///
public override void Train()
{
baseline_predictor.Train();
InitModel();
if (correlation is IBinaryDataCorrelationMatrix)
{
((IBinaryDataCorrelationMatrix)correlation).ComputeCorrelations(BinaryDataMatrix);
}
else
{
((IRatingCorrelationMatrix)correlation).ComputeCorrelations(Ratings, Entity);
}
}
///
public override void Train()
{
// init factor matrices
user_factors = new Matrix <float>(MaxUserID + 1, NumFactors);
item_factors = new Matrix <float>(MaxItemID + 1, NumFactors);
// init+train global effects model
global_effects.Ratings = ratings;
global_effects.Train();
// learn model parameters
num_learned_factors = 0;
for (int i = 0; i < NumIter; i++)
{
Iterate();
}
}
public static void Main(string[] args)
{
// load the data
var user_mapping = new EntityMapping();
var item_mapping = new EntityMapping();
var training_data = MyMediaLite.IO.RatingPrediction.Read(args[0], user_mapping, item_mapping);
var test_data = MyMediaLite.IO.RatingPrediction.Read(args[1], user_mapping, item_mapping);
// set up the recommender
var recommender = new UserItemBaseline();
recommender.Ratings = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = RatingEval.Evaluate(recommender, test_data);
Console.WriteLine("RMSE={0} MAE={1}", results["RMSE"], results["MAE"]);
// 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 = RatingData.Read(args[0]);
var test_data = RatingData.Read(args[1]);
// set up the recommender
var recommender = new UserItemBaseline();
recommender.Ratings = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = recommender.Evaluate(test_data);
Console.WriteLine("RMSE={0} MAE={1}", results["RMSE"], results["MAE"]);
Console.WriteLine(results);
// make a prediction for a certain user and item
Console.WriteLine(recommender.Predict(1, 1));
var bmf = new BiasedMatrixFactorization {Ratings = training_data};
Console.WriteLine(bmf.DoCrossValidation());
}
///
public override void Train()
{
// init factor matrices
user_factors = new Matrix <double>(Ratings.MaxUserID + 1, NumFactors);
item_factors = new Matrix <double>(Ratings.MaxItemID + 1, NumFactors);
// init+train global effects model
global_effects.Ratings = Ratings;
global_effects.Train();
global_bias = Ratings.Average;
// initialize learning data structure
residuals = new double[Ratings.Count];
// learn model parameters
num_learned_factors = 0;
for (int i = 0; i < NumIter; i++)
{
Iterate();
}
}