<commit_before>//Multi-Dimensional Parametrisation and Fitting
//Authors: <NAME>, <NAME>
   
#include "Riostream.h"
#include "TROOT.h"
#include "TApplication.h"
#include "TCanvas.h"
#include "TH1.h"
#include "TSystem.h"
#include "TBrowser.h"
#include "TFile.h"
#include "TRandom.h"
#include "TMultiDimFit.h"
#include "TVectorD.h"
#include "TMath.h"

//____________________________________________________________________
void makeData(Double_t* x, Double_t& d, Double_t& e) 
{
  // Make data points 
  Double_t upp[5] = { 10, 10, 10, 10,  1 };
  Double_t low[5] = {  0,  0,  0,  0, .1 };
  for (int i = 0; i < 4; i++) 
    x[i] = (upp[i] - low[i]) * gRandom->Rndm() + low[i]; 
  
  d = x[0] * TMath::Sqrt(x[1] * x[1] + x[2] * x[2] + x[3] * x[3]);
  
  e = gRandom->Gaus(upp[4],low[4]);
}

//____________________________________________________________________
int CompareResults(TMultiDimFit *fit)
{ 
   //Compare results with reference run
   
   // the right coefficients
  double GoodCoeffs[] = {
  -4.37056,
  43.1468,
  13.432,
  13.4632,
  13.3964,
  13.328,
  13.3016,
  13.3519,
  4.49724,
  4.63876,
  4.89036,
  -3.69982,
  -3.98618,
  -3.86195,
  4.36054,
  -4.02597,
  4.57037,
  4.69845,
  2.83819,
  -3.48855,
  -3.97612
};

// Good Powers
  int GoodPower[] = {
  1,  1,  1,  1,
  2,  1,  1,  1,
  1,  1,  1,  2,
  1,  1,  2,  1,
  1,  2,  1,  1,
  2,  2,  1,  1,
  2,  1,  1,  2,
  2,  1,  2,  1,
  1,  1,  1,  3,
  1,  3,  1,  1,
  1,  1,  5,  1,
  1,  1,  2,  2,
  1,  2,  1,  2,
  1,  2,  2,  1,
  2,  1,  1,  3,
  2,  2,  1,  2,
  2,  1,  3,  1,
  2,  3,  1,  1,
  1,  2,  2,  2,
  2,  1,  2,  2,
  2,  2,  2,  1
};

  Int_t nc = fit->GetNCoefficients();
  Int_t nv = fit->GetNVariables();
  const Int_t *powers = fit->GetPowers();
  const Int_t *pindex = fit->GetPowerIndex();
  if (nc != 21) return 1;
  const TVectorD *coeffs = fit->GetCoefficients();
  int k = 0;
  for (Int_t i=0;i<nc;i++) {
     if (TMath::Abs((*coeffs)[i] - GoodCoeffs[i]) > 5e-5) return 2;
     for (Int_t j=0;j<nv;j++) {
        if (powers[pindex[i]*nv+j] != GoodPower[k]) return 3;
        k++;
     }
  }
  
  // now test the result of the generated function
  gROOT->ProcessLine(".L MDF.C");
  Double_t x[]    = {5,5,5,5};
  Double_t refMDF = 43.98;
  Double_t rMDF   = MDF(x);
  if (TMath::Abs(rMDF -refMDF) > 1e-2) return 4;
  return 0;     
}

//____________________________________________________________________
Int_t multidimfit() 
{

  cout << "*************************************************" << endl; 
  cout << "*             Multidimensional Fit              *" << endl;
  cout << "*                                               *" << endl;
  cout << "* By <NAME> <<EMAIL>> 14/10/00     *" << endl;
  cout << "*************************************************" << endl; 
  cout << endl;

  // Initialize global TRannom object. 
  gRandom = new TRandom();

  // Open output file 
  TFile* output = new TFile("mdf.root", "RECREATE");
  
  // Global data parameters 
  Int_t nVars       = 4;
  Int_t nData       = 500;
  Double_t x[4];

  // make fit object and set parameters on it. 
  TMultiDimFit* fit = new TMultiDimFit(nVars, TMultiDimFit::kMonomials,"v");

  Int_t mPowers[]   = { 6 , 6, 6, 6 };
  fit->SetMaxPowers(mPowers);
  fit->SetMaxFunctions(1000);
  fit->SetMaxStudy(1000);
  fit->SetMaxTerms(30);
  fit->SetPowerLimit(1);
  fit->SetMinAngle(10);
  fit->SetMaxAngle(10);
  fit->SetMinRelativeError(.01);

  // variables to hold the temporary input data 
  Double_t d;
  Double_t e;
  
  // Print out the start parameters
  fit->Print("p");

  // Create training sample 
  Int_t i;
  for (i = 0; i < nData ; i++) {

    // Make some data 
    makeData(x,d,e);

    // Add the row to the fit object
    fit->AddRow(x,d,e);
  }

  // Print out the statistics
  fit->Print("s");

