public static float LaunchFirst(NN net, int[] layers, float[] chosen)
{
StreamReader sk = new StreamReader(@"weightValues1.txt", System.Text.Encoding.GetEncoding(1257));
net.ReadWeights(sk);
sk.Close();
float output = ((net.FeedForward(chosen)[0]) + 1f) * 2.5f; //gaunamas outputas is neural network
Console.WriteLine("First AI result: {0}", output);
List <float> possibilities = new List <float>(); //galimos vietos ikelti pamoka
for (int i = 0; i < 5; i++)
{
if (chosen[i * 3 + 2] == 0f || chosen[i * 3] + chosen[i * 3 + 1] == 8f)
{
possibilities.Add(i);
}
if (chosen[i * 3 + 2] == 0f && chosen[i * 3] + chosen[i * 3 + 1] == 8f)
{
possibilities.Add(i);
}
}
float nearest = ClosestTo(possibilities, output);
return(nearest);
}
public static int LaunchSecond(NN net, int[] layers, ref string[] chosen, string reference)
{
StreamReader sk = new StreamReader(@"weightValues2.txt", System.Text.Encoding.GetEncoding(1257));
net.ReadWeights(sk);
sk.Close();
const int dataCount = 8; //kiek pradiniu duomenu
float[] primalData = new float[dataCount]; //naujas duomenu masyvas NN'ui
for (int i = 0; i < dataCount; i++)
{
primalData[i] = 1f;
}
primalData = TurnSecond(chosen, reference); //jau suzymetos klases ir langai paverciami i duomenis NN'ui
float output = ((net.FeedForward(primalData)[0]) + 1f) * 4f; //gaunamas outputas is neural network
Console.WriteLine("Second AI result: {0}", output);
List <float> possibilities = new List <float>(); //galimos vietos ikelti pamoka
for (int j = 0; j < dataCount; j++)
{
if (chosen[j] == "-")
{
possibilities.Add(j); //surenkamos visos tuscios vietos
}
}
float nearest = ClosestTo(possibilities, output); //surandama artimiausia tuscia vieta pamokai
return((int)nearest);
}