Merge pull request 'GeneticsAlgorithm' (#9) from GeneticsAlgorithm into master

Reviewed-on: #9

Assets/CSC.rsp.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 0b862470b61f4d04bb2a318bb695207e
+timeCreated: 1622480696

Assets/Logic/Agent/Waitress.cs

@@ -58,7 +58,7 @@ namespace Logic.Agent
             if (isFollowingPath) return;
             
             isFollowingPath = true;
-            
+
             if (_currentState.AgentPosition is Table && _previousState != null)
             {
                 _currentState.AgentPosition = _previousState.AgentPosition;
@@ -66,12 +66,12 @@ namespace Logic.Agent
 
             var goalState = new State();
             goalState.AgentPosition = table;
-            goalState.AgentRotation = Rotation.Right;
+            goalState.AgentRotation = Rotation.Left;
 
             Stack<AgentAction> actions;
             if (SceneContext.Instance.useAStar)
             {
-                var priorityQueue = new FringeNodeHeap(SceneContext.Instance.map.Count * 3);
+                var priorityQueue = new FringeNodeHeap(SceneContext.Instance.map.Count * 4);
                 actions = _pathFinder.GraphSearchWithCost(priorityQueue, new HashSet<FringeNode>(), _currentState, goalState);
             }
             else

Assets/Logic/CustomerTableManager.cs

@@ -13,6 +13,8 @@ namespace Logic
     {
         public static CustomerTableManager Instance;
         public int customerSpawnTime = 10;
+        [SerializeField] private GameObject tablePrefab;
+        [SerializeField] private GameObject mapGameObject;
         [SerializeField] private List<CustomerTable> customerTables;
         [SerializeField] private List<Recipe> availableRecipes;
 
@@ -34,6 +36,13 @@ namespace Logic
             StartCoroutine(HandleCustomerSpawn(customerSpawnTime));
         }
 
+        public void SpawnNewTable(int posX, int posZ, int posY = 0)
+        {
+            var itemObject = Instantiate(tablePrefab, mapGameObject.transform);
+            // customerTables.Add(itemObject);
+            itemObject.transform.localPosition = new Vector3(posX, posY, posZ);
+        }
+
         public CustomerTable GETTable(int index)
         {
             return customerTables[index];
@@ -48,6 +57,7 @@ namespace Logic
         {
             while (true)
             {
+                if (customerTables.Count == 0) continue; 
                 int randomTableNumber = Random.Range(0, customerTables.Count);
                 if (customerTables[randomTableNumber].state == TableState.Empty)
                 {

Assets/Logic/Data/Food.cs

@@ -7,6 +7,7 @@ namespace Logic.Data
     {
         public string foodName;
         public int tableNumber;
+        public Texture2D predictSprite;
         public Money moneyItem;
     }
 }

Assets/Logic/GeneticAlgorithm.meta

@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: b578289f08a878145b01fadc5f70d82d
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Assets/Logic/GeneticAlgorithm/DNA.cs

@@ -0,0 +1,59 @@
+
+
+using System;
+
+namespace Logic.GeneticAlgorithm
+{
+    public class Dna<T>
+    {
+        private readonly Func<T> _getRandomGene;
+        private readonly Func<int, float> _fitnessFunction;
+        private Random _random;
+        public T[] Genes { get; private set; }
+        public float Fitness { get; private set; }
+    
+        public Dna(int size, Random random, Func<T> getRandomGene, Func<int, float> fitnessFunction, bool shouldInitGens = true)
+        {
+            _getRandomGene = getRandomGene;
+            _fitnessFunction = fitnessFunction;
+            _random = random;
+            Genes = new T[size];
+            if (shouldInitGens)
+            {
+                for (int i = 0; i < size; i++)
+                {
+                    Genes[i] = getRandomGene();
+                }
+            }
+        }
+
+        public float CalculateFitness(int index)
+        {
+            Fitness = _fitnessFunction(index);
+            return Fitness;
+        }
+
+        public Dna<T> Crossover(Dna<T> other)
+        {
+            Dna<T> child = new Dna<T>(Genes.Length, _random, _getRandomGene, _fitnessFunction,false);
+            
+            for (int i = 0; i < Genes.Length; i++)
+            {
+                child.Genes[i] = _random.NextDouble() < 0.5 ? Genes[i] : other.Genes[i];
+            }
+
+            return child;
+        }
+
+        public void Mutate(float mutationRate)
+        {
+            for (int i = 0; i < Genes.Length; i++)
+            {
+                if (_random.NextDouble() < mutationRate)
+                {
+                    Genes[i] = _getRandomGene();
+                }
+            }
+        }
+    }
+}

