Metadata-Version: 1.1
Name: lshash
Version: 0.0.3dev
Summary: A fast Python implementation of locality sensitive hashing with persistance support.
Home-page: UNKNOWN
Author: Kay Zhu
Author-email: me@kayzhu.com
License: Copyright 2012 Kay Zhu (a.k.a He Zhu)

Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
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The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

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SOFTWARE.

Description: ======
        LSHash
        ======
        
        :Versioon: 0.0.2dev
        
        A fast Python implementation of locality sensitive hashing with persistance
        support.
        
        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
        ============
        ``LSHash`` depends on the following libraries:
        
        - numpy
        - redis (if persistency through Redis is needed)
        - bitarray (if hamming distance is used as distance function)
        
        To install:
        
        .. code-block:: bash
        
            $ pip install lshash
        
        Quickstart
        ==========
        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)]
        
        
        Main Interface
        ==============
        
        - To initialize a ``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
        
        - To index a data point of a given ``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.
        
        - To query a data point against a given ``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.
        
        
        v0.0.3, 2012/12/28 -- Doc fixes.
        v0.0.2, 2012/12/28 -- Doc fixes and lowercase package name.
        v0.0.1, 2012/12/20 -- Initial release.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Topic :: Software Development :: Libraries
Requires: numpy
