public void AddMethodOk()
{
clsMostRecommendedFilmsCollection AllMostRecommendedFilms = new
clsMostRecommendedFilmsCollection();
clsMostRecommendedFilms TestItem = new clsMostRecommendedFilms();
TestItem.FilmId = 174055;
TestItem.TimesRecommended = 2017;
AllMostRecommendedFilms.ThisMostRecommendedFilm = TestItem;
AllMostRecommendedFilms.Add();
AllMostRecommendedFilms.ThisMostRecommendedFilm.Find(TestItem.FilmId);
Assert.AreEqual(AllMostRecommendedFilms.ThisMostRecommendedFilm, TestItem);
}
void GenerateRecommendations(int genreId)
{
//create a new instance of the MLContext class
MLContext mlContext = new MLContext();
(IDataView trainingDataView, IDataView testDataView) = LoadData(mlContext);
ITransformer model = BuildAndTrainModel(mlContext, trainingDataView);
//create an instance of the class which represents the IDataView/ DataViewRow schema
DataViewSchema modelSchema;
//find the saved model
var path = Server.MapPath(@"~/Model.zip");
//load the saved model
ITransformer trainedModel = mlContext.Model.Load(path,
out modelSchema);
//create a prediction engine for film recommendations
var predictionEngine = mlContext.Model.CreatePredictionEngine <clsFilmRating, MovieRatingPrediction>(trainedModel);
var tempUserId = Session["UserId"];
Single userId;
Boolean signedIn = false;
//check if user is signed in
if (tempUserId == null)
{
userId = 1;
}
else
{
userId = Convert.ToSingle(tempUserId);
signedIn = true;
}
//get all films from the database
clsFilmGenreCollection AllFilms = new clsFilmGenreCollection();
AllFilms.GetAllFilmsByGenre(genreId);
List <clsFilmPrediction> AllPredictions = new List <clsFilmPrediction>();
clsFilmPrediction aFilmPrediction = new clsFilmPrediction();
foreach (clsFilmGenre aFilm in AllFilms.AllFilmsByGenre)
{
var potentialRecommendation = new clsFilmRating {
UserId = userId, FilmId = aFilm.FilmId
};
var movieRatingPrediction = predictionEngine.Predict(potentialRecommendation);
//if a rating is high enough, add it to film predictions
if (Math.Round(movieRatingPrediction.Score, 1) > 4.4)
{
aFilmPrediction = new clsFilmPrediction();
aFilmPrediction.FilmId = aFilm.FilmId;
aFilmPrediction.Score = movieRatingPrediction.Score;
AllPredictions.Add(aFilmPrediction);
}
}
//sort them by score
AllPredictions.Sort();
//get the ten best recommendations
var topTenPredictions = AllPredictions.Take(10);
clsFilmRecommendationCollection FilmRecommendations = new clsFilmRecommendationCollection();
clsFilmRecommendation aRecommendationToAdd = new clsFilmRecommendation();
clsMostRecommendedFilmsCollection AllMostRecommendedFilms = new clsMostRecommendedFilmsCollection();
clsMostRecommendedFilms aMostRecommendedFilm = new clsMostRecommendedFilms();
foreach (clsFilmPrediction aTopTenPrediction in topTenPredictions)
{
if (signedIn)
{
//save the recommendations for future use
aRecommendationToAdd = new clsFilmRecommendation();
aRecommendationToAdd.FilmId = aTopTenPrediction.FilmId;
aRecommendationToAdd.UserId = Convert.ToInt32(userId);
FilmRecommendations.ThisFilmRecommendation = aRecommendationToAdd;
FilmRecommendations.Add();
}
aMostRecommendedFilm = new clsMostRecommendedFilms();
AllMostRecommendedFilms.ThisMostRecommendedFilm.FilmId = aTopTenPrediction.FilmId;
if (AllMostRecommendedFilms.ThisMostRecommendedFilm.Find(aTopTenPrediction.FilmId) == true)
{
AllMostRecommendedFilms.IncreaseTimesRecommended();
}
else
{
AllMostRecommendedFilms.Add();
}
//get imdb information for film
pnlRecommendations.Controls.Add(anImdbApi.GetImdbInformation(aTopTenPrediction.FilmId));
}
pnlRecommendations.Visible = true;
//save the model for later use
mlContext.Model.Save(trainedModel, modelSchema, path);
}