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        "description": "=======\nHaiku\n=======\n\nThis project had been deprecated. Maybe use deep learning.\n\nIntroduce\n============\n\nA Modification of LibShortText_ and LIBLINEAR_.\n\n- Uses Wissen Text Analyzer\n- Feature Selection\n- API Exported by Skitai App Engine\n- Win32 support (need MSVC)\n\n.. _LibShortText: https://www.csie.ntu.edu.tw/~cjlin/libshorttext/\n.. _LIBLINEAR: https://www.csie.ntu.edu.tw/~cjlin/liblinear/\n\n\nInstallation\n=============\n\n.. code:: bash\n\n  git clone https://gitlab.com/hansroh/haiku\n  cd haiku\n  python setup.py build install\n\n\nBasic Usage\n=============\n\n.. code:: python\n\n  import haiku\n  \n  model_path = \"./golforbed\"  \n  analyzer =  haiku.StandardAnalyzer (max_term = 200, stem_level = 2, make_lower_case = 1)\n \n  trainset = [\n      ('Golf', \"cloudy cold calm\"),\n      ('Golf', \"sunny warm\"),\n      ('Bed', \"rainy hot\"),\n      ('Golf', \"sunny hot windy\"),\n      ('Bed', \"windy cloudy cold\"),\n      ('Bed', \"rainy cloudy cold\"),\n  ]\n  \n  # training\n  h = haiku.Haiku (model_path, haiku.CL_L2, analyzer)\n  # pruning by document frequency and scoring by meth (FS_CF means category frequency)\n  h.select (data, mindf = 0, maxdf = 0, top = 0, meth = haiku.FS_CF)\n  # set training options: uni/bigram and feature representation\n  h.train (haiku.BIGRAM, haiku.FT_BIN)\n  h.close ()\n  \n  # guessing\n  h = haiku.Haiku (model_path, haiku.CL_L2, analyzer)\n  h.load ()\n  print (h.guess (\"sunny cold windy\"))\n  h.close ()\n\n\nExporting API through Skitai App Engine\n===========================================\n\nPlace model data into *app_root/resources/haikus/golforbed*.\n\n.. code:: python\n\n  import haiku\n  import skitai\n  \n  if __name__ == \"__main__\":\n    \n    pref = skitai.pref ()\n    pref.config.resource_dir = skitai.joinpath ('resources')\n    skitai.mount (\"/\", haiku, \"app\", pref)\n    skitai.run (port = 5005)\n\nGo to http://127.0.0.1:5000/haiku/golforbed/guess?q=sunny%20cold%20windy.\n\n",
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        "name": "haiku-lst",
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