/usr/lib/python2.7/dist-packages/scikit_learn-0.17.egg-info is in python-sklearn 0.17.0-4.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | Metadata-Version: 1.1
Name: scikit-learn
Version: 0.17
Summary: A set of python modules for machine learning and data mining
Home-page: http://scikit-learn.org
Author: Andreas Mueller
Author-email: amueller@ais.uni-bonn.de
License: new BSD
Download-URL: http://sourceforge.net/projects/scikit-learn/files/
Description: .. -*- mode: rst -*-
|Travis|_ |AppVeyor|_ |Coveralls|_
.. |Travis| image:: https://api.travis-ci.org/scikit-learn/scikit-learn.png?branch=master
.. _Travis: https://travis-ci.org/scikit-learn/scikit-learn
.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/github/scikit-learn/scikit-learn?branch=master&svg=true
.. _AppVeyor: https://ci.appveyor.com/project/sklearn-ci/scikit-learn/history
.. |Coveralls| image:: https://coveralls.io/repos/scikit-learn/scikit-learn/badge.svg?branch=master
.. _Coveralls: https://coveralls.io/r/scikit-learn/scikit-learn
scikit-learn
============
scikit-learn is a Python module for machine learning built on top of
SciPy and distributed under the 3-Clause BSD license.
The project was started in 2007 by David Cournapeau as a Google Summer
of Code project, and since then many volunteers have contributed. See
the AUTHORS.rst file for a complete list of contributors.
It is currently maintained by a team of volunteers.
**Note** `scikit-learn` was previously referred to as `scikits.learn`.
Important links
===============
- Official source code repo: https://github.com/scikit-learn/scikit-learn
- HTML documentation (stable release): http://scikit-learn.org
- HTML documentation (development version): http://scikit-learn.org/dev/
- Download releases: http://sourceforge.net/projects/scikit-learn/files/
- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
- Mailing list: https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
- IRC channel: ``#scikit-learn`` at ``irc.freenode.net``
Dependencies
============
scikit-learn is tested to work under Python 2.6, Python 2.7, and Python 3.4.
(using the same codebase thanks to an embedded copy of
`six <http://pythonhosted.org/six/>`_). It should also work with Python 3.3.
The required dependencies to build the software are NumPy >= 1.6.1,
SciPy >= 0.9 and a working C/C++ compiler.
For running the examples Matplotlib >= 1.1.1 is required and for running the
tests you need nose >= 1.1.2.
This configuration matches the Ubuntu Precise 12.04 LTS release from April
2012.
scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra
Subprograms library. scikit-learn comes with a reference implementation, but
the system CBLAS will be detected by the build system and used if present.
CBLAS exists in many implementations; see `Linear algebra libraries
<http://scikit-learn.org/stable/modules/computational_performance.html#linear-algebra-libraries>`_
for known issues.
Install
=======
This package uses distutils, which is the default way of installing
python modules. To install in your home directory, use::
python setup.py install --user
To install for all users on Unix/Linux::
python setup.py build
sudo python setup.py install
For more detailed installation instructions,
see the web page http://scikit-learn.org/stable/install.html
Development
===========
Code
----
GIT
~~~
You can check the latest sources with the command::
git clone https://github.com/scikit-learn/scikit-learn.git
or if you have write privileges::
git clone git@github.com:scikit-learn/scikit-learn.git
Contributing
~~~~~~~~~~~~
Quick tutorial on how to go about setting up your environment to
contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md
Before opening a Pull Request, have a look at the
full Contributing page to make sure your code complies
with our guidelines: http://scikit-learn.org/stable/developers/index.html
Testing
-------
After installation, you can launch the test suite from outside the
source directory (you will need to have the ``nose`` package installed)::
$ nosetests -v sklearn
Under Windows, it is recommended to use the following command (adjust the path
to the ``python.exe`` program) as using the ``nosetests.exe`` program can badly
interact with tests that use ``multiprocessing``::
C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn
See the web page http://scikit-learn.org/stable/install.html#testing
for more information.
Random number generation can be controlled during testing by setting
the ``SKLEARN_SEED`` environment variable.
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
|