Revize ce0940b5

Re #9034 fixing files corrupted by merge

Server/ServerApp/Predictor/PredictionController.cs

@@ -4 +4 @@
 
 using System;
 using System.Collections.Generic;
-using System.IO;
-using Microsoft.ML;
 using ServerApp.Connection.XMLProtocolHandler;
+using ServerApp.Parser.Parsers;
+using Newtonsoft.Json;
+using ServerApp.WeatherPredictionParser;
 using ServerApp.Parser.OutputInfo;
 using System.Reflection;
 using log4net;
@@ -16 +17 @@
     /// <summary>
     /// Implentation of the <c>IPredicitionController</c> interface.
     /// </summary>
-    class PredictionController : IPredictionController
+    public class PredictionController : IPredictionController
     {
         private static readonly ILog _log = LogManager.GetLogger(typeof(PredictionController));
 
         /// <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>
+        /// A weather prediction parser service
+        /// </summary>
+        private IJsonParser weatherService;
 
         /// <summary>
         /// Instantiates new prediction controller.
         /// </summary>
         /// <param name="dataParser">A data parser used to get training data.</param>
-        public PredictionController(IDataParser dataParser)
+        public PredictionController(IJsonParser weatherService, IDataParser dataParser, string pathToConfig = null)
         {
             _log.Info("Constructing a new PredictionController instance.");
             this.weatherService = weatherService;
@@ -68 +74 @@
 
             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)
@@ -180 +185 @@
             if (locationKey is null)
             // 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++)
+                DataParser.Parse(DateTime.MinValue, DateTime.MaxValue, this.Configuration.TimeResolution, wholeDay: false);
+                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);
+                    Console.WriteLine("Training predictor with {0} samples.", data.Count);
+                    this.Predictors[i].Fit(data);
                 }
-            } else
+            }
+            else
             // train specified predictor only
             {
                 throw new NotImplementedException();