sklearn-lvq


Namesklearn-lvq JSON
Version 1.1.1 PyPI version JSON
download
home_pagehttps://github.com/MrNuggelz/sklearn-lvq
SummaryScikit-Learn compatible Generalized Learning Vector Quantization (GLVQ) and Robust Soft Learning Vector Quantization implementation.
upload_time2020-10-09 16:54:43
maintainer
docs_urlNone
authorJoris Jensun
requires_python
licenseBSD-3-Clause
keywords
VCS
bugtrack_url
requirements numpy scipy scikit-learn
Travis-CI
coveralls test coverage No coveralls.
            [![Build Status](https://travis-ci.org/MrNuggelz/sklearn-lvq.svg?branch=stable)](https://travis-ci.org/MrNuggelz/sklearn-lvq)
[![Build status](https://ci.appveyor.com/api/projects/status/qiwkue1x5lgll382?svg=true)](https://ci.appveyor.com/project/MrNuggelz/sklearn-glvq)
[![CircleCI](https://circleci.com/gh/MrNuggelz/sklearn-lvq.svg?style=shield)](https://circleci.com/gh/MrNuggelz/sklearn-lvq)
[![Coverage Status](https://coveralls.io/repos/github/MrNuggelz/sklearn-lvq/badge.svg)](https://coveralls.io/github/MrNuggelz/sklearn-lvq)
[![Coverage Status](https://readthedocs.org/projects/sklearn-lvq/badge/?version=latest)](https://sklearn-lvq.readthedocs.io/en/latest/?badge=latest)
# Warning

Repository and Package Name changed to sklearn-lvq!

# Generalized Learning Vector Quantization
Scikit-learn compatible implementation of GLVQ, GRLVQ, GMLVQ, LGMLVQ
RSLVQ, MRSLVQ and LMRSLVQ.

Compatible with Python2.7, Python3.6 and above.

This implementation is based on the Matlab implementation
provided by Biehl, Schneider and Bunte (http://matlabserver.cs.rug.nl/gmlvqweb/web/).

## Important Links
- scikit-learn (http://scikit-learn.org/)
- documentation (https://sklearn-lvq.readthedocs.io/en/latest/?badge=latest)

## Installation
To install this module run:
```
pip install .
```
or
```
pip install sklearn-lvq
```

To also install the extras, use
```bash
pip install .[docs,examples,tests]
```
or
```bash
pip install -U sklearn-lvq[docs,examples,tests]
```

## Examples
To run the examples:
```
pip install -U sklearn-lvq[examples]
```
The examples can be found in the examples directory.

## Testing
To run testss:
```
pip install -U sklearn-lvq[tests]
```
Tests are located in the `sklearn_lvq/tests` folder
and can be run with the `nosetests` command in the main directory.

## Documentation
To build the documentation locally, ensure that you have sphinx, sphinx-gallery,
pillow, sphinx_rt_theme, metric_learn and matplotlib by executing:

```
pip install -U sklearn-lvq[docs]
```



