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//! 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].
//!
//! [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:
//! ```
//! 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](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.

#[macro_use]
mod macros;
mod error;
pub mod geometry;
pub mod hasher;
pub mod map;
pub mod models;
#[cfg(test)]
pub mod test_utilities;

pub use geometry::*;
pub use hasher::*;
pub use map::*;
pub use models::*;