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File indexing completed on 2024-10-04 22:55:07

 /****************************************************************************
 *
 * This is a part of TOTEM ofFileline software.
 *
 * Based on TMultiDimFet.cc (from ROOT) by Christian Holm Christense.
 *
 * Authors:
 *   Hubert Niewiadomski
 *   Jan Kašpar (jan.kaspar@gmail.com)
 *
 ****************************************************************************/
 
 #include "Riostream.h"
 #include "SimTransport/TotemRPProtonTransportParametrization/interface/TMultiDimFet.h"
 #include "FWCore/MessageLogger/interface/MessageLogger.h"
 #include "TMath.h"
 #include "TH1.h"
 #include "TH2.h"
 #include "TROOT.h"
 #include "TDecompChol.h"
 #include "TDatime.h"
 #include <iostream>
 #include <map>
 
 #define RADDEG (180. / TMath::Pi())
 #define DEGRAD (TMath::Pi() / 180.)
 #define HIST_XORIG 0
 #define HIST_DORIG 1
 #define HIST_XNORM 2
 #define HIST_DSHIF 3
 #define HIST_RX 4
 #define HIST_RD 5
 #define HIST_RTRAI 6
 #define HIST_RTEST 7
 #define PARAM_MAXSTUDY 1
 #define PARAM_SEVERAL 2
 #define PARAM_RELERR 3
 #define PARAM_MAXTERMS 4
 
 //____________________________________________________________________
 //static void mdfHelper(int&, double*, double&, double*, int);
 
 //____________________________________________________________________
 ClassImp(TMultiDimFet);
 
 //____________________________________________________________________
 // Static instance. Used with mdfHelper and TMinuit
 //TMultiDimFet* TMultiDimFet::fgInstance = nullptr;
 
 //____________________________________________________________________
 TMultiDimFet::TMultiDimFet() {
   // Empty CTOR. Do not use
   fMeanQuantity = 0;
   fMaxQuantity = 0;
   fMinQuantity = 0;
   fSumSqQuantity = 0;
   fSumSqAvgQuantity = 0;
   fPowerLimit = 1;
 
   fMaxAngle = 0;
   fMinAngle = 1;
 
   fNVariables = 0;
   fMaxVariables = 0;
   fMinVariables = 0;
   fSampleSize = 0;
 
   fMaxAngle = 0;
   fMinAngle = 0;
 
   fPolyType = kMonomials;
   fShowCorrelation = kFALSE;
 
   fIsUserFunction = kFALSE;
 
   fHistograms = nullptr;
   fHistogramMask = 0;
 
   //fFitter                  = nullptr;
   //fgInstance               = nullptr;
 }
 
 const TMultiDimFet &TMultiDimFet::operator=(const TMultiDimFet &in) {
   if (this == &in) {
     return *this;
   }
 
   fMeanQuantity = in.fMeanQuantity;  // Mean of dependent quantity
 
   fMaxQuantity = 0.0;       //! Max value of dependent quantity
   fMinQuantity = 0.0;       //! Min value of dependent quantity
   fSumSqQuantity = 0.0;     //! SumSquare of dependent quantity
   fSumSqAvgQuantity = 0.0;  //! Sum of squares away from mean
 
   fNVariables = in.fNVariables;  // Number of independent variables
 
   fMaxVariables.ResizeTo(in.fMaxVariables.GetLwb(), in.fMaxVariables.GetUpb());
   fMaxVariables = in.fMaxVariables;  // max value of independent variables
 
   fMinVariables.ResizeTo(in.fMinVariables.GetLwb(), in.fMinVariables.GetUpb());
   fMinVariables = in.fMinVariables;  // min value of independent variables
 
   fSampleSize = 0;      //! Size of training sample
   fTestSampleSize = 0;  //! Size of test sample
   fMinAngle = 1;        //! Min angle for acepting new function
   fMaxAngle = 0.0;      //! Max angle for acepting new function
 
   fMaxTerms = in.fMaxTerms;  // Max terms expected in final expr.
 
   fMinRelativeError = 0.0;  //! Min relative error accepted
 
   fMaxPowers.clear();  //! [fNVariables] maximum powers
   fPowerLimit = 1;     //! Control parameter
 
   fMaxFunctions = in.fMaxFunctions;  // max number of functions
 
   fFunctionCodes.clear();  //! [fMaxFunctions] acceptance code
   fMaxStudy = 0;           //! max functions to study
 
   fMaxPowersFinal.clear();  //! [fNVariables] maximum powers from fit;
 
   fMaxFunctionsTimesNVariables = in.fMaxFunctionsTimesNVariables;  // fMaxFunctionsTimesNVariables
   fPowers = in.fPowers;
 
   fPowerIndex = in.fPowerIndex;  // [fMaxTerms] Index of accepted powers
 
   fMaxResidual = 0.0;    //! Max redsidual value
   fMinResidual = 0.0;    //! Min redsidual value
   fMaxResidualRow = 0;   //! Row giving max residual
   fMinResidualRow = 0;   //! Row giving min residual
   fSumSqResidual = 0.0;  //! Sum of Square residuals
 
   fNCoefficients = in.fNCoefficients;  // Dimension of model coefficients
 
   fCoefficients.ResizeTo(in.fCoefficients.GetLwb(), in.fCoefficients.GetUpb());
   fCoefficients = in.fCoefficients;  // Vector of the final coefficients
 
   fRMS = 0.0;                   //! Root mean square of fit
   fChi2 = 0.0;                  //! Chi square of fit
   fParameterisationCode = 0;    //! Exit code of parameterisation
   fError = 0.0;                 //! Error from parameterization
   fTestError = 0.0;             //! Error from test
   fPrecision = 0.0;             //! Relative precision of param
   fTestPrecision = 0.0;         //! Relative precision of test
   fCorrelationCoeff = 0.0;      //! Multi Correlation coefficient
   fTestCorrelationCoeff = 0.0;  //! Multi Correlation coefficient
   fHistograms = nullptr;        //! List of histograms
   fHistogramMask = 0;           //! Bit pattern of hisograms used
   //fFitter = nullptr;            //! Fit object (MINUIT)
 
   fPolyType = in.fPolyType;                // Type of polynomials to use
   fShowCorrelation = in.fShowCorrelation;  // print correlation matrix
   fIsUserFunction = in.fIsUserFunction;    // Flag for user defined function
   fIsVerbose = in.fIsVerbose;              //
   return *this;
 }
 
 //____________________________________________________________________
 TMultiDimFet::TMultiDimFet(Int_t dimension, EMDFPolyType type, Option_t *option)
     : TNamed("multidimfit", "Multi-dimensional fit object"),
       fQuantity(dimension),
       fSqError(dimension),
       fVariables(dimension * 100),
       fMeanVariables(dimension),
       fMaxVariables(dimension),
       fMinVariables(dimension) {
   // Constructor
   // Second argument is the type of polynomials to use in
   // parameterisation, one of:
   //      TMultiDimFet::kMonomials
   //      TMultiDimFet::kChebyshev
   //      TMultiDimFet::kLegendre
   //
   // Options:
   //   K      Compute (k)correlation matrix
   //   V      Be verbose
   //
   // Default is no options.
   //
 
   //fgInstance = this;
 
   fMeanQuantity = 0;
   fMaxQuantity = 0;
   fMinQuantity = 0;
   fSumSqQuantity = 0;
   fSumSqAvgQuantity = 0;
   fPowerLimit = 1;
 
   fMaxAngle = 0;
   fMinAngle = 1;
 
   fNVariables = dimension;
   fMaxVariables = 0;
   fMaxFunctionsTimesNVariables = fMaxFunctions * fNVariables;
   fMinVariables = 0;
   fSampleSize = 0;
   fTestSampleSize = 0;
   fMinRelativeError = 0.01;
   fError = 0;
   fTestError = 0;
   fPrecision = 0;
   fTestPrecision = 0;
   fParameterisationCode = 0;
 
   fPolyType = type;
   fShowCorrelation = kFALSE;
   fIsVerbose = kFALSE;
 
   TString opt = option;
   opt.ToLower();
 
   if (opt.Contains("k"))
     fShowCorrelation = kTRUE;
   if (opt.Contains("v"))
     fIsVerbose = kTRUE;
 
   fIsUserFunction = kFALSE;
 
   fHistograms = nullptr;
   fHistogramMask = 0;
 
   fMaxPowers.resize(dimension);
   fMaxPowersFinal.resize(dimension);
   //fFitter                 = nullptr;
 }
 
 //____________________________________________________________________
 TMultiDimFet::~TMultiDimFet() {
   if (fHistograms)
     fHistograms->Clear("nodelete");
   delete fHistograms;
 }
 
 //____________________________________________________________________
 void TMultiDimFet::AddRow(const Double_t *x, Double_t D, Double_t E) {
   // Add a row consisting of fNVariables independent variables, the
   // known, dependent quantity, and optionally, the square error in
   // the dependent quantity, to the training sample to be used for the
   // parameterization.
   // The mean of the variables and quantity is calculated on the fly,
   // as outlined in TPrincipal::AddRow.
   // This sample should be representive of the problem at hand.
   // Please note, that if no error is given Poisson statistics is
   // assumed and the square error is set to the value of dependent
   // quantity.  See also the
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   if (!x)
     return;
 
   if (++fSampleSize == 1) {
     fMeanQuantity = D;
     fMaxQuantity = D;
     fMinQuantity = D;
   } else {
     fMeanQuantity *= 1 - 1. / Double_t(fSampleSize);
     fMeanQuantity += D / Double_t(fSampleSize);
     fSumSqQuantity += D * D;
 
     if (D >= fMaxQuantity)
       fMaxQuantity = D;
     if (D <= fMinQuantity)
       fMinQuantity = D;
   }
 
   // If the vector isn't big enough to hold the new data, then
   // expand the vector by half it's size.
   Int_t size = fQuantity.GetNrows();
   if (fSampleSize > size) {
     fQuantity.ResizeTo(size + size / 2);
     fSqError.ResizeTo(size + size / 2);
   }
 
