private static PreferenceArray cloneAndMergeInto(PreferenceArray delegatePrefs, long itemID, long newUserID, float value)
{
int num4;
int num = (delegatePrefs == null) ? 0 : delegatePrefs.length();
int size = num + 1;
PreferenceArray array = new GenericItemPreferenceArray(size);
array.setItemID(0, itemID);
int i = 0;
while ((i < num) && (newUserID > delegatePrefs.getUserID(i)))
{
i++;
}
for (num4 = 0; num4 < i; num4++)
{
array.setUserID(num4, delegatePrefs.getUserID(num4));
array.setValue(num4, delegatePrefs.getValue(num4));
}
array.setUserID(i, newUserID);
array.setValue(i, value);
for (num4 = i + 1; num4 < size; num4++)
{
array.setUserID(num4, delegatePrefs.getUserID(num4 - 1));
array.setValue(num4, delegatePrefs.getValue(num4 - 1));
}
return(array);
}
public override int getNumUsersWithPreferenceFor(long itemID1, long itemID2)
{
PreferenceArray array = this.preferenceForItems.get(itemID1);
if (array == null)
{
return(0);
}
PreferenceArray array2 = this.preferenceForItems.get(itemID2);
if (array2 == null)
{
return(0);
}
int num = array.length();
int num2 = array2.length();
int num3 = 0;
int i = 0;
int num5 = 0;
long num6 = array.getUserID(0);
long num7 = array2.getUserID(0);
while (true)
{
if (num6 < num7)
{
if (++i == num)
{
return(num3);
}
num6 = array.getUserID(i);
}
else if (num6 > num7)
{
if (++num5 == num2)
{
return(num3);
}
num7 = array2.getUserID(num5);
}
else
{
num3++;
if ((++i == num) || (++num5 == num2))
{
return(num3);
}
num6 = array.getUserID(i);
num7 = array2.getUserID(num5);
}
}
}
protected override FastIDSet doGetCandidateItems(long[] preferredItemIDs, DataModel dataModel)
{
FastIDSet set = new FastIDSet();
foreach (long num in preferredItemIDs)
{
PreferenceArray array = dataModel.getPreferencesForItem(num);
int num2 = array.length();
for (int i = 0; i < num2; i++)
{
set.addAll(dataModel.getItemIDsFromUser(array.getUserID(i)));
}
}
set.removeAll(preferredItemIDs);
return(set);
}
protected override FastIDSet doGetCandidateItems(long[] preferredItemIDs, DataModel dataModel)
{
IEnumerator <long> enumerator = ((IEnumerable <long>)preferredItemIDs).GetEnumerator();
if (preferredItemIDs.Length > this.maxItems)
{
double samplingRate = ((double)this.maxItems) / ((double)preferredItemIDs.Length);
log.info("preferredItemIDs.Length {0}, samplingRate {1}", new object[] { preferredItemIDs.Length, samplingRate });
enumerator = new SamplingLongPrimitiveIterator(enumerator, samplingRate);
}
FastIDSet possibleItemIDs = new FastIDSet();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
PreferenceArray array = dataModel.getPreferencesForItem(current);
int num3 = array.length();
if (num3 > this.maxUsersPerItem)
{
FixedSizeSamplingIterator <Preference> iterator = new FixedSizeSamplingIterator <Preference>(this.maxUsersPerItem, array.GetEnumerator());
while (iterator.MoveNext())
{
this.addSomeOf(possibleItemIDs, dataModel.getItemIDsFromUser(iterator.Current.getUserID()));
}
}
else
{
for (int i = 0; i < num3; i++)
{
this.addSomeOf(possibleItemIDs, dataModel.getItemIDsFromUser(array.getUserID(i)));
}
}
}
possibleItemIDs.removeAll(preferredItemIDs);
return(possibleItemIDs);
}
public override double itemSimilarity(long itemID1, long itemID2)
{
double num18;
DataModel model = base.getDataModel();
