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    "info": {
        "author": "Andrei Novikov",
        "author_email": "pyclustering@yandex.ru",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Operating System :: MacOS",
            "Operating System :: Microsoft",
            "Operating System :: POSIX :: Linux",
            "Operating System :: Unix",
            "Programming Language :: C",
            "Programming Language :: C++",
            "Programming Language :: Python :: 3",
            "Topic :: Education",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Image Recognition",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "|Documentation| |JOSS|\n\nPyClustering\n============\n\n**pyclustering** is a Python, C++ data mining library (clustering\nalgorithm, oscillatory networks, neural networks). The library provides\nPython and C++ implementations (via CCORE library) of each algorithm or\nmodel. CCORE library is a part of pyclustering and supported for\nLinux, Windows and MacOS operating systems.\n\nOfficial repository: https://github.com/annoviko/pyclustering/\n\nDocumentation: https://pyclustering.github.io/docs/0.9.2/html/\n\nDependencies\n============\n\n**Required packages**: scipy, matplotlib, numpy, Pillow\n\n**Python version**: >=3.5 (32-bit, 64-bit)\n\n**C++ version**: >= 14 (32-bit, 64-bit)\n\nPerformance\n===========\n\nEach algorithm is implemented using Python and C/C++ language, if your platform is not supported then Python\nimplementation is used, otherwise C/C++. Implementation can be chosen by **ccore** flag (by default it is always\n'True' and it means that C/C++ is used), for example:\n\n.. code:: python\n\n    # As by default - C/C++ part of the library is used\n    xmeans_instance_1 = xmeans(data_points, start_centers, 20, ccore=True);\n\n    # The same - C/C++ part of the library is used by default\n    xmeans_instance_2 = xmeans(data_points, start_centers, 20);\n\n    # Switch off core - Python is used\n    xmeans_instance_3 = xmeans(data_points, start_centers, 20, ccore=False);\n\nInstallation\n============\n\nInstallation using pip3 tool:\n\n.. code:: bash\n\n    $ pip3 install pyclustering\n\nManual installation from official repository using GCC:\n\n.. code:: bash\n\n    # get sources of the pyclustering library, for example, from repository\n    $ mkdir pyclustering\n    $ cd pyclustering/\n    $ git clone https://github.com/annoviko/pyclustering.git .\n\n    # compile CCORE library (core of the pyclustering library).\n    $ cd ccore/\n    $ make ccore_x64        # build for 64-bit OS\n\n    # $ make ccore_x86      # build for 32-bit OS\n    # $ make ccore          # build for both (32 and 64-bit)\n\n    # return to parent folder of the pyclustering library\n    cd ../\n\n    # add current folder to python path\n    PYTHONPATH=`pwd`\n    export PYTHONPATH=${PYTHONPATH}\n\nManual installation using Visual Studio:\n\n1. Clone repository from: https://github.com/annoviko/pyclustering.git\n2. Open folder pyclustering/ccore\n3. Open Visual Studio project ccore.sln\n4. Select solution platform: 'x86' or 'x64'\n5. Build 'ccore' project.\n6. Add pyclustering folder to python path.\n\n\nProposals, Questions, Bugs\n==========================\n\nIn case of any questions, proposals or bugs related to the pyclustering\nplease contact to pyclustering@yandex.ru.