private void CreateNeuralNetworkForImprovementSelection()
{
const int pps = 4;
_nn = NeuronNetwork.Make(new ChooseBetweenFirstAndSecondFavoriteNeuronNetworkConstructor() {NumberOfPastPerformances = pps}) as DetectImprovementNeuronNetwork;
_nn.AddHiddenLayer(_nn.Layers.ToList()[0].Neurons.Count()/2);
// _nn.AddHiddenLayer(5);
_nn.AddOutputLayer();
_neuralNetworkCtrl.Bind(_nn);
LoadRacesForImprovementSelection();
_neuralNetworkCtrl.Invalidate();
ClearDisplay();
UpdateScreen();
}
private void CreateNeuralNetworkForWinnerSelection()
{
const int fieldSize =6;
const int numberOfPP =3;
_nn = NeuronNetwork.Make(new DetectWinnerNeuronNetworkConstructor() { FieldSize = fieldSize, NumberOfPastPerformancesToUse = numberOfPP }) as DetectWinnerNeuronNetwork;
_nn.AddHiddenLayer(8);
_nn.AddOutputLayer();
_neuralNetworkCtrl.Bind(_nn);
LoadRaces();
_neuralNetworkCtrl.Invalidate();
ClearDisplay();
UpdateScreen();
}
private void ChooseBetweenFirstAndSecondFavorite()
{
_nn = NeuronNetwork.Make(new ChooseBetweenFirstAndSecondFavoriteNeuronNetworkConstructor() { NumberOfPastPerformances = 3 }) as ChooseBetweenFirstAndSecondFavoriteNeuronNetwork;
//_nn.AddHiddenLayer(2*_nn.Layers.ToList()[0].Neurons.Count() +1);
_nn.AddHiddenLayer(_nn.Layers.ToList()[0].Neurons.Count() /2);
_nn.AddOutputLayer();
_neuralNetworkCtrl.Bind(_nn);
ChooseBetweenFirstAndSecondFavoriteSelection();
_neuralNetworkCtrl.Invalidate();
ClearDisplay();
UpdateScreen();
}
