public static MultipartiteWeightTensor computeSimilarityTensor <T>(List <List <T> > objs, ComputeSimilarityFunction <T> similarity)
{
MultipartiteWeightTensor ret = new MultipartiteWeightTensor(objs.Count);
for (int i = 0; i < ret.noParts; i++)
{
ret.setNumPartitionElements(i, objs[i].Count);
}
for (int i = 0; i < ret.noParts - 1; i++)
{
for (int j = i + 1; j < ret.noParts; j++)
{
double[,] sim = computeSimilarities <T>(objs[i], objs[j], similarity);
ret.setWeightMatrix(i, j, sim);
}
}
return(ret);
}
public static List <MultipartiteWeightedMatch> computeAssociations <T>(List <List <T> > boundingBoxes, ComputeSimilarityFunction <T> similarity)
{
MultipartiteWeightTensor t = computeSimilarityTensor <T>(boundingBoxes, similarity);
MultipartiteWeightedMatching.GreedyMean matching = new MultipartiteWeightedMatching.GreedyMean();
List <MultipartiteWeightedMatch> ret = matching.getMatching(t);
return(ret);
}
public static double[,] computeSimilarities <T>(List <T> objList1, List <T> objList2, ComputeSimilarityFunction <T> similarity)
{
double[,] mat = new double[objList1.Count, objList2.Count];
for (int i = 0; i < objList1.Count; i++)
{
for (int j = 0; j < objList2.Count; j++)
{
mat[i, j] = similarity(objList2[j], objList1[i]); //this is a symmetric measure
}
}
return(mat);
}
