public void Cross(NeuroNetwork other)
{
for (int i = 0; i < HiddenLayers.Count; i++)
{
for (int j = 0; j < HiddenLayers[i].Count; j++)
{
if (StaticRandom.NextDouble() > 0.5)
{
HiddenLayers[i][j] = other.HiddenLayers[i][j].Copy();
}
}
}
for (int j = 0; j < Outputs.Count; j++)
{
if (StaticRandom.NextDouble() > 0.5)
{
Outputs[j] = other.Outputs[j].Copy();
}
}
}
public static double GetRandomWeight()
{
return(StaticRandom.NextDouble() * 2 - 1);
}
public void NeuroShake(double rate)
{
for (int i = 0; i < HiddenLayers.Count; i++)
{
for (int j = 0; j < HiddenLayers[i].Count; j++)
{
for (int w = 0; w < HiddenLayers[i][j].Weights.Length; w++)
{
if (StaticRandom.NextDouble() < rate)
{
HiddenLayers[i][j].Weights[w] = GetRandomWeight();
}
}
}
}
for (int i = 0; i < Outputs.Count; i++)
{
for (int w = 0; w < Outputs[i].Weights.Length; w++)
{
if (StaticRandom.NextDouble() < rate)
{
Outputs[i].Weights[w] = GetRandomWeight();
}
}
}
for (int i = 0; i < Memory.Count; i++)
{
for (int w = 0; w < Memory[i].Weights.Length; w++)
{
if (StaticRandom.NextDouble() < rate)
{
Memory[i].Weights[w] = GetRandomWeight();
}
}
}
/*for (int i = 0; i < 10 * rate; i++)
* {
* if (R.NextDouble() > 0.3)
* {
* var j = R.Next(HiddenLayers.Count);
* var k = R.Next(HiddenLayers[j].Count);
* var prevLayer = j == 0 ? Inputs : HiddenLayers[j - 1];
* var l = R.Next(prevLayer.Count);
* HiddenLayers[j][k].Weights[l] = GetRandomWeight();
* }
* else if (R.NextDouble() > 0.5)
* {
* var j = R.Next(Memory.Count);
* var k = R.Next(HiddenLayers[HiddenLayers.Count - 1].Count);
* Memory[j].Weights[k] = GetRandomWeight();
* }
* else
* {
* var j = R.Next(Outputs.Count);
* var k = R.Next(HiddenLayers[HiddenLayers.Count - 1].Count);
* Outputs[j].Weights[k] = GetRandomWeight();
* }
* }*/
}
