private KnnQueryResult InternalKnnSearch(KnnQuery query)
{
// get search area
var searchArea = GetSearchArea(query);
// query the quadtree
var docs = _quadTree.Search(searchArea);
// almost done, but remember we searched a rectangle
// so we need to exclude some of these results.
// Let's do that while calculating the distance
var results = new List <KnnQueryResultItem>();
foreach (var doc in docs)
{
var distance = query.Coordinate.CalculateDistance(doc.Coordinates);
if (distance <= query.MaxDistance)
{
results.Add(new KnnQueryResultItem(doc, distance));
}
}
// this could be done more efficiently by inserting into an ordered structure
// unfortunatelly SortedList<> doesn't allow duplicated keys.
// but there is definetely space for optimization here
results.Sort((x, y) => x.Distance.CompareTo(y.Distance));
// reduce set to max results
results = results.Take(query.MaxResults).ToList();
return(KnnQueryResult.Success(results));
}
public KnnQueryResult KnnSearch(KnnQuery query)
{
var search =
from doc in _docs
let distance = query.Coordinate.CalculateDistance(doc.Coordinates)
where distance <= query.MaxDistance
orderby distance
select new KnnQueryResultItem(doc, distance);
// list results taking top max
var results = search.Take(query.MaxResults).ToList();
return(KnnQueryResult.Success(results));
}
public KnnQueryResult KnnSearch(KnnQuery query)
{
if (query == null)
{
throw new ArgumentException("Parameter query cannot be null");
}
// validate query
if (!IsQueryValid(query))
{
return(KnnQueryResult.Fail("Query is not valid. Please verify search parameters"));
}
// execute search
var result = InternalKnnSearch(query);
return(result);
}
public KnnQueryResult KnnSearch(KnnQuery query)
{
return(KnnQueryResult.ZeroResults());
}
