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        "author": "IAZero",
        "author_email": "aluizio@cos.ufrj.br",
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        "description": "# wisardpkg [![](https://img.shields.io/pypi/v/wisardpkg.svg)](https://pypi.org/project/wisardpkg/) [![](https://travis-ci.org/IAZero/wisardpkg.svg?branch=master)](https://travis-ci.org/IAZero/wisardpkg)\n\n## Description:\nThis is a project to make available the different models based on WiSARD,\nwith high performance, easy to use and to install and following a pattern of use.\nThese provided models are machine learning models,\nwith supervised, unsupervised and semi-supervised learning.\n\n## to install:\npython:\n```\npip install wisardpkg\n```\nWorks to python2 and pyhton3.  \nIf you are on Linux and not in a virtual environment, you may need to run as superuser.\n\n#### obs:\nTo install on windows platform you can use [anaconda](https://anaconda.org/) and do:\n```\npython -m pip install wisardpkg\n```\nc++:\ncopy the file wisardpkg.hpp inside your project \n```\ninclude/wisardpkg.hpp\n```\n\n## to uninstall:\n```\npip uninstall wisardpkg\n```\n\n## to import:\npython:\n```python\nimport wisardpkg as wp\n```\nc++:\n```c++\n# include \"wisardpkg.hpp\"\n\nnamespace wp = wisardpkg;\n```\n\n## to use:\n### WiSARD\n\nWiSARD with bleaching by default:\n\npython:\n```python\n# load input data, just zeros and ones  \nX = [\n      [1,1,1,0,0,0,0,0],\n      [1,1,1,1,0,0,0,0],\n      [0,0,0,0,1,1,1,1],\n      [0,0,0,0,0,1,1,1]\n    ]\n\n# load label data, which must be a string array\ny = [\n      \"cold\",\n      \"cold\",\n      \"hot\",\n      \"hot\"\n    ]\n\n\naddressSize = 3     # number of addressing bits in the ram\nignoreZero  = False # optional; causes the rams to ignore the address 0\n\n# False by default for performance reasons,\n# when True, WiSARD prints the progress of train() and classify()\nverbose = True\n\nwsd = wp.Wisard(addressSize, ignoreZero=ignoreZero, verbose=verbose)\n\n\n\n# train using the input data\nwsd.train(X,y)\n\n# classify some data\nout = wsd.classify(X)\n\n# the output of classify is a string list in the same sequence as the input\nfor i,d in enumerate(X):\n    print(out[i],d)\n```\nc++:\n```c++\n\nvector<vector<int>> X(4);\nX[0] = {1,1,1,0,0,0,0,0};\nX[1] = {1,1,1,1,0,0,0,0};\nX[2] = {0,0,0,0,1,1,1,1};\nX[3] = {0,0,0,0,0,1,1,1};\n\nvector<string> y = {\n      \"cold\",\n      \"cold\",\n      \"hot\",\n      \"hot\"\n};\n\n\nwp::Wisard w(3, {\n      {\"ignoreZero\", false},\n      {\"verbose\", true}\n});\n\nw.train(X,y);\n\nvector<string> out = w.classify(X);\n\nfor(int i=0; i<4; i++){\n      cout << \"i: \" << i << \"; class: \" << out[i] << endl;\n}\n\n```\n### ClusWiSARD\n\nClusWiSARD with bleaching by default:\n```python\naddressSize        = 3    # number of addressing bits in the ram.\nminScore           = 0.1  # min score of training process\nthreshold          = 10   # limit of training cycles by discriminator\ndiscriminatorLimit = 5    # limit of discriminators by clusters\n\n# False by default for performance reasons\n# when enabled,e ClusWiSARD prints the progress of train() and classify()\nverbose = True\n\nclus = ClusWisard(addressSize, minScore, threshold, discriminatorLimit, verbose=True)\n\n\n\n# train using the input data\nclus.train(X,y)\n\n# optionally you can train using arbitrary labels for the data\n# input some labels in a dict,\n# the keys must be integer indices indicating which input array the entry is associated to,\n# the values are the labels which must be strings\ny2 = {\n  1: \"cold\",\n  3: \"hot\"\n}\n\nclus.train(X,y2)\n\n# classify some data\nout = clus.classify(X)\n\n# the output of classify is a string list in the same sequence as the input\nfor i,d in enumerate(X):\n    print(out[i], d)\n```\n\n## Documentation:\nYou can find the complete documentation in the [page](https://iazero.github.io/wisardpkg/).\n\n## Build on libraries:\n[pybind11](https://github.com/pybind/pybind11)\n[nlohmann/json](https://github.com/nlohmann/json)",
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