PreferenceArray array = model.getPreferencesForItem(itemID1);
PreferenceArray array2 = model.getPreferencesForItem(itemID2);
int num = array.length();
int num2 = array2.length();
if ((num == 0) || (num2 == 0))
{
return(double.NaN);
}
long num3 = array.getUserID(0);
long num4 = array2.getUserID(0);
int i = 0;
int num6 = 0;
double num7 = 0.0;
double num8 = 0.0;
double num9 = 0.0;
double num10 = 0.0;
double sumXY = 0.0;
double num12 = 0.0;
int n = 0;
while (true)
{
int num14 = (num3 < num4) ? -1 : ((num3 > num4) ? 1 : 0);
if (num14 == 0)
{
double num15 = array.getValue(i);
double num16 = array2.getValue(num6);
sumXY += num15 * num16;
num7 += num15;
num8 += num15 * num15;
num9 += num16;
num10 += num16 * num16;
double num17 = num15 - num16;
num12 += num17 * num17;
n++;
}
if (num14 <= 0)
{
if (++i == num)
{
break;
}
num3 = array.getUserID(i);
}
if (num14 >= 0)
{
if (++num6 == num2)
{
break;
}
num4 = array2.getUserID(num6);
}
}
if (this.centerData)
{
double num19 = n;
double num20 = num7 / num19;
double num21 = num9 / num19;
double num22 = sumXY - (num21 * num7);
double num23 = num8 - (num20 * num7);
double num24 = num10 - (num21 * num9);
num18 = this.computeResult(n, num22, num23, num24, num12);
}
else
{
num18 = this.computeResult(n, sumXY, num8, num10, num12);
}
if (!double.IsNaN(num18))
{
num18 = this.normalizeWeightResult(num18, n, this.cachedNumUsers);
}
return(num18);
}
protected override float doEstimatePreference(long theUserID, PreferenceArray preferencesFromUser, long itemID)
{
DataModel dataModel = getDataModel();
int size = preferencesFromUser.length();
FastIDSet possibleItemIDs = new FastIDSet(size);
for (int i = 0; i < size; i++)
{
possibleItemIDs.add(preferencesFromUser.getItemID(i));
}
possibleItemIDs.remove(itemID);
List <RecommendedItem> mostSimilar = mostSimilarItems(itemID, possibleItemIDs.GetEnumerator(), neighborhoodSize, null);
long[] theNeighborhood = new long[mostSimilar.Count() + 1];
theNeighborhood[0] = -1;
List <long> usersRatedNeighborhood = new List <long>();
int nOffset = 0;
foreach (RecommendedItem rec in mostSimilar)
{
theNeighborhood[nOffset++] = rec.getItemID();
}
if (mostSimilar.Count != 0)
{
theNeighborhood[mostSimilar.Count] = itemID;
for (int i = 0; i < theNeighborhood.Length; i++)
{
PreferenceArray usersNeighborhood = dataModel.getPreferencesForItem(theNeighborhood[i]);
int size1 = usersRatedNeighborhood.Count == 0 ? usersNeighborhood.length() : usersRatedNeighborhood.Count;
for (int j = 0; j < size1; j++)
{
if (i == 0)
{
usersRatedNeighborhood.Add(usersNeighborhood.getUserID(j));
}
else
{
if (j >= usersRatedNeighborhood.Count)
{
break;
}
long index = usersRatedNeighborhood[j];
if (!usersNeighborhood.hasPrefWithUserID(index) || index == theUserID)
{
usersRatedNeighborhood.Remove(index);
j--;
}
}
}
}
}
double[] weights = null;
if (mostSimilar.Count != 0)
{
weights = getInterpolations(itemID, theNeighborhood, usersRatedNeighborhood);
}
int n = 0;
double preference = 0.0;
double totalSimilarity = 0.0;
foreach (long jitem in theNeighborhood)
{
float?pref = dataModel.getPreferenceValue(theUserID, jitem);
if (pref != null)
{
double weight = weights[n];
preference += pref.Value * weight;
totalSimilarity += weight;
}
n++;
}
return(totalSimilarity == 0.0 ? float.NaN : (float)(preference / totalSimilarity));
}