private float doEstimatePreference(long itemID)
{
lock (this)
{
RunningAverage average = this.itemAverages.get(itemID);
return((average == null) ? float.NaN : ((float)average.getAverage()));
}
}
private float doEstimatePreference(long userID, long itemID)
{
double count = 0.0;
double totalPreference = 0.0;
PreferenceArray prefs = getDataModel().getPreferencesFromUser(userID);
RunningAverage[] averages = diffStorage.getDiffs(userID, itemID, prefs);
int size = prefs.length();
for (int i = 0; i < size; i++)
{
RunningAverage averageDiff = averages[i];
if (averageDiff != null)
{
double averageDiffValue = averageDiff.getAverage();
if (weighted)
{
double weight = averageDiff.getCount();
if (stdDevWeighted)
{
var raaStdDev = averageDiff as RunningAverageAndStdDev;
if (raaStdDev != null)
{
double stdev = raaStdDev.getStandardDeviation();
if (!Double.IsNaN(stdev))
{
weight /= 1.0 + stdev;
}
}
// If stdev is NaN, then it is because count is 1. Because we're weighting by count,
// the weight is already relatively low. We effectively assume stdev is 0.0 here and
// that is reasonable enough. Otherwise, dividing by NaN would yield a weight of NaN
// and disqualify this pref entirely
// (Thanks Daemmon)
}
totalPreference += weight * (prefs.getValue(i) + averageDiffValue);
count += weight;
}
else
{
totalPreference += prefs.getValue(i) + averageDiffValue;
count += 1.0;
}
}
}
if (count <= 0.0)
{
RunningAverage itemAverage = diffStorage.getAverageItemPref(itemID);
return(itemAverage == null ? float.NaN : (float)itemAverage.getAverage());
}
else
{
return((float)(totalPreference / count));
}
}
private float doEstimatePreference(long userID, long itemID)
{
lock (this)
{
RunningAverage average = this.itemAverages.get(itemID);
if (average == null)
{
return(float.NaN);
}
RunningAverage average2 = this.userAverages.get(userID);
if (average2 == null)
{
return(float.NaN);
}
double num = average2.getAverage() - this.overallAveragePrefValue.getAverage();
return((float)(average.getAverage() + num));
}
}