public void TestDefaultBehaviorIsNoDecay()
{
var afm = new SigmoidCombinedAsymmetricFactorModel()
{
LearnRate = 1.1f, NumIter = 10, Ratings = TestUtils.CreateRatings()
};
afm.Train();
Assert.AreEqual(1.1f, afm.current_learnrate);
}
public void TestCurrentLearnRate()
{
var afm = new SigmoidCombinedAsymmetricFactorModel()
{
LearnRate = 1.1f, Ratings = TestUtils.CreateRatings()
};
afm.InitModel();
Assert.AreEqual(1.1f, afm.LearnRate);
Assert.AreEqual(1.1f, afm.current_learnrate);
}
public void TestDecay()
{
var afm = new SigmoidCombinedAsymmetricFactorModel()
{
LearnRate = 1.0f, Decay = 0.5f,
NumIter = 1, Ratings = TestUtils.CreateRatings()
};
afm.Train();
Assert.AreEqual(0.5f, afm.current_learnrate);
afm.Iterate();
Assert.AreEqual(0.25f, afm.current_learnrate);
}
public void TestMatrixInit()
{
var afm = new SigmoidCombinedAsymmetricFactorModel()
{
Ratings = TestUtils.CreateRatings()
};
afm.InitModel();
Assert.IsNotNull(afm.user_factors);
Assert.IsNotNull(afm.item_factors);
Assert.IsNotNull(afm.x);
Assert.IsNotNull(afm.y);
Assert.IsNotNull(afm.user_bias);
Assert.IsNotNull(afm.item_bias);
}
public void TestFoldIn()
{
var afm = new SigmoidCombinedAsymmetricFactorModel()
{
Ratings = TestUtils.CreateRatings()
};
afm.Train();
var user_ratings = new List <Tuple <int, float> >();
user_ratings.Add(new Tuple <int, float>(0, 4.0f));
var candidate_items = new List <int> {
0, 1
}; // have a known and an unknown item
var results = afm.ScoreItems(user_ratings, candidate_items);
Assert.AreEqual(2, results.Count);
}
