private static void DrawNeuron(this Network network, Neuron neuron, Graphics graphic)
{
Pen pen = new Pen(Color.Black);
Point point = Translate(network, neuron);
Size size = new Size(10, 10);
Rectangle r = new Rectangle(point, size);
Color color = Blend(Color.Gray, Color.Red, (neuron.Signal - 0.5));
Brush brush = new SolidBrush(color);
graphic.FillEllipse(brush, r);
graphic.DrawEllipse(pen, r);
// Draw signal text
Font font = new Font(FontFamily.GenericSerif, 8);
Brush fontbrush = new SolidBrush(Color.DarkBlue);
point = point.Shift(0, 10);
graphic.TextRenderingHint = System.Drawing.Text.TextRenderingHint.SingleBitPerPixelGridFit;
graphic.DrawString(neuron.Signal.ToString("f2"), font, fontbrush, point);
// Draw Error:
point = point.Shift(0, 10);
fontbrush = new SolidBrush(Color.Red);
graphic.DrawString(neuron.ErrorGradient.ToString("f2"), font, fontbrush, point);
}
public static void Link(this Neuron a, Neuron b)
{
Axon axon = new Axon();
axon.Source = a;
axon.Target = b;
a.Axons.Add(axon);
b.Dendrites.Add(axon);
}
public Layer(int size)
{
for (int i = 0; i < size; i++)
{
Neuron neuron = new Neuron();
Neurons.Add(neuron);
}
}
public static double CalcOutputErrorGradient(Neuron neuron) // for an output neuron
{
double signal = neuron.Signal;
double desired = neuron.Desired;
double delta = (desired - signal);
double gradient = signal * (1 - signal);
double result = gradient * delta;
return result;
}
public Layer LayerOf(Neuron neuron)
{
foreach(Layer layer in Layers)
{
layer.Neurons.Contains(neuron);
return layer;
}
return null;
}
public static Point Translate(Network network, Neuron neuron)
{
// translate a network coordinate to a screen coordinate
Layer layer = network.LayerOf(neuron);
Point p = neuron.Coordinate.Multiply(100, 25).Add(50);
return p;
}
public static double CalcErrorGradient(Neuron neuron) // for a non-output neuron
{
// this function needs to be called through the network from output towards input!!! (backpropagated)
double signal = neuron.Signal;
double deltasum = 0;
foreach (Axon axon in neuron.Axons)
{
deltasum += axon.Weight * axon.Target.ErrorGradient;
}
double gradient = signal * (1 - signal);
double result = gradient * deltasum;
return result;
}