\n\nIssue tracker: https://github.com/annoviko/pyclustering/issues\n\n\nLibrary Content\n===============\n\n**Clustering algorithms (module pyclustering.cluster):** \n\n- **Agglomerative** (pyclustering.cluster.agglomerative);\n- **BANG** (pyclustering.cluster.bang);\n- **BIRCH** (pyclustering.cluster.birch);\n- **BSAS** (pyclustering.cluster.bsas);\n- **CLARANS** (pyclustering.cluster.clarans);\n- **CLIQUE** (pyclustering.cluster.clique);\n- **CURE** (pyclustering.cluster.cure);\n- **DBSCAN** (pyclustering.cluster.dbscan);\n- **Elbow** (pyclustering.cluster.elbow);\n- **EMA** (pyclustering.cluster.ema);\n- **Fuzzy C-Means** (pyclustering.cluster.fcm);\n- **GA (Genetic Algorithm)** (pyclustering.cluster.ga);\n- **G-Means** (pyclustering.cluster.gmeans);\n- **HSyncNet** (pyclustering.cluster.hsyncnet);\n- **K-Means** (pyclustering.cluster.kmeans);\n- **K-Means++** (pyclustering.cluster.center_initializer);\n- **K-Medians** (pyclustering.cluster.kmedians);\n- **K-Medoids** (pyclustering.cluster.kmedoids);\n- **MBSAS** (pyclustering.cluster.mbsas);\n- **OPTICS** (pyclustering.cluster.optics);\n- **ROCK** (pyclustering.cluster.rock);\n- **Silhouette** (pyclustering.cluster.silhouette);\n- **SOM-SC** (pyclustering.cluster.somsc);\n- **SyncNet** (pyclustering.cluster.syncnet);\n- **Sync-SOM** (pyclustering.cluster.syncsom);\n- **TTSAS** (pyclustering.cluster.ttsas);\n- **X-Means** (pyclustering.cluster.xmeans);\n\n\n**Oscillatory networks and neural networks (module pyclustering.nnet):**\n\n- **Oscillatory network based on Hodgkin-Huxley model** (pyclustering.nnet.hhn);\n- **fSync: Oscillatory Network based on Landau-Stuart equation and Kuramoto model** (pyclustering.nnet.fsync);\n- **Hysteresis Oscillatory Network** (pyclustering.nnet.hysteresis);\n- **LEGION: Local Excitatory Global Inhibitory Oscillatory Network** (pyclustering.nnet.legion);\n- **PCNN: Pulse-Coupled Neural Network** (pyclustering.nnet.pcnn);\n- **SOM: Self-Organized Map** (pyclustering.nnet.som);\n- **Sync: Oscillatory Network based on Kuramoto model** (pyclustering.nnet.sync);\n- **SyncPR: Oscillatory Network based on Kuramoto model for pattern recognition** (pyclustering.nnet.syncpr);\n- **SyncSegm: Oscillatory Network based on Kuramoto model for image segmentation** (pyclustering.nnet.syncsegm);\n\n**Graph Coloring Algorithms (module pyclustering.gcolor):**\n\n- **DSATUR** (pyclustering.gcolor.dsatur);\n- **Hysteresis Oscillatory Network for graph coloring** (pyclustering.gcolor.hysteresis);\n- **Sync: Oscillatory Network based on Kuramoto model for graph coloring** (pyclustering.gcolor.sync);\n\n**Containers (module pyclustering.container):**\n\n- **CF-Tree** (pyclustering.container.cftree);\n- **KD-Tree** (pyclustering.container.kdtree);\n\n\nCite the Library\n================\n\nIf you are using pyclustering library in a scientific paper, please, cite the library:\n\nNovikov, A., 2019. PyClustering: Data Mining Library. Journal of Open Source Software, 4(36), p.1230. Available at: http://dx.doi.org/10.21105/joss.01230.\n\nBibTeX entry:\n\n.. code::\n\n    @article{Novikov2019,\n        doi         = {10.21105/joss.01230},\n        url         = {https://doi.org/10.21105/joss.01230},\n        year        = 2019,\n        month       = {apr},\n        publisher   = {The Open Journal},\n        volume      = {4},\n        number      = {36},\n        pages       = {1230},\n        author      = {Andrei Novikov},\n        title       = {{PyClustering}: Data Mining Library},\n        journal     = {Journal of Open Source Software}\n    }\n\n\n.. |Documentation| image:: https://codedocs.xyz/annoviko/pyclustering.svg\n   :target: https://codedocs.xyz/annoviko/pyclustering/\n.. |JOSS| image:: http://joss.theoj.org/papers/10.21105/joss.01230/status.svg\n   :target: https://doi.org/10.21105/joss.01230",
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