IFoldInRatingPredictor CreateRecommender()
{
var training_data = RatingData.Read("../../../../data/ml-100k/u.data");
var recommender = new MatrixFactorization();
recommender.Ratings = training_data;
recommender.NumFactors = 4;
recommender.NumIter = 5;
recommender.Train();
return recommender;
}
public void TestDecay()
{
var mf = new MatrixFactorization()
{
LearnRate = 1.0f, Decay = 0.5f,
NumIter = 1, Ratings = TestUtils.CreateRatings()
};
mf.Train();
Assert.AreEqual(0.5f, mf.current_learnrate);
mf.Iterate();
Assert.AreEqual(0.25f, mf.current_learnrate);
}
public void TestDefaultBehaviorIsNoDecay()
{
var mf = new MatrixFactorization() { LearnRate = 1.1f, NumIter = 10, Ratings = TestUtils.CreateRatings() };
mf.Train();
Assert.AreEqual(1.1f, mf.current_learnrate);
}
