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);
}
protected override float doEstimatePreference(long theUserID, long[] theNeighborhood, long itemID)
{
if (theNeighborhood.Length == 0)
{
return(float.NaN);
}
DataModel model = this.getDataModel();
UserSimilarity similarity = base.getSimilarity();
float num = 0f;
bool flag = false;
foreach (long num2 in theNeighborhood)
{
if ((num2 != theUserID) && model.getPreferenceValue(num2, itemID).HasValue)
{
flag = true;
num += (float)similarity.userSimilarity(theUserID, num2);
}
}
return(flag ? num : float.NaN);
}
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());
}