   // Store the value
   fQuantity(fSampleSize - 1) = D;
   fSqError(fSampleSize - 1) = (E == 0 ? D : E);
 
   // Store data point in internal vector
   // If the vector isn't big enough to hold the new data, then
   // expand the vector by half it's size
   size = fVariables.GetNrows();
   if (fSampleSize * fNVariables > size)
     fVariables.ResizeTo(size + size / 2);
 
   // Increment the data point counter
   Int_t i, j;
   for (i = 0; i < fNVariables; i++) {
     if (fSampleSize == 1) {
       fMeanVariables(i) = x[i];
       fMaxVariables(i) = x[i];
       fMinVariables(i) = x[i];
     } else {
       fMeanVariables(i) *= 1 - 1. / Double_t(fSampleSize);
       fMeanVariables(i) += x[i] / Double_t(fSampleSize);
 
       // Update the maximum value for this component
       if (x[i] >= fMaxVariables(i))
         fMaxVariables(i) = x[i];
 
       // Update the minimum value for this component
       if (x[i] <= fMinVariables(i))
         fMinVariables(i) = x[i];
     }
 
     // Store the data.
     j = (fSampleSize - 1) * fNVariables + i;
     fVariables(j) = x[i];
   }
 }
 
 //____________________________________________________________________
 void TMultiDimFet::AddTestRow(const Double_t *x, Double_t D, Double_t E) {
   // Add a row consisting of fNVariables independent variables, the
   // known, dependent quantity, and optionally, the square error in
   // the dependent quantity, to the test sample to be used for the
   // test of the parameterization.
   // This sample needn't be representive of the problem at hand.
   // Please note, that if no error is given Poisson statistics is
   // assumed and the square error is set to the value of dependent
   // quantity.  See also the
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   if (fTestSampleSize++ == 0) {
     fTestQuantity.ResizeTo(fNVariables);
     fTestSqError.ResizeTo(fNVariables);
     fTestVariables.ResizeTo(fNVariables * 100);
   }
 
   // If the vector isn't big enough to hold the new data, then
   // expand the vector by half it's size.
   Int_t size = fTestQuantity.GetNrows();
   if (fTestSampleSize > size) {
     fTestQuantity.ResizeTo(size + size / 2);
     fTestSqError.ResizeTo(size + size / 2);
   }
 
   // Store the value
   fTestQuantity(fTestSampleSize - 1) = D;
   fTestSqError(fTestSampleSize - 1) = (E == 0 ? D : E);
 
   // Store data point in internal vector
   // If the vector isn't big enough to hold the new data, then
   // expand the vector by half it's size
   size = fTestVariables.GetNrows();
   if (fTestSampleSize * fNVariables > size)
     fTestVariables.ResizeTo(size + size / 2);
 
   // Increment the data point counter
   Int_t i, j;
   for (i = 0; i < fNVariables; i++) {
     j = fNVariables * (fTestSampleSize - 1) + i;
     fTestVariables(j) = x[i];
 
     if (x[i] > fMaxVariables(i))
       Warning("AddTestRow", "variable %d (row: %d) too large: %f > %f", i, fTestSampleSize, x[i], fMaxVariables(i));
     if (x[i] < fMinVariables(i))
       Warning("AddTestRow", "variable %d (row: %d) too small: %f < %f", i, fTestSampleSize, x[i], fMinVariables(i));
   }
 }
 
 //____________________________________________________________________
 void TMultiDimFet::Clear(Option_t *option) {
   // Clear internal structures and variables
   Int_t i, j, n = fNVariables, m = fMaxFunctions;
 
   // Training sample, dependent quantity
   fQuantity.Zero();
   fSqError.Zero();
   fMeanQuantity = 0;
   fMaxQuantity = 0;
   fMinQuantity = 0;
   fSumSqQuantity = 0;
   fSumSqAvgQuantity = 0;
 
   // Training sample, independent variables
   fVariables.Zero();
   fNVariables = 0;
   fSampleSize = 0;
   fMeanVariables.Zero();
   fMaxVariables.Zero();
   fMinVariables.Zero();
 
   // Test sample
   fTestQuantity.Zero();
   fTestSqError.Zero();
   fTestVariables.Zero();
   fTestSampleSize = 0;
 
   // Functions
   fFunctions.Zero();
   fMaxFunctions = 0;
   fMaxStudy = 0;
   fMaxFunctionsTimesNVariables = 0;
   fOrthFunctions.Zero();
   fOrthFunctionNorms.Zero();
 
   // Control parameters
   fMinRelativeError = 0;
   fMinAngle = 0;
   fMaxAngle = 0;
   fMaxTerms = 0;
 
   // Powers
   for (i = 0; i < n; i++) {
     fMaxPowers[i] = 0;
     fMaxPowersFinal[i] = 0;
     for (j = 0; j < m; j++)
       fPowers[i * n + j] = 0;
   }
   fPowerLimit = 0;
 
   // Residuals
   fMaxResidual = 0;
   fMinResidual = 0;
   fMaxResidualRow = 0;
   fMinResidualRow = 0;
   fSumSqResidual = 0;
 
   // Fit
   fNCoefficients = 0;
   fOrthCoefficients = 0;
   fOrthCurvatureMatrix = 0;
   fRMS = 0;
   fCorrelationMatrix.Zero();
   fError = 0;
   fTestError = 0;
   fPrecision = 0;
   fTestPrecision = 0;
 
   // Coefficients
   fCoefficients.Zero();
   fCoefficientsRMS.Zero();
   fResiduals.Zero();
   fHistograms->Clear(option);
 
   // Options
   fPolyType = kMonomials;
   fShowCorrelation = kFALSE;
   fIsUserFunction = kFALSE;
 }
 
 //____________________________________________________________________
 Double_t TMultiDimFet::Eval(const Double_t *x, const Double_t *coeff) const {
   // Evaluate parameterization at point x. Optional argument coeff is
   // a vector of coefficients for the parameterisation, fNCoefficients
   // elements long.
   //   int fMaxFunctionsTimesNVariables = fMaxFunctions * fNVariables;
   Double_t returnValue = fMeanQuantity;
   Double_t term = 0;
   Int_t i, j;
 
   for (i = 0; i < fNCoefficients; i++) {
     // Evaluate the ith term in the expansion
     term = (coeff ? coeff[i] : fCoefficients(i));
     for (j = 0; j < fNVariables; j++) {
       // Evaluate the factor (polynomial) in the j-th variable.
       Int_t p = fPowers[fPowerIndex[i] * fNVariables + j];
       Double_t y = 1 + 2. / (fMaxVariables(j) - fMinVariables(j)) * (x[j] - fMaxVariables(j));
       term *= EvalFactor(p, y);
     }
     // Add this term to the final result
     returnValue += term;
   }
   return returnValue;
 }
 
 void TMultiDimFet::ReducePolynomial(double error) {
   if (error == 0.0)
     return;
   else {
     ZeroDoubiousCoefficients(error);
   }
 }
 
 void TMultiDimFet::ZeroDoubiousCoefficients(double error) {
   typedef std::multimap<double, int> cmt;
   cmt m;
 
   for (int i = 0; i < fNCoefficients; i++) {
     m.insert(std::pair<double, int>(TMath::Abs(fCoefficients(i)), i));
   }
 
   double del_error_abs = 0;
   int deleted_terms_count = 0;
 
   for (cmt::iterator it = m.begin(); it != m.end() && del_error_abs < error; ++it) {
     if (TMath::Abs(it->first) + del_error_abs < error) {
       fCoefficients(it->second) = 0.0;
       del_error_abs = TMath::Abs(it->first) + del_error_abs;
       deleted_terms_count++;
     } else
       break;
   }
 
   int fNCoefficients_new = fNCoefficients - deleted_terms_count;
   TVectorD fCoefficients_new(fNCoefficients_new);
   std::vector<Int_t> fPowerIndex_new;
 
   int ind = 0;
   for (int i = 0; i < fNCoefficients; i++) {
     if (fCoefficients(i) != 0.0) {
       fCoefficients_new(ind) = fCoefficients(i);
       fPowerIndex_new.push_back(fPowerIndex[i]);
       ind++;
     }
   }
   fNCoefficients = fNCoefficients_new;
   fCoefficients.ResizeTo(fNCoefficients);
   fCoefficients = fCoefficients_new;
   fPowerIndex = fPowerIndex_new;
   edm::LogInfo("TMultiDimFet") << deleted_terms_count << " terms removed"
                                << "\n";
 }
 
 //____________________________________________________________________
 Double_t TMultiDimFet::EvalControl(const Int_t *iv) {
   // PRIVATE METHOD:
   // Calculate the control parameter from the passed powers
   Double_t s = 0;
   Double_t epsilon = 1e-6;  // a small number
   for (Int_t i = 0; i < fNVariables; i++) {
     if (fMaxPowers[i] != 1)
       s += (epsilon + iv[i] - 1) / (epsilon + fMaxPowers[i] - 1);
   }
   return s;
 }
 
 //____________________________________________________________________
 Double_t TMultiDimFet::EvalFactor(Int_t p, Double_t x) const {
   // PRIVATE METHOD:
   // Evaluate function with power p at variable value x
   Int_t i = 0;
   Double_t p1 = 1;
   Double_t p2 = 0;
   Double_t p3 = 0;
   Double_t r = 0;
 
   switch (p) {
     case 1:
       r = 1;
       break;
     case 2:
       r = x;
       break;
     default:
       p2 = x;
       for (i = 3; i <= p; i++) {
         p3 = p2 * x;
         if (fPolyType == kLegendre)
           p3 = ((2 * i - 3) * p2 * x - (i - 2) * p1) / (i - 1);
         else if (fPolyType == kChebyshev)
           p3 = 2 * x * p2 - p1;
         p1 = p2;
         p2 = p3;
       }
       r = p3;
   }
 
   return r;
 }
 
 //____________________________________________________________________
 void TMultiDimFet::FindParameterization(double precision) {
   // Find the parameterization
   //
   // Options:
   //     None so far
   //
   // For detailed description of what this entails, please refer to the
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   MakeNormalized();
   MakeCandidates();
   MakeParameterization();
   MakeCoefficients();
   ReducePolynomial(precision);
 }
 
