# pyLSHash

# pyLSHash

A fast Python implementation of locality sensitive hashing.

I am using https://github.com/kayzhu/LSHash, but it stops to update since 2013.

So I maintain it myself.

## Highlights

- Fast hash calculation for large amount of high dimensional data through the use of
`numpy`

arrays. - Built-in support for persistency through Redis.
- Multiple hash indexes support.
- Built-in support for common distance/objective functions for ranking outputs.

## Installation

`pyLSHash`

depends on the following libraries:

- numpy
- redis (if persistency through Redis is needed)
- bitarray (if hamming distance is used as distance function)

To install:

`$ pip install pyLSHash`

## Quickstart

To create 6-bit hashes for input data of 8 dimensions:

```
from pyLSHash import LSHash
lsh = LSHash(6, 8)
lsh.index([1, 2, 3, 4, 5, 6, 7, 8])
lsh.index([2, 3, 4, 5, 6, 7, 8, 9])
lsh.index([10, 12, 99, 1, 5, 31, 2, 3])
lsh.query([1, 2, 3, 4, 5, 6, 7, 7])
```

[((1, 2, 3, 4, 5, 6, 7, 8), 1.0), ((2, 3, 4, 5, 6, 7, 8, 9), 11)]

## Main Interface

- To initialize a
`LSHash`

instance:

`LSHash(hash_size, input_dim, num_of_hashtables=1, storage=None)`

parameters:

`hash_size`

: The length of the resulting binary hash.`input_dim`

: The dimension of the input vector.`num_hashtables = 1`

: (optional) The number of hash tables used for multiple lookups.`storage = None`

: (optional) Specify the name of the storage to be used for the index storage. Options include "redis".

To index a data point of a given `LSHash`

instance, e.g., `lsh`

:

`lsh.index(input_point, extra_data=None)`

parameters:

`input_point`

: The input data point is an array or tuple of numbers of input_dim.`extra_data = None`

: (optional) Extra data to be added along with the input_point.

To query a data point against a given `LSHash`

instance, e.g., `lsh`

:

`lsh.query(query_point, num_results=None, distance_func="euclidean")`

parameters:

`query_point`

: The query data point is an array or tuple of numbers of input_dim.`num_results = None`

: (optional) The number of query results to return in ranked order. By default all results will be returned.`distance_func = "euclidean"`

: (optional) Distance function to use to rank the candidates. By default euclidean distance function will be used.