# GMSDB Clusterer
# Use
pip install gmsdb
## Simple use (sklearn -like style), no speed improvement, recommended significance level alpha=0.1.
Maximal number of gaussian components=50:
from gmsdb import GMSDB
clf=GMSDB(n_components=50)
clf.fit(X)
Y=clf.predict(X)
## Complex use (with speed improvement for stages 1 and 3):
clf=GMSDB(min_components=2,step_components=100,n_components=900,rand_search=1000,rand_level=0.5)
## Complex use (with custom significance level alpha=0.15):
clf=GMSDB(n_components=50,alpha_stage2=0.15,alpha_stage4=0.15)
## Verbose use (show debug information):
clf=GMSDB(n_components=50,verbose=True)
# Desciption:
https://arxiv.org/abs/2309.02623
Raw data
{
"_id": null,
"home_page": "https://github.com/berng/GMSDB",
"name": "gmsdb",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "",
"keywords": "clusterization",
"author": "Oleg I.Berngardt",
"author_email": "berng@rambler.ru",
"download_url": "https://files.pythonhosted.org/packages/6e/39/d1701ac39ca25ae154f0e177ea0d7523b352f42c4ac2d1d682fca8796bbc/gmsdb-2.1.tar.gz",
"platform": null,
"description": "# GMSDB Clusterer\n\n# Use\npip install gmsdb\n\n## Simple use (sklearn -like style), no speed improvement, recommended significance level alpha=0.1. \nMaximal number of gaussian components=50:\n\nfrom gmsdb import GMSDB\n\nclf=GMSDB(n_components=50)\n\nclf.fit(X)\n\nY=clf.predict(X)\n\n## Complex use (with speed improvement for stages 1 and 3):\n\nclf=GMSDB(min_components=2,step_components=100,n_components=900,rand_search=1000,rand_level=0.5)\n\n## Complex use (with custom significance level alpha=0.15):\n\nclf=GMSDB(n_components=50,alpha_stage2=0.15,alpha_stage4=0.15)\n\n## Verbose use (show debug information):\n\nclf=GMSDB(n_components=50,verbose=True)\n\n\n# Desciption:\nhttps://arxiv.org/abs/2309.02623\n\n\n\n",
"bugtrack_url": null,
"license": "",
"summary": "GMSDB Clusterer",
"version": "2.1",
"project_urls": {
"Homepage": "https://github.com/berng/GMSDB"
},
"split_keywords": [
"clusterization"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "7490b454003a6f2cf57c7dfa6ac7d9850485bc1069d0af4087a863fbd7a5b993",
"md5": "c50877b199962ea22d7b1b1a516a1a79",
"sha256": "3352ba857da8f3ce4ecb69e4294aaab37f0a6f7197f2828a17bd3aa9e632407e"
},
"downloads": -1,
"filename": "gmsdb-2.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "c50877b199962ea22d7b1b1a516a1a79",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 7628,
"upload_time": "2023-10-28T02:54:22",
"upload_time_iso_8601": "2023-10-28T02:54:22.616427Z",
"url": "https://files.pythonhosted.org/packages/74/90/b454003a6f2cf57c7dfa6ac7d9850485bc1069d0af4087a863fbd7a5b993/gmsdb-2.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "6e39d1701ac39ca25ae154f0e177ea0d7523b352f42c4ac2d1d682fca8796bbc",
"md5": "73a1df47cb78670454c41ef66e61d875",
"sha256": "152fb4cc88b6c14d0b8729adfa2c4bd4122423ca991d61773b2007806a41a60f"
},
"downloads": -1,
"filename": "gmsdb-2.1.tar.gz",
"has_sig": false,
"md5_digest": "73a1df47cb78670454c41ef66e61d875",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 7494,
"upload_time": "2023-10-28T02:54:23",
"upload_time_iso_8601": "2023-10-28T02:54:23.846135Z",
"url": "https://files.pythonhosted.org/packages/6e/39/d1701ac39ca25ae154f0e177ea0d7523b352f42c4ac2d1d682fca8796bbc/gmsdb-2.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-28 02:54:23",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "berng",
"github_project": "GMSDB",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "gmsdb"
}