Revize 4977ce53

Re #8832 Refactoring

Refactored existing classes to comply with the coding conventions

Server/ServerApp/Predictor/NaiveBayesClassifier.cs

@@ -1 @@
-using System;
+//
+// Author: Roman Kalivoda
+//
+
+using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
@@ -9 +13 @@
 
 namespace ServerApp.Predictor
 {
+    /// <summary>
+    /// Implementation of the naive Bayes classifier in ML.NET.
+    /// </summary>
     class NaiveBayesClassifier : IPredictor
     {
+        /// <summary>
+        /// Context of the ML.NET framework.
+        /// </summary>
         private MLContext mlContext;
 
+        /// <summary>
+        /// Model instance
+        /// </summary>
         private ITransformer model;
 
+        /// <summary>
+        /// Instantiates new <c>MLContext</c>.
+        /// </summary>
         public NaiveBayesClassifier()
         {
             mlContext = new MLContext();
 
         }
 
+        /// <summary>
+        /// Extracts list of feature vectors from parsed info objects.
+        /// </summary>
+        /// <param name="weatherInfos">List of weather info objects.</param>
+        /// <param name="activityInfos">List of info objects about activities at the site.</param>
+        /// <returns>A list of feature vectors for model training.</returns>
         public IEnumerable<ModelInput> ExtractModelInput(List<WeatherInfo> weatherInfos, List<ActivityInfo> activityInfos)
         {
             return weatherInfos.Select(e => new ModelInput(){
@@ -29 +51 @@
             }).ToList();
         }
 
-        public void Fit(IEnumerable<ModelInput> trainingData)
+        public void Fit(IEnumerable<ModelInput> trainInput)
         {
-            IDataView trainingDataView = mlContext.Data.LoadFromEnumerable(trainingData);
+            IDataView trainingDataView = mlContext.Data.LoadFromEnumerable(trainInput);
             var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey(nameof(ModelInput.Label))
                 .Append(mlContext.Transforms.Concatenate("Features", new[] { "temp" })
                 .Append(mlContext.Transforms.NormalizeMinMax("Features", "Features")));
@@ -43 +65 @@
 
         }
 
-        public String[] Predict(IEnumerable<ModelInput> input)
+        public IDataView Predict(IEnumerable<ModelInput> input)
         {
             var data = mlContext.Data.LoadFromEnumerable(input);
             IDataView result = model.Transform(data);
-            String[] prediction = result.GetColumn<String>("prediction").ToArray();
-
-            return prediction;
+            return result;
         }
     }
 }