Metadata-Version: 2.0
Name: gensim
Version: 0.8.8
Summary: Python framework for fast Vector Space Modelling
Home-page: http://radimrehurek.com/gensim
Author: Radim Rehurek
Author-email: radimrehurek@seznam.cz
License: LGPL
Download-URL: http://pypi.python.org/pypi/gensim
Keywords: Singular Value Decomposition,Latent Semantic Indexing,SVD,LSA,LSI,LDA,Latent Dirichlet Allocation,VSM,Random Projections,TFIDF,word2vec
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.5
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: Linguistic
Requires-Dist: scipy (>=0.7.0)
Provides-Extra: distributed
Requires-Dist: Pyro4 (>=4.8); extra == 'distributed'

==============================================
gensim -- Python Framework for Topic Modelling
==============================================


Gensim is a Python library for *topic modelling*, *document indexing* and *similarity retrieval* with large corpora.
Target audience is the *natural language processing* (NLP) and *information retrieval* (IR) community.

For a Python3 port of gensim by Parikshit Samant, visit `this fork <https://github.com/samantp/gensimPy3>`_.

Features
---------

* All algorithms are **memory-independent** w.r.t. the corpus size (can process input larger than RAM),
* **Intuitive interfaces**

  * easy to plug in your own input corpus/datastream (trivial streaming API)
  * easy to extend with other Vector Space algorithms (trivial transformation API)

* Efficient implementations of popular algorithms, such as online **Latent Semantic Analysis**,
  **Latent Dirichlet Allocation** or **Random Projections**
* **Distributed computing**: can run *Latent Semantic Analysis* and *Latent Dirichlet Allocation* on a cluster of computers.
* Extensive `HTML documentation and tutorials <http://radimrehurek.com/gensim/>`_.


If this feature list left you scratching your head, you can first read more about the `Vector
Space Model <http://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised
document analysis <http://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia.

Installation
------------

This software depends on `NumPy and Scipy <http://www.scipy.org/Download>`_, two Python packages for scientific computing.
You must have them installed prior to installing `gensim`.

It is also recommended you install a fast BLAS library prior to installing NumPy. This is optional, but using an optimized BLAS such as `ATLAS <http://math-atlas.sourceforge.net/>`_ or `OpenBLAS <http://xianyi.github.io/OpenBLAS/>`_ is known to improve performance by as much as an order of magnitude.

The simple way to install `gensim` is::

    sudo easy_install gensim

Or, if you have instead downloaded and unzipped the `source tar.gz <http://pypi.python.org/pypi/gensim>`_ package,
you'll need to run::

    python setup.py test
    sudo python setup.py install


For alternative modes of installation (without root privileges, development
installation, optional install features), see the `documentation <http://radimrehurek.com/gensim/install.html>`_.

This version has been tested under Python 2.5, 2.6 and 2.7, and should run on any 2.5 <= Python < 3.0.

Documentation
-------------

Manual for the gensim package is available in `HTML <http://radimrehurek.com/gensim/>`_. It
contains a walk-through of all its features and a complete reference section.
It is also included in the source distribution package.

----------------

Gensim is open source software released under the `GNU LGPL license <http://www.gnu.org/licenses/lgpl.html>`_.
Copyright (c) 2009-2013 Radim Rehurek


