public override long[] GetUserNeighborhood(long userID)
{
IDataModel dataModel = getDataModel();
IUserSimilarity userSimilarityImpl = getUserSimilarity();
TopItems.IEstimator <long> estimator = new Estimator(userSimilarityImpl, userID, minSimilarity);
var userIDs = SamplinglongPrimitiveIterator.MaybeWrapIterator(dataModel.GetUserIDs(),
getSamplingRate());
return(TopItems.GetTopUsers(n, userIDs, null, estimator));
}
public override long[] GetUserNeighborhood(long userID)
{
IDataModel dataModel = getDataModel();
FastIDSet neighborhood = new FastIDSet();
var usersIterable = SamplinglongPrimitiveIterator.MaybeWrapIterator(dataModel
.GetUserIDs(), getSamplingRate());
IUserSimilarity userSimilarityImpl = getUserSimilarity();
while (usersIterable.MoveNext())
{
long otherUserID = usersIterable.Current;
if (userID != otherUserID)
{
double theSimilarity = userSimilarityImpl.UserSimilarity(userID, otherUserID);
if (!Double.IsNaN(theSimilarity) && theSimilarity >= threshold)
{
neighborhood.Add(otherUserID);
}
}
}
return(neighborhood.ToArray());
}
public static LoadStatistics runLoad(IRecommender recommender, int howMany)
{
IDataModel dataModel = recommender.GetDataModel();
int numUsers = dataModel.GetNumUsers();
double sampleRate = 1000.0 / numUsers;
var userSampler =
SamplinglongPrimitiveIterator.MaybeWrapIterator(dataModel.GetUserIDs(), sampleRate);
if (userSampler.MoveNext())
{
recommender.Recommend(userSampler.Current, howMany); // Warm up
}
var callables = new List <Action>();
while (userSampler.MoveNext())
{
callables.Add(new LoadCallable(recommender, userSampler.Current).call);
}
AtomicInteger noEstimateCounter = new AtomicInteger();
IRunningAverageAndStdDev timing = new FullRunningAverageAndStdDev();
AbstractDifferenceRecommenderEvaluator.execute(callables, noEstimateCounter, timing);
return(new LoadStatistics(timing));
}