  // Book histograms 
  fit->MakeHistograms();

  // Find the parameterization 
  fit->FindParameterization();

  // Print coefficents 
  fit->Print("rc");

  // Get the min and max of variables from the training sample, used
  // for cuts in test sample. 
  Double_t *xMax = new Double_t[nVars];
  Double_t *xMin = new Double_t[nVars];
  for (i = 0; i < nVars; i++) {
    xMax[i] = (*fit->GetMaxVariables())(i);
    xMin[i] = (*fit->GetMinVariables())(i);
  }

  nData = fit->GetNCoefficients() * 100;
  Int_t j;

  // Create test sample 
  for (i = 0; i < nData ; i++) {
    // Make some data 
    makeData(x,d,e);

    for (j = 0; j < nVars; j++) 
      if (x[j] < xMin[j] || x[j] > xMax[j])
	break;

    // If we get through the loop above, all variables are in range 
    if (j == nVars)  
      // Add the row to the fit object
      fit->AddTestRow(x,d,e);
    else
      i--;
  }
  //delete gRandom;

  // Test the parameterizatio and coefficents using the test sample. 
  fit->Fit();

  // Print result 
  fit->Print("fc");

  // Write code to file 
  fit->MakeCode();

  // Write histograms to disk, and close file 
  output->Write();
  output->Close();
  delete output;
  
  // Compare results with reference run
  Int_t compare = CompareResults(fit);
  if (!compare) {
     printf("\nmultidimfit ..............................................  OK\n");
  } else {
     printf("\nmultidimfit ..............................................  fails case %d\n",compare);
  }

  // We're done 
  delete fit;
  return compare;
}
<commit_msg>Fix multi dim fit tutorial for cling<commit_after>//Multi-Dimensional Parametrisation and Fitting
//Authors: <NAME>, <NAME>
   
#include "Riostream.h"
#include "TROOT.h"
#include "TApplication.h"
#include "TCanvas.h"
#include "TH1.h"
#include "TSystem.h"
#include "TBrowser.h"
#include "TFile.h"
#include "TRandom.h"
#include "TMultiDimFit.h"
#include "TVectorD.h"
#include "TMath.h"


//____________________________________________________________________
void makeData(Double_t* x, Double_t& d, Double_t& e) 
{
  // Make data points 
  Double_t upp[5] = { 10, 10, 10, 10,  1 };
  Double_t low[5] = {  0,  0,  0,  0, .1 };
  for (int i = 0; i < 4; i++) 
    x[i] = (upp[i] - low[i]) * gRandom->Rndm() + low[i]; 
  
  d = x[0] * TMath::Sqrt(x[1] * x[1] + x[2] * x[2] + x[3] * x[3]);
  
  e = gRandom->Gaus(upp[4],low[4]);
}

//____________________________________________________________________
int CompareResults(TMultiDimFit *fit, bool doFit)
{ 
   //Compare results with reference run
 

   // the right coefficients (before fit) 
  double GoodCoeffsNoFit[] = {
  -4.37056,
  43.1468,
  13.432,
  13.4632,
  13.3964,
  13.328,
  13.3016,
  13.3519,
  4.49724,
  4.63876,
  4.89036,
  -3.69982,
  -3.98618,
  -3.86195,
  4.36054,
  -4.02597,
  4.57037,
  4.69845,
  2.83819,
  -3.48855,
  -3.97612
};

   // the right coefficients (after fit) 
  double GoodCoeffs[] = {
     -4.399,  
     43.15,
     13.41,
     13.49,
     13.4,
     13.23,
     13.34,
     13.29,
     4.523,
     4.659,
     4.948,
     -4.026,
     -4.045,
     -3.939,
     4.421,
     -4.006,
     4.626,
     4.378,
     3.516,
     -4.111,
     -3.823,    
};

// Good Powers
  int GoodPower[] = {
  1,  1,  1,  1,
  2,  1,  1,  1,
  1,  1,  1,  2,
  1,  1,  2,  1,
  1,  2,  1,  1,
  2,  2,  1,  1,
  2,  1,  1,  2,
  2,  1,  2,  1,
  1,  1,  1,  3,
  1,  3,  1,  1,
  1,  1,  5,  1,
  1,  1,  2,  2,
  1,  2,  1,  2,
  1,  2,  2,  1,
  2,  1,  1,  3,
  2,  2,  1,  2,
  2,  1,  3,  1,
  2,  3,  1,  1,
  1,  2,  2,  2,
  2,  1,  2,  2,
  2,  2,  2,  1
};

