public void ResetTraining()
{
Substrate substrate = SubstrateFactory.factorSandwichSubstrate(11, 11);
BoxesScore score = new BoxesScore(11);
pop = new NEATPopulation(substrate, 500);
pop.ActivationCycles = 4;
pop.Reset();
train = NEATUtil.ConstructNEATTrainer(pop, score);
OriginalNEATSpeciation speciation = new OriginalNEATSpeciation();
train.Speciation = new OriginalNEATSpeciation();
}
public void Render()
{
NEATGenome genome = (NEATGenome)this.pop.BestGenome;
Substrate substrate = SubstrateFactory.factorSandwichSubstrate(resolution, resolution);
HyperNEATCODEC codec = new HyperNEATCODEC();
NEATNetwork phenotype = (NEATNetwork)codec.Decode(this.pop, substrate, genome);
TrialEvaluation trial = new TrialEvaluation(phenotype, this.testCase);
IntPair actualPos = trial.Query(resolution);
// clear what was there before
GridCanvas.Children.Clear();
//
double boxWidth = GridCanvas.ActualWidth / resolution;
double boxHeight = GridCanvas.ActualHeight / resolution;
double delta = 2.0 / resolution;
int index = 0;
for (int row = 0; row < resolution; row++)
{
double y = -1 + (row * delta);
double boxY = row * boxHeight;
for (int col = 0; col < resolution; col++)
{
double x = -1 + (col * delta);
double boxX = col * boxWidth;
Rectangle r = new Rectangle();
r.SetValue(Canvas.LeftProperty, boxX);
r.SetValue(Canvas.TopProperty, boxY);
r.Width = boxWidth;
r.Height = boxHeight;
if (this.testCase.GetPixel(x, y) > 0)
{
r.Fill = Brushes.Blue;
}
else
{
double d = trial.Output[index];
int c = trial.Normalize(d, 255);
SolidColorBrush b = new SolidColorBrush(Color.FromRgb(255, (byte)c, 255));
r.Fill = b;
r.Stroke = Brushes.Black;
}
GridCanvas.Children.Add(r);
index++;
}
}
Rectangle target = new Rectangle();
target.SetValue(Canvas.LeftProperty, actualPos.X * boxWidth);
target.SetValue(Canvas.TopProperty, actualPos.Y * boxHeight);
target.Width = boxWidth;
target.Height = boxHeight;
target.Fill = Brushes.Red;
GridCanvas.Children.Add(target);
}