 //____________________________________________________________________
 /*
 void TMultiDimFet::Fit(Option_t *option)
 {
    // Try to fit the found parameterisation to the test sample.
    //
    // Options
    //     M     use Minuit to improve coefficients
    //
    // Also, refer to
    // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
 
    //fgInstance = this;
 
    Int_t i, j;
    Double_t*      x    = new Double_t[fNVariables];
    Double_t  sumSqD    = 0;
    Double_t    sumD    = 0;
    Double_t  sumSqR    = 0;
    Double_t    sumR    = 0;
 
    // Calculate the residuals over the test sample
    for (i = 0; i < fTestSampleSize; i++) {
       for (j = 0; j < fNVariables; j++)
          x[j] = fTestVariables(i * fNVariables + j);
       Double_t res =  fTestQuantity(i) - Eval(x);
       sumD         += fTestQuantity(i);
       sumSqD       += fTestQuantity(i) * fTestQuantity(i);
       sumR         += res;
       sumSqR       += res * res;
       if (TESTBIT(fHistogramMask,HIST_RTEST))
          ((TH1D*)fHistograms->FindObject("res_test"))->Fill(res);
    }
    Double_t dAvg         = sumSqD - (sumD * sumD) / fTestSampleSize;
    Double_t rAvg         = sumSqR - (sumR * sumR) / fTestSampleSize;
    fTestCorrelationCoeff = (dAvg - rAvg) / dAvg;
    fTestError            = sumSqR;
    fTestPrecision        = sumSqR / sumSqD;
 
    TString opt(option);
    opt.ToLower();
 
    if (!opt.Contains("m"))
       MakeChi2();
 
    if (fNCoefficients * 50 > fTestSampleSize)
       Warning("Fit", "test sample is very small");
 
    if (!opt.Contains("m"))
       return;
 
    fFitter = TVirtualFitter::Fitter(nullptr,fNCoefficients);
    fFitter->SetFCN(mdfHelper);
 
    const Int_t  maxArgs = 16;
    Int_t           args = 1;
    Double_t*   arglist  = new Double_t[maxArgs];
    arglist[0]           = -1;
    fFitter->ExecuteCommand("SET PRINT",arglist,args);
 
    for (i = 0; i < fNCoefficients; i++) {
       Double_t startVal = fCoefficients(i);
       Double_t startErr = fCoefficientsRMS(i);
       fFitter->SetParameter(i, Form("coeff%02d",i),
          startVal, startErr, 0, 0);
    }
 
    args                 = 1;
    fFitter->ExecuteCommand("MIGRAD",arglist,args);
 
    for (i = 0; i < fNCoefficients; i++) {
       Double_t val = 0, err = 0, low = 0, high = 0;
       fFitter->GetParameter(i, Form("coeff%02d",i),
          val, err, low, high);
       fCoefficients(i)    = val;
       fCoefficientsRMS(i) = err;
    }
 }
 */
 
 //____________________________________________________________________
 void TMultiDimFet::MakeCandidates() {
   // PRIVATE METHOD:
   // Create list of candidate functions for the parameterisation. See
   // also
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   Int_t i = 0;
   Int_t j = 0;
   Int_t k = 0;
 
   // The temporary array to store the powers in. We don't need to
   // initialize this array however.
   assert(fNVariables > 0);
   Int_t *powers = new Int_t[fNVariables * fMaxFunctions];
 
   // store of `control variables'
   Double_t *control = new Double_t[fMaxFunctions];
 
   // We've better initialize the variables
   Int_t *iv = new Int_t[fNVariables];
   for (i = 0; i < fNVariables; i++)
     iv[i] = 1;
 
   if (!fIsUserFunction) {
     // Number of funcs selected
     Int_t numberFunctions = 0;
 
     while (kTRUE) {
       // Get the control value for this function
       Double_t s = EvalControl(iv);
 
       if (s <= fPowerLimit) {
         // Call over-loadable method Select, as to allow the user to
         // interfere with the selection of functions.
         if (Select(iv)) {
           numberFunctions++;
 
           // If we've reached the user defined limit of how many
           // functions we can consider, break out of the loop
           if (numberFunctions > fMaxFunctions)
             break;
 
           // Store the control value, so we can sort array of powers
           // later on
           control[numberFunctions - 1] = Int_t(1.0e+6 * s);
 
           // Store the powers in powers array.
           for (i = 0; i < fNVariables; i++) {
             j = (numberFunctions - 1) * fNVariables + i;
             powers[j] = iv[i];
           }
         }  // if (Select())
       }  // if (s <= fPowerLimit)
 
       for (i = 0; i < fNVariables; i++)
         if (iv[i] < fMaxPowers[i])
           break;
 
       // If all variables have reached their maximum power, then we
       // break out of the loop
       if (i == fNVariables) {
         fMaxFunctions = numberFunctions;
         fMaxFunctionsTimesNVariables = fMaxFunctions * fNVariables;
         break;
       }
 
       // Next power in variable i
       iv[i]++;
 
       for (j = 0; j < i; j++)
         iv[j] = 1;
     }  // while (kTRUE)
   } else {
     // In case the user gave an explicit function
     for (i = 0; i < fMaxFunctions; i++) {
       // Copy the powers to working arrays
       for (j = 0; j < fNVariables; j++) {
         powers[i * fNVariables + j] = fPowers[i * fNVariables + j];
         iv[j] = fPowers[i * fNVariables + j];
       }
 
       control[i] = Int_t(1.0e+6 * EvalControl(iv));
     }
   }
 
   // Now we need to sort the powers according to least `control
   // variable'
   Int_t *order = new Int_t[fMaxFunctions];
   for (i = 0; i < fMaxFunctions; i++)
     order[i] = i;
   fPowers.resize(fMaxFunctions * fNVariables);
 
   for (i = 0; i < fMaxFunctions; i++) {
     Double_t x = control[i];
     Int_t l = order[i];
     k = i;
 
     for (j = i; j < fMaxFunctions; j++) {
       if (control[j] <= x) {
         x = control[j];
         l = order[j];
         k = j;
       }
     }
 
     if (k != i) {
       control[k] = control[i];
       control[i] = x;
       order[k] = order[i];
       order[i] = l;
     }
   }
 
   for (i = 0; i < fMaxFunctions; i++)
     for (j = 0; j < fNVariables; j++)
       fPowers[i * fNVariables + j] = powers[order[i] * fNVariables + j];
 
   delete[] control;
   delete[] powers;
   delete[] order;
   delete[] iv;
 }
 
 Double_t TMultiDimFet::MakeChi2(const Double_t *coeff) {
   // Calculate Chi square over either the test sample. The optional
   // argument coeff is a vector of coefficients to use in the
   // evaluation of the parameterisation. If coeff == 0, then the found
   // coefficients is used.
   // Used my MINUIT for fit (see TMultDimFit::Fit)
   fChi2 = 0;
   Int_t i, j;
   Double_t *x = new Double_t[fNVariables];
   for (i = 0; i < fTestSampleSize; i++) {
     // Get the stored point
     for (j = 0; j < fNVariables; j++)
       x[j] = fTestVariables(i * fNVariables + j);
 
     // Evaluate function. Scale to shifted values
     Double_t f = Eval(x, coeff);
 
     // Calculate contribution to Chic square
     fChi2 += 1. / TMath::Max(fTestSqError(i), 1e-20) * (fTestQuantity(i) - f) * (fTestQuantity(i) - f);
   }
 
   // Clean up
   delete[] x;
 
   return fChi2;
 }
 
 //____________________________________________________________________
 void TMultiDimFet::MakeCode(const char *filename, Option_t *option) {
   // Generate the file <filename> with .C appended if argument doesn't
   // end in .cxx or .C. The contains the implementation of the
   // function:
   //
   //   Double_t <funcname>(Double_t *x)
   //
   // which does the same as TMultiDimFet::Eval. Please refer to this
   // method.
   //
   // Further, the static variables:
   //
   //     Int_t    gNVariables
   //     Int_t    gNCoefficients
   //     Double_t gDMean
   //     Double_t gXMean[]
   //     Double_t gXMin[]
   //     Double_t gXMax[]
   //     Double_t gCoefficient[]
   //     Int_t    gPower[]
   //
   // are initialized. The only ROOT header file needed is Rtypes.h
   //
   // See TMultiDimFet::MakeRealCode for a list of options
 
   TString outName(filename);
   if (!outName.EndsWith(".C") && !outName.EndsWith(".cxx"))
     outName += ".C";
 
   MakeRealCode(outName.Data(), "", option);
 }
 
 void TMultiDimFet::MakeCoefficientErrors() {
   // PRIVATE METHOD:
   // Compute the errors on the coefficients. For this to be done, the
   // curvature matrix of the non-orthogonal functions, is computed.
   Int_t i = 0;
   Int_t j = 0;
   Int_t k = 0;
   TVectorD iF(fSampleSize);
   TVectorD jF(fSampleSize);
   fCoefficientsRMS.ResizeTo(fNCoefficients);
 
   TMatrixDSym curvatureMatrix(fNCoefficients);
 
   // Build the curvature matrix
   for (i = 0; i < fNCoefficients; i++) {
     iF = TMatrixDRow(fFunctions, i);
     for (j = 0; j <= i; j++) {
       jF = TMatrixDRow(fFunctions, j);
       for (k = 0; k < fSampleSize; k++)
         curvatureMatrix(i, j) += 1 / TMath::Max(fSqError(k), 1e-20) * iF(k) * jF(k);
       curvatureMatrix(j, i) = curvatureMatrix(i, j);
     }
   }
 
   // Calculate Chi Square
   fChi2 = 0;
   for (i = 0; i < fSampleSize; i++) {
     Double_t f = 0;
     for (j = 0; j < fNCoefficients; j++)
       f += fCoefficients(j) * fFunctions(j, i);
     fChi2 += 1. / TMath::Max(fSqError(i), 1e-20) * (fQuantity(i) - f) * (fQuantity(i) - f);
   }
 
   // Invert the curvature matrix
   const TVectorD diag = TMatrixDDiag_const(curvatureMatrix);
   curvatureMatrix.NormByDiag(diag);
 
   TDecompChol chol(curvatureMatrix);
   if (!chol.Decompose())
     Error("MakeCoefficientErrors", "curvature matrix is singular");
   chol.Invert(curvatureMatrix);
 
   curvatureMatrix.NormByDiag(diag);
 
   for (i = 0; i < fNCoefficients; i++)
     fCoefficientsRMS(i) = TMath::Sqrt(curvatureMatrix(i, i));
 }
 
 //____________________________________________________________________
 void TMultiDimFet::MakeCoefficients() {
   // PRIVATE METHOD:
   // Invert the model matrix B, and compute final coefficients. For a
   // more thorough discussion of what this means, please refer to the
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   //
   // First we invert the lower triangle matrix fOrthCurvatureMatrix
   // and store the inverted matrix in the upper triangle.
 
