Expand description
This crate implements the Learned SPatial HashMap(LSPH), a high performance spatial index uses HashMap with learned model.
The original paper of LSPH can be found here.
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:
use lsph::{LearnedHashMap, LinearModel};
let point_data = vec![[1., 1.], [2., 1.], [3., 2.], [4., 4.]];
let (mut map, points) = LearnedHashMap::<LinearModel<f64>, 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);
§License
Licensed under either of
- Apache License, Version 2.0, (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
- MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)
at your option.