BruteSearch

  K-nearest neighbors 搜索 聚类时经常使用的一种方法 国外网站转载( The following utilities are provided: - Nearest neighbor - K-Nearest neighbors - Radius Search They al supports N-dimensions and work on double, it is possible to choose if return the distances. Here is a time comparison with a vectrized m-code: N=1000000 number of reference points Nq=100 number of query points dim=3 dimension of points k=3 number of neighbor tic [idc,dist]=BruteSearchMex(p ,qp , k ,k) MEX toc tic [idc,dist]=knnsearch(qp,p,k) VECTORIZED M-CODE toc p=rand(N,dim) qp=rand(Nq,dim) Output: Elapsed time is 0.962640 seconds. Elapsed time is 18.813100 seconds. )