            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/MrNuggelz/sklearn-lvq",
    "name": "sklearn-lvq",
    "maintainer": "",
    "docs_url": null,
    "requires_python": "",
    "maintainer_email": "",
    "keywords": "",
    "author": "Joris Jensun",
    "author_email": "jjensen@techfak.uni-bielefeld.de",
    "download_url": "https://files.pythonhosted.org/packages/0b/f7/a4785ee044f3e5bb50e14b553dbaff9d0f895ed62e18a581159cfe5be2fb/sklearn-lvq-1.1.1.tar.gz",
    "platform": "",
    "description": "[![Build Status](https://travis-ci.org/MrNuggelz/sklearn-lvq.svg?branch=stable)](https://travis-ci.org/MrNuggelz/sklearn-lvq)\n[![Build status](https://ci.appveyor.com/api/projects/status/qiwkue1x5lgll382?svg=true)](https://ci.appveyor.com/project/MrNuggelz/sklearn-glvq)\n[![CircleCI](https://circleci.com/gh/MrNuggelz/sklearn-lvq.svg?style=shield)](https://circleci.com/gh/MrNuggelz/sklearn-lvq)\n[![Coverage Status](https://coveralls.io/repos/github/MrNuggelz/sklearn-lvq/badge.svg)](https://coveralls.io/github/MrNuggelz/sklearn-lvq)\n[![Coverage Status](https://readthedocs.org/projects/sklearn-lvq/badge/?version=latest)](https://sklearn-lvq.readthedocs.io/en/latest/?badge=latest)\n# Warning\n\nRepository and Package Name changed to sklearn-lvq!\n\n# Generalized Learning Vector Quantization\nScikit-learn compatible implementation of GLVQ, GRLVQ, GMLVQ, LGMLVQ\nRSLVQ, MRSLVQ and LMRSLVQ.\n\nCompatible with Python2.7, Python3.6 and above.\n\nThis implementation is based on the Matlab implementation\nprovided by Biehl, Schneider and Bunte (http://matlabserver.cs.rug.nl/gmlvqweb/web/).\n\n## Important Links\n- scikit-learn (http://scikit-learn.org/)\n- documentation (https://sklearn-lvq.readthedocs.io/en/latest/?badge=latest)\n\n## Installation\nTo install this module run:\n```\npip install .\n```\nor\n```\npip install sklearn-lvq\n```\n\nTo also install the extras, use\n```bash\npip install .[docs,examples,tests]\n```\nor\n```bash\npip install -U sklearn-lvq[docs,examples,tests]\n```\n\n## Examples\nTo run the examples:\n```\npip install -U sklearn-lvq[examples]\n```\nThe examples can be found in the examples directory.\n\n## Testing\nTo run testss:\n```\npip install -U sklearn-lvq[tests]\n```\nTests are located in the `sklearn_lvq/tests` folder\nand can be run with the `nosetests` command in the main directory.\n\n## Documentation\nTo build the documentation locally, ensure that you have sphinx, sphinx-gallery,\npillow, sphinx_rt_theme, metric_learn and matplotlib by executing:\n\n```\npip install -U sklearn-lvq[docs]\n```\n\n\n",
    "bugtrack_url": null,
    "license": "BSD-3-Clause",
    "summary": "Scikit-Learn compatible Generalized Learning Vector Quantization (GLVQ) and Robust Soft Learning Vector Quantization implementation.",
    "version": "1.1.1",
    "project_urls": {
        "Download": "https://github.com/MrNuggelz/sklearn-lvq/releases/tag/1.1.0",
        "Homepage": "https://github.com/MrNuggelz/sklearn-lvq"
    },
    "split_keywords": [],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "4f7c8ecc19d3b45fb1da4eb2926464d05fa2725d675759304744a897e2781a61",
                "md5": "7c03ee15e7f62c9cf843bb6dde211ac0",
                "sha256": "2c8305cfad6cb0db64c0fdcdebc1e824d2f61c23704c91c0b4f607ae2a3e575f"
            },
            "downloads": -1,
            "filename": "sklearn_lvq-1.1.1-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "7c03ee15e7f62c9cf843bb6dde211ac0",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": null,
            "size": 38144,
            "upload_time": "2020-10-09T16:54:41",
            "upload_time_iso_8601": "2020-10-09T16:54:41.781680Z",
            "url": "https://files.pythonhosted.org/packages/4f/7c/8ecc19d3b45fb1da4eb2926464d05fa2725d675759304744a897e2781a61/sklearn_lvq-1.1.1-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "0bf7a4785ee044f3e5bb50e14b553dbaff9d0f895ed62e18a581159cfe5be2fb",
                "md5": "0eb11e472605e0be8d16befd57b37a75",
                "sha256": "c62df832a8c59761bd1550e5550e0af2c22f6ed79c164ef668d4c1e97fa05cd4"
            },
            "downloads": -1,
            "filename": "sklearn-lvq-1.1.1.tar.gz",
            "has_sig": false,
            "md5_digest": "0eb11e472605e0be8d16befd57b37a75",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 20542,
            "upload_time": "2020-10-09T16:54:43",
            "upload_time_iso_8601": "2020-10-09T16:54:43.646778Z",
            "url": "https://files.pythonhosted.org/packages/0b/f7/a4785ee044f3e5bb50e14b553dbaff9d0f895ed62e18a581159cfe5be2fb/sklearn-lvq-1.1.1.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2020-10-09 16:54:43",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "MrNuggelz",
    "github_project": "sklearn-lvq",
    "travis_ci": true,
    "coveralls": false,
    "github_actions": false,
    "circle": true,
    "appveyor": true,
    "requirements": [
        {
            "name": "numpy",
            "specs": []
        },
        {
            "name": "scipy",
            "specs": []
        },
        {
            "name": "scikit-learn",
            "specs": []
        }
    ],
    "lcname": "sklearn-lvq"
}
        
Elapsed time: 0.51744s