Metadata-Version: 1.0
Name: sparsesvd
Version: 0.1.9
Summary: Python module that wraps SVDLIBC, a library for sparse Singular Value Decomposition.
Home-page: http://pypi.python.org/pypi/sparsesvd
Author: Radim Rehurek
Author-email: radimrehurek@seznam.cz
License: BSD
Download-URL: http://pypi.python.org/pypi/sparsesvd
Description: =================================================
        sparsesvd -- Sparse Singular Value Decomposition
        =================================================
        
        **sparsesvd** is a Python wrapper around the `SVDLIBC <http://tedlab.mit.edu/~dr/SVDLIBC/>`_
        library by Doug Rohde, which is itself based on Michael Berry's `SVDPACK <http://www.netlib.org/svdpack/>`_.
        
        sparsesvd uses SciPy's sparse CSC (Compressed Sparse Column) matrix format as input to SVD.
        This is the same format used internally by SVDLIBC, so that no extra data copies need to be
        made by the Python wrapper (memory-efficient).
        
        Installation
        ------------
        
        In order to install `sparsesvd`, you'll need NumPy, Scipy and Cython.
        
        Install `sparsesvd` and its dependencies with::
        
            pip install numpy
            pip install scipy
            pip install cython
            pip install sparsesvd
        
        In case of problems, see `<http://www.scipy.org/Download>`_ for instructions on installing
        SciPy on various platforms.
        
        If you have instead downloaded and unzipped the `source tar.gz <http://pypi.python.org/pypi/sparsesvd>`_ package, run::
        
            python setup.py test
            sudo python setup.py install
        
        This version has been tested under Python 2.6 and 3.2, but should run on any
        later versions of both 2.x and 3.x series.
        
        Documentation
        --------------
        
        The `sparsesvd` module offers a single function, `sparsesvd`, which accepts two parameters.
        One is a sparse matrix in the `scipy.sparse.csc_matrix` format, the other the number
        of requested factors (an integer):
        
        >>> import numpy, scipy.sparse
        >>> from sparsesvd import sparsesvd
        >>> mat = numpy.random.rand(200, 100) # create a random matrix
        >>> smat = scipy.sparse.csc_matrix(mat) # convert to sparse CSC format
        >>> ut, s, vt = sparsesvd(smat, 100) # do SVD, asking for 100 factors
        >>> assert numpy.allclose(mat, numpy.dot(ut.T, numpy.dot(numpy.diag(s), vt)))
        
        
        -------
        
        Original wrapper by Lubos Kardos, package updated and maintained by Radim Rehurek, Cython and Python 3.x port by Alejandro Pulver. For an application of sparse SVD to Latent Semantic Analysis, see the `gensim <http://pypi.python.org/pypi/gensim>`_ package.
        
        You can use this code under the `simplified BSD license <http://www.opensource.org/licenses/bsd-license.php>`_.
        
Keywords: Singular Value Decomposition,SVD,sparse SVD
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.5
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.2
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: Linguistic
Classifier: License :: OSI Approved :: BSD License
