private static BiasMachine GetBiasMachine()
{
BiasMachine machine = new BiasMachine(Input, Output);
machine.ChangeFunctionsRate = 0.25;
return(machine);
}
private static BiasMachine Learn(double RequiredError)
{
const int iteration = 10000;
const int steps = 100;
const int count = 100;
const int best = 10;
BiasMachine[] population = new BiasMachine[count];
BiasMachine[] successful = new BiasMachine[best];
RequiredError = Math.Abs(RequiredError);
for (int j = default; j < count; ++j)
{
population[j] = GetBiasMachine();
}
for (int i = default; i < iteration; ++i)
{
List <KeyValuePair <BiasMachine, double> > scores = new List <KeyValuePair <BiasMachine, double> >(count);
double[] error = new double[count];
for (int u = default; u < steps; ++u)
{
double[] input = GetInput();
double[] result = GetOutput(input);
for (int j = default; j < count; ++j)
{
double[] solution = population[j].Compute(input);
for (int z = default; z < Output; ++z)
{
error[j] += Math.Abs(solution[z] - result[z]);
}
}
}
for (int j = default; j < count; ++j)
{
error[j] /= (steps * Output);
scores.Add(new KeyValuePair <BiasMachine, double>(population[j], error[j]));
}
scores.Sort((x, y) => x.Value.CompareTo(y.Value));
if (i % 20 == default)
{
Console.Clear();
Console.WriteLine("Itteration: " + (i + 1) + " error: " + Format(scores[0].Value) + "\n\n");
}
if (Math.Abs(scores[default].Value) <= RequiredError)
