{
    "info": {
        "author": "Xinwei Sun",
        "author_email": "sxwxiaoxiaohehe@pku.edu.cn",
        "bugtrack_url": null,
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            "Development Status :: 1 - Planning",
            "Intended Audience :: Science/Research",
            "License :: Free For Educational Use",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
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        "description": "Citing libra_py\n=============\n\nThe library libra_py is an academic project. The time and resources spent developing fastFM are therefore justified \nby the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry `citation.bib <http://jmlr.org/papers/v17/15-355.bib>`_).\n\n    Bayer, I. \"fastFM: A Library for Factorization Machines\" Journal of Machine Learning Research 17, pp. 1-5 (2016)\n\n\nlibra_py: A Package for sparsity problem\n============================================\n\n\n\nSupported Operating Systems\n---------------------------\nfastFM has a continuous integration / testing servers (Travis) for **Linux (Ubuntu 14.04 LTS)**\nand **OS X Mavericks**. Other OS are not actively supported.\n\nUsage\n-----\n.. code-block:: python\n\n    from fastFM import als\n    fm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=2, l2_reg_w=0.1, l2_reg_V=0.5)\n    fm.fit(X_train, y_train)\n    y_pred = fm.predict(X_test)\n\n\nTutorials and other information are available `here <http://arxiv.org/abs/1505.00641>`_.\nThe C code is available as `subrepository <https://github.com/ibayer/fastFM-core>`_ and provides \na stand alone command line interface. If you have still **questions** after reading the documentation please open a issue at GitHub.\n\n+----------------+------------------+-----------------------------+\n| Family         | Solver           | Loss                        |\n+================+==================+=============================+\n| Gaussian       | LBI_Linear       | Square Loss                 |\n+----------------+------------------+-----------------------------+\n| Binomial       | LBI_Logit        | Logit Model                 |\n+----------------+------------------+-----------------------------+\n\n*Supported solvers and tasks*\n\nInstallation\n------------\n\n**binary install**\n\n``pip install libra_py``\n\n\nTests\n-----\n\n",
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        "keywords": "sparsity regularization path Lasso variable-selection",
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        "name": "libra_py",
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