bla.m

function [ ] = pr1()
  m = 50
  for punct=1:m
    X(1, punct) = unifrnd(-1, 1)
    X(2, punct) = unifrnd(-1, 1)
  end

  for punct=1:m
    determinant = 2*X(1,punct) - X(2,punct)
    clasa = (determinant < 0)
    if (clasa)
      T(punct) = -1
    else
      T(punct) = 1
    end
  end

  
  neuralnet1 = newp([-1 1; -1 1], 1, 'hardlims', 'learnp');
  neuralnet1.trainParam.epochs = 1000;
  neuralnet1.trainParam.showWindow = false;

  neuralnet1 = train(neuralnet1, X, T);
  plotpv(X, hardlim(T));
  plotpc(neuralnet1.IW{1, 1}, neuralnet1.b{1});

  Y1 = confusionmat(hardlim(T), hardlim(SIM(neuralnet1, X)))

  
  neuralnet2 = newp([-1 1; -1 1], 1, 'hardlims', 'learnpn');
  neuralnet2.trainParam.epochs = 1000;
  neuralnet2.trainParam.showWindow = false;
  
  neuralnet2 = train(neuralnet2, X, T);
  plotpv(X, hardlim(T));
  plotpc(neuralnet2.IW{1, 1}, neuralnet2.b{1});

  Y2 = confusionmat(hardlim(T), hardlim(SIM(neuralnet2, X)))
end