{ "info": { "author": "Christof Angermueller", "author_email": "cangermueller@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", "Operating System :: MacOS", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Bio-Informatics", "Topic :: Scientific/Engineering :: Image Recognition", "Topic :: Scientific/Engineering :: Information Analysis" ], "description": "=======================================================\nvbmfa: Variational Bayesian Mixture of Factor Analysers\n=======================================================\n\nVariational Bayesian Mixture of Factor Analysers for dimensionality reduction\nand clustering.\n\nFactor analysis (FA) is a method for dimensionality reduction, similar to\nprinciple component analysis (PCA), singular value decomposition (SVD), or\nindependent component analysis (ICA). Applications include visualization, image\ncompression, or feature learning. A mixture of factor analysers consists of\nseveral factor analysers, and allows both dimensionality reduction and\nclustering. Variational Bayesian learning of model parameters prevents\noverfitting compared with maximum likelihood methods such as expectation\nmaximization (EM), and allows to learn the dimensionality of the lower\ndimensional subspace by automatic relevance determination (ARD). A detailed\nexplanation of the model can be found `here\n`_.\n\nNote\n----\nThe current version is still under development, and needs to be optimized for\nlarge-scale data sets. I am open for any suggestions, and happy about every\nbug report!\n\nInstallation\n------------\nThe easiest way to install vbmfa is to use PyPI::\n\n pip install vbmfa\n\nAlternatively, you can checkout the repository from Github::\n\n git clone https://github.com/cangermueller/vbmfa.git\n\nExamples\n--------\nThe folder ``examples/`` contains example ipython notebooks:\n\n- `VbFa `_, a single Variational Bayesian Factor Analyser\n- `VbMfa `_, a mixture of Variational Bayesian Factors\n Analysers\n\nReferences\n----------\n.. [1] `Ghahramani, Zoubin, Matthew J Beal, Gatsby Computational, and Neuroscience\n Unit. \u201cVariational Inference for Bayesian Mixtures of Factor Analysers.\u201d NIPS,\n 1999. `_\n.. [2] `Bishop, Christopher M. \u201cVariational Principal Components,\u201d 1999.\n `_\n.. [3] `Beal, Matthew J. \u201cVariational Algorithms For Approximate Bayesian\n Inference,\u201d 2003. `_\n\nContact\n-------\nChristof Angermueller\n\nhttps://github.com/cangermueller", "description_content_type": null, "docs_url": "https://pythonhosted.org/vbmfa/", "download_url": "UNKNOWN", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/cangermueller/vbmfa", "keywords": "Factor Analysis,PCA,Probabilistic PCA,Dimensionality Reduction,Clustering", "license": "GNU GPLv3+", "maintainer": null, "maintainer_email": null, "name": "vbmfa", "package_url": "https://pypi.org/project/vbmfa/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/vbmfa/", "project_urls": { "Download": "UNKNOWN", "Homepage": "https://github.com/cangermueller/vbmfa" }, "release_url": "https://pypi.org/project/vbmfa/0.0.1/", "requires_dist": null, "requires_python": null, "summary": "Variational Bayesian Mixture of Factor Analysers", "version": "0.0.1" }, "last_serial": 1248852, "releases": { "0.0.1": [ { "comment_text": "built for Darwin-13.4.0", "digests": { "md5": "b27cdbd68b7131787bc347df83a863b0", "sha256": "ecedd9996f8119a8b021ad9121bb6a1a7fd6bdca784a2816fa89262bdb7a3504" }, "downloads": -1, "filename": "vbmfa-0.0.1.macosx-10.9-x86_64.tar.gz", "has_sig": false, "md5_digest": "b27cdbd68b7131787bc347df83a863b0", "packagetype": "bdist_dumb", "python_version": "any", "requires_python": null, "size": 17916, "upload_time": "2014-10-05T21:34:08", "url": "https://files.pythonhosted.org/packages/84/b0/c551bd5857cc390b5744a492d18e79929a9da2cf0c4ee3f9512fa591b840/vbmfa-0.0.1.macosx-10.9-x86_64.tar.gz" }, { "comment_text": "", "digests": { "md5": "694bc6d6eab5577fadd210723655c87a", "sha256": "64a931a01f1b671d18a49d5d6f78d6080c2c391b525579f84af100faa8c06812" }, "downloads": -1, "filename": "vbmfa-0.0.1.tar.gz", "has_sig": false, "md5_digest": "694bc6d6eab5577fadd210723655c87a", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 33226, "upload_time": "2014-10-05T21:34:04", "url": "https://files.pythonhosted.org/packages/25/f0/3f89c0523c9d9c0efdcc2ee11fd6086ae9c2095ddfda27ba7950e971aa62/vbmfa-0.0.1.tar.gz" } ] }, "urls": [ { "comment_text": "built for Darwin-13.4.0", "digests": { "md5": "b27cdbd68b7131787bc347df83a863b0", "sha256": "ecedd9996f8119a8b021ad9121bb6a1a7fd6bdca784a2816fa89262bdb7a3504" }, "downloads": -1, "filename": "vbmfa-0.0.1.macosx-10.9-x86_64.tar.gz", "has_sig": false, "md5_digest": "b27cdbd68b7131787bc347df83a863b0", "packagetype": "bdist_dumb", "python_version": "any", "requires_python": null, "size": 17916, "upload_time": "2014-10-05T21:34:08", "url": "https://files.pythonhosted.org/packages/84/b0/c551bd5857cc390b5744a492d18e79929a9da2cf0c4ee3f9512fa591b840/vbmfa-0.0.1.macosx-10.9-x86_64.tar.gz" }, { "comment_text": "", "digests": { "md5": "694bc6d6eab5577fadd210723655c87a", "sha256": "64a931a01f1b671d18a49d5d6f78d6080c2c391b525579f84af100faa8c06812" }, "downloads": -1, "filename": "vbmfa-0.0.1.tar.gz", "has_sig": false, "md5_digest": "694bc6d6eab5577fadd210723655c87a", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 33226, "upload_time": "2014-10-05T21:34:04", "url": "https://files.pythonhosted.org/packages/25/f0/3f89c0523c9d9c0efdcc2ee11fd6086ae9c2095ddfda27ba7950e971aa62/vbmfa-0.0.1.tar.gz" } ] }