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;
// 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);
}