private float FeedRNNPair(NeuralGenome genome, int[] values)
{
genome.ResetNeuronsValues();
genome.FeedNeuralNetwork(new float[] { values[0] });
genome.FeedNeuralNetwork(new float[] { values[1] });
return(genome.Outputs.First().Value);
}
private float ComputeFitness(NeuralGenome genome)
{
genome.NetworkOperationBaker.BakeNetwork(genome);
var fitness = 0d;
for (var i = 0; i < 2; i++)
{
for (var j = 0; j < 2; j++)
{
genome.FeedNeuralNetwork(new float[] { i, j });
var output = genome.Outputs.Select(x => x.Value).First();
var targetValue = i ^ j;
var delta = Math.Abs(targetValue - output);
var gradient = (i == j && i == 1) ? 5 : 1;
fitness -= delta * gradient;
}
}
if (fitness >= -0.01f)
{
targetReached = true;
}
return((float)fitness);
}
private void MoveFromNetwork()
{
if (neuralGenome == null)
{
return;
}
neuralGenome.FeedNeuralNetwork(GenerateNetworkInputs());
var outputs = neuralGenome.Outputs.Select(x => x.Value).ToArray();
cartRb.AddForce(Vector2.right * outputs[0] * xVelocity * Time.fixedDeltaTime);
}
private float ComputeFitness(NeuralGenome genome)
{
var fitness = 0d;
for (var i = 0; i < Math.Pow(2, inputs) - 1; i++)
{
genome.NetworkOperationBaker.BakeNetwork(genome);
genome.FeedNeuralNetwork(GetBits(i).Select(x => (float)x).ToArray());
var expectedOutput = GetBits(i + 1);
fitness -= genome.Outputs.Select(x => x.Value)
.Zip(expectedOutput, (o, e) => Math.Abs(o - e))
.Sum();
}
return((float)fitness);
}
private float ComputeFitness(NeuralGenome genome)
{
genome.NetworkOperationBaker.BakeNetwork(genome);
var fitness = 0d;
// Continuous input.
foreach (var dataset in datasets)
{
genome.ResetNeuronsValues();
int expectedResult = 0;
foreach (var input in dataset)
{
expectedResult ^= input;
genome.FeedNeuralNetwork(new float[] { input });
var delta = Math.Abs(expectedResult - genome.Outputs[0].Value);
fitness -= delta;
}
}
// Pair input
//foreach (var pair in samplePairs)
//{
// var output = FeedRNNPair(genome, pair);
// var expectedResult = pair[0] ^ pair[1];
// var delta = Math.Abs(expectedResult - output);
// fitness -= delta;
//}
if (fitness >= -1f)
{
targetReached = true;
}
return((float)fitness);
}
