/// \file
/// \ingroup tutorial_math
/// \notebook
/// Principal Components Analysis (PCA) example
///
/// Example of using TPrincipal as a stand alone class.
///
/// We create n-dimensional data points, where c = trunc(n / 5) + 1
/// are  correlated with the rest n - c randomly distributed variables.
///
/// \macro_output
/// \macro_code
///
/// \authors Rene Brun, Christian Holm Christensen

#include "TPrincipal.h"

void principal(Int_t n=4, Int_t m=150)
{
   cout << "*************************************************" << endl;
   cout << "*         Principal Component Analysis          *" << endl;
   cout << "*                                               *" << endl;
   cout << "*  Number of variables:           " << setw(4) << n
       << "          *" << endl;
   cout << "*  Number of data points:         " << setw(8) << m
       << "      *" << endl;
   cout << "*  Number of dependent variables: " << setw(4) << c
       << "          *" << endl;
   cout << "*                                               *" << endl;
   cout << "*************************************************" << endl;

   TPrincipal* principal = new TPrincipal(n,"ND");

   // Make the m data-points
   // Make a variable to hold our data
   // Allocate memory for the data point
   Double_t* data = new Double_t[n];

  ifstream inFile;
  inFile.open("path/to/iris.data");
  if (!inFile)
  {
      cout << "\nError opening file.\n";
      return 13;
  }

   for (Int_t i = 0; i < m; i++) // loop over file
   {
      // First we create the un-correlated, random variables, according
      // to one of three distributions
      for (Int_t j = 0; j < n ; j++)
      {
         double val;
         inFile >> val;
         data[j] = val;
      }
      TString label;
      inFile >> label;
      // Finally we're ready to add this datapoint to the PCA
      principal->AddRow(data);
      principal->GetRow(m);
   }

   // We delete the data after use, since TPrincipal got it by now.
   delete [] data;

   // Do the actual analysis
   principal->MakePrincipals();

   // Print out the result on
   principal->Print();

   // Test the PCA
   principal->Test();

   // Make some histograms of the orginal, principal, residue, etc data
   principal->MakeHistograms("pca","XP");

   // Make two functions to map between feature and pattern space
   principal->MakeCode();

   const TVectorD *td = principal->GetUserData();

   // Start a browser, so that we may browse the histograms generated
   // above
   TBrowser* b = new TBrowser("principalBrowser", principal);
}