Revize 66c3e0df

re #8598

Update Server/ServerApp/ServerApp.csproj
fixed corrupted dependencies
working simple classification

Server/ServerApp/Predictor/NaiveBayesClassifier.cs

@@ -24 +24 @@
         public IEnumerable<ModelInput> ExtractModelInput(List<WeatherInfo> weatherInfos, List<ActivityInfo> activityInfos)
         {
             return weatherInfos.Select(e => new ModelInput(){
-                Col1 = e.temp,
-                LabelCol = "Empty",
+                Temp = (float)e.temp,
+                Label = e.temp > 15.0 ? "Full" : "Empty",
             }).ToList();
         }
 
         public void Fit(IEnumerable<ModelInput> trainingData)
         {
             IDataView trainingDataView = mlContext.Data.LoadFromEnumerable(trainingData);
-            var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey("label", "label")
-                .Append(mlContext.Transforms.CopyColumns("Features", "temp"))
-                .Append(mlContext.Transforms.NormalizeMinMax("Features", "Features"));
+            var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey(nameof(ModelInput.Label))
+                .Append(mlContext.Transforms.Concatenate("Features", new[] { "temp" })
+                .Append(mlContext.Transforms.NormalizeMinMax("Features", "Features")));
             var trainer = mlContext.MulticlassClassification.Trainers.NaiveBayes();
-            var traininingPipeline = dataProcessPipeline.Append(trainer);
+            var traininingPipeline = dataProcessPipeline.Append(trainer)
+                .Append(mlContext.Transforms.Conversion.MapKeyToValue("prediction", "PredictedLabel"));
 
             this.model = traininingPipeline.Fit(trainingDataView);
 
         }
 
-        public ModelOutput Predict(IEnumerable<ModelInput> input)
+        public String[] Predict(IEnumerable<ModelInput> input)
         {
             var data = mlContext.Data.LoadFromEnumerable(input);
-            var prediction = model.Transform(data);
-            var result = mlContext.Data.CreateEnumerable<ModelOutput>(prediction, reuseRowObject: false);
-            return (ModelOutput)result;
+            IDataView result = model.Transform(data);
+            String[] prediction = result.GetColumn<String>("prediction").ToArray();
+
+            return prediction;
         }
     }
 }