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 void addItemPref(long userID, long itemIDA, float prefValue)
{
PreferenceArray userPreferences = dataModel.getPreferencesFromUser(userID);
try
{
buildAverageDiffsLock.EnterWriteLock();
FastByIDMap <RunningAverage> aMap = averageDiffs.get(itemIDA);
if (aMap == null)
{
aMap = new FastByIDMap <RunningAverage>();
averageDiffs.put(itemIDA, aMap);
}
int length = userPreferences.length();
for (int i = 0; i < length; i++)
{
long itemIDB = userPreferences.getItemID(i);
float bValue = userPreferences.getValue(i);
if (itemIDA < itemIDB)
{
RunningAverage average = aMap.get(itemIDB);
if (average == null)
{
average = buildRunningAverage();
aMap.put(itemIDB, average);
}
average.addDatum(bValue - prefValue);
}
else
{
FastByIDMap <RunningAverage> bMap = averageDiffs.get(itemIDB);
if (bMap == null)
{
bMap = new FastByIDMap <RunningAverage>();
averageDiffs.put(itemIDB, bMap);
}
RunningAverage average = bMap.get(itemIDA);
if (average == null)
{
average = buildRunningAverage();
bMap.put(itemIDA, average);
}
average.addDatum(prefValue - bValue);
}
}
}
finally
{
buildAverageDiffsLock.ExitWriteLock();
}
}
private long processOneUser(long averageCount, long userID)
{
log.debug("Processing prefs for user {}", userID);
// Save off prefs for the life of this loop iteration
PreferenceArray userPreferences = dataModel.getPreferencesFromUser(userID);
int length = userPreferences.length();
for (int i = 0; i < length - 1; i++)
{
float prefAValue = userPreferences.getValue(i);
long itemIDA = userPreferences.getItemID(i);
FastByIDMap <RunningAverage> aMap = averageDiffs.get(itemIDA);
if (aMap == null)
{
aMap = new FastByIDMap <RunningAverage>();
averageDiffs.put(itemIDA, aMap);
}
for (int j = i + 1; j < length; j++)
{
// This is a performance-critical block
long itemIDB = userPreferences.getItemID(j);
RunningAverage average = aMap.get(itemIDB);
if (average == null && averageCount < maxEntries)
{
average = buildRunningAverage();
aMap.put(itemIDB, average);
averageCount++;
}
if (average != null)
{
average.addDatum(userPreferences.getValue(j) - prefAValue);
}
}
RunningAverage itemAverage = averageItemPref.get(itemIDA);
if (itemAverage == null)
{
itemAverage = buildRunningAverage();
averageItemPref.put(itemIDA, itemAverage);
}
itemAverage.addDatum(prefAValue);
}
return(averageCount);
}
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);
}
}
}