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        "author": "Ingo Steinwart, Philipp Thomann",
        "author_email": "philipp.thomann@mathematik.uni-stuttgart.de",
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        "description": "liquidSVM for Python\n====================\n\nWelcome to the Python bindings for liquidSVM.\n\nSummary:\n\n-  Install it using any of the following variants:\n\n   ::\n\n       pip install --user --upgrade liquidSVM\n       easy_install --user --upgrade liquidSVM\n\n-  If you want to compile liquidSVM for your machine download\n   http://www.isa.uni-stuttgart.de/software/python/liquidSVM-python.tar.gz.\n   For Windows there are binaries at\n   `liquidSVM-python.win-amd64.zip <http://www.isa.uni-stuttgart.de/software/python/liquidSVM-python.win-amd64.zip>`__,\n   for Mac at\n   `liquidSVM-python.macosx.tar.gz <http://www.isa.uni-stuttgart.de/software/python/liquidSVM-python.macosx.tar.gz>`__\n\nThen to try it out issue on the command line\n\n::\n\n    python -m liquidSVM covtype.1000 mc --display=1\n\n    **NOTE**: it might be possible that there is a problem with the last\n    line if there are files called ``liquidSVM*`` in the current\n    directory, so change to some other or a newly created one.\n\nOr use it in an interactive shell\n\n.. code:: python\n\n    from liquidSVM import *\n    model = mcSVM(iris, iris_labs, display=1,threads=2)\n    result, err = model.test(iris, iris_labs)\n    result = model.predict(iris)\n\n    reg = LiquidData('reg-1d')\n    model = lsSVM(reg.test, display=1)\n    result, err = model.test(reg.test)\n\nMore Information can be found in the\n`demo <http://www.isa.uni-stuttgart.de/software/python/demo.html>`__\n`[jupyter\nnotebook] <http://www.isa.uni-stuttgart.de/software/python/demo.ipynb>`__\nand in\n\n.. code:: python\n\n    from liquidSVM import *\n    help(SVM)\n    help(doc.configuration)\n\nBoth liquidSVM and these bindings are provided under the AGPL 3.0\nlicense.\n\nNative Library Compilation\n--------------------------\n\nliquidSVM is implemented in C++ therefore a native library needs to be\ncompiled and included in the Python process. Binaries for Windows are\nincluded, however if it is possible for you, we recommend you compile it\nfor every machine to get full performance.\n\nTo set compiler options use the the environment variable\n``LIQUIDSVM_CONFIGURE_ARGS``. The first word in it can be any of the\nfollowing:\n\n``native``\n    usually the fastest, but the resulting library is usually not\n    portable to other machines.\n``generic``\n    should be portable to most machines, yet slower (factor 2 to 4?)\n``debug``\n    compiles with debugging activated (can be debugged e.g. with gdb)\n``empty``\n    No special compilation options activated.\n\nThe remainder of the environment variable will be passed to the\ncompiler. Extract\nhttp://www.isa.uni-stuttgart.de/software/python/liquidSVM-python.tar.gz\nand change into the directory. On Linux and MacOS X command line use for\ninstance:\n\n::\n\n    LIQUIDSVM_CONFIGURE_ARGS=\"native -mavx2\" python setup.py bdist\n    LIQUIDSVM_CONFIGURE_ARGS=generic python setup.py bdist\n\n*MacOS*:\n    Install Xcode and then the optional command line tools are installed\n    from therein.\n\n*Windows*:\n    If you have VisualStudio installed then you should have an\n    environment variable like ``%VS90COMNTOOLS%`` (for VisualStudio\n    2015). Still it seems that setup.py needs to have this information\n    in ``%VS90COMNTOOLS%`` so copy that environment variable or use for\n    example:\n\n    ::\n\n        set VS90COMNTOOLS=%VS140COMNTOOLS%\n\n        **Note:** At the moment the Visual Studio for Python only gives\n        Version 9.0 and this is too old for compilation.",
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        "license": "AGPL v3",
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        "name": "liquidSVM",
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        "summary": "Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non- parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, inclusion of a variety of different classification and regression scenarios, and full flexibility for experts.",
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