add: NeuralSolver

InverseOfLife.csproj

@@ -2,10 +2,18 @@
 
     <PropertyGroup>
         <TargetFramework>net8.0</TargetFramework>
+        <RuntimeIdentifier>linux-x64</RuntimeIdentifier>
         <ImplicitUsings>enable</ImplicitUsings>
         <Nullable>disable</Nullable>
         <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
         <Version>0.0.2</Version>
+        <CopyLocalLockFileAssemblies>true</CopyLocalLockFileAssemblies>
     </PropertyGroup>
 
+    <ItemGroup>
+        <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.16.0" />
+        <PackageReference Include="TensorFlow.Keras" Version="0.15.0" />
+        <PackageReference Include="TensorFlow.NET" Version="0.150.0" />
+    </ItemGroup>
+
 </Project>

src/Board.cs

@@ -4,6 +4,7 @@ namespace InverseOfLife;
 
 public class Board
 {
+
     [SetsRequiredMembers]
     public Board(int w, int h, bool qx = false, bool qy = false, bool useTracer = false)
     {
@@ -128,6 +129,24 @@ public class Board
         return builder.ToString();
     }
 
+    public HashSet<(int, int)>[] Frames(int steps)
+    {
+        HashSet<(int, int)>[] res = new HashSet<(int, int)>[steps];
+        for (int i = 0; i < steps; i++)
+        {
+            Evaluate();
+            res[i] = CopyLives();
+        }
+        return res;
+    }
+
+    private HashSet<(int, int)> CopyLives()
+    {
+        HashSet<(int, int)> res = new();
+        foreach ((int, int) cell in Lives)
+            res.Add(cell);
+        return res;
+    }
 
     public void Play(int generations, int delay = 200)
     {

src/Generator.cs

@@ -32,7 +32,7 @@ public class Generator
             {
                 if (idx > layers * split)
                     break;
-                (Gene g, double x) = s.Solve(mode: mode);
+                (Gene g, double x) = s.Solve(2, 10, 20, 0.2f, mode: mode);
                 res[idx] = g.Restore(target.Width, target.Height, target.QuotientX, target.QuotientY);
                 idx += 1;
                 Console.WriteLine($"Progress: {idx}/{layers * split}");
@@ -45,13 +45,13 @@ public class Generator
             result[i] = new Dictionary<(int, int), float>();
             foreach (Board b in res)
             {
+                b.Evaluate();
                 foreach ((int, int) cell in b.Lives)
                 {
                     if(!result[i].Keys.Contains(cell))
                         result[i][cell] = 0f;
                     result[i][cell] += 1f/(layers * split);
                 }
-                b.Evaluate();
             }
             
