//private static TransferFunction Transfer = new ReLUFunction();
//private static TransferFunction Transfer = new SigmoidFunction();
private static void Main(string[] args)
{
var data = DataReader.ReadFromFile("data/iris.data").OrderByDescending(i => i.SepalWidth).ToArray();
data = NormalizeData(data, Transfer.XMin, Transfer.XMax).ToArray();
var networks = GenerateNetworks().ToArray();
var genetic = new Genetic.Genetic(new ConstantParamsProvider(0.02F, 0.001F).GetParams, NextFloat);
var indivs = new Individual[NetworkCount];
int genIndx = 0;
float avgFitness;
float bestFitness;
var startT = Environment.TickCount;
do
{
var t = Environment.TickCount;
Console.Write($"\nGen: {genIndx}");
Parallel.For(0, NetworkCount, (n) => {
var curNet = networks[n];
var grade = Grade(curNet, data);
indivs[n] = curNet.ToIndividual(grade);
});
var res = genetic.Apply(indivs).ToArray();
for (int n = 0; n < NetworkCount; n++)
{
networks[n].SetWeights(res[n]);
}
avgFitness = indivs.Average(ind => ind.Fitness);
bestFitness = indivs.Max(ind => ind.Fitness);
Console.Write($" - Fitness: avg={avgFitness:N3} max={bestFitness:N3}, variety: {indivs.AvgVariety():N3}, time {Environment.TickCount - t}Ms");
genIndx++;
} while(bestFitness < 0.96);
Console.Write($"\n\nDone in {Environment.TickCount - startT}Ms");
Console.ReadKey();
}