Assets/Logic/GeneticAlgorithm/DNA.cs.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 317237ed714e4395800d31fbdf1d2135
+timeCreated: 1623410038

Assets/Logic/GeneticAlgorithm/GeneticAlgorithm.cs

@@ -0,0 +1,135 @@
+using System;
+using System.Collections.Generic;
+
+namespace Logic.GeneticAlgorithm
+{
+public class GeneticAlgorithm<T>
+{
+	public List<Dna<T>> Population { get; private set; }
+	public int Generation { get; private set; }
+	public float BestFitness { get; private set; }
+	public T[] BestGenes { get; private set; }
+
+	public int Elitism;
+	public float MutationRate;
+	
+    private Random _random;
+	private List<Dna<T>> _newPopulation;
+    private float _fitnessSum;
+	private readonly int _dnaSize;
+	private readonly Func<T> _getRandomGene;
+	private readonly Func<int, float> _fitnessFunction;
+
+	public GeneticAlgorithm(int populationSize, int dnaSize, Random random, Func<T> getRandomGene, Func<int, float> fitnessFunction,
+		int elitism, float mutationRate = 0.01f)
+	{
+		Generation = 1;
+		Elitism = elitism;
+		MutationRate = mutationRate;
+		Population = new List<Dna<T>>(populationSize);
+		_newPopulation = new List<Dna<T>>(populationSize);
+        _dnaSize = dnaSize;
+        _random = random;
+		_getRandomGene = getRandomGene;
+		_fitnessFunction = fitnessFunction;
+
+		BestGenes = new T[dnaSize];
+
+		for (int i = 0; i < populationSize; i++)
+		{
+			Population.Add(new Dna<T>(dnaSize, _random, getRandomGene, fitnessFunction, true));
+		}
+	}
+
+	public void NewGeneration(int numNewDna = 0, bool crossoverNewDna = false)
+	{
+		int finalCount = Population.Count + numNewDna;
+
+		if (finalCount <= 0) {
+			return;
+		}
+
+		if (Population.Count > 0) {
+			CalculateFitness();
+			Population.Sort(CompareDna);
+		}
+		_newPopulation.Clear();
+
+		for (int i = 0; i < Population.Count; i++)
+		{
+			if (i < Elitism && i < Population.Count)
+			{
+				_newPopulation.Add(Population[i]);
+			}
+			else if (i < Population.Count || crossoverNewDna)
+			{
+				Dna<T> parent1 = ChooseParent();
+				Dna<T> parent2 = ChooseParent();
+
+				Dna<T> child = parent1.Crossover(parent2);
+
+				child.Mutate(MutationRate);
+
+				_newPopulation.Add(child);
+			}
+			else
+			{
+				_newPopulation.Add(new Dna<T>(_dnaSize, _random, _getRandomGene, _fitnessFunction, true));
+			}
+		}
+
+		List<Dna<T>> tmpList = Population;
+		Population = _newPopulation;
+		_newPopulation = tmpList;
+
+		Generation++;
+	}
+	
+	private int CompareDna(Dna<T> a, Dna<T> b)
+	{
+		if (a.Fitness > b.Fitness) {
+			return -1;
+		} else if (a.Fitness < b.Fitness) {
+			return 1;
+		} else {
+			return 0;
+		}
+	}
+
+	private void CalculateFitness()
+	{
+		_fitnessSum = 0;
+		Dna<T> best = Population[0];
+
+		for (int i = 0; i < Population.Count; i++)
+		{
+			_fitnessSum += Population[i].CalculateFitness(i);
+
+			if (Population[i].Fitness > best.Fitness)
+			{
+				best = Population[i];
+			}
+		}
+
+		BestFitness = best.Fitness;
+		best.Genes.CopyTo(BestGenes, 0);
+	}
+
+	private Dna<T> ChooseParent()
+	{
+		double randomNumber = _random.NextDouble() * _fitnessSum;
+
+		for (int i = 0; i < Population.Count; i++)
+		{
+			if (randomNumber < Population[i].Fitness)
+			{
+				return Population[i];
+			}
+
+			randomNumber -= Population[i].Fitness;
+		}
+
+		return null;
+	}
+}
+}

