:Version: 0.0.4dev
A fast Python implementation of locality sensitive hashing with persistance support.
numpy
arrays.LSHash
depends on the following libraries:
To install:
.. code-block:: bash
$ pip install lshash
To create 6-bit hashes for input data of 8 dimensions:
.. code-block:: python
>>> 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)]
LSHash
instance:.. code-block:: python
LSHash(hash_size, input_dim, num_of_hashtables=1, storage=None, matrices_filename=None, overwrite=False)
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".
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
LSHash
instance, e.g., lsh
:.. code-block:: python
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.
LSHash
instance, e.g., lsh
:.. code-block:: python
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.