# anndists

This crate provides distances computations used in some related crates [hnsw_rs](https://crates.io/crates/hnsw_rs), [annembed](https://crates.io/crates/annembed) and [coreset](https://github.com/jean-pierreBoth/coreset)

All distances implement the trait **Distance**:


```rust
pub trait Distance<T: Send + Sync> {  
    fn eval(&self, va: &[T], vb: &[T]) -> f32;
}
```


## Functionalities

The crate provides:

* usual distances as L1, L2, Cosine, Jaccard, Hamming for vectors of standard numeric types, Levenshtein distance on u16.

* Hellinger distance and Jeffreys divergence between probability distributions (f32 and f64). It must be noted that the Jeffreys divergence
(a symetrized Kullback-Leibler divergence) do not satisfy the triangle inequality. (Neither Cosine distance !).

* Jensen-Shannon distance between probability distributions (f32 and f64). It is defined as the **square root** of the Jensen-Shannon divergence and is a bounded metric. See [Nielsen F. in Entropy 2019, 21(5), 485](https://doi.org/10.3390/e21050485).

* A Trait to enable the user to implement its own distances.
  It takes as data slices of types T satisfying T:Serialize+Clone+Send+Sync. It is also possible to use C extern functions or closures.

* Simd implementation is provided for the most often used case.

## Implementation

Simd support is provided with the [simdeez](https://crates.io/crates/simdeez) crate on Intel and partial implementation with **std::simd** for general case.

## Building

### Simd

* The simd provided by the simdeez crate is accessible with the feature "simdeez_f" for x86_64 processors.
Compile with **cargo build --release --features "simdeez_f"** ....
To compile this crate on a M1 chip just do not activate this feature.

* It is nevertheless possible to experiment with std::simd. Compiling with the feature stdsimd
  (**cargo build --release --features "stdsimd"**), activates the  portable_simd feature on rust nightly. **This requires nightly compiler**. 
  Only the Hamming distance with the u32x16 and u64x8 types and DistL1,DistL2 and DistDot on f32*16 are provided for now.




## Benchmarks and Examples

The speed is illustated in the [hnsw_rs](https://crates.io/crates/hnsw_rs), [annembed](https://crates.io/crates/annembed) crates




## Contributions

Petter Egesund added the DistLevenshtein distance.


## 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.

