A fast Python implementation of locality sensitive hashing with persistance (Redis) support.
A fast Python implementation of locality sensitive hashing with persistance support.
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.
LSHash depends on the following libraries:
redis (if persistency through Redis is needed)
bitarray (if hamming distance is used as distance function)
$ pip install lshash
To create 6-bit hashes for input data of 8 dimensions:
>>> from lshash 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)]
To initialize a LSHash instance:
LSHash(hash_size, input_dim, num_of_hashtables=1, storage=None, matrices_filename=None, overwrite=False)
The length of the resulting binary hash.
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”.
- matrices_filename = None:
(optional) Specify the path to the .npz file random matrices are stored or to be stored if the file does not exist yet
- overwrite = False:
(optional) Whether to overwrite the matrices file if it already exist
To index a data point of a given LSHash instance, e.g., lsh:
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"):
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.
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