public static void WriteNs(NeuralNet NN)
{
FileStream fs = new FileStream(NPath, FileMode.Create, FileAccess.Write, FileShare.None);
StreamWriter sw = new StreamWriter(fs);
foreach (Neuron n in NN.Neurons)
{
sw.WriteLine("Neuron" + ' ' + n.layer);
if (n.layer == 0)
{
for (int i = 0; i <= 7; i++)
{
for (int ii = 0; ii <= 7; ii++)
{
sw.Write(Math.Abs(n.weights[i, ii]).ToString());
if (ii < 7)
{
sw.Write(" ");
}
}
sw.WriteLine();
}
}
else
{
int count = 1;
//Layweights are all the same?
foreach (KeyValuePair <Neuron, double> kvp in n.layWeights)
{
sw.Write(Math.Abs(kvp.Value).ToString());
if (count < n.layWeights.Count())
{
sw.Write(" ");
}
count++;
}
sw.WriteLine();
}
}
sw.Close(); fs.Close();
}
public static void Play(Board b)
{
Board b2 = GoDiePointers.DeepClone(b);
Player PW = new Player(true); NeuralNet NNW = new NeuralNet(PW, 3, 10);
Data.ReadNs(NNW);
Player PB = new Player(false); NeuralNet NNB = new NeuralNet(PB, 3, 10);
Data.ReadNs(NNB);
foreach (Piece p in b2.Pieces)
{
if (p is Empty)
{
continue;
}
if (p.Player.IsW == true)
{
p.Player = PW;
}
else
{
p.Player = PB;
}
}
List <Neuron> BestNeurons = GoDiePointers.DeepClone(NNW.Neurons);
Random random = new Random();
//Amount of weights to change
int changeCount = 5;
for (int j = 0; j < changeCount; j++)
{
//For neurons
//It increases/decreases the weight by rand.next(x, y)% [normally]
//It currently is used as an input for the sigmoid (as a randomizing factor)
double randomVal = random.Next(-14, 14);
//For pieces
double pieceRVal = random.Next(1, 19) / 10.00;
int randNeuron = random.Next(0, NNW.Neurons.Count);
int randthing = random.Next(1, 2);
int X = random.Next(0, 7);
int Y = random.Next(0, 7);
try
{
if (randthing == 1)
{
if (BestNeurons[randNeuron].layer == 0)
{
NNW.Neurons[randNeuron].weights[X, Y] =
Sigmoid.sigmoid(randomVal);
}
else
{
KeyValuePair <Neuron, double> kvp = NNW.Neurons[randNeuron].layWeights.ElementAt(random.Next(0, NNW.Neurons[randNeuron].layWeights.Count));
NNW.Neurons[randNeuron].layWeights[kvp.Key] =
Sigmoid.sigmoid(randomVal);
}
}
if (randthing == 2)
{
if (BestNeurons[randNeuron].layer == 0)
{
NNB.Neurons[randNeuron].weights[X, Y] = Sigmoid.sigmoid(randomVal);
}
else
{
KeyValuePair <Neuron, double> kvp = NNB.Neurons[randNeuron].layWeights.ElementAt(random.Next(0, NNB.Neurons[randNeuron].layWeights.Count));
NNB.Neurons[randNeuron].layWeights[kvp.Key] =
Sigmoid.sigmoid(randomVal);
}
}
/*
* Disabled for now
* also, it has a 50% chance of selecting the empty squares with the current x/y randomizer
* //Changing class values?
* if (randthing == 3)
* {
* b.Pieces[X, Y].CVal = (int)(pieceRVal * (GoDiePointers.DeepClone(b.Pieces[X, Y].CVal)));
* }
* //Repeat to equalize chances of neuron vs piece
* if (randthing == 4)
* {
* b2.Pieces[Y, X].CVal = (int)(pieceRVal * (GoDiePointers.DeepClone(b.Pieces[X, Y].CVal)));
* }
*/
}
catch (Exception ex) { Console.WriteLine(ex); return; }
}
//At movecap, end playing, and write whoever had a higher score to the weight list file
int moveCap = 100;
int i = 1;
//While it has not moved too many times, and while no-one has won, play
//Run in parallel?
