using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Transforms.TimeSeries;
using Newtonsoft.Json;

namespace AnomalyDetectionUsingSrCnn
{
    class Program
    {
        static void Main(string[] args)
        {
            var mlContext = new MLContext();

            //Load Json DataSet and Create an IEnumerable object
            List<DataPointModel> dataPoints = LoadJson();

            IEnumerable<DataPointModel> enumerableDataSet = dataPoints; 

            //Convert the IEnumerable Object into IDataView
            IDataView dataView  = mlContext.Data.LoadFromEnumerable(enumerableDataSet);

            SrCnnAnomalyEstimator srCnnAnomalyEstimator = mlContext.Transforms.DetectAnomalyBySrCnn(nameof(DataPointPredictionModel.Prediction), nameof(DataPointModel.DataPoint), 10, 5, 5, 3, 5, 0.1);

            ITransformer srCnnAnomalyDetector = srCnnAnomalyEstimator.Fit(dataView);

            var transformedData = srCnnAnomalyDetector.Transform(dataView);
            
            var predictions = mlContext.Data.CreateEnumerable<DataPointPredictionModel>(transformedData, reuseRowObject: false);
            
            Console.WriteLine("Data\tAlert\tScore\tMag");
            
            var k = 0;
            
            foreach (var p in predictions)
            {
                var results = "";
                
                results += $"{dataPoints[k++].DataPoint}\t{p.Prediction[0]}\t{p.Prediction[1]:f2}\t{p.Prediction[2]:F2}";

                if (p.Prediction[0] == 1)
                {

                    results += "<------anomaly detected";
                }

                Console.WriteLine(results);
            }
            Console.WriteLine("");


        }
        
        static IDataView CreateEmptyDataView(MLContext mlContext)
        {
            // Create empty DataView. We just need the schema to call Fit() for the time series transforms
            IEnumerable<DataPointModel> enumerableData = new List<DataPointModel>();
            
            return mlContext.Data.LoadFromEnumerable(enumerableData);
        }

        
        public static List<DataPointModel> LoadJson()
        {
            using (var r = new StreamReader("/root/.config/JetBrains/Rider2021.2/scratches/scratch.json"))
            {
                var json = r.ReadToEnd();
                
                return JsonConvert.DeserializeObject<List<DataPointModel>>(json);
            }
        }
        
        
    }
}