Metadata-Version: 2.1
Name: divik
Version: 2.1.6b0
Summary: Divisive iK-means algorithm implementation
Home-page: https://github.com/gmrukwa/divik
Author: Grzegorz Mrukwa
Author-email: g.mrukwa@gmail.com
License: UNKNOWN
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        # divik
        
        Python implementation of Divisive iK-means (DiviK) algorithm.
        
        # Tools within this package
        
        > This section will be further developed soon.
        
        1) [`divik`](divik/cli/divik.md) - runs DiviK in one of many scenarios
        2) [`kmeans`](divik/cli/auto_kmeans.md) - runs K-means
        3) `linkage` - runs agglomerative clustering
        4) [`inspect`](divik/cli/inspect.md) - visualizes DiviK result
        5) `visualize` - generates `.png` file with visualization of clusters for 2D
        maps
        6) [`spectral`](divik/cli/spectral.md) - generates spectral embedding of a
        dataset
        
        # Installation
        
        ## Docker
        
        The recommended way to use this software is through
        [Docker](https://www.docker.com/). This is the most convenient way, if you want
        to use `divik` application, since it requires *MATLAB Compiler Runtime*
        and more dependencies.
        
        To install latest stable version use:
        
        ```bash
        docker pull gmrukwa/divik
        ```
        
        To install specific version, you can specify it in the command, e.g.:
        
        ```bash
        docker pull gmrukwa/divik:1.12.0
        ```
        
        ## Python package
        
        Prerequisites for installation of base package:
        
        - Python 3.5
        
        These are required for using `divik` application and GMM-based filtering:
        
        - [MATLAB Compiler Runtime](https://www.mathworks.com/products/compiler/matlab-runtime.html),
        version 2016b or newer, installed to default path
        - [compiled package with legacy code](https://github.com/spectre-team/matlab-legacy/releases/tag/legacy-v4.0.9)
        
        Installation process may be clearer with insight into Docker images used for
        application deployment:
        
        - [`python_mcr` image](https://github.com/spectre-team/python_mcr) - installs
        MCR r2016b onto Python 3.5 image
        - [`python_msi` image](https://github.com/spectre-team/python_msi) - installs
        compiled legacy code onto MCR image
        - [`divik` image](https://github.com/spectre-team/spectre-divik/blob/master/dockerfile) -
        installs DiviK software onto legacy code image
        
        Having prerequisites installed, one can install latest base version of the
        package:
        
        ```bash
        pip install divik
        ```
        
        or any stable tagged version, e.g.:
        
        ```bash
        pip install divik==2.0.0
        ```
        
        # References
        
        This software is part of contribution made by [Data Mining Group of Silesian
        University of Technology](http://www.zaed.polsl.pl/), rest of which is
        published [here](https://github.com/ZAEDPolSl).
        
        + [P. Widlak, G. Mrukwa, M. Kalinowska, M. Pietrowska, M. Chekan, J. Wierzgon, M.
        Gawin, G. Drazek and J. Polanska, "Detection of molecular signatures of oral
        squamous cell carcinoma and normal epithelium - application of a novel
        methodology for unsupervised segmentation of imaging mass spectrometry data,"
        Proteomics, vol. 16, no. 11-12, pp. 1613-21, 2016][1]
        + [M. Pietrowska, H. C. Diehl, G. Mrukwa, M. Kalinowska-Herok, M. Gawin, M.
        Chekan, J. Elm, G. Drazek, A. Krawczyk, D. Lange, H. E. Meyer, J. Polanska, C.
        Henkel, P. Widlak, "Molecular profiles of thyroid cancer subtypes:
        Classification based on features of tissue revealed by mass spectrometry
        imaging," Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 2016][2]
        + [G. Mrukwa, G. Drazek, M. Pietrowska, P. Widlak and J. Polanska, "A Novel
        Divisive iK-Means Algorithm with Region-Driven Feature Selection as a Tool for
        Automated Detection of Tumour Heterogeneity in MALDI IMS Experiments," in
        International Conference on Bioinformatics and Biomedical Engineering, 2016][3]
        
        [1]: http://onlinelibrary.wiley.com/doi/10.1002/pmic.201500458/pdf
        [2]: http://www.sciencedirect.com/science/article/pii/S1570963916302175
        [3]: http://link.springer.com/chapter/10.1007/978-3-319-31744-1_11
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.4,<3.6
Description-Content-Type: text/markdown
