k-d tree (A k-dimensional tree) #805
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## master #805 +/- ##
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+ Hits 21437 21666 +229
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@bomenderick: thanks for interesting contribution.
Before this will be merged some work needs to be done. First of all: please add missing tests. Especially the functionality finding the closest point has to be exercised quite brutally.
Could you also describe/document in the code, which operations does your implementation support?
src/data_structures/kd_tree.rs
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| impl<T: PartialOrd + Copy, const K: usize> KDTree<T, K> { | ||
| // Create and empty kd-tree | ||
| // #[must_use] |
| // Returns true if point found, false otherwise | ||
| pub fn contains(&self, point: &[T; K]) -> bool { | ||
| search_rec(&self.root, point, 0) | ||
| } |
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Contains is useful to search for the presence of a point in a k-d tree.
Or do you mean the additional call of search_rec?
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Yes for interaction with the outside world. However, contains is just to complete CRUD operations on a k-d tree.
Indeed, the current k-d tree implementation doesn't make use of contains. However, its implementation could help as it is just a synonym of the search method of a k-d tree.
Would you recommend I make it private for now or remove it?
src/data_structures/kd_tree.rs
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| search_rec(&self.root, point, 0) | ||
| } | ||
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| // Returns true if successfully delete a point, false otherwise |
src/data_structures/kd_tree.rs
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| } | ||
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| // Returns the number of points in a kd-tree | ||
| // #[must_use] |
src/data_structures/kd_tree.rs
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| } | ||
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| // Returns the depth a kd-tree | ||
| // #[must_use] |
src/data_structures/kd_tree.rs
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| pub fn depth(&self) -> usize { | ||
| depth_rec(&self.root, 0, 0) | ||
| } |
src/data_structures/kd_tree.rs
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| } | ||
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| // Determine whether there exist points in a kd-tree or not | ||
| // #[must_use] |
src/data_structures/kd_tree.rs
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| } | ||
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| // Returns a kd-tree built from a vector points | ||
| // #[must_use] |
src/data_structures/kd_tree.rs
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| /// Returns a `KDTree` containing both trees | ||
| /// Merging two KDTrees by collecting points and rebuilding | ||
| // #[must_use] |
src/data_structures/kd_tree.rs
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| pub fn merge(&mut self, other: &mut Self) -> Self { | ||
| let mut points: Vec<[T; K]> = Vec::new(); | ||
| collect_points(&self.root, &mut points); | ||
| collect_points(&other.root, &mut points); | ||
| KDTree::build(points) | ||
| } |
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This pull request has been automatically marked as abandoned because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions. |
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Please ping one of the maintainers once you commit the changes requested or make improvements on the code. If this is not the case and you need some help, feel free to ask for help in our Gitter channel. Thank you for your contributions! |
Description
A K-D Tree(also known as a K-Dimensional Tree) is a binary search tree where data in each node is a K-Dimensional point in space. In short, it is a space partitioning data structure for organizing points in a K-Dimensional space in other to facilitate nearest neighbor search of points.
In addition to insert, search, and delete methods, the implementation also supports nearest neighbors search, median finding for insertion in other to keep the k-d tree balanced, and a merge method to combine two k-d trees by collecting their points and building a balanced k-d tree from it.
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