Assets/Logic/GeneticAlgorithm/GeneticAlgorithm.cs.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: f9467a7335bc48dbb6b642d8d49aa70a
+timeCreated: 1623410750

Assets/Logic/GeneticAlgorithm/NodeGen.cs

@@ -0,0 +1,14 @@
+namespace Logic.GeneticAlgorithm
+{
+    public class NodeGen
+    {
+        public NodeGen(int posX, int posY)
+        {
+            PosX = posX;
+            PosY = posY;
+        }
+
+        public int PosX { get; set; }
+        public int PosY { get; set; }
+    }
+}

Assets/Logic/GeneticAlgorithm/NodeGen.cs.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: a0a6b688100640c88528d67eefc933a6
+timeCreated: 1623414035

Assets/Logic/GeneticAlgorithm/TestGenetics.cs

@@ -0,0 +1,112 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using Logic.Utils;
+using UnityEngine;
+
+namespace Logic.GeneticAlgorithm
+{
+    [ExecuteInEditMode]
+    public class TestGenetics: MonoBehaviour
+    {
+        [Header("Genetic Algorithm")]
+        [SerializeField] private int populationSize = 200;
+        [SerializeField] private float mutationRate = 0.01f;
+        [SerializeField] private int elitism = 0;
+        [SerializeField] private int tables = 10;
+        [SerializeField] private int maxGeneration = 10;
+
+        [Header("Map Options")]
+        [SerializeField] private int mapStartX = 2;
+        [SerializeField] private int mapEndX = 10;
+        [SerializeField] private int mapStartY = -3;
+        [SerializeField] private int mapEndY = 3;
+        [SerializeField] private Vector3 kitchenNode = new Vector3(-2, 0, 3);
+        [SerializeField] private Vector3 kitchenMinNode = new Vector3(10, 0, -3);
+        [SerializeField] private Vector3 kitchenMaxNode = new Vector3(2, 0, 3);
+        [SerializeField] private Vector3 bathroomNode = new Vector3(7, 0, -4);
+        [SerializeField] private Vector3 bathroomMinNode = new Vector3(2, 0, 3);
+        [SerializeField] private Vector3 bathroomMaxNode = new Vector3(7, 0, -3);
+        
+        private float _maxDistance;
+        private float _minDistance;
+        private System.Random _random;
+
+        private GeneticAlgorithm<NodeGen> _ga;
+
+        private void Awake()
+        {
+            float maxTableKitchenDistance = Vector3.Distance(kitchenNode, kitchenMaxNode);
+            float maxTableBathroomDistance = Vector3.Distance(bathroomNode, bathroomMaxNode);
+            float minTableKitchenDistance = Vector3.Distance(kitchenNode, kitchenMinNode);
+            float minTableBathroomDistance = Vector3.Distance(bathroomNode, bathroomMinNode);
+            _maxDistance = (maxTableBathroomDistance + maxTableKitchenDistance) / 2;
+            _minDistance = (minTableBathroomDistance + minTableKitchenDistance) / 2;
+            _random = new System.Random();
+            _ga = new GeneticAlgorithm<NodeGen>(populationSize, tables, _random, GetRandomGene, FitnessFunction, elitism, mutationRate);
+        }
+
+        public void Generate()
+        {
+            StartCoroutine(GenerateCoroutine(SpawnTables));
+        }
+
+        private void SpawnTables(NodeGen[] genes)
+        {
+            StartCoroutine(SpawnTablesCoroutine(genes));
+        }
+
+        private IEnumerator GenerateCoroutine(Action<NodeGen[]> callback)
+        {
+            while (true)
+            {
+                _ga.NewGeneration();
+                Debug.Log($" Generation: {_ga.Generation}");
+                if (_ga.BestFitness >= 1 || _ga.Generation == maxGeneration)
+                {
+                    callback(_ga.BestGenes);
+                    yield break;
+                }
+                yield return new WaitForEndOfFrame();
+            }
+        }
+
+        private IEnumerator SpawnTablesCoroutine(NodeGen[] genes)
+        {
+            while (true)
+            {
+                foreach (NodeGen gene in genes)
+                {
+                    Debug.Log($"X = {gene.PosX}, Y = {gene.PosY}");
+                    CustomerTableManager.Instance.SpawnNewTable(gene.PosX, gene.PosY);
+                }
+                yield break;
+            }
+        }
+
+        private NodeGen GetRandomGene()
+        {
+            var posX = Randomizer.GetRandomIntInRange(mapStartX, mapEndX);
+            var posY = Randomizer.GetRandomIntInRange(mapStartY, mapEndY);
+            return new NodeGen(posX, posY);
+        }
+
+        private float FitnessFunction(int index)
+        {
+            float score = 0;
+            
+            Dna<NodeGen> dna = _ga.Population[index];
+
+            foreach (var t in dna.Genes)
+            {
+                float distance = (Vector3.Distance(kitchenNode, new Vector3(t.PosX, 0, t.PosY)) +
+                                Vector3.Distance(bathroomNode, new Vector3(t.PosX, 0, t.PosY))) / 2;
+                // score += (distance - _minDistance) / (_maxDistance - _minDistance); // MAX DISTANCE
+                score += (distance - _maxDistance) / (_minDistance - _maxDistance); // MIN DISTANCE
+            }
+            score /= tables;
+            score = (Mathf.Pow(2, score) - 1) / (2 - 1);
+            return score;
+        }
+    }
+}

