Metadata-Version: 1.0
Name: gensim
Version: 0.7.3
Summary: Python framework for fast Vector Space Modelling
Home-page: http://nlp.fi.muni.cz/projekty/gensim
Author: Radim Rehurek
Author-email: radimrehurek@seznam.cz
License: LGPL
Download-URL: http://pypi.python.org/pypi/gensim
Description: ==============================================
        gensim -- Python Framework for Topic Modelling
        ==============================================
        
        
        
        Gensim is a Python library for *Vector Space Modelling* with very large corpora.
        Target audience is the *Natural Language Processing* (NLP) community.
        
        
        Features
        ---------
        
        * All algorithms are **memory-independent** w.r.t. the corpus size (can process input larger than RAM),
        * **Intuitive interfaces** (think `java`... then think again)
        
        * easy to plug in your own input corpus/datastream (simple streaming API)
        * easy to extend with other Vector Space algorithms (simple transformation API)
        
        * Efficient streaming implementations of popular algorithms, such as online **Latent Semantic Analysis**,
        **Latent Dirichlet Allocation** or **Random Projections**
        * Can run Latent Semantic Analysis on a cluster of computers (**distributed computing**).
        * Extensive `HTML documentation and tutorials <http://nlp.fi.muni.cz/projekty/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.
        
        .. note::
        
        `gensim`'s target audience is the NLP research community and interested general public;
        `gensim` is not meant to be a production tool for commercial environments.
        
        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`.
        
        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 priviledges, development
        installation, optional install features), see the `documentation <http://nlp.fi.muni.cz/projekty/gensim/install.html>`_.
        
        This version has been tested under Python 2.5 and 2.6, but should run on any 2.5 <= Python < 3.0.
        
        Documentation
        -------------
        
        Manual for the gensim package is available in `HTML <http://nlp.fi.muni.cz/projekty/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, and has been released under the
        `GNU LPGL license <http://www.gnu.org/licenses/lgpl.html>`_.
        Copyright (c) 2010 Radim Rehurek
        
Keywords: Singular Value Decomposition,Latent Semantic Indexing,SVD,LSA,LSI,LDA,Latent Dirichlet Allocation,VSM,Random Projections,TFIDF
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
