示例#1
0
    public static void Main(string[] args)
    {
        // load the data
        var user_mapping   = new EntityMapping();
        var item_mapping   = new EntityMapping();
        var training_data  = ItemRecommendation.Read(args[0], user_mapping, item_mapping);
        var relevant_users = training_data.AllUsers;         // users that will be taken into account in the evaluation
        var relevant_items = training_data.AllItems;         // items that will be taken into account in the evaluation
        var test_data      = ItemRecommendation.Read(args[1], user_mapping, item_mapping);

        // set up the recommender
        var recommender = new MostPopular();

        recommender.Feedback = training_data;
        recommender.Train();

        // measure the accuracy on the test data set
        var results = ItemPredictionEval.Evaluate(recommender, test_data, training_data, relevant_users, relevant_items);

        foreach (var key in results.Keys)
        {
            Console.WriteLine("{0}={1}", key, results[key]);
        }

        // make a prediction for a certain user and item
        Console.WriteLine(recommender.Predict(user_mapping.ToInternalID(1), item_mapping.ToInternalID(1)));
    }
示例#2
0
        protected void Page_Load(object sender, EventArgs e)
        {
            string sortBy = null;

            if (Request.QueryString["active"] == null)
            {
                _activeLetter = "A";
            }
            else
            {
                _activeLetter = Request.QueryString["active"];
            }

            if (Request.QueryString["sortBy"] == null)
            {
                sortBy = "a";
            }
            else
            {
                sortBy = Request.QueryString["sortBy"];
            }

            _sort = TryParseSortChannelsBy(sortBy);

            GetSortingLinks();

            BLClient client = null;

            // access the DB for most popular channels and bind "most popular" once
            if (!Page.IsPostBack)
            {
                try
                {
                    client = new BLClient();

                    MostPopular.DataSource = client.GetChannelMostPopular();
                    MostPopular.DataBind();
                }
                finally
                {
                    client.Dispose();
                }

                GetChannels();
            }

            Panel activeLetterPanel = GetActivePanel(_activeLetter);

            AddSortingInfoToAlphabet(sortBy);

            activeLetterPanel.CssClass = "ActiveLetter";
        }
	public static void Main(string[] args)
	{
		// load the data
		var training_data = ItemData.Read(args[0]);
		var test_data = ItemData.Read(args[1]);

		// set up the recommender
		var recommender = new MostPopular();
		recommender.Feedback = training_data;
		recommender.Train();

		// measure the accuracy on the test data set
		var results = recommender.Evaluate(test_data, training_data);
		foreach (var key in results.Keys)
			Console.WriteLine("{0}={1}", key, results[key]);
		Console.WriteLine(results);

		// make a score prediction for a certain user and item
		Console.WriteLine(recommender.Predict(1, 1));
	}
示例#4
0
    public static void Main(string[] args)
    {
        // load the data
        var user_mapping = new EntityMapping();
        var item_mapping = new EntityMapping();
        var training_data = ItemRecommendation.Read(args[0], user_mapping, item_mapping);
        var relevant_users = training_data.AllUsers; // users that will be taken into account in the evaluation
        var relevant_items = training_data.AllItems; // items that will be taken into account in the evaluation
        var test_data = ItemRecommendation.Read(args[1], user_mapping, item_mapping);

        // set up the recommender
        var recommender = new MostPopular();
        recommender.Feedback = training_data;
        recommender.Train();

        // measure the accuracy on the test data set
        var results = ItemPredictionEval.Evaluate(recommender, test_data, training_data, relevant_users, relevant_items);
        foreach (var key in results.Keys)
            Console.WriteLine("{0}={1}", key, results[key]);

        // make a prediction for a certain user and item
        Console.WriteLine(recommender.Predict(user_mapping.ToInternalID(1), item_mapping.ToInternalID(1)));
    }
    public static void Main(string[] args)
    {
        // load the data
        var training_data = ItemData.Read(args[0]);
        var test_data     = ItemData.Read(args[1]);

        // set up the recommender
        var recommender = new MostPopular();

        recommender.Feedback = training_data;
        recommender.Train();

        // measure the accuracy on the test data set
        var results = recommender.Evaluate(test_data, training_data);

        foreach (var key in results.Keys)
        {
            Console.WriteLine("{0}={1}", key, results[key]);
        }
        Console.WriteLine(results);

        // make a score prediction for a certain user and item
        Console.WriteLine(recommender.Predict(1, 1));
    }
        static void Main(string[] args)
        {
            var processor = new MostPopular();

            processor.Setup();
        }