Assets/Logic/GeneticAlgorithm/TestGenetics.cs.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 0533ff90ce1545e09aa48af5fb181c2e
+timeCreated: 1623411577

Assets/Logic/GeneticAlgorithm/TestGeneticsEditor.cs

@@ -0,0 +1,19 @@
+using UnityEditor;
+using UnityEngine;
+
+namespace Logic.GeneticAlgorithm
+{
+    
+    [CustomEditor(typeof(TestGenetics))]
+    public class TestGeneticsEditor: Editor
+    {
+        public override void OnInspectorGUI() {
+            DrawDefaultInspector();
+            TestGenetics myScript = (TestGenetics)target;
+            if (GUILayout.Button("Generate"))
+            {
+                myScript.Generate();
+            }
+        }
+    }
+}

Assets/Logic/GeneticAlgorithm/TestGeneticsEditor.cs.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: bdb34492157d46aabf7a5070f053b045
+timeCreated: 1623614282

Assets/Logic/Graph/KitchenTable.cs

@@ -1,9 +1,10 @@
 
 using System.Collections;
 using System.Collections.Generic;
+using System.IO;
 using System.Linq;
-using Logic.Agent;
 using Logic.Data;
+using Logic.NeuralNetwork;
 using Logic.UI;
 using UnityEngine;
 
@@ -12,12 +13,48 @@ namespace Logic.Graph
     
     public class KitchenTable : Table
     {
-        
+
+        public GameObject texturePlaceholder;
+        public Camera camera;
+        private Preprocess _preprocess;
+
+        private void Awake()
+        {
+            _preprocess = new Preprocess();
+        }
+
         public void Start()
         {
             StartCoroutine(PrepareMeal());
         }
 
+        private void SetSprite(Texture2D texture2D)
+        {
+            texture2D = _preprocess.DeCompress(texture2D);
+            texture2D = _preprocess.ScaleTexture(texture2D, 180, 180);
+            texturePlaceholder.gameObject.GetComponent<SpriteRenderer>().sprite = 
+                Sprite.Create(texture2D, new Rect(0, 0, texture2D.width, texture2D.height), new Vector2(0.5f, 0.5f));
+            texturePlaceholder.gameObject.SetActive(true);
+        }
+
+        private void PredictSnapshot()
+        {
+            int resHeight = 900;
+            int resWidth = 900;
+            
+            RenderTexture rt = new RenderTexture(resWidth, resHeight, 24);
+            camera.targetTexture = rt;
+            Texture2D screenShot = new Texture2D(resWidth, resHeight, TextureFormat.RGB24, false);
+            camera.Render();
+            RenderTexture.active = rt;
+            screenShot.ReadPixels(new Rect(0, 0, resWidth, resHeight), 0, 0);
+            camera.targetTexture = null;
+            RenderTexture.active = null;
+            Destroy(rt);
+            var current = CurrentItem as Food;
+            NeuralNetwork.NeuralNetwork.Instance.Predict(current.predictSprite);
+        }
+        
         public void HandleSpaceClick()
         {
             List<Recipe> recipes = AgentManager.Instance.GetRecipes();
@@ -31,10 +68,12 @@ namespace Logic.Graph
                 bool isSuccess = AgentManager.Instance.AddItem(CurrentItem);
                 if (isSuccess)
                 {
+                    PredictSnapshot();
                     ClearItemObject();
                     IsFull = false;
                     CurrentItem = null;
                     UIManager.Instance.uiQueueManager.Remove(QueueType.ReadyQueue);
+                    texturePlaceholder.gameObject.SetActive(false);
                 }
                 SpawnReadyMeal();
             }
@@ -55,7 +94,6 @@ namespace Logic.Graph
                         KitchenTableManager.Instance.AddToQueue(food, QueueType.ReadyQueue);
                     }
                 }
-
                 SpawnReadyMeal();
             }
         }
@@ -65,6 +103,8 @@ namespace Logic.Graph
             if (!IsFull && KitchenTableManager.Instance.readyQueue.Any())
             {
                 CurrentItem = KitchenTableManager.Instance.readyQueue.Dequeue();
+                var current = CurrentItem as Food;
+                SetSprite(current.predictSprite);
                 Spawn();
             }
         }