   Int_t i = 0, j = 0;
   Int_t col = 0, row = 0;
 
   // Invert the B matrix
   for (col = 1; col < fNCoefficients; col++) {
     for (row = col - 1; row > -1; row--) {
       fOrthCurvatureMatrix(row, col) = 0;
       for (i = row; i <= col; i++)
         fOrthCurvatureMatrix(row, col) -= fOrthCurvatureMatrix(i, row) * fOrthCurvatureMatrix(i, col);
     }
   }
 
   // Compute the final coefficients
   fCoefficients.ResizeTo(fNCoefficients);
 
   for (i = 0; i < fNCoefficients; i++) {
     Double_t sum = 0;
     for (j = i; j < fNCoefficients; j++)
       sum += fOrthCurvatureMatrix(i, j) * fOrthCoefficients(j);
     fCoefficients(i) = sum;
   }
 
   // Compute the final residuals
   fResiduals.ResizeTo(fSampleSize);
   for (i = 0; i < fSampleSize; i++)
     fResiduals(i) = fQuantity(i);
 
   for (i = 0; i < fNCoefficients; i++)
     for (j = 0; j < fSampleSize; j++)
       fResiduals(j) -= fCoefficients(i) * fFunctions(i, j);
 
   // Compute the max and minimum, and squared sum of the evaluated
   // residuals
   fMinResidual = 10e10;
   fMaxResidual = -10e10;
   Double_t sqRes = 0;
   for (i = 0; i < fSampleSize; i++) {
     sqRes += fResiduals(i) * fResiduals(i);
     if (fResiduals(i) <= fMinResidual) {
       fMinResidual = fResiduals(i);
       fMinResidualRow = i;
     }
     if (fResiduals(i) >= fMaxResidual) {
       fMaxResidual = fResiduals(i);
       fMaxResidualRow = i;
     }
   }
 
   fCorrelationCoeff = fSumSqResidual / fSumSqAvgQuantity;
   fPrecision = TMath::Sqrt(sqRes / fSumSqQuantity);
 
   // If we use histograms, fill some more
   if (TESTBIT(fHistogramMask, HIST_RD) || TESTBIT(fHistogramMask, HIST_RTRAI) || TESTBIT(fHistogramMask, HIST_RX)) {
     for (i = 0; i < fSampleSize; i++) {
       if (TESTBIT(fHistogramMask, HIST_RD))
         ((TH2D *)fHistograms->FindObject("res_d"))->Fill(fQuantity(i), fResiduals(i));
       if (TESTBIT(fHistogramMask, HIST_RTRAI))
         ((TH1D *)fHistograms->FindObject("res_train"))->Fill(fResiduals(i));
 
       if (TESTBIT(fHistogramMask, HIST_RX))
         for (j = 0; j < fNVariables; j++)
           ((TH2D *)fHistograms->FindObject(Form("res_x_%d", j)))->Fill(fVariables(i * fNVariables + j), fResiduals(i));
     }
   }  // If histograms
 }
 
 //____________________________________________________________________
 void TMultiDimFet::MakeCorrelation() {
   // PRIVATE METHOD:
   // Compute the correlation matrix
   if (!fShowCorrelation)
     return;
 
   fCorrelationMatrix.ResizeTo(fNVariables, fNVariables + 1);
 
   Double_t d2 = 0;
   Double_t ddotXi = 0;   // G.Q. needs to be reinitialized in the loop over i fNVariables
   Double_t xiNorm = 0;   // G.Q. needs to be reinitialized in the loop over i fNVariables
   Double_t xidotXj = 0;  // G.Q. needs to be reinitialized in the loop over j fNVariables
   Double_t xjNorm = 0;   // G.Q. needs to be reinitialized in the loop over j fNVariables
 
   Int_t i, j, k, l, m;  // G.Q. added m variable
   for (i = 0; i < fSampleSize; i++)
     d2 += fQuantity(i) * fQuantity(i);
 
   for (i = 0; i < fNVariables; i++) {
     ddotXi = 0.;  // G.Q. reinitialisation
     xiNorm = 0.;  // G.Q. reinitialisation
     for (j = 0; j < fSampleSize; j++) {
       // Index of sample j of variable i
       k = j * fNVariables + i;
       ddotXi += fQuantity(j) * (fVariables(k) - fMeanVariables(i));
       xiNorm += (fVariables(k) - fMeanVariables(i)) * (fVariables(k) - fMeanVariables(i));
     }
     fCorrelationMatrix(i, 0) = ddotXi / TMath::Sqrt(d2 * xiNorm);
 
     for (j = 0; j < i; j++) {
       xidotXj = 0.;  // G.Q. reinitialisation
       xjNorm = 0.;   // G.Q. reinitialisation
       for (k = 0; k < fSampleSize; k++) {
         // Index of sample j of variable i
         // l =  j * fNVariables + k;  // G.Q.
         l = k * fNVariables + j;  // G.Q.
         m = k * fNVariables + i;  // G.Q.
         // G.Q.        xidotXj += (fVariables(i) - fMeanVariables(i))
         // G.Q.          * (fVariables(l) - fMeanVariables(j));
         xidotXj +=
             (fVariables(m) - fMeanVariables(i)) * (fVariables(l) - fMeanVariables(j));  // G.Q. modified index for Xi
         xjNorm += (fVariables(l) - fMeanVariables(j)) * (fVariables(l) - fMeanVariables(j));
       }
       fCorrelationMatrix(i, j + 1) = xidotXj / TMath::Sqrt(xiNorm * xjNorm);
     }
   }
 }
 
 //____________________________________________________________________
 Double_t TMultiDimFet::MakeGramSchmidt(Int_t function) {
   // PRIVATE METHOD:
   // Make Gram-Schmidt orthogonalisation. The class description gives
   // a thorough account of this algorithm, as well as
   // references. Please refer to the
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
 
   // calculate w_i, that is, evaluate the current function at data
   // point i
   Double_t f2 = 0;
   fOrthCoefficients(fNCoefficients) = 0;
   fOrthFunctionNorms(fNCoefficients) = 0;
   Int_t j = 0;
   Int_t k = 0;
 
   for (j = 0; j < fSampleSize; j++) {
     fFunctions(fNCoefficients, j) = 1;
     fOrthFunctions(fNCoefficients, j) = 0;
     // First, however, we need to calculate f_fNCoefficients
     for (k = 0; k < fNVariables; k++) {
       Int_t p = fPowers[function * fNVariables + k];
       Double_t x = fVariables(j * fNVariables + k);
       fFunctions(fNCoefficients, j) *= EvalFactor(p, x);
     }
 
     // Calculate f dot f in f2
     f2 += fFunctions(fNCoefficients, j) * fFunctions(fNCoefficients, j);
     // Assign to w_fNCoefficients f_fNCoefficients
     fOrthFunctions(fNCoefficients, j) = fFunctions(fNCoefficients, j);
   }
 
   // the first column of w is equal to f
   for (j = 0; j < fNCoefficients; j++) {
     Double_t fdw = 0;
     // Calculate (f_fNCoefficients dot w_j) / w_j^2
     for (k = 0; k < fSampleSize; k++) {
       fdw += fFunctions(fNCoefficients, k) * fOrthFunctions(j, k) / fOrthFunctionNorms(j);
     }
 
     fOrthCurvatureMatrix(fNCoefficients, j) = fdw;
     // and subtract it from the current value of w_ij
     for (k = 0; k < fSampleSize; k++)
       fOrthFunctions(fNCoefficients, k) -= fdw * fOrthFunctions(j, k);
   }
 
   for (j = 0; j < fSampleSize; j++) {
     // calculate squared length of w_fNCoefficients
     fOrthFunctionNorms(fNCoefficients) += fOrthFunctions(fNCoefficients, j) * fOrthFunctions(fNCoefficients, j);
 
     // calculate D dot w_fNCoefficients in A
     fOrthCoefficients(fNCoefficients) += fQuantity(j) * fOrthFunctions(fNCoefficients, j);
   }
 
   // First test, but only if didn't user specify
   if (!fIsUserFunction)
     if (TMath::Sqrt(fOrthFunctionNorms(fNCoefficients) / (f2 + 1e-10)) < TMath::Sin(fMinAngle * DEGRAD))
       return 0;
 
   // The result found by this code for the first residual is always
   // much less then the one found be MUDIFI. That's because it's
   // supposed to be. The cause is the improved precision of Double_t
   // over DOUBLE PRECISION!
   fOrthCurvatureMatrix(fNCoefficients, fNCoefficients) = 1;
   Double_t b = fOrthCoefficients(fNCoefficients);
   fOrthCoefficients(fNCoefficients) /= fOrthFunctionNorms(fNCoefficients);
 
