MyMediaLite.ItemRecommendation.UserKNN.Train C# (CSharp) Method

        public override void Train()
        {
            this.correlation = BinaryCosine.Create(Feedback.UserMatrix);

            int num_users = MaxUserID + 1;
            this.nearest_neighbors = new int[num_users][];
            for (int u = 0; u < num_users; u++)
                nearest_neighbors[u] = correlation.GetNearestNeighbors(u, k);
        }

    private static void startUserKNN(string data)
    {
        MyMediaLite.Data.Mapping user_mapping = new MyMediaLite.Data.Mapping();
        MyMediaLite.Data.Mapping item_mapping = new MyMediaLite.Data.Mapping();
        ITimedRatings            all_data     = readDataMapped(data, ref user_mapping, ref item_mapping);

        removeUserThreshold(ref all_data);
        Console.WriteLine("Start iteration Test UserKNN");
        //for (int i = 0; i < 5; i++) {
        ITimedRatings validation_data = new TimedRatings();    // 10%
        ITimedRatings test_data       = new TimedRatings();    // 20%
        ITimedRatings training_data   = new TimedRatings();    // 70%

        readAndSplitData(all_data, ref test_data, ref training_data, ref validation_data);
        IPosOnlyFeedback training_data_pos = new PosOnlyFeedback <SparseBooleanMatrix> ();        // 80%

        for (int index = 0; index < training_data.Users.Count; index++)
        {
            training_data_pos.Add(training_data.Users [index], training_data.Items [index]);
        }


        MyMediaLite.ItemRecommendation.UserKNN recommender = new MyMediaLite.ItemRecommendation.UserKNN();
        recommender.K           = 80;
        recommender.Q           = 1;
        recommender.Weighted    = false;
        recommender.Alpha       = 0.5f;
        recommender.Correlation = MyMediaLite.Correlation.BinaryCorrelationType.Jaccard;
        recommender.Feedback    = training_data_pos;
        DateTime start_time = DateTime.Now;

        recommender.Train();

        Console.Write("Total Training time needed:");
        Console.WriteLine(((TimeSpan)(DateTime.Now - start_time)).TotalMilliseconds);
        Console.WriteLine("Final results in this iteration:");
        var results = MyMediaLite.Eval.ItemsWeatherItemRecommender.EvaluateTime(recommender, validation_data, training_data, "VALIDATION ", false);

        results = MyMediaLite.Eval.ItemsWeatherItemRecommender.EvaluateTime(recommender, test_data, training_data, "TEST ", false);
        //}
    }