旨在在 Matlab 中快速运行。 可以在0.66秒内处理30k本地化。 在所有核心点上递归调用函数 expandcluster。 从Andrea Tagliasacchi的kd-trees实现中调用函数kdtree_build.m和kdtree_ball_query.m： https : //github.com/ataiya/kdtree/ 不需要任何工具箱。 欢迎在我的 GitHub 存储库中提供有关如何改进代码或其他问题的任何提示。

并行KNN 使用 KD 树/球树并行实现 KNN 图 该存储库是可扩展机器学习的最终项目。 提供了 4 种解决并行 KNN 问题的方法。 KDTrees with OpenMP：使用 OpenMP 并行化使用 KDTrees 的 KNN 构建 KDTrees with Galois : 使用 Galois 来构造和并行化 KNN 构造，使用 KDTrees Ball Trees with OpenMP：使用 OpenMP 并行化使用 Ball Trees 的 KNN 构造 KDTrees with Galois : 使用 Galois 来构造和并行化 KNN 构造，使用 KDTrees 所有实验都在 Stampede（TACC 的一部分）上运行。 基线是 KNN 图的蛮力构造。 有关每种方法的详细信息，请参阅报告。 报告和数据集可以在找到

Kd树 算法，第一部分，第 5 周 提交 提交时间 Thu-05-Mar 06:46:38 Raw Score 91.46 / 100.00 反馈 有关如何阅读本报告的信息，请参阅评估指南。 评估总结汇编：通过 风格：通过 Findbugs：没有发现潜在的错误。 API：通过 正确性：21/21 次测试通过 内存：8/8 次测试通过 计时：27/41 次测试通过 总分：91.46% [正确性：65%，记忆：10%，时间：25%，风格：0%] 评估详情 提交以下文件： 总计 20K -rw-r--r-- 1 12K Mar 5 14:48 KdTree.java -rw-r--r-- 1 2.1K Mar 5 14:48 PointSET.java -rw-r--r-- 1 2.7K Mar 5 14:48 studentSubmission.zip 编译 % javac PointSE

For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with only minimal guidance on selecting an algorithm and its parameters for any given problem. In this paper, we describe a system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data? ” Our system will take any given dataset and desired degree of precision and use these to automatically determine the best algorithm and parameter values. We also describe a new algorithm that applies priority search on hierarchical k-means trees, which we have found to provide the best known performance on many datasets. After testing a range of alternatives, we have found that multiple randomized k-d trees provide the best performance for other datasets. We are releasing public domain code that implements these approaches. This library provides about one order of magnitude improvement in query time over the best previously available software and provides fully automated parameter selection.