  Int_t nc = fit->GetNCoefficients();
  Int_t nv = fit->GetNVariables();
  const Int_t *powers = fit->GetPowers();
  const Int_t *pindex = fit->GetPowerIndex();
  if (nc != 21) return 1;
  const TVectorD *coeffs = fit->GetCoefficients();
  int k = 0;
  for (Int_t i=0;i<nc;i++) {
     if (doFit) {
        if (!TMath::AreEqualRel((*coeffs)[i],GoodCoeffs[i],1e-3)) return 2;
     }
     else {
        if (TMath::Abs((*coeffs)[i] - GoodCoeffsNoFit[i]) > 5e-5) return 2;
     }
     for (Int_t j=0;j<nv;j++) {
        if (powers[pindex[i]*nv+j] != GoodPower[k]) return 3;
        k++;
     }
  }
  
  // now test the result of the generated function
  gROOT->ProcessLine(".L MDF.C");
 
  Double_t refMDF = (doFit) ? 43.95 : 43.98;
  // this does not work in CLing since the function is not defined 
  //Double_t x[]    = {5,5,5,5};
  //Double_t rMDF   = MDF(x);
  //LM:  need to return the address of the result since it is casted to a long (this should not be in a tutorial !)
  Long_t iret = gROOT->ProcessLine(" Double_t x[] = {5,5,5,5}; double result=MDF(x); &result;");
  Double_t rMDF = * ( (Double_t*)iret);
  //printf("%f\n",rMDF);
  if (TMath::Abs(rMDF -refMDF) > 1e-2) return 4;
  return 0;     
}

//____________________________________________________________________
Int_t multidimfit(bool doFit = true) 
{

  cout << "*************************************************" << endl; 
  cout << "*             Multidimensional Fit              *" << endl;
  cout << "*                                               *" << endl;
  cout << "* By Christian Holm <<EMAIL>> 14/10/00     *" << endl;
  cout << "*************************************************" << endl; 
  cout << endl;

  // Initialize global TRannom object. 
  gRandom = new TRandom();

  // Open output file 
  TFile* output = new TFile("mdf.root", "RECREATE");
  
  // Global data parameters 
  Int_t nVars       = 4;
  Int_t nData       = 500;
  Double_t x[4];

  // make fit object and set parameters on it. 
  TMultiDimFit* fit = new TMultiDimFit(nVars, TMultiDimFit::kMonomials,"v");

  Int_t mPowers[]   = { 6 , 6, 6, 6 };
  fit->SetMaxPowers(mPowers);
  fit->SetMaxFunctions(1000);
  fit->SetMaxStudy(1000);
  fit->SetMaxTerms(30);
  fit->SetPowerLimit(1);
  fit->SetMinAngle(10);
  fit->SetMaxAngle(10);
  fit->SetMinRelativeError(.01);

  // variables to hold the temporary input data 
  Double_t d;
  Double_t e;
  
  // Print out the start parameters
  fit->Print("p");

  printf("======================================\n");

  // Create training sample 
  Int_t i;
  for (i = 0; i < nData ; i++) {

    // Make some data 
    makeData(x,d,e);

    // Add the row to the fit object
    fit->AddRow(x,d,e);
  }

  // Print out the statistics
  fit->Print("s");

  // Book histograms 
  fit->MakeHistograms();

  // Find the parameterization 
  fit->FindParameterization();

  // Print coefficents 
  fit->Print("rc");

  // Get the min and max of variables from the training sample, used
  // for cuts in test sample. 
  Double_t *xMax = new Double_t[nVars];
  Double_t *xMin = new Double_t[nVars];
  for (i = 0; i < nVars; i++) {
    xMax[i] = (*fit->GetMaxVariables())(i);
    xMin[i] = (*fit->GetMinVariables())(i);
  }

  nData = fit->GetNCoefficients() * 100;
  Int_t j;

  // Create test sample 
  for (i = 0; i < nData ; i++) {
    // Make some data 
    makeData(x,d,e);

    for (j = 0; j < nVars; j++) 
      if (x[j] < xMin[j] || x[j] > xMax[j])
	break;

    // If we get through the loop above, all variables are in range 
    if (j == nVars)  
      // Add the row to the fit object
      fit->AddTestRow(x,d,e);
    else
      i--;
  }
  //delete gRandom;

  // Test the parameterizatio and coefficents using the test sample. 
  if (doFit) 
     fit->Fit("M");

  // Print result 
  fit->Print("fc v");

  // Write code to file 
  fit->MakeCode();

  // Write histograms to disk, and close file 
  output->Write();
  output->Close();
  delete output;
  
  // Compare results with reference run
  Int_t compare = CompareResults(fit, doFit);
  if (!compare) {
     printf("\nmultidimfit ..............................................  OK\n");
  } else {
     printf("\nmultidimfit ..............................................  fails case %d\n",compare);
  }

  // We're done 
  delete fit;
  return compare;
}

{'fraction_non_alphanumeric': 0.19748438435868915, 'fraction_numerical': 0.05886882861298879, 'mean_word_length': 2.307300509337861, 'pattern_counts': {'":': 0, '<': 72, '<?xml version=': 0, '>': 81, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 4, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7631060', 'n_tokens_mistral': 5014, 'n_tokens_neox': 4577, 'n_words': 1187}