{ "info": { "author": "F\u00e1bio Mac\u00eado Mendes", "author_email": "fabiomacedomendes@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: GNU General Public License (GPL)", "Operating System :: POSIX", "Programming Language :: Python", "Topic :: Software Development :: Libraries" ], "description": "Library for detecting plagiarism in source code. Userful for online judges, \nteachers, developers and maybe lawyers.\n\nPlagiarism uses the method described [here](http://...). The basic idea is to \nclassify each submitted file according to different metrics and perform a \nseries of k-means based clusterizations to determine which objects are most \nsimilar to each other. This approach has a N log N cost and scales fairly well \nto big samples.\n\nThe algorithm can be applied to natural text, source code and can even be \nadapted to run on arbitrary data structures (such as the parse tree of a \ncomputer program, ASM output, even binary executables). It requires some tuning\nfor each application and accuracy may vary widely depending on application.\nYou should expect better results grading Python and C source code. Performance\non other programming languages or even in other domains may vary. \n", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "plagiarism", "package_url": "https://pypi.org/project/plagiarism/", "platform": "any", "project_url": "https://pypi.org/project/plagiarism/", "project_urls": null, "release_url": "https://pypi.org/project/plagiarism/0.1.0/", "requires_dist": null, "requires_python": "", "summary": "A short description for your project.", "version": "0.1.0" }, "last_serial": 2503767, "releases": { "0.1.0": [ { "comment_text": "", "digests": { "md5": "b94411c1ebbac96bc9fd9ac52f1f9db5", "sha256": "ef158ebc755f323d4d371ddc230ab2e346462e2b96a7f6cfa4181e215450c3a4" }, "downloads": -1, "filename": "plagiarism-0.1.0.tar.gz", "has_sig": false, "md5_digest": "b94411c1ebbac96bc9fd9ac52f1f9db5", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 21060, "upload_time": "2016-12-06T23:39:12", "url": "https://files.pythonhosted.org/packages/9e/99/b1db825ef1f24fa80e0535528f6b1f7eda1b11e61000c50260660a58f294/plagiarism-0.1.0.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "b94411c1ebbac96bc9fd9ac52f1f9db5", "sha256": "ef158ebc755f323d4d371ddc230ab2e346462e2b96a7f6cfa4181e215450c3a4" }, "downloads": -1, "filename": "plagiarism-0.1.0.tar.gz", "has_sig": false, "md5_digest": "b94411c1ebbac96bc9fd9ac52f1f9db5", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 21060, "upload_time": "2016-12-06T23:39:12", "url": "https://files.pythonhosted.org/packages/9e/99/b1db825ef1f24fa80e0535528f6b1f7eda1b11e61000c50260660a58f294/plagiarism-0.1.0.tar.gz" } ] }