         }

src/NeuralSolver.cs

@@ -0,0 +1,198 @@
+using Tensorflow;
+using Tensorflow.Gradients;
+using Tensorflow.Keras.Engine;
+using Tensorflow.NumPy;
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;  
+namespace InverseOfLife;
+
+public class NeuralSolver
+{
+    private int Width { get; set; }
+    private int Height { get; set; }
+    private int Steps { get; set; }
+    private bool QuotientX { get; set; }
+    private bool QuotientY { get; set; }
+    private IOptimizer Optimizer { get; set; }
+
+    private IModel ForwardModel { get; set; }
+    private IModel ReverseModel { get; set; }
+
+    private void BuildForwardModel()
+    {
+        Tensors inputs = keras.Input(shape: new Shape(Height, Width, 1), name: "InitialState");
+        Tensors hidden = keras.layers.Conv2D(32, kernel_size: 3, padding:"same", activation: keras.activations.Relu).Apply(inputs);
+        hidden = keras.layers.Conv2D(32, kernel_size: 3, padding: "same", activation: keras.activations.Relu).Apply(hidden);
+        Tensors outputs = keras.layers.Conv2D(1, kernel_size: 1, padding: "same", activation: keras.activations.Sigmoid).Apply(hidden);
+        ForwardModel = keras.Model(inputs, outputs, name: "ForwardModel");
+    }
+
+    private void BuildReverseModel()
+    {
+        Tensors inputs = keras.Input(shape: new Shape(Height, Width, 1), name: "FinalState");
+        Tensors hidden = keras.layers.Conv2D(32, kernel_size: 3, padding: "same", activation: keras.activations.Relu).Apply(inputs);
+        hidden = keras.layers.Conv2D(32, kernel_size:3, padding:"same", activation: keras.activations.Relu).Apply(hidden);
+        Tensors outputs = keras.layers.Conv2D(1, kernel_size: 1, padding:"same", activation: keras.activations.Sigmoid).Apply(hidden);
+        ReverseModel = keras.Model(inputs, outputs, name: "ReverseModel");
+    }
+
+    public NeuralSolver(int width, int height, int steps, bool quotientX, bool quotientY)
+    {
+        Width = width;
+        Height = height;
+        Steps = steps;
+        QuotientX = quotientX;
+        QuotientY = quotientY;
+        BuildForwardModel();
+        BuildReverseModel();
+    }
+
+    public void SaveModel(string basePath)
+    {
+        ForwardModel.save($"{basePath}/FM{Width}x{Height}_{Steps}_{QuotientX}_{QuotientY}");
+        ReverseModel.save($"{basePath}/RM{Width}x{Height}_{Steps}_{QuotientX}_{QuotientY}");
+    }
+    public void LoadModel(string basePath)
+    {
+        ForwardModel = keras.models.load_model($"{basePath}/FM{Width}x{Height}_{Steps}_{QuotientX}_{QuotientY}");
+        ReverseModel = keras.models.load_model($"{basePath}/RM{Width}x{Height}_{Steps}_{QuotientX}_{QuotientY}");
+    }
+    public (NDArray, NDArray) GenerateTrainingData(int datasetSize)
+    {
+        Random rnd = new Random();
+
+        float[] inputsData = new float[datasetSize * Height * Width];
+        float[] labelsData = new float[datasetSize * Height * Width];
+
+        for (int idx = 0; idx < datasetSize; idx++)
+        {
+            Board board = new Board(Width, Height, QuotientX, QuotientY);
+            int randomCells = rnd.Next(1, Width * Height / 4);
+            for (int c = 0; c < randomCells; c++)
+            {
+                int x = rnd.Next(0, Width);
+                int y = rnd.Next(0, Height);
+                board.Toggle(x, y, true);
+            }
+
+            int offsetLabel = idx * Width * Height;
+            foreach ( (int x, int y) in board.Lives)
+            {
+                int pos = y * Width + x;
+                labelsData[offsetLabel + pos] = 1f;
+            }
+
+            board.Evaluate(Steps);
+
+            int offsetInput = idx * Width * Height;
+            foreach (var (x, y) in board.Lives)
+                inputsData[offsetInput + (y * Width + x)] = 1f;
+        }
+        NDArray inputsTensor = np.array(inputsData).reshape(new Shape(datasetSize, Height, Width, 1));
+        NDArray labelsTensor = np.array(labelsData).reshape(new Shape(datasetSize, Height, Width, 1));
+        return (inputsTensor, labelsTensor);
