private void SortReviews(Dictionary <int, Reviewer> reviewersById)
{
foreach (var reviewer in Reviews.Keys
.Select(revId => reviewersById[revId])
.OrderBy(reviewer => reviewer.HilbertOrder)
)
{
ReviewerPositionsSorted.Add(reviewer.HilbertOrder);
ReviewerIdsSorted.Add(reviewer.ReviewerId);
}
}
/// <summary>
/// From among the Reviewers who have reviewed this Movie, find those that have the
/// most similar opinions about other movies, if such an overlap exists.
/// </summary>
/// <param name="reviewer">Reviewer whose opinion of the Movie we do not know and hope to guess.</param>
/// <param name="k">Number of similar Reviewers to search for.</param>
/// <param name="window">Half of the maximum number of Reviewers to compare.
/// If Count is not greater than twice the window, all Reviewers of this Movie will be checked.
/// Otherwise, the closest Reviewer according to a Binary search by Hilbert position will
/// be checked, plus a number of Reviewers immediately before it in Hilbert order equal to the window size, and
/// an identical number after it in Hilbert order.
/// </param>
/// <param name="reviewersById">This can be all the known Reviewers or just those drawn from a single cluster if the data has been clustered.
/// This set contains not just those that have reviewed this Movie.</param>
/// <returns>Up to K Reviewers whose opinions are similar to the given Reviewer, drawn from an intersection
/// between reviewersById and the set of reviewers for this movie,
/// sorted from nearest to farthest, associated with the square distance between the Reviewer and its neighbor.
/// The List may be empty, if no overlap exists.</returns>
public List <Tuple <Reviewer, long> > KNearest(Reviewer reviewer, int k, int window, Dictionary <int, Reviewer> reviewersById)
{
var point = reviewer.Point;
// NOTE: This assumes that both the Point and the HilbertOrder for each Reviewer has already been computed.
if (point == null)
{
throw new NullReferenceException($"The Point for reviewer {reviewer.ReviewerId} has not yet been calculated.");
}
if (reviewer.HilbertOrder < 0)
{
throw new ApplicationException($"Reviewer {reviewer.ReviewerId} has not yet had its HilbertOrder calculated.");
}
IEnumerable <int> idsToSort;
var overlappingReviewerIds = ReviewerIdsSorted
.Where(id => reviewersById.ContainsKey(id))
.ToList();
if (!overlappingReviewerIds.Any())
{
return(new List <Tuple <Reviewer, long> >());
}
if (overlappingReviewerIds.Count <= 2 * k)
{
// Sort all reviewers of this movie by distance from near to far.
idsToSort = overlappingReviewerIds;
}
else
{
// Only sort some reviewers, those within window positions on either side
// of the insertion point where this reviewer would go if added to the list
// in Hilbert order.
var insertionPosition = overlappingReviewerIds.BinarySearch(reviewer.HilbertOrder);
if (insertionPosition < 0)
{
insertionPosition = ~insertionPosition;
}
var lowPosition = Max(0, insertionPosition - window);
idsToSort = overlappingReviewerIds.Skip(lowPosition).Take(2 * window);
}
return(idsToSort
.Select(revId => reviewersById[revId])
.Select(r => new Tuple <Reviewer, long>(r, point.Measure(r.Point)))
.OrderBy(tup => tup.Item2)
.Take(k)
.ToList());
}
