public static void evaluate(Recommender recommender, DataModel model, int samples, RunningAverage tracker, string tag)
{
printHeader();
IEnumerator <long> enumerator = recommender.getDataModel().getUserIDs();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
List <RecommendedItem> items = recommender.recommend(current, model.getNumItems());
PreferenceArray prefs = model.getPreferencesFromUser(current);
prefs.sortByValueReversed();
FastIDSet modelSet = new FastIDSet();
long num2 = setBits(modelSet, items, samples);
FastIDSet set2 = new FastIDSet();
num2 = Math.Max(num2, setBits(set2, prefs, samples));
int max = Math.Min(mask(modelSet, set2, num2), samples);
if (max >= 2)
{
long[] itemsL = getCommonItems(modelSet, items, max);
long[] itemsR = getCommonItems(modelSet, prefs, max);
double datum = scoreCommonSubset(tag, current, samples, max, itemsL, itemsR);
tracker.addDatum(datum);
}
}
}
public CachingRecommender(Recommender recommender)
{
Action refreshRunnable = null;
this.recommender = recommender;
this.maxHowMany = new int[] { 1 };
int maxEntries = recommender.getDataModel().getNumUsers();
this.recommendationsRetriever = new RecommendationRetriever(this);
this.recommendationCache = new Cache <long, Recommendations>(this.recommendationsRetriever, maxEntries);
this.estimatedPrefCache = new Cache <Tuple <long, long>, float>(new EstimatedPrefRetriever(this), maxEntries);
if (refreshRunnable == null)
{
refreshRunnable = () => this.clear();
}
this.refreshHelper = new RefreshHelper(refreshRunnable);
this.refreshHelper.addDependency(recommender);
}
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));
}
public static void evaluate(Recommender recommender1, Recommender recommender2, int samples, RunningAverage tracker, string tag)
{
printHeader();
IEnumerator <long> enumerator = recommender1.getDataModel().getUserIDs();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
List <RecommendedItem> items = recommender1.recommend(current, samples);
List <RecommendedItem> list2 = recommender2.recommend(current, samples);
FastIDSet modelSet = new FastIDSet();
long num2 = setBits(modelSet, items, samples);
FastIDSet set2 = new FastIDSet();
num2 = Math.Max(num2, setBits(set2, list2, samples));
int max = Math.Min(mask(modelSet, set2, num2), samples);
if (max >= 2)
{
long[] itemsL = getCommonItems(modelSet, items, max);
long[] itemsR = getCommonItems(modelSet, list2, max);
double datum = scoreCommonSubset(tag, current, samples, max, itemsL, itemsR);
tracker.addDatum(datum);
}
}
}
