diresa


Namediresa JSON
Version 1.1.0 PyPI version JSON
download
home_pageNone
SummaryDiresa - distance-regularized siamese twin autoencoder
upload_time2025-01-02 13:03:02
maintainerNone
docs_urlNone
authorNone
requires_python>=3.8
licenseNone
keywords climate learning machine tensorflow weather
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # *DIRESA*

![test](https://gitlab.com/etrovub/ai4wcm/public/diresa/badges/master/pipeline.svg?ignore_skipped=true&key_text=test&key_width=35)
![release](https://gitlab.com/etrovub/ai4wcm/public/diresa/-/badges/release.svg?key_text=pypi&key_width=35)
![python](https://img.shields.io/badge/python-3.8%20|%203.9%20|%203.10%20|%203.11%20|%203.12-blue)
![tensorflow](https://img.shields.io/badge/tensorflow-2.12%20|%202.13%20|%202.14%20|%202.15%20|%202.16%20|%202.17%20|%202.18-orange)
![mit](https://img.shields.io/badge/license-MIT-yellow)

### Overview

*DIRESA* is a Python package for dimension reduction based on 
[TensorFlow](https://www.tensorflow.org). The distance-regularized 
Siamese twin autoencoder architecture is designed to preserve distance 
(ordering) in latent space while capturing the non-linearities in
the datasets.


### Install *DIRESA*

Install *DIRESA* with the following command:

``` bash
  pip install diresa
```

### Documentation

The *DIRESA* documentation can be found on [Read the Docs](https://diresa-learn.readthedocs.io)
            

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "diresa",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.8",
    "maintainer_email": null,
    "keywords": "climate, learning, machine, tensorflow, weather",
    "author": null,
    "author_email": "Geert De Paepe <geert.de.paepe@vub.be>",
    "download_url": "https://files.pythonhosted.org/packages/22/9c/99410ba90371fca3a933c3c4d7b09eda05b1b2494796bbb85257406ae402/diresa-1.1.0.tar.gz",
    "platform": null,
    "description": "# *DIRESA*\n\n![test](https://gitlab.com/etrovub/ai4wcm/public/diresa/badges/master/pipeline.svg?ignore_skipped=true&key_text=test&key_width=35)\n![release](https://gitlab.com/etrovub/ai4wcm/public/diresa/-/badges/release.svg?key_text=pypi&key_width=35)\n![python](https://img.shields.io/badge/python-3.8%20|%203.9%20|%203.10%20|%203.11%20|%203.12-blue)\n![tensorflow](https://img.shields.io/badge/tensorflow-2.12%20|%202.13%20|%202.14%20|%202.15%20|%202.16%20|%202.17%20|%202.18-orange)\n![mit](https://img.shields.io/badge/license-MIT-yellow)\n\n### Overview\n\n*DIRESA* is a Python package for dimension reduction based on \n[TensorFlow](https://www.tensorflow.org). The distance-regularized \nSiamese twin autoencoder architecture is designed to preserve distance \n(ordering) in latent space while capturing the non-linearities in\nthe datasets.\n\n\n### Install *DIRESA*\n\nInstall *DIRESA* with the following command:\n\n``` bash\n  pip install diresa\n```\n\n### Documentation\n\nThe *DIRESA* documentation can be found on [Read the Docs](https://diresa-learn.readthedocs.io)",
    "bugtrack_url": null,
    "license": null,
    "summary": "Diresa - distance-regularized siamese twin autoencoder",
    "version": "1.1.0",
    "project_urls": {
        "Homepage": "https://gitlab.com/etrovub/ai4wcm/public/diresa",
        "Issues": "https://gitlab.com/etrovub/ai4wcm/public/diresa/-/issues"
    },
    "split_keywords": [
        "climate",
        " learning",
        " machine",
        " tensorflow",
        " weather"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "1525e4f205e644368cd680754829e53a494616593a4b9b026f04f2648d297581",
                "md5": "e5934e2dee361162c8f9d4738bb2aa67",
                "sha256": "7a53ece184a3517619510350fdd287364f4807de8fdba1fb5f56557f72f44a92"
            },
            "downloads": -1,
            "filename": "diresa-1.1.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "e5934e2dee361162c8f9d4738bb2aa67",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.8",
            "size": 12176,
            "upload_time": "2025-01-02T13:03:00",
            "upload_time_iso_8601": "2025-01-02T13:03:00.716865Z",
            "url": "https://files.pythonhosted.org/packages/15/25/e4f205e644368cd680754829e53a494616593a4b9b026f04f2648d297581/diresa-1.1.0-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "229c99410ba90371fca3a933c3c4d7b09eda05b1b2494796bbb85257406ae402",
                "md5": "cf37135d24bb4be3afc6a8341647e683",
                "sha256": "fab719402524730887c6645792cc4ab7d0b150152e9ed7a4bfc54920466562ee"
            },
            "downloads": -1,
            "filename": "diresa-1.1.0.tar.gz",
            "has_sig": false,
            "md5_digest": "cf37135d24bb4be3afc6a8341647e683",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.8",
            "size": 1616421,
            "upload_time": "2025-01-02T13:03:02",
            "upload_time_iso_8601": "2025-01-02T13:03:02.770906Z",
            "url": "https://files.pythonhosted.org/packages/22/9c/99410ba90371fca3a933c3c4d7b09eda05b1b2494796bbb85257406ae402/diresa-1.1.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-01-02 13:03:02",
    "github": false,
    "gitlab": true,
    "bitbucket": false,
    "codeberg": false,
    "gitlab_user": "etrovub",
    "gitlab_project": "ai4wcm",
    "lcname": "diresa"
}
        
Elapsed time: 0.69115s