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 FastIDSet getRelevantItemsIDs(long userID, int at, double relevanceThreshold, DataModel dataModel) { PreferenceArray array = dataModel.getPreferencesFromUser(userID); FastIDSet set = new FastIDSet(at); array.sortByValueReversed(); for (int i = 0; (i < array.length()) && (set.size() < at); i++) { if (array.getValue(i) >= relevanceThreshold) { set.add(array.getItemID(i)); } } return(set); }