{ "info": { "author": "Hirotaka Niitsuma", "author_email": "hirotaka.niitsuma@gmail.com", "bugtrack_url": null, "classifiers": [ "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Mathematics" ], "description": "## Delayed Sparse Matrix\n\nEfficient sparse matrix implementation for various \"Principal Component Analysis\".\nAnd demo usages of the efficient implementation for \n\n* Correspondence Analysis(CA) \n* Principal Component Analysis (PCA)\n* Canonical Correlation Analysis (CCA)\n\n\nTo compare with existing methods, you can execute demo.sh.\n```sh\n>>> bash demo.sh\n```\n\nThis library is effective when the input matrix size ls large.\nBut, in order to demonstrations, the demo programs use only a small matrix.\nYou can test more large matrix by setting SIZE variable in demo-*.sh\n\n\nWhen the input matrix size is large, \nthe program of this library will finish within in few minutes, \nbut the existing methods take hours.\n\n\n\nYou can find more general description about CA and PCA in\nhttps://github.com/MaxHalford/prince\n\n\n## Installation\n\n**Via PyPI**\n\n```sh\n>>> pip install delayedsparse\n```\n\n**Via GitHub for the latest development version**\n\n```sh\n>>> pip install git+https://github.com/niitsuma/delayedsparse \n```\n\n\n## Requirements\n\n```sh\n>>> pip3 install sklearn\n```\n\nIn order to execute demo.sh, you need install /usr/bin/time and orange library\n\n```sh\n>>> apt-get install time\n>>> pip3 install orange\n```\n\n\n## License\n\n@2018 Hirotaka Niirtsuma.\n\n\nYou can use these codes olny for self evaluation.\nCannot use these codes for commercial and academical use.\n\n* pantent pending\n * https://patentscope2.wipo.int/search/ja/detail.jsf?docId=JP225380312\n * Japan patent office:patent number 2017-007741\n\n\n\n## Author\nHirotaka Niitsuma.\n\n\n@2018 Hirotaka Niirtsuma.", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/niitsuma/delayedsparse", "keywords": "", "license": "You can use these codes olny for self evaluation. 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