ml model created

Game1/Game1.cs

@@ -74,6 +74,7 @@ namespace Game1
             cropTypesNames[11] = "Wheat";
 
             Engine.init();
+            //Sources.ML_Joel.Engine.CreateModel();
 
 
 

Game1/Game1.csproj

@@ -92,6 +92,7 @@
     <Compile Include="Sources\ML\Engine.cs" />
     <Compile Include="Sources\ML_Joel\DataModel\Input.cs" />
     <Compile Include="Sources\ML_Joel\DataModel\Output.cs" />
+    <Compile Include="Sources\ML_Joel\Engine.cs" />
     <Compile Include="Sources\ML_Joel\Model.cs" />
     <Compile Include="Sources\Objects\DayNightCycle.cs" />
     <Compile Include="Sources\Objects\Fertilizer.cs" />

Game1/Sources/Crops/Farm.cs

@@ -74,8 +74,6 @@ class Farm
             init(Size, housepos);
         RainPosition.X = r.Next(0, 1900);
         RainPosition.Y = r.Next(0, 1950);
-        WindSpeed.X = r.Next(-1, 1);
-        WindSpeed.Y = r.Next(-1, 1);
         RainfallMap = PerlinNoise.LoadImage("C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/Rainfall.png");
     }
 

Game1/Sources/ML_Joel/DataModel/Output.cs

@@ -9,7 +9,12 @@ namespace Game1.Sources.ML_Joel
 {
     class Output
     {
-        [ColumnName("PredictedLabel")]
+        //[ColumnName("PredictedLabel")]
         public float Prediction { get; set; }
+        public float Score { get; set; }
+
+        //[ColumnName("Score")]
+       // public float[] Score { get; set; }
+
     }
 }

Game1/Sources/ML_Joel/Engine.cs

@@ -0,0 +1,44 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using System.Threading.Tasks;
+using Microsoft.ML;
+
+
+namespace Game1.Sources.ML_Joel
+{
+    static class Engine
+    {
+        private static MLContext mlContext = new MLContext(seed: 1);
+        private static PredictionEngine<ModelInput, ModelOutput> PredictionEngine;
+
+        public static void CreateModel()
+        {
+            Model.CreateModel();
+        }
+
+        public static void init()
+        {
+            PredictionEngine = MLModel.CreateEngine();
+        }
+
+        public static string PredictFertilizer(Crops crop, CropTypes cropTypes)
+        {
+            ModelInput modelInput = new ModelInput
+            {
+                Temperature = crop.getSoilProperties().Temperature,
+                Humidity = crop.getSoilProperties().Humidity,
+                Moisture = crop.getSoilProperties().Moisture,
+                Soil_Type = crop.getSoilProperties().soilType,
+                Crop_Type = cropTypes.CropName,
+                Nitrogen = crop.getSoilProperties().Nitrogen,
+                Potassium = crop.getSoilProperties().Potassium,
+                Phosporous = crop.getSoilProperties().Phosphorous
+            };
+
+            return PredictionEngine.Predict(modelInput).Prediction;
+        }
+    }
+}
+

Game1/Sources/ML_Joel/Model.cs

@@ -13,56 +13,75 @@ namespace Game1.Sources.ML_Joel
     class Model
     {
         private static MLContext mlContext = new MLContext(seed: 1);
+        /*
         private static string path = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/Rainfall.csv";
         private static string modelpath = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodel_Joel";
         private static string report = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report_Joel";
+        */
+        private static string path = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/Rainfall.csv";
+        private static string modelpath = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/MLmodel_Joel";
+        private static string report = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/report_Joel";
 
         // Loading data, creatin and saving ML model for smaller dataset (100)
         public static void CreateModel()
         {
 
-            IDataView trainingDataView = mlContext.Data.LoadFromTextFile<ModelInput>(
+            IDataView trainingDataView = mlContext.Data.LoadFromTextFile<Input>(
                 path: path,
                 hasHeader: true,
                 separatorChar: ',',
                 allowQuoting: true,
                 allowSparse: false);
 