Assets/Logic/NeuralNetwork.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: ec5ee6e4e612424881c05088ca4c9807
+timeCreated: 1622205136

Assets/Logic/NeuralNetwork/ImageInputData.cs

@@ -0,0 +1,13 @@
+using System.Drawing;
+using Microsoft.ML.Data;
+using Microsoft.ML.Transforms.Image;
+
+namespace Logic.NeuralNetwork
+{
+    public class ImageInputData
+    {
+        [LoadColumn(0)] 
+        [ImageType(180, 180)]
+        public Bitmap Image { get; set; }
+    }
+}

Assets/Logic/NeuralNetwork/ImageInputData.cs.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: ce6b5da43aca4c70941815398c42edd2
+timeCreated: 1622478385

Assets/Logic/NeuralNetwork/ModelBuilder.cs

@@ -0,0 +1,55 @@
+using System.Collections.Generic;
+using System.Drawing;
+using Microsoft.ML;
+
+namespace Logic.NeuralNetwork
+{
+    public class ModelBuilder
+    { 
+        private readonly MLContext _mlContext;
+        private readonly NetworkSettings _networkSettings;
+        private readonly ITransformer _mlModel;
+        
+
+        public ModelBuilder(MLContext mlContext, NetworkSettings networkSettings, string tensorFlowModelFilePath)
+        {
+            _mlContext = mlContext;
+            _networkSettings = networkSettings;
+            _mlModel = SetupMlnetModel(tensorFlowModelFilePath);
+        }
+
+        private ITransformer SetupMlnetModel(string tensorFlowModelFilePath)
+        {
+            var pipeline =
+                _mlContext.Transforms.ResizeImages(
+                    outputColumnName: _networkSettings.InputTensor, 
+                    imageWidth: _networkSettings.ImageWidth, 
+                    imageHeight: _networkSettings.ImageHeight, 
+                    inputColumnName: nameof(ImageInputData.Image))
+                .Append(_mlContext.Transforms.ExtractPixels(
+                    outputColumnName: _networkSettings.InputTensor, 
+                    interleavePixelColors: _networkSettings.ChannelsLast, 
+                    offsetImage: _networkSettings.Mean))
+                .Append(_mlContext.Model
+                    .LoadTensorFlowModel(tensorFlowModelFilePath)
+                    .ScoreTensorFlowModel(
+                        outputColumnNames: new[] { _networkSettings.OutputTensor },
+                        inputColumnNames: new[] { _networkSettings.InputTensor },
+                        addBatchDimensionInput: false)
+                    );
+            ITransformer mlModel = pipeline.Fit(CreateEmptyDataView());
+            return mlModel;
+        }
+        private IDataView CreateEmptyDataView()
+        {
+            List<ImageInputData> list = new List<ImageInputData>();
+            list.Add(new ImageInputData() { Image = new Bitmap(_networkSettings.ImageWidth, _networkSettings.ImageHeight) });
+            IEnumerable<ImageInputData> enumerableData = list;
+            return _mlContext.Data.LoadFromEnumerable<ImageInputData>(list);
+        }
+        public void SaveMlNetModel(string mlnetModelFilePath)
+        {
+            _mlContext.Model.Save(_mlModel, null, mlnetModelFilePath);
+        }
+    }
+}