   // Calculate the residual from including this fNCoefficients.
   Double_t dResidur = fOrthCoefficients(fNCoefficients) * b;
 
   return dResidur;
 }
 
 //____________________________________________________________________
 void TMultiDimFet::MakeHistograms(Option_t *option) {
   // Make histograms of the result of the analysis. This message
   // should be sent after having read all data points, but before
   // finding the parameterization
   //
   // Options:
   //     A         All the below
   //     X         Original independent variables
   //     D         Original dependent variables
   //     N         Normalised independent variables
   //     S         Shifted dependent variables
   //     R1        Residuals versus normalised independent variables
   //     R2        Residuals versus dependent variable
   //     R3        Residuals computed on training sample
   //     R4        Residuals computed on test sample
   //
   // For a description of these quantities, refer to
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   TString opt(option);
   opt.ToLower();
 
   if (opt.Length() < 1)
     return;
 
   if (!fHistograms)
     fHistograms = new TList;
 
   // Counter variable
   Int_t i = 0;
 
   // Histogram of original variables
   if (opt.Contains("x") || opt.Contains("a")) {
     SETBIT(fHistogramMask, HIST_XORIG);
     for (i = 0; i < fNVariables; i++)
       if (!fHistograms->FindObject(Form("x_%d_orig", i)))
         fHistograms->Add(
             new TH1D(Form("x_%d_orig", i), Form("Original variable # %d", i), 100, fMinVariables(i), fMaxVariables(i)));
   }
 
   // Histogram of original dependent variable
   if (opt.Contains("d") || opt.Contains("a")) {
     SETBIT(fHistogramMask, HIST_DORIG);
     if (!fHistograms->FindObject("d_orig"))
       fHistograms->Add(new TH1D("d_orig", "Original Quantity", 100, fMinQuantity, fMaxQuantity));
   }
 
   // Histograms of normalized variables
   if (opt.Contains("n") || opt.Contains("a")) {
     SETBIT(fHistogramMask, HIST_XNORM);
     for (i = 0; i < fNVariables; i++)
       if (!fHistograms->FindObject(Form("x_%d_norm", i)))
         fHistograms->Add(new TH1D(Form("x_%d_norm", i), Form("Normalized variable # %d", i), 100, -1, 1));
   }
 
   // Histogram of shifted dependent variable
   if (opt.Contains("s") || opt.Contains("a")) {
     SETBIT(fHistogramMask, HIST_DSHIF);
     if (!fHistograms->FindObject("d_shifted"))
       fHistograms->Add(
           new TH1D("d_shifted", "Shifted Quantity", 100, fMinQuantity - fMeanQuantity, fMaxQuantity - fMeanQuantity));
   }
 
   // Residual from training sample versus independent variables
   if (opt.Contains("r1") || opt.Contains("a")) {
     SETBIT(fHistogramMask, HIST_RX);
     for (i = 0; i < fNVariables; i++)
       if (!fHistograms->FindObject(Form("res_x_%d", i)))
         fHistograms->Add(new TH2D(Form("res_x_%d", i),
                                   Form("Computed residual versus x_%d", i),
                                   100,
                                   -1,
                                   1,
                                   35,
                                   fMinQuantity - fMeanQuantity,
                                   fMaxQuantity - fMeanQuantity));
   }
 
   // Residual from training sample versus. dependent variable
   if (opt.Contains("r2") || opt.Contains("a")) {
     SETBIT(fHistogramMask, HIST_RD);
     if (!fHistograms->FindObject("res_d"))
       fHistograms->Add(new TH2D("res_d",
                                 "Computed residuals vs Quantity",
                                 100,
                                 fMinQuantity - fMeanQuantity,
                                 fMaxQuantity - fMeanQuantity,
                                 35,
                                 fMinQuantity - fMeanQuantity,
                                 fMaxQuantity - fMeanQuantity));
   }
 
   // Residual from training sample
   if (opt.Contains("r3") || opt.Contains("a")) {
     SETBIT(fHistogramMask, HIST_RTRAI);
     if (!fHistograms->FindObject("res_train"))
       fHistograms->Add(new TH1D("res_train",
                                 "Computed residuals over training sample",
                                 100,
                                 fMinQuantity - fMeanQuantity,
                                 fMaxQuantity - fMeanQuantity));
   }
   if (opt.Contains("r4") || opt.Contains("a")) {
     SETBIT(fHistogramMask, HIST_RTEST);
     if (!fHistograms->FindObject("res_test"))
       fHistograms->Add(new TH1D("res_test",
                                 "Distribution of residuals from test",
                                 100,
                                 fMinQuantity - fMeanQuantity,
                                 fMaxQuantity - fMeanQuantity));
   }
 }
 
 //____________________________________________________________________
 void TMultiDimFet::MakeMethod(const Char_t *classname, Option_t *option) {
   // Generate the file <classname>MDF.cxx which contains the
   // implementation of the method:
   //
   //   Double_t <classname>::MDF(Double_t *x)
   //
   // which does the same as  TMultiDimFet::Eval. Please refer to this
   // method.
   //
   // Further, the public static members:
   //
   //   Int_t    <classname>::fgNVariables
   //   Int_t    <classname>::fgNCoefficients
   //   Double_t <classname>::fgDMean
   //   Double_t <classname>::fgXMean[]       //[fgNVariables]
   //   Double_t <classname>::fgXMin[]        //[fgNVariables]
   //   Double_t <classname>::fgXMax[]        //[fgNVariables]
   //   Double_t <classname>::fgCoefficient[] //[fgNCoeffficents]
   //   Int_t    <classname>::fgPower[]       //[fgNCoeffficents*fgNVariables]
   //
   // are initialized, and assumed to exist. The class declaration is
   // assumed to be in <classname>.h and assumed to be provided by the
   // user.
   //
   // See TMultiDimFet::MakeRealCode for a list of options
   //
   // The minimal class definition is:
   //
   //   class <classname> {
   //   public:
   //     Int_t    <classname>::fgNVariables;     // Number of variables
   //     Int_t    <classname>::fgNCoefficients;  // Number of terms
   //     Double_t <classname>::fgDMean;          // Mean from training sample
   //     Double_t <classname>::fgXMean[];        // Mean from training sample
   //     Double_t <classname>::fgXMin[];         // Min from training sample
   //     Double_t <classname>::fgXMax[];         // Max from training sample
   //     Double_t <classname>::fgCoefficient[];  // Coefficients
   //     Int_t    <classname>::fgPower[];        // Function powers
   //
   //     Double_t Eval(Double_t *x);
   //   };
   //
   // Whether the method <classname>::Eval should be static or not, is
   // up to the user.
 
   MakeRealCode(Form("%sMDF.cxx", classname), classname, option);
 }
 
 //____________________________________________________________________
 void TMultiDimFet::MakeNormalized() {
   // PRIVATE METHOD:
   // Normalize data to the interval [-1;1]. This is needed for the
   // classes method to work.
 
   Int_t i = 0;
   Int_t j = 0;
   Int_t k = 0;
 
   for (i = 0; i < fSampleSize; i++) {
     if (TESTBIT(fHistogramMask, HIST_DORIG))
       ((TH1D *)fHistograms->FindObject("d_orig"))->Fill(fQuantity(i));
 
     fQuantity(i) -= fMeanQuantity;
     fSumSqAvgQuantity += fQuantity(i) * fQuantity(i);
 
     if (TESTBIT(fHistogramMask, HIST_DSHIF))
       ((TH1D *)fHistograms->FindObject("d_shifted"))->Fill(fQuantity(i));
 
     for (j = 0; j < fNVariables; j++) {
       Double_t range = 1. / (fMaxVariables(j) - fMinVariables(j));
       k = i * fNVariables + j;
 
       // Fill histograms of original independent variables
       if (TESTBIT(fHistogramMask, HIST_XORIG))
         ((TH1D *)fHistograms->FindObject(Form("x_%d_orig", j)))->Fill(fVariables(k));
 
       // Normalise independent variables
       fVariables(k) = 1 + 2 * range * (fVariables(k) - fMaxVariables(j));
 
       // Fill histograms of normalised independent variables
       if (TESTBIT(fHistogramMask, HIST_XNORM))
         ((TH1D *)fHistograms->FindObject(Form("x_%d_norm", j)))->Fill(fVariables(k));
     }
   }
   // Shift min and max of dependent variable
   fMaxQuantity -= fMeanQuantity;
   fMinQuantity -= fMeanQuantity;
 
   // Shift mean of independent variables
   for (i = 0; i < fNVariables; i++) {
     Double_t range = 1. / (fMaxVariables(i) - fMinVariables(i));
     fMeanVariables(i) = 1 + 2 * range * (fMeanVariables(i) - fMaxVariables(i));
   }
 }
 
 //____________________________________________________________________
 void TMultiDimFet::MakeParameterization() {
   // PRIVATE METHOD:
   // Find the parameterization over the training sample. A full account
   // of the algorithm is given in the
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
 
   Int_t i = -1;
   Int_t j = 0;
   Int_t k = 0;
   Int_t maxPass = 3;
   Int_t studied = 0;
   Double_t squareResidual = fSumSqAvgQuantity;
   fNCoefficients = 0;
   fSumSqResidual = fSumSqAvgQuantity;
   fFunctions.ResizeTo(fMaxTerms, fSampleSize);
   fOrthFunctions.ResizeTo(fMaxTerms, fSampleSize);
   fOrthFunctionNorms.ResizeTo(fMaxTerms);
   fOrthCoefficients.ResizeTo(fMaxTerms);
   fOrthCurvatureMatrix.ResizeTo(fMaxTerms, fMaxTerms);
   fFunctions = 1;
 
   fFunctionCodes.resize(fMaxFunctions);
   fPowerIndex.resize(fMaxTerms);
   Int_t l;
   for (l = 0; l < fMaxFunctions; l++)
     fFunctionCodes[l] = 0;
   for (l = 0; l < fMaxTerms; l++)
     fPowerIndex[l] = 0;
 
   if (fMaxAngle != 0)
     maxPass = 100;
   if (fIsUserFunction)
     maxPass = 1;
 