-            ModelInput sample = mlContext.Data.CreateEnumerable<ModelInput>(trainingDataView, false).ElementAt(0);
+            var splitData = mlContext.Data.TrainTestSplit(trainingDataView, testFraction: 0.2);
-            ITransformer MLModel = BuildAndTrain(mlContext, trainingDataView, sample, report);
+            trainingDataView = splitData.TrainSet;
+            IDataView testDataView = splitData.TestSet;
+            Input sample = mlContext.Data.CreateEnumerable<Input>(trainingDataView, false).ElementAt(0);
+            ITransformer MLModel = BuildAndTrain(mlContext, trainingDataView, testDataView, sample, report);
             SaveModel(mlContext, MLModel, modelpath, trainingDataView.Schema);
         }
 
-        // Building and training ML model, very small dataset (100 entries)
+        // Building and training ML model
-        public static ITransformer BuildAndTrain(MLContext mLContext, IDataView trainingDataView, ModelInput sample, string reportPath)
+        public static ITransformer BuildAndTrain(MLContext mLContext, IDataView trainingDataView, IDataView testDataView, Input sample, string reportPath)
         {
 
-            var options = new LightGbmMulticlassTrainer.Options
+            var options = new LightGbmRegressionTrainer.Options
             {
-                MaximumBinCountPerFeature = 8,
+                MaximumBinCountPerFeature = 40,
-                LearningRate = 0.00025,
+                LearningRate = 0.00020,
-                NumberOfIterations = 40000,
+                NumberOfIterations = 50000,
-                NumberOfLeaves = 10,
+                NumberOfLeaves = 55,
-                LabelColumnName = "Fertilizer_NameF",
+                LabelColumnName = "Production",
                 FeatureColumnName = "Features",
+                //EarlyStoppingRound = 20,
+                //UseCategoricalSplit = true,
+                //L2CategoricalRegularization = 1,
+                //CategoricalSmoothing = 1,
 
                 Booster = new DartBooster.Options()
                 {
-                    MaximumTreeDepth = 10
+                    MaximumTreeDepth = 20
                 }
             };
 
             var pipeline = mlContext.Transforms
-                    .Text.FeaturizeText("Soil_TypeF", "Soil_Type")
+                    .Text.FeaturizeText("SeasonF", "Season")
-                    .Append(mlContext.Transforms.Text.FeaturizeText("Crop_TypeF", "Crop_Type"))
+                    .Append(mlContext.Transforms.Text.FeaturizeText("CropF", "Crop"))
-                    .Append(mlContext.Transforms.Concatenate("Features", "Temperature", "Humidity", "Moisture", "Soil_TypeF", "Crop_TypeF", "Nitrogen", "Potassium", "Phosphorous"))
+                    .Append(mlContext.Transforms.Concatenate("Features", "SeasonF", "CropF", "Rainfall"))
-                    .Append(mlContext.Transforms.Conversion.MapValueToKey("Fertilizer_NameF", "Fertilizer_Name"), TransformerScope.TrainTest)
+                    //.Append(mlContext.Transforms.Conversion.MapValueToKey("ProductionF", "Production"), TransformerScope.TrainTest)
-                    .AppendCacheCheckpoint(mLContext)
+                    //.AppendCacheCheckpoint(mLContext)
-                    .Append(mLContext.MulticlassClassification.Trainers.LightGbm(options))
+                    .Append(mLContext.Regression.Trainers.LightGbm(options));
-                    .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));
+            //.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));
+
+            //Evaluate(mlContext, trainingDataView, pipeline, 10, reportPath, "ProductionF");
+            //var Evaluate = mlContext.Regression.CrossValidate(trainingDataView, pipeline, numberOfFolds: 100, labelColumnName: "Production");
+            //var Evaluate = mlContext.Regression.Evaluate(testDataView, labelColumnName: "Production", scoreColumnName: "Score");
+            //var metricsInMultipleFolds = Evaluate.Select(r => r.Metrics);
 
-            Evaluate(mlContext, trainingDataView, pipeline, 10, reportPath, "Fertilizer_NameF");
             ITransformer MLModel = pipeline.Fit(trainingDataView);
+            var testEval = MLModel.Transform(testDataView);
+            var Evaluate = mlContext.Regression.Evaluate(testEval, labelColumnName: "Production");
+
 
             return MLModel;
         }
@@ -104,10 +123,10 @@ namespace Game1.Sources.ML_Joel
              return mlContext.Model.Load(modelpath, out DataViewSchema inputSchema);
         }
 
