public double getSimilarity(FastIDSet cluster1, FastIDSet cluster2)
{
if (cluster1.isEmpty() || cluster2.isEmpty())
{
return(Double.NaN);
}
double leastSimilarity = Double.PositiveInfinity;
var someUsers = SamplingLongPrimitiveIterator.maybeWrapIterator(cluster1.GetEnumerator(), samplingRate);
while (someUsers.MoveNext())
{
long userID1 = someUsers.Current;
var it2 = cluster2.GetEnumerator();
while (it2.MoveNext())
{
double theSimilarity = similarity.userSimilarity(userID1, it2.Current);
if (theSimilarity < leastSimilarity)
{
leastSimilarity = theSimilarity;
}
}
}
// We skipped everything? well, at least try comparing the first Users to get some value
if (leastSimilarity == Double.PositiveInfinity)
{
return(similarity.userSimilarity(cluster1.GetEnumerator().Current, cluster2.GetEnumerator().Current));
}
return(leastSimilarity);
}
public override long[] getUserNeighborhood(long userID)
{
DataModel model = this.getDataModel();
TopItems.Estimator <long> estimator = new Estimator(this.getUserSimilarity(), userID, this.minSimilarity);
IEnumerator <long> allUserIDs = SamplingLongPrimitiveIterator.maybeWrapIterator(model.getUserIDs(), this.getSamplingRate());
return(TopItems.getTopUsers(this.n, allUserIDs, null, estimator));
}
public override long[] getUserNeighborhood(long userID)
{
DataModel model = this.getDataModel();
FastIDSet set = new FastIDSet();
IEnumerator <long> enumerator = SamplingLongPrimitiveIterator.maybeWrapIterator(model.getUserIDs(), this.getSamplingRate());
UserSimilarity similarity = this.getUserSimilarity();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
if (userID != current)
{
double d = similarity.userSimilarity(userID, current);
if (!(double.IsNaN(d) || (d < this.threshold)))
{
set.add(current);
}
}
}
return(set.toArray());
}
public static LoadStatistics runLoad(Recommender recommender, int howMany)
{
DataModel model = recommender.getDataModel();
int num = model.getNumUsers();
double samplingRate = 1000.0 / ((double)num);
IEnumerator <long> enumerator = SamplingLongPrimitiveIterator.maybeWrapIterator(model.getUserIDs(), samplingRate);
if (enumerator.MoveNext())
{
recommender.recommend(enumerator.Current, howMany);
}
List <Action> callables = new List <Action>();
while (enumerator.MoveNext())
{
callables.Add(new Action(new LoadCallable(recommender, enumerator.Current).call));
}
AtomicInteger noEstimateCounter = new AtomicInteger();
RunningAverageAndStdDev timing = new FullRunningAverageAndStdDev();
AbstractDifferenceRecommenderEvaluator.execute(callables, noEstimateCounter, timing);
return(new LoadStatistics(timing));
}