https://github.com/jackson211/LearnedSPatialHashMap

Learned SPatial Hashmap
https://github.com/jackson211/LearnedSPatialHashMap

data-structures hashmap index spatial

Last synced: 2 months ago
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Learned SPatial Hashmap

• Host: GitHub
• URL: https://github.com/jackson211/LearnedSPatialHashMap
• Owner: jackson211
• License: other
• Created: 2021-09-12T09:48:33.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-04-17T14:41:00.000Z (9 months ago)
• Last Synced: 2024-11-07T20:03:03.076Z (2 months ago)
• Topics: data-structures, hashmap, index, spatial
• Language: Rust
• Homepage:
• Size: 209 KB
• Stars: 11
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE-APACHE

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README

        
# [LSPH](https://crates.io/crates/lsph) - Learned SPatial HashMap

**fast 2d point query powered by hashmap and statistic model**

![Github Workflow](https://github.com/jackson211/lsph/actions/workflows/rust.yml/badge.svg)

[![crates.io version](https://img.shields.io/crates/v/lsph)](https://crates.io/crates/lsph)

[![dos.io](https://img.shields.io/docsrs/lsph)](https://docs.rs/lsph)

[![dependency status](https://deps.rs/repo/github/jackson211/lsph/status.svg)](https://deps.rs/repo/github/jackson211/lsph)

The original paper of LSPH can be found [here].

[here]: https://minerva-access.unimelb.edu.au/items/beb5c0ee-2a8d-5bd2-b349-1190a335ef1a

The LSPH uses a learned model such as a linear regression model as the hash function to predict the index in a hashmap. As a result, the learned model is more fitted to the data that stored in the hashmap, and reduces the

chance of hashing collisions. Moreover, if the learned model is monotonic function(e.g. linear regression), the hash indexes are increasing as the input data increases. This property can be used to create a sorted order

of buckets in a hashmap, which allow us to do range searches in a hashmap.

The LSPH supports:

- Point Query

- Rectange Query

- Radius Range Query

- Nearest Neighbor Query

## Example:

```rust

use lsph::{LearnedHashMap, LinearModel};

let point_data = vec![[1., 1.], [2., 1.], [3., 2.], [4., 4.]];

let (mut map, points) = LearnedHashMap::, f64>::with_data(&point_data).unwrap();

assert_eq!(map.get(&[1., 1.]).is_some(), true);

assert_eq!(map.get(&[3., 1.]).is_none(), true);

assert_eq!(map.range_search(&[0., 0.], &[3., 3.]).is_some(), true);

assert_eq!(map.radius_range(&[2., 1.], 1.).is_some(), true);

assert_eq!(map.nearest_neighbor(&[2., 1.]).is_some(), true);

```

## To Run Benchmark:

```bash

cargo bench

```

# License

Licensed under either of

- Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)

- MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)

at your option.