//Using two boards to allow for different piece cvals, unless I want to put that into the NN class?
while (i <= moveCap && !b.WWin && !b.BWin && !b2.WWin && !b2.BWin && !b.Stale && !b2.Stale)
{
if (b.WTurn)
{
b2.Pieces = NNW.Move(b, true).Pieces; Board.PrintBoard(b2); b2.WTurn = false; i++;
}
if (!b2.WTurn)
{
b.Pieces = NNB.Move(b2, false).Pieces; Board.PrintBoard(b); b.WTurn = true; i++;
}
else
{
Console.WriteLine("NN Failure"); break;
}
}
//Will need to check whether pieces read/write properly in the future
//If white won, write white's data
if (b.WWin || b2.WWin) /*Data.WritePieces(b);*/ Data {
public static void ReadNs(NeuralNet NN)
{
NN.Neurons = new List <Neuron>();
FileStream fs = new FileStream(NPath, FileMode.Open, FileAccess.Read, FileShare.None);
StreamReader sr = new StreamReader(fs);
while (!sr.EndOfStream)
{
string line = sr.ReadLine();
string[] splitLine = line.Split(' ');
if (splitLine[0] == "Neuron" && splitLine[1] == "0")
{
//fs.Position++;
int.TryParse(splitLine[1], out int result);
Neuron n = new Neuron(NN, 0, result);
for (int j = 0; j <= 7; j++)
{
line = sr.ReadLine();
splitLine = line.Split(' ');
for (int i = 0; i <= 7; i++)
{
try
{
double.TryParse(splitLine[i], out double result2);
n.weights[j, i] = result2;
}
catch { }
}
}
}
if (splitLine[0] == "Neuron" && splitLine[1] != "0")
{
Dictionary <Neuron, double> layerwets = new Dictionary <Neuron, double>();
int.TryParse(splitLine[1], out int result);
line = sr.ReadLine();
splitLine = line.Split(' ');
//MAGIC CODE, DO NOT TOUCH
for (int i = (splitLine.Count() * (result - 1)); i <= (splitLine.Count() * result) - 1; i++)
{
if (!layerwets.ContainsKey(NN.Neurons[i]))
{
if (result == NN.Neurons[i].layer + 1)
{
double.TryParse(splitLine[i - (splitLine.Count() * (result - 1))], out double result2);
layerwets.Add(NN.Neurons[i], result2);
}
}
else
{
Console.WriteLine("Failure to copy neuron " + NN.Neurons[i].ToString() + " " + NN.Neurons[i].layer.ToString());
}
}
Neuron n = new Neuron(NN, layerwets, 0, result);
if (result == 3)
{
NN.Output = n;
}
}
}
sr.Close(); fs.Close();
}
public static void activeUI()
{
Console.WriteLine("Command?");
string command = Console.ReadLine();
try
{
if (command[0] == 'm')
{
bool x = int.TryParse(command[1].ToString(), out int result);
bool y = int.TryParse(command[2].ToString(), out int result2);
bool z = int.TryParse(command[3].ToString(), out int result3);
bool q = int.TryParse(command[4].ToString(), out int result4);
if (x && y && z && q)
{
ActiveBoard = (ActiveBoard.Pieces[result, result2]).Move(ActiveBoard, result3, result4);
}
}
if (command.ToLower() == "learn")
{
NeuralNet.Play(ActiveBoard);
}
if (command[0] == 'p' || command[0] == 'P')
{
if (ActiveBoard.WTurn)
{
NeuralNet NN = new NeuralNet(player1, 3, 10);
Data.ReadNs(NN);
ActiveBoard = NN.Move(ActiveBoard, NN.Player.IsW);
}
else
{
NeuralNet NN = new NeuralNet(player2, 3, 10);
Data.ReadNs(NN);
ActiveBoard = NN.Move(ActiveBoard, NN.Player.IsW);
}
}
if (command == "initialize")
{
NeuralNet NN = new NeuralNet(player2, 3, 10);
NN.initNN();
Data.WriteNs(NN);
}
if (command == "load")
{
NeuralNet NN = new NeuralNet(player2, 3, 10);
Data.ReadNs(NN);
Data.WriteNs(NN);
}
//'s' to stop learning
if (command.Length >= 2 && command[0] == 'p' && command[1] == 'm')
{
bool x = int.TryParse(command[2].ToString(), out int result);
bool y = int.TryParse(command[3].ToString(), out int result2);
bool z = int.TryParse(command[4].ToString(), out int result3);
bool q = int.TryParse(command[5].ToString(), out int result4);
if (x && y && z && q)
{
ActiveBoard.Pieces[result, result2].Move(ActiveBoard, result3, result4);
}
}
}
catch (Exception ex) { Console.WriteLine("Failure"); Console.WriteLine(ex); }
finally
{
Board.PrintBoard(ActiveBoard);
activeUI();
}
}
