Metadata-Version: 1.1
Name: pyclustering
Version: 0.8.0
Summary: pyclustring is a python data mining library
Home-page: https://github.com/annoviko/pyclustering
Author: Andrei Novikov
Author-email: pyclustering@yandex.ru
License: GNU Public License
Description-Content-Type: UNKNOWN
Description: |Documentation| |DOI|
        
        PyClustering
        ============
        
        **pyclustering** is a Python, C++ data mining library (clustering
        algorithm, oscillatory networks, neural networks). The library provides
        Python and C++ implementations (via CCORE library) of each algorithm or
        model. CCORE library is a part of pyclustering and supported only for
        32, 64-bit Linux and 32, 64-bit Windows operating systems.
        
        Official repository: https://github.com/annoviko/pyclustering/
        
        Dependencies
        ============
        
        **Required packages**: scipy, matplotlib, numpy, PIL
        
        **Python version**: >=3.4 (32-bit, 64-bit)
        
        **C++ version**: >= 14 (32-bit, 64-bit)
        
        Performance
        ===========
        
        Each algorithm is implemented using Python and C/C++ language, if your platform is not supported then Python
        implementation is used, otherwise C/C++. Implementation can be chosen by **ccore** flag (by default it is always
        'True' and it means that C/C++ is used), for example:
        
        .. code:: python
        
            xmeans_instance = xmeans(data_points, start_centers, 20, ccore = True);
        
        Installation
        ============
        
        Installation using pip3 tool:
        
        .. code:: bash
        
            $ pip3 install pyclustering
        
        Manual installation from official repository using GCC:
        
        .. code:: bash
        
            # get sources of the pyclustering library, for example, from repository
            $ mkdir pyclustering
            $ cd pyclustering/
            $ git clone https://github.com/annoviko/pyclustering.git .
        
            # compile CCORE library (core of the pyclustering library).
            $ cd pyclustering/ccore
            $ make ccore_x64        # build for 64-bit OS
        
            # $ make ccore_x86      # build for 32-bit OS
            # $ make ccore          # build for both (32 and 64-bit)
        
            # return to parent folder of the pyclustering library
            cd ../
        
            # add current folder to python path
            PYTHONPATH=`pwd`
            export PYTHONPATH=${PYTHONPATH}
        
        Manual installation using Visual Studio:
        
        1. Clone repository from: https://github.com/annoviko/pyclustering.git
        2. Open folder pyclustering/ccore
        3. Open Visual Studio project ccore.sln
        4. Select solution platform: 'x86' or 'x64'
        5. Build 'ccore' project.
        6. Add pyclustering folder to python path.
        
        
        Proposals, Questions, Bugs
        ==========================
        
        In case of any questions, proposals or bugs related to the pyclustering
        please contact to pyclustering@yandex.ru.
        
        Issue tracker: https://github.com/annoviko/pyclustering/issues
        
        
        Library Content
        ===============
        
        **Clustering algorithms (module pyclustering.cluster):** 
        
        - **Agglomerative** (pyclustering.cluster.agglomerative);
        - **BIRCH** (pyclustering.cluster.birch);
        - **CLARANS** (pyclustering.cluster.clarans);
        - **CURE** (pyclustering.cluster.cure);
        - **DBSCAN** (pyclustering.cluster.dbscan);
        - **EMA** (pyclustering.cluster.ema);
        - **GA (Genetic Algorithm)** (pyclustering.cluster.ga);
        - **HSyncNet** (pyclustering.cluster.hsyncnet);
        - **K-Means** (pyclustering.cluster.kmeans);
        - **K-Means++** (pyclustering.cluster.center_initializer);
        - **K-Medians** (pyclustering.cluster.kmedians);
        - **K-Medoids (PAM)** (pyclustering.cluster.kmedoids);
        - **OPTICS** (pyclustering.cluster.optics);
        - **ROCK** (pyclustering.cluster.rock);
        - **SOM-SC** (pyclustering.cluster.somsc);
        - **SyncNet** (pyclustering.cluster.syncnet);
        - **Sync-SOM** (pyclustering.cluster.syncsom);
        - **X-Means** (pyclustering.cluster.xmeans);
        
        
        **Oscillatory networks and neural networks (module pyclustering.nnet):**
        
        - **Oscillatory network based on Hodgkin-Huxley model** (pyclustering.nnet.hhn);
        - **fSync: Oscillatory Network based on Landau-Stuart equation and Kuramoto model** (pyclustering.nnet.fsync);
        - **Hysteresis Oscillatory Network** (pyclustering.nnet.hysteresis);
        - **LEGION: Local Excitatory Global Inhibitory Oscillatory Network** (pyclustering.nnet.legion);
        - **PCNN: Pulse-Coupled Neural Network** (pyclustering.nnet.pcnn);
        - **SOM: Self-Organized Map** (pyclustering.nnet.som);
        - **Sync: Oscillatory Network based on Kuramoto model** (pyclustering.nnet.sync);
        - **SyncPR: Oscillatory Network based on Kuramoto model for pattern recognition** (pyclustering.nnet.syncpr);
        - **SyncSegm: Oscillatory Network based on Kuramoto model for image segmentation** (pyclustering.nnet.syncsegm);
        
        **Graph Coloring Algorithms (module pyclustering.gcolor):**
        
        - **DSATUR** (pyclustering.gcolor.dsatur);
        - **Hysteresis Oscillatory Network for graph coloring** (pyclustering.gcolor.hysteresis);
        - **Sync: Oscillatory Network based on Kuramoto model for graph coloring** (pyclustering.gcolor.sync);
        
        **Containers (module pyclustering.container):**
        
        - **CF-Tree** (pyclustering.container.cftree);
        - **KD-Tree** (pyclustering.container.kdtree);
        
        
        .. |Documentation| image:: https://codedocs.xyz/annoviko/pyclustering.svg
           :target: https://codedocs.xyz/annoviko/pyclustering/
        .. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1183636.svg
           :target: https://doi.org/10.5281/zenodo.1183636
        
Keywords: pyclustering data-mining clustering cluster-analysis neural-network oscillatory-network
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: Microsoft :: Windows :: Windows 7
Classifier: Operating System :: Microsoft :: Windows :: Windows 8
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: C
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
