pr2.m

function [ ] = pr2()
  m = 50;
  
  for punct = 1:m
    X(1, punct) = unifrnd(-1, 1);
    X(2, punct) = unifrnd(-1, 1);
    X(3, punct) = unifrnd(-1, 1);
  end
  
  for punct = 1:m
    determinant = 2 * X(1, punct) - X(2, punct) + X(3, punct)
    clasa = (determinant < 0)
    if (clasa)
      T(punct) = -1;
    else
      T(punct) = 1;
    end
  end
  
  
  neuralnet1 = newp([-1 1; -1 1; -1 1], 1, 'hardlims', 'learnpn');
  neuralnet1.adaptParam.passes = 1000
  neuralnet1.adaptParam.showWindow = false;
  
  neuralnet1 = adapt(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 1], 1, 'hardlims', 'learnp');
  neuralnet2.adaptParam.passes = 1000
  neuralnet2.adaptParam.showWindow = false;
  
  neuralnet2 = adapt(neuralnet2, X, T);
  plotpv(X, hardlim(T));
  plotpc(net.IW{1, 1}, net.b{1});
  Y2 = confusionmat(hardlim(T), hardlim(sim(neuralnet2, X)))
end