public FrmPlay(NNFeedForwardNetwork network)
{
InitializeComponent();
initWindow();
this.network = network;
gameloopThread = new System.Threading.Thread(gameloop);
gameloopThread.Start();
}
private void gameloop()
{
while (!stop)
{
double[] res = new double[4];
RefreshDelegate refreshDelegate = new RefreshDelegate(refr);
while (!snake.isGameOver() && !quit)
{
System.Threading.Thread.Sleep(100);
if (network != null)
{
double[] gc = snake.getGameCharacteristics();
res = network.propagateToEnd(gc);
if (res[0] >= res[1] && res[0] >= res[2] && res[0] >= res[3])
{
currentMovement = (int)Keys.Up;
}
else if (res[1] >= res[0] && res[1] >= res[2] && res[1] >= res[3])
{
currentMovement = (int)Keys.Left;
}
else if (res[2] >= res[0] && res[2] >= res[1] && res[2] >= res[3])
{
currentMovement = (int)Keys.Down;
}
else if (res[3] >= res[0] && res[3] >= res[1] && res[3] >= res[2])
{
currentMovement = (int)Keys.Right;
}
}
switch (currentMovement)
{
case (int)Keys.Up:
if (trainByPlay)
{
gamechars.AddLast(snake.getGameCharacteristics());
labels.AddLast(new double[] { 1.0, 0.0, 0.0, 0.0 });
}
snake.moveUp();
break;
case (int)Keys.Left:
if (trainByPlay)
{
gamechars.AddLast(snake.getGameCharacteristics());
labels.AddLast(new double[] { 0.0, 1.0, 0.0, 0.0 });
}
snake.moveLeft();
break;
case (int)Keys.Down:
if (trainByPlay)
{
gamechars.AddLast(snake.getGameCharacteristics());
labels.AddLast(new double[] { 0.0, 0.0, 1.0, 0.0 });
}
snake.moveDown();
break;
case (int)Keys.Right:
if (trainByPlay)
{
gamechars.AddLast(snake.getGameCharacteristics());
labels.AddLast(new double[] { 0.0, 0.0, 0.0, 1.0 });
}
snake.moveRight();
break;
}
Application.DoEvents();
if (!stop)
{
this.Invoke(refreshDelegate);
}
}
if (trainByPlay)
{
MessageBox.Show("Learning ...");
double[][] ar_trainingset = gamechars.ToArray();
double[][] ar_labels = labels.ToArray();
/*
* network = new NNDeepBeliefNetwork(new int[] { ar_trainingset[0].Length, 10, 5, 4 }, new int[] { 4, 4 });
* for (int i = 0; i < ((NNDeepBeliefNetwork)network).getUnsupervisedLayerCount(); i++)
* {
* ((NNDeepBeliefNetwork)network).trainUnsupervised(ar_trainingset, i, 10000, 0.1);
* }
* ((NNDeepBeliefNetwork)network).trainSupervised(ar_trainingset, ar_labels, 1000, 1.0);
*/
network = new NNAccordInterface(new int[] { ar_trainingset[0].Length, 10, 5, 4 });
((NNAccordInterface)network).train(ar_trainingset, ar_labels, 10000, 0.1);
/*
* network = new NNFeedForwardNetwork(new int[] { ar_trainingset[0].Length, 5, 4 });
* ((NNFeedForwardNetwork)network).randomizeWeights();
* ((NNFeedForwardNetwork)network).train(ar_trainingset, ar_labels, 1000, 1.0f);
*/
// new FrmNetworkVisualizer(((NNDeepBeliefNetwork)network).getSupervisedNetwork()).Show();
MessageBox.Show("Learning finished. Playing ...");
trainByPlay = false;
quit = false;
}
snake.restart();
}
}