   // Loop over the number of functions we want to study.
   // increment inspection counter
   while (kTRUE) {
     // Reach user defined limit of studies
     if (studied++ >= fMaxStudy) {
       fParameterisationCode = PARAM_MAXSTUDY;
       break;
     }
 
     // Considered all functions several times
     if (k >= maxPass) {
       fParameterisationCode = PARAM_SEVERAL;
       break;
     }
 
     // increment function counter
     i++;
 
     // If we've reached the end of the functions, restart pass
     if (i == fMaxFunctions) {
       if (fMaxAngle != 0)
         fMaxAngle += (90 - fMaxAngle) / 2;
       i = 0;
       studied--;
       k++;
       continue;
     }
     if (studied == 1)
       fFunctionCodes[i] = 0;
     else if (fFunctionCodes[i] >= 2)
       continue;
 
     // Print a happy message
     if (fIsVerbose && studied == 1)
       edm::LogInfo("TMultiDimFet") << "Coeff   SumSqRes    Contrib   Angle      QM   Func"
                                    << "     Value        W^2  Powers"
                                    << "\n";
 
     // Make the Gram-Schmidt
     Double_t dResidur = MakeGramSchmidt(i);
 
     if (dResidur == 0) {
       // This function is no good!
       // First test is in MakeGramSchmidt
       fFunctionCodes[i] = 1;
       continue;
     }
 
     // If user specified function, assume she/he knows what he's doing
     if (!fIsUserFunction) {
       // Flag this function as considered
       fFunctionCodes[i] = 2;
 
       // Test if this function contributes to the fit
       if (!TestFunction(squareResidual, dResidur)) {
         fFunctionCodes[i] = 1;
         continue;
       }
     }
 
     // If we get to here, the function currently considered is
     // fNCoefficients, so we increment the counter
     // Flag this function as OK, and store and the number in the
     // index.
     fFunctionCodes[i] = 3;
     fPowerIndex[fNCoefficients] = i;
     fNCoefficients++;
 
     // We add the current contribution to the sum of square of
     // residuals;
     squareResidual -= dResidur;
 
     // Calculate control parameter from this function
     for (j = 0; j < fNVariables; j++) {
       if (fNCoefficients == 1 || fMaxPowersFinal[j] <= fPowers[i * fNVariables + j] - 1)
         fMaxPowersFinal[j] = fPowers[i * fNVariables + j] - 1;
     }
     Double_t s = EvalControl(&fPowers[i * fNVariables]);
 
     // Print the statistics about this function
     if (fIsVerbose) {
       edm::LogVerbatim("TMultiDimFet") << std::setw(5) << fNCoefficients << " " << std::setw(10) << std::setprecision(4)
                                        << squareResidual << " " << std::setw(10) << std::setprecision(4) << dResidur
                                        << " " << std::setw(7) << std::setprecision(3) << fMaxAngle << " "
                                        << std::setw(7) << std::setprecision(3) << s << " " << std::setw(5) << i << " "
                                        << std::setw(10) << std::setprecision(4) << fOrthCoefficients(fNCoefficients - 1)
                                        << " " << std::setw(10) << std::setprecision(4)
                                        << fOrthFunctionNorms(fNCoefficients - 1) << " " << std::flush;
       for (j = 0; j < fNVariables; j++)
         edm::LogInfo("TMultiDimFet") << " " << fPowers[i * fNVariables + j] - 1 << std::flush;
       edm::LogInfo("TMultiDimFet") << "\n";
     }
 
     if (fNCoefficients >= fMaxTerms /* && fIsVerbose */) {
       fParameterisationCode = PARAM_MAXTERMS;
       break;
     }
 
     Double_t err = TMath::Sqrt(TMath::Max(1e-20, squareResidual) / fSumSqAvgQuantity);
     if (err < fMinRelativeError) {
       fParameterisationCode = PARAM_RELERR;
       break;
     }
   }
 
   fError = TMath::Max(1e-20, squareResidual);
   fSumSqResidual -= fError;
   fRMS = TMath::Sqrt(fError / fSampleSize);
 }
 
 //____________________________________________________________________
 void TMultiDimFet::MakeRealCode(const char *filename, const char *classname, Option_t *) {
   // PRIVATE METHOD:
   // This is the method that actually generates the code for the
   // evaluation the parameterization on some point.
   // It's called by TMultiDimFet::MakeCode and TMultiDimFet::MakeMethod.
   //
   // The options are: NONE so far
   Int_t i, j;
 
   Bool_t isMethod = (classname[0] == '\0' ? kFALSE : kTRUE);
   const char *prefix = (isMethod ? Form("%s::", classname) : "");
   const char *cv_qual = (isMethod ? "" : "static ");
 
   std::ofstream outFile(filename, std::ios::out | std::ios::trunc);
   if (!outFile) {
     Error("MakeRealCode", "couldn't open output file '%s'", filename);
     return;
   }
 
   if (fIsVerbose)
     edm::LogInfo("TMultiDimFet") << "Writing on file \"" << filename << "\" ... " << std::flush;
   //
   // Write header of file
   //
   // Emacs mode line ;-)
   outFile << "// -*- mode: c++ -*-"
           << "\n";
   // Info about creator
   outFile << "// "
           << "\n"
           << "// File " << filename << " generated by TMultiDimFet::MakeRealCode"
           << "\n";
   // Time stamp
   TDatime date;
   outFile << "// on " << date.AsString() << "\n";
   // ROOT version info
   outFile << "// ROOT version " << gROOT->GetVersion() << "\n"
           << "//"
           << "\n";
   // General information on the code
   outFile << "// This file contains the function "
           << "\n"
           << "//"
           << "\n"
           << "//    double  " << prefix << "MDF(double *x); "
           << "\n"
           << "//"
           << "\n"
           << "// For evaluating the parameterization obtained"
           << "\n"
           << "// from TMultiDimFet and the point x"
           << "\n"
           << "// "
           << "\n"
           << "// See TMultiDimFet class documentation for more "
           << "information "
           << "\n"
           << "// "
           << "\n";
   // Header files
   if (isMethod)
     // If these are methods, we need the class header
     outFile << "#include \"" << classname << ".h\""
             << "\n";
 
   //
   // Now for the data
   //
   outFile << "//"
           << "\n"
           << "// Static data variables"
           << "\n"
           << "//"
           << "\n";
   outFile << cv_qual << "int    " << prefix << "gNVariables    = " << fNVariables << ";"
           << "\n";
   outFile << cv_qual << "int    " << prefix << "gNCoefficients = " << fNCoefficients << ";"
           << "\n";
   outFile << cv_qual << "double " << prefix << "gDMean         = " << fMeanQuantity << ";"
           << "\n";
 
   // Assignment to mean vector.
   outFile << "// Assignment to mean vector."
           << "\n";
   outFile << cv_qual << "double " << prefix << "gXMean[] = {"
           << "\n";
   for (i = 0; i < fNVariables; i++)
     outFile << (i != 0 ? ", " : "  ") << fMeanVariables(i) << std::flush;
   outFile << " };"
           << "\n"
           << "\n";
 
   // Assignment to minimum vector.
   outFile << "// Assignment to minimum vector."
           << "\n";
   outFile << cv_qual << "double " << prefix << "gXMin[] = {"
           << "\n";
   for (i = 0; i < fNVariables; i++)
     outFile << (i != 0 ? ", " : "  ") << fMinVariables(i) << std::flush;
   outFile << " };"
           << "\n"
           << "\n";
 
   // Assignment to maximum vector.
   outFile << "// Assignment to maximum vector."
           << "\n";
   outFile << cv_qual << "double " << prefix << "gXMax[] = {"
           << "\n";
   for (i = 0; i < fNVariables; i++)
     outFile << (i != 0 ? ", " : "  ") << fMaxVariables(i) << std::flush;
   outFile << " };"
           << "\n"
           << "\n";
 
   // Assignment to coefficients vector.
   outFile << "// Assignment to coefficients vector."
           << "\n";
   outFile << cv_qual << "double " << prefix << "gCoefficient[] = {" << std::flush;
   for (i = 0; i < fNCoefficients; i++)
     outFile << (i != 0 ? "," : "") << "\n"
             << "  " << fCoefficients(i) << std::flush;
   outFile << "\n"
           << " };"
           << "\n"
           << "\n";
 
   // Assignment to powers vector.
   outFile << "// Assignment to powers vector."
           << "\n"
           << "// The powers are stored row-wise, that is"
           << "\n"
           << "//  p_ij = " << prefix << "gPower[i * NVariables + j];"
           << "\n";
   outFile << cv_qual << "int    " << prefix << "gPower[] = {" << std::flush;
   for (i = 0; i < fNCoefficients; i++) {
     for (j = 0; j < fNVariables; j++) {
       if (j != 0)
         outFile << std::flush << "  ";
       else
         outFile << "\n"
                 << "  ";
       outFile << fPowers[fPowerIndex[i] * fNVariables + j]
               << (i == fNCoefficients - 1 && j == fNVariables - 1 ? "" : ",") << std::flush;
     }
   }
   outFile << "\n"
           << "};"
           << "\n"
           << "\n";
 
