8 releases

0.1.7 Nov 3, 2022
0.1.6 Apr 23, 2022

#431 in Data structures

27 downloads per month


1.5K SLoC

LSPH - Learned SPatial HashMap

fast 2d point query powered by hashmap and statistic model

Github Workflow crates.io version dos.io dependency status

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


use lsph::{LearnedHashMap, LinearModel};
let point_data = vec![[1., 1.], [2., 1.], [3., 2.], [4., 4.]];
let (mut map, points) = LearnedHashMap::<LinearModel<f32>, 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:

cargo bench


Licensed under either of

at your option.