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Singular Value Decomposition (SVD) / Latent Semantic Analysis (LSA) / Latent Semantic Indexing (LSI)"],"readme":"=================================================\nsparsesvd -- Sparse Singular Value Decomposition\n=================================================\n\n**sparsesvd** is a Python wrapper around the `SVDLIBC \u003chttp://tedlab.mit.edu/~dr/SVDLIBC/\u003e`_\nlibrary by Doug Rohde, which is itself based on Michael Berry's `SVDPACK \u003chttp://www.netlib.org/svdpack/\u003e`_.\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\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 `\u003chttp://www.scipy.org/Download\u003e`_ for instructions on installing\nSciPy on various platforms.\n\nIf you have instead downloaded and unzipped the `source tar.gz \u003chttp://pypi.python.org/pypi/sparsesvd\u003e`_ 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\u003e\u003e\u003e import numpy, scipy.sparse\n\u003e\u003e\u003e from sparsesvd import sparsesvd\n\u003e\u003e\u003e mat = numpy.random.rand(200, 100) # create a random matrix\n\u003e\u003e\u003e smat = scipy.sparse.csc_matrix(mat) # convert to sparse CSC format\n\u003e\u003e\u003e ut, s, vt = sparsesvd(smat, 100) # do SVD, asking for 100 factors\n\u003e\u003e\u003e 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. For an application of sparse SVD to Latent Semantic Analysis, see the `gensim \u003chttp://pypi.python.org/pypi/gensim\u003e`_ package.\n\nYou can use this code under the `simplified BSD license \u003chttp://www.opensource.org/licenses/bsd-license.php\u003e`_.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpiskvorky%2Fsparsesvd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpiskvorky%2Fsparsesvd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpiskvorky%2Fsparsesvd/lists"}