/*public void SetMaxUsers(int i)
* {
* _engine.MaxUsers = i;
* }
*
* public void SetSimilaritySaf(double p)
* {
* _engine.MinSimilarity = p;
* }*/
//TODO
public Dictionary <PredictionMethod, double> ComputeRMSE(List <PredictionMethod> lMethods, out Dictionary <PredictionMethod, Dictionary <PredictionMethod, double> > dConfidence)
{
_allTest = _test.GetAllData();
Dictionary <PredictionMethod, double> res = new Dictionary <PredictionMethod, double>();
foreach (var m in lMethods)
{
res.Add(m, ComputeRMSE(m, _trainEngine, _allTest));
}
//TODO
dConfidence = new Dictionary <PredictionMethod, Dictionary <PredictionMethod, double> >();
foreach (var m1 in lMethods)
{
dConfidence.Add(m1, new Dictionary <PredictionMethod, double>());
foreach (var m2 in lMethods)
{
if (m1 != m2)
{
dConfidence[m1].Add(m2, SignTest(m1, m2));
}
}
}
return(res);
}
public void TrainBaseModel(int cFeatures)
{
double avg = (_train.SumRanks() + _test.SumRanks()) / (_train.NumOfRanks() + _test.NumOfRanks());
var svd = new SVD(avg, cFeatures);
RankingDB train = new RankingDB();
RankingDB validation = new RankingDB();
DivideDB(_train, 0.95, train, validation);
var ranks = train.GetAllData();
double RMSE = double.MaxValue; double LastRMSE = double.MaxValue;
while (RMSE <= LastRMSE)
{
LastRMSE = RMSE;
svd.Train(ranks);
_trainEngine.setSVD(svd);
RMSE = ComputeRMSE(PredictionMethod.SVD, _trainEngine, validation.GetAllData());
}
//_engine.setSVD(svd);
}
private double ComputeRMSE(PredictionMethod m, RankingDB db)
{
return(ComputeRMSE(m, new RecommenderSystemEngine(db), db.GetAllData()));
}