private int QuerySizeToInt(QuerySize size, string clazz)
{
int count = 0;
// Calculate the size based on the given enum.
switch (size)
{
case QuerySize.KBest:
count = Math.Min(Settings.KBestResults, FeatureCount);
break;
case QuerySize.Class:
count = Settings.FileManager.ShapesInClass(clazz);
break;
case QuerySize.All:
count = FeatureCount;
break;
}
// Edge case: The value is still 0 for whatever reason.
if (count == 0)
{
count = Math.Min(Settings.KBestResults, FeatureCount);
}
return(count);
}
/// <summary>
/// Compares all the featurevectors in the database with each other.
/// </summary>
/// <param name="size">The size of best number of elements.</param>
/// <returns>A <see cref="QueryResult"/> array which contains the K-best similair
/// matches from the entire database for these entries.</returns>
public QueryResult[] InternalCompare(QuerySize size)
{
ANN HNSW = new ANN(features);
ConcurrentBag <QueryResult> results = new ConcurrentBag <QueryResult>();
Parallel.ForEach(VectorDictionary, pair => {
string name = pair.Key;
FeatureVector vector = pair.Value;
string clazz = Settings.FileManager.ClassByShapeName(name);
int count = QuerySizeToInt(size, clazz);
results.Add(HNSW.RunANNQuery(name, vector, count));
});
return(results.ToArray());
}
/// <summary>
/// Compares the given meshes with all the saved meshes in this <see cref="FeatureManager"/>
/// and returns all the meshes ordered by the similarity.
/// </summary>
/// <param name="meshes">The mesh that should be compared with all other meshes
/// in the database.</param>
/// <returns>A <see cref="QueryResult"/> array which contains the K-best similair
/// matches from the entire database for these entries.</returns>
public QueryResult[] CalculateResults(QuerySize size, IEnumerable <MeshEntry> meshes)
{
if (meshes == null)
{
throw new ArgumentNullException(nameof(meshes));
}
ANN HNSW = new ANN(features);
ConcurrentBag <QueryResult> results = new ConcurrentBag <QueryResult>();
Parallel.ForEach(meshes, entry => {
FeatureVector queryVector = CreateVector(entry);
queryVector = NormaliseVector(queryVector);
int count = QuerySizeToInt(size, entry.Class);
results.Add(HNSW.RunANNQuery(entry.Name,
queryVector,
count));
});
QueryResult[] array = results.ToArray();
Array.Sort(array);
return(array);
}
/// <summary> /// Compares the given meshes with all the saved meshes in this /// <see cref="FeatureManager"/> and returns all the meshes ordered by the /// similarity. /// </summary> /// <param name="count">The number of elements to return in the result. /// -1 identifies that all the results should be returned.</param> /// <param name="meshes">The mesh that should be compared with all other meshes in the database.</param> /// <returns>A <see cref="QueryResult"/> array which contains the K-best similair /// matches from the entire database for these entries.</returns> public QueryResult[] CalculateResults(QuerySize size, params MeshEntry[] meshes) => CalculateResults(size, (IEnumerable <MeshEntry>)meshes);
/// <summary>
/// Compares the given mesh with all the saved meshes in this <see cref="FeatureManager"/>
/// and returns all the meshes ordered by the similarity.
/// </summary>
/// <param name="size">The size of the return value for the query specified
/// by an enum constant.</param>
/// <param name="mesh">The mesh that should be compared with all other meshes in the database.</param>
/// <returns>A <see cref="QueryResult"/> which contains the K-best similair
/// matches from the entire database for this entry.</returns>
public QueryResult CalculateResults(QuerySize size, MeshEntry mesh) =>
CalculateResults(size, new MeshEntry[] { mesh })[0];