   //
   // Finally we reach the function itself
   //
   outFile << "// "
           << "\n"
           << "// The " << (isMethod ? "method " : "function ") << "  double " << prefix << "MDF(double *x)"
           << "\n"
           << "// "
           << "\n";
   outFile << "double " << prefix << "MDF(double *x) {"
           << "\n"
           << "  double returnValue = " << prefix << "gDMean;"
           << "\n"
           << "  int    i = 0, j = 0, k = 0;"
           << "\n"
           << "  for (i = 0; i < " << prefix << "gNCoefficients ; i++) {"
           << "\n"
           << "    // Evaluate the ith term in the expansion"
           << "\n"
           << "    double term = " << prefix << "gCoefficient[i];"
           << "\n"
           << "    for (j = 0; j < " << prefix << "gNVariables; j++) {"
           << "\n"
           << "      // Evaluate the polynomial in the jth variable."
           << "\n"
           << "      int power = " << prefix << "gPower[" << prefix << "gNVariables * i + j]; "
           << "\n"
           << "      double p1 = 1, p2 = 0, p3 = 0, r = 0;"
           << "\n"
           << "      double v =  1 + 2. / (" << prefix << "gXMax[j] - " << prefix << "gXMin[j]) * (x[j] - " << prefix
           << "gXMax[j]);"
           << "\n"
           << "      // what is the power to use!"
           << "\n"
           << "      switch(power) {"
           << "\n"
           << "      case 1: r = 1; break; "
           << "\n"
           << "      case 2: r = v; break; "
           << "\n"
           << "      default: "
           << "\n"
           << "        p2 = v; "
           << "\n"
           << "        for (k = 3; k <= power; k++) { "
           << "\n"
           << "          p3 = p2 * v;"
           << "\n";
   if (fPolyType == kLegendre)
     outFile << "          p3 = ((2 * i - 3) * p2 * v - (i - 2) * p1)"
             << " / (i - 1);"
             << "\n";
   if (fPolyType == kChebyshev)
     outFile << "          p3 = 2 * v * p2 - p1; "
             << "\n";
   outFile << "          p1 = p2; p2 = p3; "
           << "\n"
           << "        }"
           << "\n"
           << "        r = p3;"
           << "\n"
           << "      }"
           << "\n"
           << "      // multiply this term by the poly in the jth var"
           << "\n"
           << "      term *= r; "
           << "\n"
           << "    }"
           << "\n"
           << "    // Add this term to the final result"
           << "\n"
           << "    returnValue += term;"
           << "\n"
           << "  }"
           << "\n"
           << "  return returnValue;"
           << "\n"
           << "}"
           << "\n"
           << "\n";
 
   // EOF
   outFile << "// EOF for " << filename << "\n";
 
   // Close the file
   outFile.close();
 
   if (fIsVerbose)
     edm::LogInfo("TMultiDimFet") << "done"
                                  << "\n";
 }
 
 //____________________________________________________________________
 void TMultiDimFet::Print(Option_t *option) const {
   // Print statistics etc.
   // Options are
   //   P        Parameters
   //   S        Statistics
   //   C        Coefficients
   //   R        Result of parameterisation
   //   F        Result of fit
   //   K        Correlation Matrix
   //   M        Pretty print formula
   //
   Int_t i = 0;
   Int_t j = 0;
 
   TString opt(option);
   opt.ToLower();
 
   if (opt.Contains("p")) {
     // Print basic parameters for this object
     edm::LogInfo("TMultiDimFet") << "User parameters:"
                                  << "\n"
                                  << "----------------"
                                  << "\n"
                                  << " Variables:                    " << fNVariables << "\n"
                                  << " Data points:                  " << fSampleSize << "\n"
                                  << " Max Terms:                    " << fMaxTerms << "\n"
                                  << " Power Limit Parameter:        " << fPowerLimit << "\n"
                                  << " Max functions:                " << fMaxFunctions << "\n"
                                  << " Max functions to study:       " << fMaxStudy << "\n"
                                  << " Max angle (optional):         " << fMaxAngle << "\n"
                                  << " Min angle:                    " << fMinAngle << "\n"
                                  << " Relative Error accepted:      " << fMinRelativeError << "\n"
                                  << " Maximum Powers:               " << std::flush;
     for (i = 0; i < fNVariables; i++)
       edm::LogInfo("TMultiDimFet") << " " << fMaxPowers[i] - 1 << std::flush;
     edm::LogInfo("TMultiDimFet") << "\n"
                                  << "\n"
                                  << " Parameterisation will be done using " << std::flush;
     if (fPolyType == kChebyshev)
       edm::LogInfo("TMultiDimFet") << "Chebyshev polynomials"
                                    << "\n";
     else if (fPolyType == kLegendre)
       edm::LogInfo("TMultiDimFet") << "Legendre polynomials"
                                    << "\n";
     else
       edm::LogInfo("TMultiDimFet") << "Monomials"
                                    << "\n";
     edm::LogInfo("TMultiDimFet") << "\n";
   }
 
   if (opt.Contains("s")) {
     // Print statistics for read data
     edm::LogInfo("TMultiDimFet") << "Sample statistics:"
                                  << "\n"
                                  << "------------------"
                                  << "\n"
                                  << "                 D" << std::flush;
     for (i = 0; i < fNVariables; i++)
       edm::LogInfo("TMultiDimFet") << " " << std::setw(10) << i + 1 << std::flush;
     edm::LogInfo("TMultiDimFet") << "\n"
                                  << " Max:   " << std::setw(10) << std::setprecision(7) << fMaxQuantity << std::flush;
     for (i = 0; i < fNVariables; i++)
       edm::LogInfo("TMultiDimFet") << " " << std::setw(10) << std::setprecision(4) << fMaxVariables(i) << std::flush;
     edm::LogInfo("TMultiDimFet") << "\n"
                                  << " Min:   " << std::setw(10) << std::setprecision(7) << fMinQuantity << std::flush;
     for (i = 0; i < fNVariables; i++)
       edm::LogInfo("TMultiDimFet") << " " << std::setw(10) << std::setprecision(4) << fMinVariables(i) << std::flush;
     edm::LogInfo("TMultiDimFet") << "\n"
                                  << " Mean:  " << std::setw(10) << std::setprecision(7) << fMeanQuantity << std::flush;
     for (i = 0; i < fNVariables; i++)
       edm::LogInfo("TMultiDimFet") << " " << std::setw(10) << std::setprecision(4) << fMeanVariables(i) << std::flush;
     edm::LogInfo("TMultiDimFet") << "\n"
                                  << " Function Sum Squares:         " << fSumSqQuantity << "\n"
                                  << "\n";
   }
 
   if (opt.Contains("r")) {
     edm::LogInfo("TMultiDimFet") << "Results of Parameterisation:"
                                  << "\n"
                                  << "----------------------------"
                                  << "\n"
                                  << " Total reduction of square residuals    " << fSumSqResidual << "\n"
                                  << " Relative precision obtained:           " << fPrecision << "\n"
                                  << " Error obtained:                        " << fError << "\n"
                                  << " Multiple correlation coefficient:      " << fCorrelationCoeff << "\n"
                                  << " Reduced Chi square over sample:        " << fChi2 / (fSampleSize - fNCoefficients)
                                  << "\n"
                                  << " Maximum residual value:                " << fMaxResidual << "\n"
                                  << " Minimum residual value:                " << fMinResidual << "\n"
                                  << " Estimated root mean square:            " << fRMS << "\n"
                                  << " Maximum powers used:                   " << std::flush;
     for (j = 0; j < fNVariables; j++)
       edm::LogInfo("TMultiDimFet") << fMaxPowersFinal[j] << " " << std::flush;
     edm::LogInfo("TMultiDimFet") << "\n"
                                  << " Function codes of candidate functions."
                                  << "\n"
                                  << "  1: considered,"
                                  << "  2: too little contribution,"
                                  << "  3: accepted." << std::flush;
     for (i = 0; i < fMaxFunctions; i++) {
       if (i % 60 == 0)
         edm::LogInfo("TMultiDimFet") << "\n"
                                      << " " << std::flush;
       else if (i % 10 == 0)
         edm::LogInfo("TMultiDimFet") << " " << std::flush;
       edm::LogInfo("TMultiDimFet") << fFunctionCodes[i];
     }
     edm::LogInfo("TMultiDimFet") << "\n"
                                  << " Loop over candidates stopped because " << std::flush;
     switch (fParameterisationCode) {
       case PARAM_MAXSTUDY:
         edm::LogInfo("TMultiDimFet") << "max allowed studies reached"
                                      << "\n";
         break;
       case PARAM_SEVERAL:
         edm::LogInfo("TMultiDimFet") << "all candidates considered several times"
                                      << "\n";
         break;
       case PARAM_RELERR:
         edm::LogInfo("TMultiDimFet") << "wanted relative error obtained"
                                      << "\n";
         break;
       case PARAM_MAXTERMS:
         edm::LogInfo("TMultiDimFet") << "max number of terms reached"
                                      << "\n";
         break;
       default:
         edm::LogInfo("TMultiDimFet") << "some unknown reason"
                                      << "\n";
         break;
     }
     edm::LogInfo("TMultiDimFet") << "\n";
   }
 
   if (opt.Contains("f")) {
     edm::LogInfo("TMultiDimFet") << "Results of Fit:"
                                  << "\n"
                                  << "---------------"
                                  << "\n"
                                  << " Test sample size:                      " << fTestSampleSize << "\n"
                                  << " Multiple correlation coefficient:      " << fTestCorrelationCoeff << "\n"
                                  << " Relative precision obtained:           " << fTestPrecision << "\n"
                                  << " Error obtained:                        " << fTestError << "\n"
                                  << " Reduced Chi square over sample:        " << fChi2 / (fSampleSize - fNCoefficients)
                                  << "\n"
                                  << "\n";
     /*
       if (fFitter) {
          fFitter->PrintResults(1,1);
          edm::LogInfo("TMultiDimFet") << "\n";
       }
 */
   }
 