Assets/Logic/NeuralNetwork/ModelBuilder.cs.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: fa73e93dd6eb47789c940b5c2a53e8cf
+timeCreated: 1622477810

Assets/Logic/NeuralNetwork/NetworkSettings.cs

@@ -0,0 +1,17 @@
+using System.Collections.Generic;
+
+namespace Logic.NeuralNetwork
+{
+    public class NetworkSettings
+    {
+        public int ImageWidth { get; set; }
+        public int ImageHeight { get; set; }
+        public int Epochs { get; set; }
+        public int BatchSize { get; set; }
+        public List<string> ClassNames { get; set; }
+        public string InputTensor { get; set; }
+        public string OutputTensor { get; set; }
+        public float Mean = 117;
+        public bool ChannelsLast = true;
+    }
+}

Assets/Logic/NeuralNetwork/NetworkSettings.cs.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 6d476584622c45db80ed63515ea037ab
+timeCreated: 1622554779

Assets/Logic/NeuralNetwork/NeuralNetwork.cs

@@ -0,0 +1,86 @@
+using System;
+using System.Collections;
+using System.Drawing;
+using System.IO;
+using System.Linq;
+using Microsoft.ML;
+using Newtonsoft.Json;
+using TransferLearningTF;
+using UnityEngine;
+
+namespace Logic.NeuralNetwork
+{
+    public class NeuralNetwork: MonoBehaviour
+    {
+        public static NeuralNetwork Instance;
+        public bool debugNetwork;
+        public Texture2D[] textures;
+        private Preprocess _preprocess;
+        private int _currentTexture = 0;
+        private Coroutine _co;
+        private NetworkSettings _networkSettings;
+        
+        private void Awake()
+        {
+            Instance = this;
+            
+            using (StreamReader r = new StreamReader(Path.Combine(Application.dataPath, "NeuralNetwork", "settings.json")))
+            {
+                string json = r.ReadToEnd();
+                _networkSettings = JsonConvert.DeserializeObject<NetworkSettings>(json);
+            }
+            
+            _preprocess = new Preprocess();
+            var mlContext = new MLContext();
+            var modelBuilder = new ModelBuilder(mlContext, _networkSettings, Path.Combine(Application.dataPath,  "NeuralNetwork", "frozen_model.pb"));
+            modelBuilder.SaveMlNetModel(Path.Combine(Application.dataPath,  "NeuralNetwork", "PredictionModel.zip"));
+
+            if (debugNetwork)
+            {
+                _co = StartCoroutine(Test());
+            }
+        }
+
+        public void Predict(Texture2D texture)
+        {
+            DataViewSchema predictionPipelineSchema;
+            var mlContext = new MLContext();
+            var mlModel = mlContext.Model.Load(Path.Combine(Application.dataPath, "NeuralNetwork", "PredictionModel.zip"), out predictionPipelineSchema);
+            var predictionEngine = mlContext.Model
+                .CreatePredictionEngine<ImageInputData, ImageLabelPredictions>(mlModel);
+            
+            var input = new ImageInputData();
+            texture = _preprocess.DeCompress(texture);
+            using (MemoryStream mStream = new MemoryStream())
+            {
+                mStream.Write(texture.EncodeToPNG(), 0, texture.EncodeToPNG().Length);
+                mStream.Seek(0, SeekOrigin.Begin);
+                input.Image = new Bitmap(mStream);
+            }
+            File.WriteAllBytes(Path.Combine(Application.dataPath, "NeuralNetwork", "predict.png"), texture.EncodeToPNG());
+            var prediction = predictionEngine.Predict(input);
+            var maxProbability = prediction.PredictedLabels.Max();
+            var labelIndex = prediction.PredictedLabels.AsSpan().IndexOf(maxProbability);
+            var classifiedLabel = _networkSettings.ClassNames[labelIndex];
+            Debug.Log($"Image predicted as '{classifiedLabel}'");
+        }
+        
+        private IEnumerator Test()
+        {
+            while (true)
+            {
+                if (_currentTexture == textures.Length)
+                {
+                    StopCoroutine(_co);
+                }
+                else
+                {
+                    Debug.Log(textures[_currentTexture].name + "AS -> ");
+                    Predict(textures[_currentTexture]);
+                    _currentTexture++;
+                }
+                yield return new WaitForSeconds(2);
+            }
+        }
+    }
+}