-        public static Microsoft.ML.PredictionEngine<ModelInput, ModelOutput> CreateEngine()
+        public static Microsoft.ML.PredictionEngine<Input, Output> CreateEngine()
         {
             ITransformer mlModel = LoadModel(false);
-            return mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);
+            return mlContext.Model.CreatePredictionEngine<Input, Output>(mlModel);
         }
 
     }

Game1/Sources/Objects/FertilizerHolder.cs

@@ -16,72 +16,72 @@ class FertilizerHolder
         {
             ID = 999,
             Name = "None",
-            Nitrogen = 0.0f / 5,
+            Nitrogen = 0.0f / 2,
-            Phosphorus = 0 * 0.436f / 5,
+            Phosphorus = 0 * 0.436f / 2,
-            Potassium = 0 * 0.83f / 5
+            Potassium = 0 * 0.83f / 2
         };
 
         FertilizerType[1] = new Fertilizer
         {
             ID = 0,
             Name = "10-26-26",
-            Nitrogen = 10.0f / 5,
+            Nitrogen = 10.0f / 2,
-            Phosphorus = 26 * 0.436f / 5,
+            Phosphorus = 26 * 0.436f / 2,
-            Potassium = 26 * 0.83f / 5
+            Potassium = 26 * 0.83f / 2
         };
 
         FertilizerType[2] = new Fertilizer
         {
             ID = 1,
             Name = "14-35-14",
-            Nitrogen = 14.0f / 5,
+            Nitrogen = 14.0f / 2,
-            Phosphorus = 35 * 0.436f / 5,
+            Phosphorus = 35 * 0.436f / 2,
-            Potassium = 14 * 0.83f / 5
+            Potassium = 14 * 0.83f / 2
         };
 
         FertilizerType[3] = new Fertilizer
         {
             ID = 2,
             Name = "17-17-17",
-            Nitrogen = 17.0f / 5,
+            Nitrogen = 17.0f / 2,
-            Phosphorus = 17 * 0.436f / 5,
+            Phosphorus = 17 * 0.436f / 2,
-            Potassium = 17 * 0.83f / 5
+            Potassium = 17 * 0.83f / 2
         };
 
         FertilizerType[4] = new Fertilizer
         {
             ID = 3,
             Name = "20-20",
-            Nitrogen = 20.0f / 5,
+            Nitrogen = 20.0f / 2,
-            Phosphorus = 20 * 0.436f / 5,
+            Phosphorus = 20 * 0.436f / 2,
-            Potassium = 0 * 0.83f / 5
+            Potassium = 0 * 0.83f / 2
         };
 
         FertilizerType[5] = new Fertilizer
         {
             ID = 4,
             Name = "28-28",
-            Nitrogen = 28.0f / 5,
+            Nitrogen = 28.0f / 2,
-            Phosphorus = 28 * 0.436f / 5,
+            Phosphorus = 28 * 0.436f / 2,
-            Potassium = 0 * 0.83f / 5
+            Potassium = 0 * 0.83f / 2
         };
 
         FertilizerType[6] = new Fertilizer
         {
             ID = 5,
             Name = "DAP",
-            Nitrogen = 18.0f / 5,
+            Nitrogen = 18.0f / 2,
-            Phosphorus = 46 * 0.436f / 5,
+            Phosphorus = 46 * 0.436f / 2,
-            Potassium = 0 * 0.83f / 5
+            Potassium = 0 * 0.83f / 2
         };
 
         FertilizerType[7] = new Fertilizer
         {
             ID = 6,
             Name = "Urea",
-            Nitrogen = 46.0f / 5,
+            Nitrogen = 46.0f / 2,
-            Phosphorus = 0 * 0.436f / 5,
+            Phosphorus = 0 * 0.436f / 2,
-            Potassium = 0 * 0.83f / 5
+            Potassium = 0 * 0.83f / 2
         };
     }
     */
@@ -89,72 +89,72 @@ class FertilizerHolder
         {
             ID = 999,
             Name = "None",
-            Nitrogen = 0.0f / 5,
+            Nitrogen = 0.0f / 2,
-            Phosphorus = 0 * 0.436f / 5,
+            Phosphorus = 0 * 0.436f / 2,
-            Potassium = 0 * 0.83f / 5
+            Potassium = 0 * 0.83f / 2
         };
 
