forked from s425077/PotatoPlan
fixes
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94c2792ef3
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0b7ff8a949
@ -172,7 +172,7 @@ namespace Game1
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Time.updateTime(tractorUnit.getSpeed());
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//System.Threading.Thread updatethread = new System.Threading.Thread(new System.Threading.ParameterizedThreadStart(Update));
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base.Update(gameTime);
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}
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@ -20,7 +20,7 @@ class Farm
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private float[][] whiteNoise;
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private float[][] perlinNoise;
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private DayNightCycle Time;
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private float updatePerc = 0.25f;
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private float updatePerc = 0.5f;
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private float updateProgress = 0;
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private int nextUpdate = 0;
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private int productionUpdate = 0;
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@ -147,13 +147,14 @@ class Farm
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crops[i, j].updateProductionRate();
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}
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}
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updateProgress = updateProgress + updatePerc;
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updateProgress = updateProgress + nextUpdate;
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if (updateProgress > 1)
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{
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updateProgress = 0;
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}
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updateRainMapPosition(Size);
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productionUpdate = 0;
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nextUpdate = 0;
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}
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productionUpdate++;
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}
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@ -310,7 +311,7 @@ class Farm
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public float getProductionRate(int x, int y, int Type)
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{
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return crops[x, y].getProductionRate(PresetCrops.getPresetCropTypes(Type));
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return crops[x, y]. getProductionRate(PresetCrops.getPresetCropTypes(Type));
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}
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public float GetRandomNumber(double minimum, double maximum)
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@ -11,7 +11,7 @@ using Microsoft.ML.Trainers.LightGbm;
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class MLModel
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{
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private static MLContext mlContext = new MLContext(seed: 1);
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/*
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private static string path = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/Fertilizer_Prediction.csv";
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private static string modelpath = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/MLmodel";
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private static string report = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/report";
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@ -19,7 +19,7 @@ class MLModel
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private static string pathBig = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/BigFertPredict.csv";
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private static string modelpathBig = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/MLmodelBig";
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private static string reportBig = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/report_BigModel";
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*/
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/*
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private static string pathBig = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/BigFertPredict.csv";
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private static string modelpathBig = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodelBig";
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private static string reportBig = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report_BigModel";
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@ -27,7 +27,7 @@ class MLModel
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private static string path = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/Fertilizer_Prediction.csv";
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private static string modelpath = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodel";
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private static string report = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report";
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*/
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// Loading data, creatin and saving ML model for smaller dataset (100)
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public static void CreateModel()
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{
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@ -9,12 +9,7 @@ namespace Game1.Sources.ML_Joel
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{
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class OutputArea
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{
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//[ColumnName("PredictedLabel")]
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public float Prediction { get; set; }
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public float Score { get; set; }
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//[ColumnName("Score")]
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// public float[] Score { get; set; }
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}
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}
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@ -41,7 +41,7 @@ namespace Game1.Sources.ML_Joel
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{
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Season = Time.getTimeOfYear(),
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Area = soilProperties.Area,
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Rainfall = soilProperties.Humidity,
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Rainfall = soilProperties.prevRainfall,
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};
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return PredictionEngineArea.Predict(modelInput).Score;
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@ -13,19 +13,24 @@ namespace Game1.Sources.ML_Joel
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class Model
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{
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private static MLContext mlContext = new MLContext(seed: 1);
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private static string path = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/Rainfall.csv";
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private static string modelpath = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/MLmodel_Joel";
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private static string report = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/report_Joel";
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private static string path_area = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/Rainfall_area.csv";
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private static string modelpath_area = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/MLmodel_Joel_area";
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private static string report_area = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/report_Joel_area";
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/*
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private static string path = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/Rainfall.csv";
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private static string modelpath = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodel_Joel";
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private static string report = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report_Joel";
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*/
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private static string path = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/Rainfall.csv";
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private static string modelpath = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodel_Joel";
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private static string report = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report_Joel";
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private static string path_area = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/Rainfall_area.csv";
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private static string modelpath_area = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodel_Joel_area";
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private static string report_area = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report_Joel_area";
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*/
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// Loading data, creatin and saving ML model for smaller dataset (100)
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public static void CreateModel()
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{
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@ -142,7 +147,7 @@ namespace Game1.Sources.ML_Joel
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var eval = mlContext.Regression.Evaluate(trainingDataView, labelColumnName: labelColumnName);
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var report = File.CreateText(reportPath);
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report.Write("Mean Absolute Error: " + eval.MeanAbsoluteError + '\n' + "Mean Squared Error: " + eval.MeanSquaredError + '\n' + "R Squared: " + eval.RSquared, 0, 0);
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report.Write("Mean Absolute Error: " + eval.MeanAbsoluteError + '\n' + "R Squared: " + eval.RSquared, 0, 0);
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report.Flush();
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report.Close();
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}
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