public void SetNewNeuralNet(RMP_Net net)
{
NeuralNet = net;
NeuralNet.RemoveAllHiddenNeurons();
foreach (var inputN in NeuralNet.InputNeurons)
{
inputN.Connections.Clear();
}
var neuronList = new List <RMP_Neuron>();
neuronList.AddRange(net.HiddenNeurons);
neuronList.AddRange(net.InputNeurons);
neuronList.AddRange(net.OutputNeurons);
neurons = neuronList.ToArray();
const int inputHiddenMargin = 10;
const int hiddenOutputMargin = 30;
neuronPositions = new Vector2[neurons.Length];
const int inputNeuronMargin = 2;
int inputY = 0;
int i;
for (i = 0; i < net.HiddenNeurons.Count + net.InputNeurons.Count; i++)
{
neuronPositions[i].Y = inputY;
inputY += inputNeuronMargin + Circle.Height;
}
const int outputNeuronMargin = 2;
int outputY = 0;
for (; i < net.HiddenNeurons.Count + net.InputNeurons.Count + net.OutputNeurons.Count; i++)
{
neuronPositions[i].X = Circle.Width + inputHiddenMargin + Circle.Width + hiddenOutputMargin;
neuronPositions[i].Y = outputY;
outputY += outputNeuronMargin + Circle.Height;;
}
for (i = 0; i < neuronPositions.Length; i++)
{
if (neuronPositions[i].X > graphWidth)
{
graphWidth = neuronPositions[i].X;
}
if (neuronPositions[i].Y > graphHeight)
{
graphHeight = neuronPositions[i].Y;
}
}
}
public void SetNewNeuralNet(RMP_Net net)
{
NeuralNet = net;
var neuronList = new List<RMP_Neuron>();
neuronList.AddRange(net.HiddenNeurons);
neuronList.AddRange(net.InputNeurons);
neuronList.AddRange(net.OutputNeurons);
neurons = neuronList.ToArray();
const int neuronMargin = 15;
const int inputHiddenMargin = 10;
const int hiddenOutputMargin = 10;
double radius = (net.HiddenNeurons.Count * (neuronMargin + Circle.Width) / Math.PI / 2);
double rotationStep = Math.PI * 2 / net.HiddenNeurons.Count;
double rotation = 0;
neuronPositions = new Vector2[neurons.Length];
int i;
for (i = 0; i < net.HiddenNeurons.Count; i++)
{ //Margin between input neurons and hidden neurons.
neuronPositions[i].X = (float)(Math.Cos(rotation) * radius) + Circle.Width + inputHiddenMargin + (float)radius;
neuronPositions[i].Y = (float)(Math.Sin(rotation) * radius);
rotation += rotationStep;
}
const int inputNeuronMargin = 2;
int inputY = 0;
for (; i < net.HiddenNeurons.Count + net.InputNeurons.Count; i++)
{
neuronPositions[i].Y = inputY;
inputY += inputNeuronMargin + Circle.Height;
}
const int outputNeuronMargin = 2;
int outputY = 0;
for (; i < net.HiddenNeurons.Count + net.InputNeurons.Count + net.OutputNeurons.Count; i++)
{
neuronPositions[i].X = Circle.Width + inputHiddenMargin + Circle.Width + (float)radius * 2 + hiddenOutputMargin;
neuronPositions[i].Y = outputY;
outputY += outputNeuronMargin + Circle.Height; ;
}
for (i = 0; i < neuronPositions.Length; i++)
{
if (neuronPositions[i].X > graphWidth)
graphWidth = neuronPositions[i].X;
if (neuronPositions[i].Y > graphHeight)
graphHeight = neuronPositions[i].Y;
}
}