   if (opt.Contains("c")) {
     edm::LogInfo("TMultiDimFet") << "Coefficients:"
                                  << "\n"
                                  << "-------------"
                                  << "\n"
                                  << "   #         Value        Error   Powers"
                                  << "\n"
                                  << " ---------------------------------------"
                                  << "\n";
     for (i = 0; i < fNCoefficients; i++) {
       edm::LogInfo("TMultiDimFet") << " " << std::setw(3) << i << "  " << std::setw(12) << fCoefficients(i) << "  "
                                    << std::setw(12) << fCoefficientsRMS(i) << "  " << std::flush;
       for (j = 0; j < fNVariables; j++)
         edm::LogInfo("TMultiDimFet") << " " << std::setw(3) << fPowers[fPowerIndex[i] * fNVariables + j] - 1
                                      << std::flush;
       edm::LogInfo("TMultiDimFet") << "\n";
     }
     edm::LogInfo("TMultiDimFet") << "\n";
   }
   if (opt.Contains("k") && fCorrelationMatrix.IsValid()) {
     edm::LogInfo("TMultiDimFet") << "Correlation Matrix:"
                                  << "\n"
                                  << "-------------------";
     fCorrelationMatrix.Print();
   }
 
   if (opt.Contains("m")) {
     edm::LogInfo("TMultiDimFet") << std::setprecision(25);
     edm::LogInfo("TMultiDimFet") << "Parameterization:"
                                  << "\n"
                                  << "-----------------"
                                  << "\n"
                                  << "  Normalised variables: "
                                  << "\n";
     for (i = 0; i < fNVariables; i++)
       edm::LogInfo("TMultiDimFet") << "\ty" << i << "\t:= 1 + 2 * (x" << i << " - " << fMaxVariables(i) << ") / ("
                                    << fMaxVariables(i) << " - " << fMinVariables(i) << ")"
                                    << "\n";
     edm::LogInfo("TMultiDimFet") << "\n"
                                  << "  f[";
     for (i = 0; i < fNVariables; i++) {
       edm::LogInfo("TMultiDimFet") << "y" << i;
       if (i != fNVariables - 1)
         edm::LogInfo("TMultiDimFet") << ", ";
     }
     edm::LogInfo("TMultiDimFet") << "] := ";
     for (Int_t i = 0; i < fNCoefficients; i++) {
       if (i != 0)
         edm::LogInfo("TMultiDimFet") << " " << (fCoefficients(i) < 0 ? "- " : "+ ") << TMath::Abs(fCoefficients(i));
       else
         edm::LogInfo("TMultiDimFet") << fCoefficients(i);
       for (Int_t j = 0; j < fNVariables; j++) {
         Int_t p = fPowers[fPowerIndex[i] * fNVariables + j];
         switch (p) {
           case 1:
             break;
           case 2:
             edm::LogInfo("TMultiDimFet") << " * y" << j;
             break;
           default:
             switch (fPolyType) {
               case kLegendre:
                 edm::LogInfo("TMultiDimFet") << " * L" << p - 1 << "(y" << j << ")";
                 break;
               case kChebyshev:
                 edm::LogInfo("TMultiDimFet") << " * C" << p - 1 << "(y" << j << ")";
                 break;
               default:
                 edm::LogInfo("TMultiDimFet") << " * y" << j << "^" << p - 1;
                 break;
             }
         }
       }
     }
     edm::LogInfo("TMultiDimFet") << "\n";
   }
 }
 
 //____________________________________________________________________
 void TMultiDimFet::PrintPolynomialsSpecial(Option_t *option) const {
   //   M        Pretty print formula
   //
   Int_t i = 0;
   //   Int_t j = 0;
 
   TString opt(option);
   opt.ToLower();
 
   if (opt.Contains("m")) {
     edm::LogInfo("TMultiDimFet") << std::setprecision(25);
     edm::LogInfo("TMultiDimFet") << "Parameterization:"
                                  << "\n"
                                  << "-----------------"
                                  << "\n"
                                  << "  Normalised variables: "
                                  << "\n";
     for (i = 0; i < fNVariables; i++)
       edm::LogInfo("TMultiDimFet") << "\tdouble y" << i << "\t=1+2*(x" << i << "-" << fMaxVariables(i) << ")/("
                                    << fMaxVariables(i) << "-" << fMinVariables(i) << ");"
                                    << "\n";
     edm::LogInfo("TMultiDimFet") << "\n"
                                  << "  f[";
     for (i = 0; i < fNVariables; i++) {
       edm::LogInfo("TMultiDimFet") << "y" << i;
       if (i != fNVariables - 1)
         edm::LogInfo("TMultiDimFet") << ", ";
     }
     edm::LogInfo("TMultiDimFet") << "] := " << fMeanQuantity << " + ";
     for (Int_t i = 0; i < fNCoefficients; i++) {
       if (i != 0)
         edm::LogInfo("TMultiDimFet") << " " << (fCoefficients(i) < 0 ? "-" : "+") << TMath::Abs(fCoefficients(i));
       else
         edm::LogInfo("TMultiDimFet") << fCoefficients(i);
       for (Int_t j = 0; j < fNVariables; j++) {
         Int_t p = fPowers[fPowerIndex[i] * fNVariables + j];
         switch (p) {
           case 1:
             break;
           case 2:
             edm::LogInfo("TMultiDimFet") << "*y" << j;
             break;
           default:
             switch (fPolyType) {
               case kLegendre:
                 edm::LogInfo("TMultiDimFet") << "*Leg(" << p - 1 << ",y" << j << ")";
                 break;
               case kChebyshev:
                 edm::LogInfo("TMultiDimFet") << "*C" << p - 1 << "(y" << j << ")";
                 break;
               default:
                 edm::LogInfo("TMultiDimFet") << "*y" << j << "**" << p - 1;
                 break;
             }
         }
       }
       edm::LogInfo("TMultiDimFet") << "\n";
     }
     edm::LogInfo("TMultiDimFet") << "\n";
   }
 }
 
 //____________________________________________________________________
 Bool_t TMultiDimFet::Select(const Int_t *) {
   // Selection method. User can override this method for specialized
   // selection of acceptable functions in fit. Default is to select
   // all. This message is sent during the build-up of the function
   // candidates table once for each set of powers in
   // variables. Notice, that the argument array contains the powers
   // PLUS ONE. For example, to De select the function
   //     f = x1^2 * x2^4 * x3^5,
   // this method should return kFALSE if given the argument
   //     { 3, 4, 6 }
   return kTRUE;
 }
 
 //____________________________________________________________________
 void TMultiDimFet::SetMaxAngle(Double_t ang) {
   // Set the max angle (in degrees) between the initial data vector to
   // be fitted, and the new candidate function to be included in the
   // fit.  By default it is 0, which automatically chooses another
   // selection criteria. See also
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   if (ang >= 90 || ang < 0) {
     Warning("SetMaxAngle", "angle must be in [0,90)");
     return;
   }
 
   fMaxAngle = ang;
 }
 
 //____________________________________________________________________
 void TMultiDimFet::SetMinAngle(Double_t ang) {
   // Set the min angle (in degrees) between a new candidate function
   // and the subspace spanned by the previously accepted
   // functions. See also
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   if (ang > 90 || ang <= 0) {
     Warning("SetMinAngle", "angle must be in [0,90)");
     return;
   }
 
   fMinAngle = ang;
 }
 
 //____________________________________________________________________
 void TMultiDimFet::SetPowers(const Int_t *powers, Int_t terms) {
   // Define a user function. The input array must be of the form
   // (p11, ..., p1N, ... ,pL1, ..., pLN)
   // Where N is the dimension of the data sample, L is the number of
   // terms (given in terms) and the first number, labels the term, the
   // second the variable.  More information is given in the
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   fIsUserFunction = kTRUE;
   fMaxFunctions = terms;
   fMaxTerms = terms;
   fMaxStudy = terms;
   fMaxFunctionsTimesNVariables = fMaxFunctions * fNVariables;
   fPowers.resize(fMaxFunctions * fNVariables);
   Int_t i, j;
   for (i = 0; i < fMaxFunctions; i++)
     for (j = 0; j < fNVariables; j++)
       fPowers[i * fNVariables + j] = powers[i * fNVariables + j] + 1;
 }
 
 //____________________________________________________________________
 void TMultiDimFet::SetPowerLimit(Double_t limit) {
   // Set the user parameter for the function selection. The bigger the
   // limit, the more functions are used. The meaning of this variable
   // is defined in the
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   fPowerLimit = limit;
 }
 
 //____________________________________________________________________
 void TMultiDimFet::SetMaxPowers(const Int_t *powers) {
   // Set the maximum power to be considered in the fit for each
   // variable. See also
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   if (!powers)
     return;
 
   for (Int_t i = 0; i < fNVariables; i++)
     fMaxPowers[i] = powers[i] + 1;
 }
 
 //____________________________________________________________________
 void TMultiDimFet::SetMinRelativeError(Double_t error) {
   // Set the acceptable relative error for when sum of square
   // residuals is considered minimized. For a full account, refer to
   // the
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
   fMinRelativeError = error;
 }
 
 //____________________________________________________________________
 Bool_t TMultiDimFet::TestFunction(Double_t squareResidual, Double_t dResidur) {
   // PRIVATE METHOD:
   // Test whether the currently considered function contributes to the
   // fit. See also
   // Begin_Html<a href="#TMultiDimFet:description">class description</a>End_Html
 
   if (fNCoefficients != 0) {
     // Now for the second test:
     if (fMaxAngle == 0) {
       // If the user hasn't supplied a max angle do the test as,
       if (dResidur < squareResidual / (fMaxTerms - fNCoefficients + 1 + 1E-10)) {
         return kFALSE;
       }
     } else {
       // If the user has provided a max angle, test if the calculated
       // angle is less then the max angle.
       if (TMath::Sqrt(dResidur / fSumSqAvgQuantity) < TMath::Cos(fMaxAngle * DEGRAD)) {
         return kFALSE;
       }
     }
   }
   // If we get here, the function is OK
   return kTRUE;
 }
 
 //____________________________________________________________________
 //void mdfHelper(int& /*npar*/, double* /*divs*/, double& chi2,
 //               double* coeffs, int /*flag*/)
 /*{
    // Helper function for doing the minimisation of Chi2 using Minuit
 
    // Get pointer  to current TMultiDimFet object.
   // TMultiDimFet* mdf = TMultiDimFet::Instance();
    //chi2     = mdf->MakeChi2(coeffs);
 }
 */

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