         FertilizerType[1] = new Fertilizer
         {
             ID = 0,
             Name = "10-26-26",
-            Nitrogen = 17.21f / 5,
+            Nitrogen = 17.21f / 2,
-            Phosphorus = 12.14f / 5,
+            Phosphorus = 12.14f / 2,
-            Potassium = 0.64f / 5
+            Potassium = 0.64f / 2
         };
 
         FertilizerType[2] = new Fertilizer
         {
             ID = 1,
             Name = "14-35-14",
-            Nitrogen = 16.89f / 5,
+            Nitrogen = 16.89f / 2,
-            Phosphorus = 6.21f / 5,
+            Phosphorus = 6.21f / 2,
-            Potassium = 5.21f / 5
+            Potassium = 5.21f / 2
         };
 
         FertilizerType[3] = new Fertilizer
         {
             ID = 2,
             Name = "17-17-17",
-            Nitrogen = 14.92f / 5,
+            Nitrogen = 14.92f / 2,
-            Phosphorus = 14.42f / 5,
+            Phosphorus = 14.42f / 2,
-            Potassium = 3.0f / 5
+            Potassium = 3.0f / 2
         };
 
         FertilizerType[4] = new Fertilizer
         {
             ID = 3,
             Name = "20-20",
-            Nitrogen = 15.39f / 5,
+            Nitrogen = 15.39f / 2,
-            Phosphorus = 15.21f / 5,
+            Phosphorus = 15.21f / 2,
-            Potassium = 9.5f / 5
+            Potassium = 9.5f / 2
         };
 
         FertilizerType[5] = new Fertilizer
         {
             ID = 4,
             Name = "28-28",
-            Nitrogen = 9.67f / 5,
+            Nitrogen = 9.67f / 2,
-            Phosphorus = 10.47f / 5,
+            Phosphorus = 10.47f / 2,
-            Potassium = 9.5f / 5
+            Potassium = 9.5f / 2
         };
 
         FertilizerType[6] = new Fertilizer
         {
             ID = 5,
             Name = "DAP",
-            Nitrogen = 14.52f / 5,
+            Nitrogen = 14.52f / 2,
-            Phosphorus = 1.77f / 5,
+            Phosphorus = 1.77f / 2,
-            Potassium = 9.5f / 5
+            Potassium = 9.5f / 2
         };
 
         FertilizerType[7] = new Fertilizer
         {
             ID = 6,
             Name = "Urea",
-            Nitrogen = 1.81f / 5,
+            Nitrogen = 1.81f / 2,
-            Phosphorus = 21.0f / 5,
+            Phosphorus = 21.0f / 2,
-            Potassium = 9.5f / 5
+            Potassium = 9.5f / 2
         };
     }
 

Game1/Sources/Objects/InventorySystem/Cargo.cs

@@ -7,7 +7,7 @@ using System.Threading.Tasks;
 
 class Cargo
 {
-    private Items[,] items = new Items[2, 351];
+    private Items[,] items = new Items[2, 281];
     private int[] Count = new int[2];
 
 

Game1/Sources/Objects/InventorySystem/Inventory.cs

@@ -11,7 +11,7 @@ using Microsoft.Xna.Framework.Input;
 class Inventory
 {
     private int Weight = 0;
-    private int maxWeight = 350;
+    private int maxWeight = 280;
     private int[] totalHarvested = new int[11];
     private int[] totalFertilizerUsed = new int[8];
     private Cargo cargo = new Cargo();

Game1/Sources/Smart/AI.cs

@@ -54,11 +54,16 @@ class AI
         int testsize = 2;
         Path newTarget;
         Nodes nodes;
+        Random random = new Random();
 
         if (astar.GetAdjacentNodes(tractorPos).Count == 0)
             nodes = new Nodes(housePos);
         else
-            nodes = astar.GetAdjacentNodes(tractorPos)[0];
+        {
+            List<Nodes> templist = astar.GetAdjacentNodes(tractorPos);
+            nodes = templist[random.Next(0, templist.Count())];
+            templist.Clear();
+        }
 
         if (tractorPos != housePos)
             if (inventory.getWeight() == inventory.getMaxWeight() || inventory.isMissingFertilizer())
@@ -168,7 +173,7 @@ class AI
             saturationScore = -100;
         }
 
-        return score + (-aproxDistance * 5) + statusScore + timerScore + saturationScore;
+        return score + (-aproxDistance * 10) + statusScore + timerScore + saturationScore;
     }
 
     private float norm(float min, float max, float val)