+    }
+
+    public void Train(int datasetSize = 1000, int batchSize = 8, int epochs = 10)
+    { 
+        (NDArray trainFinal, NDArray trainInitial) = GenerateTrainingData(datasetSize);
+        Optimizer = keras.optimizers.Adam(learning_rate: 0.001f);
+        for (int epoch = 0; epoch < epochs; epoch++)
+        {
+            for (int i = 0; i < datasetSize; i += batchSize)
+            {
+                NDArray initialBatch = trainInitial[$"{i}:{i + batchSize}"];
+                NDArray finalBatch = trainFinal[$"{i}:{i + batchSize}"];
+                using (GradientTape tape = tf.GradientTape())
+                {
+                    Tensors predictedFinal = ForwardModel.Apply(initialBatch);
+                    Tensors predictedInitial = ReverseModel.Apply(finalBatch);
+                    Tensors reconstructedFinal = ForwardModel.Apply(predictedInitial);
+                    
+                    Tensor forwardLoss = keras.losses.BinaryCrossentropy().Call(finalBatch, predictedFinal);
+                    Tensor reverseLoss = keras.losses.BinaryCrossentropy().Call(initialBatch, predictedInitial);
+                    Tensor cycleLoss = keras.losses.BinaryCrossentropy().Call(finalBatch, reconstructedFinal);
+                    Tensor totalLoss = forwardLoss + reverseLoss + cycleLoss;
+                    Tensor[] gradients = tape.gradient(totalLoss, ForwardModel.TrainableVariables.Concat(ReverseModel.TrainableVariables));
+                    Optimizer.apply_gradients(zip(gradients, ForwardModel.TrainableVariables.Concat(ReverseModel.TrainableVariables)));
+                    Console.WriteLine($"Epoch {epoch + 1}, Batch {i / batchSize + 1}, Loss: {totalLoss.numpy()}");
+                }
+                
+            }
+        }
+
+        
+    }
+
+    public Board Predict(Board target)
+    {
+        float[] inputData = new float[Height * Width];
+        foreach (var (x, y) in target.Lives)
+            inputData[y * Width + x] = 1f;
+        NDArray input = np.array(inputData).reshape(new Shape(1, Height, Width, 1));
+        Tensors pred = ReverseModel.predict(input);
+        float[] predData = pred.ToArray<float>();
+        Board res = new Board(Width, Height);
+        for (int i = 0; i < predData.Length; i++)
+        {
+            int x = i % Width;
+            int y = i / Width;
+            if (predData[i] > 0.5f)
+                res.Lives.Add((x, y));
+        }
+        return res;
+    }
+    
+    public static IEnumerable<(int, int)> Circle()
+    {
+        int centerX = 10;
+        int centerY = 10;
+        int radius = 7;
+        int x = 0;
+        int y = radius;
+        
+        int d = 1 - radius;
+        
+        IEnumerable<(int, int)> PlotCirclePoints(int cx, int cy, int px, int py)
+        {
+            yield return (cx + px, cy + py);
+            yield return (cx - px, cy + py);
+            yield return (cx + px, cy - py);
+            yield return (cx - px, cy - py);
+            yield return (cx + py, cy + px);
+            yield return (cx - py, cy + px);
+            yield return (cx + py, cy - px);
+            yield return (cx - py, cy - px);
+        }
+        
+        foreach (var point in PlotCirclePoints(centerX, centerY, x, y))
+            yield return point;
+        
+        while (x < y)
+        {
+            x++;
+            if (d < 0)
+                d += 2 * x + 1;
+            else
+            {
+                y--;
+                d += 2 * (x - y) + 1;
+            }
+
+            foreach (var point in PlotCirclePoints(centerX, centerY, x, y))
+                yield return point;
+        }
+    }
+    public static void Run()
+    {
+        NeuralSolver solver = new NeuralSolver(20, 20, 10, false, false);
+        solver.Train(1000,8,20);
+        Board b = new Board(20, 20);
+        foreach ((int, int) cell in Circle())
+            b.Toggle(cell);
+        b.Evaluate(10);
+        Board z = solver.Predict(b);
+        Console.WriteLine(z.ToString());
+        
+    }
+}

