mih-rs

Rust implementation of multi-index hashing (MIH) for neighbor searches on 64-bit codes in the Hamming space, described in the paper

Norouzi, Punjani, and Fleet, Fast exact search in Hamming space with multi-index hashing, IEEE TPAMI, 36(6):1107– 1119, 2014.

Features

• Two types of neighbor searches: mih-rs provides the two search operations:

  • Range search finds neighbor codes whose Hamming distances to a given code are within a radius.
  • Top-K search finds the top-K codes that are closest to a given code.

• Fast and memory-efficient implementation: The data structure is built on sparse hash tables, following the original implementation.

• Parameter free: mih-rs automatically sets an optimal parameter of MIH depending on a given database (although you can set this manually).

Example

```rust use mih_rs::Index;

fn main() { // Database of codes let codes: [u64; 8] = [ 0b1111111111111111111111011111111111111111111111111011101111111111, // #zeros = 3 0b1111111111111111111111111111111101111111111011111111111111111111, // #zeros = 2 0b1111111011011101111111111111111101111111111111111111111111111111, // #zeros = 4 0b1111111111111101111111111111111111111000111111111110001111111110, // #zeros = 8 0b1101111111111111111111111111111111111111111111111111111111111111, // #zeros = 1 0b1111111111111111101111111011111111111111111101001110111111111111, // #zeros = 6 0b1111111111111111111111111111111111101111111111111111011111111111, // #zeros = 2 0b1110110101011011011111111111111101111111111111111000011111111111, // #zeros = 11 ];

// Query code
let qcode: u64 = 0b1111111111111111111111111111111111111111111111111111111111111111;

// Construct the index
let index = Index::new(&codes).unwrap();

// Find the ids of neighbor codes whose Hamming distances are within 2
let answers = index.range_search(qcode, 2);
println!("{:?}", answers); // [1, 4, 6]

// Find the ids of the top-4 nearest neighbor codes
let answers = index.topk_search(qcode, 4);
println!("{:?}", answers); // [4, 1, 6, 0]

} ```

Benchmark

timeperf_topk.rs offers the benchmark of top-K search for MIH and LinearSearch algorithms.

The following table shows the result of average search times in milliseconds per query, in the settings:

• Database: N random codes from a uniform distribution.
• Query set: 100 random codes from a uniform distribution.
• Machine: Mac Pro (Late 2013) of 6 core Intel Xeon E5 @3.5 GHz with 32 GB of RAM.

| Algorithm | N=10,000 | N=100,000 | N=1,000,000 | N=10,000,000 | | ------------ | -------: | --------: | ----------: | -----------: | | MIH (K=1) | 0.1 | 0.4 | 1.7 | 6.5 | | MIH (K=10) | 0.2 | 0.8 | 4.0 | 13.6 | | MIH (K=100) | 0.4 | 1.6 | 7.9 | 32.0 | | LinearSearch | 0.4 | 4.9 | 56.1 | 701.4 |

Licensing

This library is free software provided under MIT.