static void Main(string[] args)
{
String path_training_data = "user_ratedmovies.dat";
String path_user_data = "users.dat";
String path_atr_data = "movie_genres.dat";
String path_pred_data = "predict.dat";
var user_mapping = new Mapping();
var item_mapping = new Mapping();
//var user_mapping = new EntityMapping();
//var item_mapping = new EntityMapping();
//String path_training_data = "1.rec.train";
//String path_test_data = "1.rec.test";
//String path_training_data = "u1.base";
//String path_test_data = "u1.test";
//var training_data = ItemData.Read(path_training_data);
//var test_data = ItemData.Read(path_test_data);
var training_data = RatingData.Read(path_training_data, user_mapping, item_mapping);
var relevant_items = item_mapping.InternalIDs;
var atr_data = AttributeData.Read(path_atr_data, item_mapping);
var pred_data = RatingData.Read(path_pred_data, user_mapping, item_mapping);
//var user_data = ItemData.Read(path_user_data, user_mapping);
Console.WriteLine("I'm here");
//Console.WriteLine(String.Join(";", user_data.AllUsers));
//var test_data = RatingData.Read(path_test_data);
//var recommender = new ItemKNN();
//recommender.Feedback = training_data;
//recommender.Train();
//var recommender = new GSVDPlusPlus();
//var recommender = new MatrixFactorization();
//recommender.Ratings = training_data;
// recommender.MaxRating = 5;
// recommender.MinRating = 1;
//recommender.ItemAttributes = atr_data;
//recommender.NumFactors = 5;
//recommender.NumIter = 10;
//recommender.Train();
var recommender = new SVDPlusPlus();
recommender.Ratings = training_data;
//recommender.AdditionalFeedback = user_data;
Console.WriteLine("I'm here2");
Console.WriteLine(recommender.ToString());
//recommender.ItemAttributes = atr_data;
recommender.NumFactors = 10;
Console.WriteLine("I'm here3");
Console.WriteLine(recommender.ToString());
recommender.Train();
Console.WriteLine("I'm here4");
//var results = recommender.DoIterativeCrossValidation(recommdender, 10, 1, 1, true);
var results = recommender.DoCrossValidation(10, false, true);
////var results = recommender.Evaluate(test_data, training_data);
foreach (var key in results.Keys)
{
Console.WriteLine("{0}={1}", key, results[key]);
}
Console.WriteLine(results);
//recommender.Predict();
recommender.WritePredictions(pred_data, "test45.dat", user_mapping, item_mapping, "{0}|{1}|{2}");
Console.WriteLine(recommender.Predict(1, 1));
Console.ReadLine();
}