src/ResultData.cs

@@ -0,0 +1,123 @@
+namespace InverseOfLife;
+
+public class ResultData
+{
+    public int Width { get; set; }
+    public int Height { get; set; }
+    public bool QuotientX { get; set; }
+    public bool QuotientY { get; set; }
+    public byte[] TargetSignature { get; set; }
+    public HashSet<(int x, int y)>[] Frames { get; set; }
+    public double Score { get; set; }
+
+    public override string ToString()
+    {
+        
+        return $"""
+                {Width}x{Height}
+                {QuotientX} {QuotientY}
+                {BytesToString(TargetSignature)}
+                {FramesToString(Frames)}
+                {Score}
+                """;
+    }
+    public static ResultData Restore(string save)
+    {
+
+        var rawLines = save.Split(new[] { "\r\n", "\n" }, StringSplitOptions.None);
+        
+        var linesList = new List<string>();
+        foreach (var line in rawLines)
+        {
+            var trimmed = line.Trim();
+            if (trimmed.Length > 0)
+                linesList.Add(trimmed);
+        }
+        var lines = linesList.ToArray();
+
+        if (lines.Length < 4)
+            throw new FormatException("Input string not in expected format (less than 4 non-empty lines).");
+        
+        var line1 = lines[0];
+        var wh = line1.Split('x');
+        if (wh.Length != 2)
+            throw new FormatException($"Line1 format invalid: {line1}");
+        int width = int.Parse(wh[0]);
+        int height = int.Parse(wh[1]);
+        
+        var line2 = lines[1].Split(' ', StringSplitOptions.RemoveEmptyEntries);
+        if (line2.Length != 2)
+            throw new FormatException($"Line2 format invalid: {lines[1]}");
+        bool qx = bool.Parse(line2[0]);
+        bool qy = bool.Parse(line2[1]);
+
+        var line3 = lines[2];
+        byte[] signature;
+        if (string.IsNullOrWhiteSpace(line3))
+            signature = Array.Empty<byte>();
+        else
+        {
+            var hexes = line3.Split(' ', StringSplitOptions.RemoveEmptyEntries);
+            signature = new byte[hexes.Length];
+            for (int i = 0; i < hexes.Length; i++)
+                signature[i] = Convert.ToByte(hexes[i], 16);
+        }
+
+
+        var scoreLine = lines[lines.Length - 1];
+        double score = double.Parse(scoreLine);
+        
+        var framesList = new List<HashSet<(int, int)>>();
+        for (int i = 3; i < lines.Length - 1; i++)
+        {
+            var frameLine = lines[i].Trim();
+            if (string.IsNullOrEmpty(frameLine))
+            {
+                framesList.Add(new HashSet<(int,int)>());
+                continue;
+            }
+            
+            var tokens = frameLine.Split(' ', StringSplitOptions.RemoveEmptyEntries);
+
+            var frameSet = new HashSet<(int, int)>();
+            foreach (var token in tokens)
+            {
+                var trimmed = token.Trim('(', ')');
+                var xy = trimmed.Split(',');
+                if (xy.Length != 2)
+                    throw new FormatException($"Frame cell format invalid: {token}");
+                int cellX = int.Parse(xy[0]);
+                int cellY = int.Parse(xy[1]);
+                frameSet.Add((cellX, cellY));
+            }
+            framesList.Add(frameSet);
+        }
+        
+        return new ResultData
+        {
+            Width = width,
+            Height = height,
+            QuotientX = qx,
+            QuotientY = qy,
+            TargetSignature = signature,
+            Frames = framesList.ToArray(),
+            Score = score
+        };
+    }
+
+    private static string BytesToString(byte[] bytes) 
+        => String.Join(" ", bytes.Select(b => b.ToString("X2")));
+
+    private static string FramesToString(HashSet<(int, int)>[] frames)
+    {
+        string res = "";
+        foreach (HashSet<(int, int)> frame in frames)
+        {
+            res += String.Join(" ", frame.Select(cell => $"({cell.Item1},{cell.Item2})"));
+            res += "\n";
+        }
+
+        return res;
+    }
+    
+}

src/Solver.cs

@@ -36,7 +36,22 @@ public class Solver
         return res;
     }
 
-    [SuppressMessage("ReSharper.DPA", "DPA0002: Excessive memory allocations in SOH", MessageId = "type: Entry[System.Int32,System.Double][]; size: 14391MB")]
+    public ResultData SolveToData(int resolution, int maxGeneration, int topN, float mutationRate, string mode)
+    {
+        (Gene res, double score) = Solve(resolution, maxGeneration, topN, mutationRate, mode);
+        ResultData result = new ResultData
+        {
+            Width = Target.Width,
+            Height = Target.Height,
+            QuotientX = Target.QuotientX,
+            QuotientY = Target.QuotientY,
+            TargetSignature = new Gene(1, Target).RiboseSequence,
+            Frames = res.Restore(Target.Width, Target.Height, Target.QuotientX, Target.QuotientY).Frames(Steps),
+            Score = score,
+        };
+        return result;
+    }
+
     public (Gene, double) Solve(int withResolution=1, int maxGenerations=50, int topN=10, float mutationRate=0.1f, string mode="tp_only")
     {
         List<(Board, Gene)> currentGeneration = GetInitialGeneration()

summerizer.py

@@ -0,0 +1,30 @@
+import os
+
+ignores = [
+    'bin',
+    'obj'
+]
+def find_all_proj_files(base_path):
+    res = []
+    for root, dirs, files in os.walk(base_path):
+        dirs[:] = [d for d in dirs if not d.startswith('.') and not d in ignores]
+        for file in files:
+            if file not in ignores:
+                res.append(os.path.join(root, file))
+    return res
+
+
+def summerizer():
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+
+
+    fs = find_all_proj_files(current_dir)
+    res = ""
+    for file in fs:
+        with open(file) as f:
+            res += f"---------------------{file}-------------------------\n"
+            res += f.read()
+        res += "\n"
+    print(res)
+
+summerizer()