#include "amma/Num/NumClassifyBayes.hh"
#include "amma/Num/NumPDFNormal.hh"
#include "amma/Num/NumPerfLeaveOutOne.hh"
#include "amma/Num/NumErrorCount.hh"
#include "amma/Num/NumSelectConstant.hh"
#include "amma/Num/NumSelectGenetic.hh"
#include "amma/Vector2d.hh"

main ()
{
  // Setup training data
  NumVLDataSetC trainL;
  trainL.Append (Vector2dC(1,1),0);
  trainL.Append (Vector2dC(1,-1),0);
  trainL.Append (Vector2dC(2,0),0);
  trainL.Append (Vector2dC(1.5,0.5),0);
  trainL.Append (Vector2dC(-1,1),1);
  trainL.Append (Vector2dC(-1,-1),1);
  trainL.Append (Vector2dC(-2,0),1);
  trainL.Append (Vector2dC(-1.5,-0.5),1);
  // Setup classifier stuff
  NumPDFNormalC pdf;
  VectorC prior (2); prior.Fill (0.5);
  NumClassifyBayesC class1 (pdf,prior);
  // Setup error weighting stuff
  NumErrorCountC errorCount;
  NumPerfLeaveOutOneC perfMeasure;
  // Setup Feature selectors
  IntSArray1dC list (1); list[0] = 1;
  SArray1dC<NumSelectC> select (2);
  select[0] = NumSelectConstantC (list);
  select[1] = NumSelectGeneticC ();
  // Test selectors
  FOR_SARRAY1 (select,i) {
    select[i].Design (trainL,class1,errorCount,perfMeasure);
    cout << select[i].GetInfo () << endl;
    cout << "Input feature set PMC = " << perfMeasure.PMC (trainL,class1,errorCount) << endl;
    NumVLDataSetC subTrain (select[i].Evaluate(trainL.InputSet()),trainL.OutputSet());
    cout << "Output training set PMC = " << perfMeasure.PMC (subTrain,class1,errorCount) << endl;
    cout << "Input feature set: " << trainL.InputSet() << endl;
    cout << "Output feature set: " << subTrain.InputSet() << endl;
  }
}