{ "info": { "author": "Radim Rehurek", "author_email": "radimrehurek@seznam.cz", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Programming Language :: Python :: 2.5", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.2", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Text Processing :: Linguistic" ], "description": "=================================================\nsparsesvd -- Sparse Singular Value Decomposition\n=================================================\n\n**sparsesvd** is a Python wrapper around the `SVDLIBC `_\nlibrary by Doug Rohde, which is itself based on Michael Berry's `SVDPACK `_.\n\nsparsesvd uses SciPy's sparse CSC (Compressed Sparse Column) matrix format as input to SVD.\nThis is the same format used internally by SVDLIBC, so that no extra data copies need to be\nmade by the Python wrapper (memory-efficient).\n\nFor a more scalable implementation of truncated SVD, see the `gensim `_ package (contains an incremental, online SVD algorithm).\n\nInstallation\n------------\n\nIn order to install `sparsesvd`, you'll need NumPy, Scipy and Cython.\n\nInstall `sparsesvd` and its dependencies with::\n\n pip install numpy\n pip install scipy\n pip install cython\n pip install sparsesvd\n\nIn case of problems, see ``_ for instructions on installing\nSciPy on various platforms.\n\nIf you have instead downloaded and unzipped the `source tar.gz `_ package, run::\n\n python setup.py test\n sudo python setup.py install\n\nThis version has been tested under Python 2.6 and 3.2, but should run on any\nlater versions of both 2.x and 3.x series.\n\nDocumentation\n--------------\n\nThe `sparsesvd` module offers a single function, `sparsesvd`, which accepts two parameters.\nOne is a sparse matrix in the `scipy.sparse.csc_matrix` format, the other the number\nof requested factors (an integer):\n\n>>> import numpy, scipy.sparse\n>>> from sparsesvd import sparsesvd\n>>> mat = numpy.random.rand(200, 100) # create a random matrix\n>>> smat = scipy.sparse.csc_matrix(mat) # convert to sparse CSC format\n>>> ut, s, vt = sparsesvd(smat, 100) # do SVD, asking for 100 factors\n>>> assert numpy.allclose(mat, numpy.dot(ut.T, numpy.dot(numpy.diag(s), vt)))\n\n\n-------\n\nOriginal wrapper by Lubos Kardos, package updated and maintained by Radim Rehurek, Cython and Python 3.x port by Alejandro Pulver. 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