private static void TestNetwork()
{
NeuralNetworkChromosome network = _trainer.FittestChromosome;
double[] input = GenerateInput();
double[] output = network.Compute(input);
double[] target = CalculateOutput(input);
Console.WriteLine("After {0} generations:", _trainer.CurrentGeneration);
Console.WriteLine("calculated output: {0}", string.Join(",", output.Select(x => x.ToString("0.00"))));
Console.WriteLine("target output: {0}", string.Join(",", target.Select(x => x.ToString("0.00"))));
Console.WriteLine("Fitness Value: {0}", network.FitnessValue);
Console.WriteLine();
FitnessFunction(network);
}
private static double FitnessFunction(NeuralNetworkChromosome chromosome)
{
double fitness = 0;
int n = 3;
for (int i = 0; i < n; i++)
{
double[] input = GenerateInput();
double[] outputFromNetwork = chromosome.Compute(input);
double[] calculatedOutput = CalculateOutput(input);
double sum = 0;
for (int z = 0; z < input.Length; z++)
{
sum += Math.Abs(calculatedOutput[z] - outputFromNetwork[z]);
}
fitness += INPUT_NEURONS_COUNT - sum;
}
return(fitness / n / 2);
}