Assets/Logic/NeuralNetwork/NeuralNetwork.cs.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: ebb7000c98574a798cb572b066859125
+timeCreated: 1622205145

Assets/Logic/NeuralNetwork/Preprocess.cs

@@ -0,0 +1,54 @@
+using System.IO;
+using UnityEngine;
+using UnityEngine.Events;
+
+namespace Logic.NeuralNetwork
+{
+    public class Preprocess
+    {
+
+        private string _path = Path.Combine(Application.dataPath, "NeuralNetwork", "predict.jpg");
+        
+        public string SaveTexture(Texture2D texture, string path = null) {
+            if (path == null)
+                path = _path;
+            texture = DeCompress(texture);
+            File.WriteAllBytes(path, texture.EncodeToJPG());
+            return Path.Combine(path);
+        }
+        
+        public Texture2D DeCompress(Texture2D source)
+        {
+            RenderTexture renderTex = RenderTexture.GetTemporary(
+                source.width,
+                source.height,
+                0,
+                RenderTextureFormat.Default,
+                RenderTextureReadWrite.Linear);
+
+            Graphics.Blit(source, renderTex);
+            RenderTexture previous = RenderTexture.active;
+            RenderTexture.active = renderTex;
+            Texture2D readableText = new Texture2D(source.width, source.height);
+            readableText.ReadPixels(new Rect(0, 0, renderTex.width, renderTex.height), 0, 0);
+            readableText.Apply();
+            RenderTexture.active = previous;
+            RenderTexture.ReleaseTemporary(renderTex);
+            return readableText;
+        }
+        
+        public Texture2D ScaleTexture(Texture2D source,int targetWidth,int targetHeight) {
+            Texture2D result=new Texture2D(targetWidth,targetHeight,source.format,false);
+            float incX=(1.0f / (float)targetWidth);
+            float incY=(1.0f / (float)targetHeight);
+            for (int i = 0; i < result.height; ++i) {
+                for (int j = 0; j < result.width; ++j) {
+                    Color newColor = source.GetPixelBilinear((float)j / (float)result.width, (float)i / (float)result.height);
+                    result.SetPixel(j, i, newColor);
+                }
+            }
+            result.Apply();
+            return result;
+        }
+    }
+}

Assets/Logic/NeuralNetwork/Preprocess.cs.meta

@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 741115aafcc549aebc7fc721dfc8c131
+timeCreated: 1622208666

Assets/Logic/UI/UIQueue.cs

@@ -12,15 +12,6 @@ namespace Logic.UI
         
         public GameObject queueWrapper;
         
-        
-        // private void Start()
-        // {
-        //     foreach (var child in queueWrapper.GetComponentsInChildren<UIInventorySlot>())
-        //     {
-        //         Destroy(child.gameObject);
-        //     }
-        // }
-
         private void Start()
         {
             table = KitchenTableManager.Instance.GETTable();

Assets/Logic/Utils/Randomizer.cs

@@ -4,7 +4,7 @@ namespace Logic.Utils
 {
     public static class Randomizer
     {
-        private static readonly Random Random = new Random();
+        public static readonly Random Random = new Random();
 
         public static T GetRandomEnumElement<T>() where T : struct, IConvertible
         {
@@ -16,5 +16,10 @@ namespace Logic.Utils
         {
             return Random.Next(min, max + 1);
         }
+
+        public static double GetRandomDouble()
+        {
+            return Random.NextDouble();
+        }
     }
 }

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+Copyright (c) 2018 Patrick McCarthy
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+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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+MIT License
+
+Copyright (c) 2018 .NET Foundation
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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+ML.NET uses third-party libraries or other resources that may be
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+In the event that we accidentally failed to list a required notice, please
+bring it to our attention. Post an issue or email us:
+
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+The attached notices are provided for information only.
+
+License notice for LIBMF
+------------------------
+
+https://github.com/cjlin1/libmf
+
+Copyright (c) 2014-2015 The LIBMF Project.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
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+3. Neither name of copyright holders nor the names of its contributors
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+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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+License notice for SharpZipLib
+------------------------------
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+https://github.com/icsharpcode/SharpZipLib
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+Copyright © 2000-2018 SharpZipLib Contributors
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+Permission is hereby granted, free of charge, to any person obtaining a copy of this
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+publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons
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