(1);
- for (unsigned int i = 0; i < n; ++i) {
+ for (unsigned int i = 0; i < n; ++i) {
x[0] = pos[i];
- fPositions.push_back(x);
+ fPositions.push_back(x);
}
}
/**
-
+
Constructor which initializes the measurement points for the multi dimensional model function.
-
+
@param modelFCN the model function used for describing the data.
@param pos vector containing the x values corresponding to the
@@ -78,10 +77,9 @@ public:
*/
- FumiliStandardMaximumLikelihoodFCN(const ParametricFunction& modelFCN,
- const std::vector<std::vector<double> >& pos) :
- fErrorDef(0.5) {
- this->SetModelFunction(modelFCN);
+ FumiliStandardMaximumLikelihoodFCN(const ParametricFunction& modelFCN,
+ const std::vector<std::vector<double> >& pos) {
+ this->SetModelFunction(modelFCN);
fPositions = pos;
}
@@ -96,13 +94,13 @@ public:
/**
- Evaluates the model function for the different measurement points and
+ Evaluates the model function for the different measurement points and
the Parameter values supplied.
@param par vector of Parameter values to feed to the model function.
- @return A vector containing the model function evaluated
- for each measurement point.
+ @return A vector containing the model function evaluated
+ for each measurement point.
*/
@@ -126,7 +124,7 @@ public:
/**
- Accessor to the number of measurements used for calculating
+ Accessor to the number of measurements used for calculating
the maximum likelihood.
@return the number of measurements.
@@ -136,10 +134,10 @@ public:
virtual int GetNumberOfMeasurements() const;
/**
-
+
Evaluate function Value, Gradient and Hessian using Fumili approximation, for values of parameters p
- The resul is cached inside and is return from the FumiliFCNBase::Value , FumiliFCNBase::Gradient and
- FumiliFCNBase::Hessian methods
+ The resul is cached inside and is return from the FumiliFCNBase::Value , FumiliFCNBase::Gradient and
+ FumiliFCNBase::Hessian methods
@param par vector of parameters
@@ -151,10 +149,6 @@ public:
private:
std::vector<std::vector<double> > fPositions;
- double fErrorDef;
-
-
-
};
} // namespace Minuit2
--
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