Revize cdeee9f8

Re #8955 implemented multiple predictors support

Server/ServerApp/Predictor/PredictionController.cs

@@ -6 +6 @@
 using System.Collections.Generic;
 using ServerApp.Connection.XMLProtocolHandler;
 using ServerApp.Parser.Parsers;
+using Newtonsoft.Json;
 
 namespace ServerApp.Predictor
 {
@@ -15 +16 @@
     class PredictionController : IPredictionController
     {
         /// <summary>
-        /// A dictionary for storing trained predictors.
+        /// Configuration of the <c>Predictor</c>
         /// </summary>
-        private Dictionary<string, int> buildingsToAreas;
+        private PredictorConfiguration Configuration;
 
-        private List<IPredictor> predictors;
+        private List<IPredictor> Predictors;
 
         /// <summary>
         /// A reference to a data parser.
         /// </summary>
-        private IDataParser dataParser;
+        private IDataParser DataParser;
 
         /// <summary>
         /// A feature extractor instance.
         /// </summary>
-        private FeatureExtractor featureExtractor;
+        private FeatureExtractor FeatureExtractor;
 
         /// <summary>
         /// Instantiates new prediction controller.
         /// </summary>
         /// <param name="dataParser">A data parser used to get training data.</param>
-        public PredictionController(IDataParser dataParser)
+        public PredictionController(IDataParser dataParser, string pathToConfig = null)
         {
-            this.dataParser = dataParser;
-            this.predictors = new List<IPredictor>();
-            this.buildingsToAreas = new Dictionary<string, int>();
-            this.featureExtractor = new FeatureExtractor(this.dataParser, buildingsToAreas);
+            // load config or get the default one
+            if (pathToConfig is null)
+            {
+                pathToConfig = PredictorConfiguration.DEFAULT_CONFIG_PATH;
+            }
+            try
+            {
+                string json = System.IO.File.ReadAllText(pathToConfig);
+                this.Configuration = JsonConvert.DeserializeObject<PredictorConfiguration>(json);
+            } catch (System.IO.IOException e)
+            {
+                Console.WriteLine(e.ToString());
+                this.Configuration = PredictorConfiguration.GetDefaultConfig();
+            }
+
+            this.DataParser = dataParser;
+            this.Predictors = new List<IPredictor>();
+            this.FeatureExtractor = new FeatureExtractor(this.DataParser, this.Configuration);
 
-            // fill predictors with all available locationKeys
-            // TODO Currently all locations use the same predictor. Try dividing locations into subareas with separate predictors.
-            var locationKeys = TagInfo.buildings;
-            foreach (string key in locationKeys)
+            for (int i = 0; i < this.Configuration.PredictorCount; i++)
             {
-                buildingsToAreas.Add(key, 0);
+                Predictors.Add(new NaiveBayesClassifier());
             }
-            IPredictor predictor = new NaiveBayesClassifier();
-            predictors.Add(predictor);
+            PredictorConfiguration.SaveConfig(PredictorConfiguration.DEFAULT_CONFIG_PATH, Configuration);
         }
         public List<string> GetPredictors()
         {
-            return new List<string>(buildingsToAreas.Keys);
+            return new List<string>(this.Configuration.BuildingsToAreas.Keys);
         }
 
         public void Load(string locationKey = null, string path = null)
@@ -81 +92 @@
             // train all predictors
             {
                 // TODO A single predictor is used for all areas, so training is done only once now.
-                for (int i = 0; i < this.predictors.Count; i++)
+                for (int i = 0; i < this.Predictors.Count; i++)
                 {
                     // train on all available data
                     // TODO the train/test split is used just temporarily for demonstration.
-                    List<ModelInput> data = featureExtractor.PrepareTrainingInput(i, DateTime.MinValue, DateTime.MaxValue);
-                    List<ModelInput> trainingData = data.GetRange(index: 0, count: 500);
-                    List<ModelInput> testData = data.GetRange(index: 500, count: 94);
-                    Console.WriteLine("Training predictor with {0} samples.", trainingData.Count);
-                    this.predictors[i].Fit(trainingData);
-
-                    Console.WriteLine("Evaluating predictor with {0} samples.", testData.Count);
-                    this.predictors[i].Evaluate(testData);
+                    List<ModelInput> data = FeatureExtractor.PrepareTrainingInput(i, DateTime.MinValue, DateTime.MaxValue);
+                    Console.WriteLine("Training predictor with {0} samples.", data.Count);
+                    this.Predictors[i].Fit(data);
                 }
             }
             else