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Merge pull request #6419 from nelson-liu/fix_linkcheck
[MRG+1] DOC: Fix broken links
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CONTRIBUTING.md

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-----------------
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The preferred way to contribute to scikit-learn is to fork the
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[main repository](http://github.com/scikit-learn/scikit-learn/) on
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[main repository](https://github.com/scikit-learn/scikit-learn) on
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GitHub:
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1. Fork the [project repository](http://github.com/scikit-learn/scikit-learn):
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1. Fork the [project repository](https://github.com/scikit-learn/scikit-learn):
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click on the 'Fork' button near the top of the page. This creates
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a copy of the code under your account on the GitHub server.
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doc/README

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The configuration of this server is managed at:
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http://github.com/scikit-learn/sklearn-docbuilder
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https://github.com/scikit-learn/sklearn-docbuilder
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doc/about.rst

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Funding
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-------
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`INRIA <http://www.inria.fr>`_ actively supports this project. It has
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`INRIA <https://www.inria.fr>`_ actively supports this project. It has
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provided funding for Fabian Pedregosa (2010-2012), Jaques Grobler
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(2012-2013) and Olivier Grisel (2013-2015) to work on this project
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full-time. It also hosts coding sprints and other events.
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:width: 200pt
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:align: center
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The following students were sponsored by `Google <http://code.google.com/opensource/>`_
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The following students were sponsored by `Google <https://developers.google.com/open-source/>`_
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to work on scikit-learn through the
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`Google Summer of Code <http://en.wikipedia.org/wiki/Google_Summer_of_Code>`_
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`Google Summer of Code <https://en.wikipedia.org/wiki/Google_Summer_of_Code>`_
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program.
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- 2007 - David Cournapeau
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It also provided funding for sprints and events around scikit-learn. If
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you would like to participate in the next Google Summer of code
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program, please see `this page
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<http://github.com/scikit-learn/scikit-learn/wiki/SummerOfCode>`_
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<https://github.com/scikit-learn/scikit-learn/wiki/SummerOfCode>`_
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The `NeuroDebian <http://neuro.debian.net>`_ project providing `Debian
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<http://www.debian.org>`_ packaging and contributions is supported by
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`Dr. James V. Haxby <http://haxbylab.dartmouth.edu/>`_ (`Dartmouth
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College <http://www.dartmouth.edu/~psych/>`_).
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College <http://pbs.dartmouth.edu>`_).
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The `PSF <http://www.python.org/psf/>`_ helped find and manage funding for our
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The `PSF <https://www.python.org/psf/>`_ helped find and manage funding for our
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2011 Granada sprint. More information can be found `here
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<https://github.com/scikit-learn/scikit-learn/wiki/Past-sprints#granada-19th-21th-dec-2011>`__
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~~~~~~~~~~~~~~~~~~~~~~~
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If you are interested in donating to the project or to one of our code-sprints, you can use
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the *Paypal* button below or the `NumFOCUS Donations Page <http://numfocus.org/donatejoin/>`_ (if you use the latter, please indicate that you are donating for the scikit-learn project).
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the *Paypal* button below or the `NumFOCUS Donations Page <http://www.numfocus.org/support-numfocus.html>`_ (if you use the latter, please indicate that you are donating for the scikit-learn project).
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All donations will be handled by `NumFOCUS
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<http://numfocus.org/donations>`_, a non-profit-organization which is
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<http://www.numfocus.org>`_, a non-profit-organization which is
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managed by a board of `Scipy community members
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<http://numfocus.org/board>`_. NumFOCUS's mission is to foster
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<http://www.numfocus.org/board>`_. NumFOCUS's mission is to foster
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scientific computing software, in particular in Python. As a fiscal home
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of scikit-learn, it ensures that money is available when needed to keep
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the project funded and available while in compliance with tax regulations.

doc/datasets/twenty_newsgroups.rst

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ready-to-use tfidf features instead of file names.
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.. _`20 newsgroups website`: http://people.csail.mit.edu/jrennie/20Newsgroups/
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.. _`TF-IDF`: http://en.wikipedia.org/wiki/Tf-idf
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.. _`TF-IDF`: https://en.wikipedia.org/wiki/Tf-idf
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Filtering text for more realistic training

doc/developers/advanced_installation.rst

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~~~~~~~~~~~~~~~~~~~
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download the source package from
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`pypi <http://pypi.python.org/pypi/scikit-learn/>`_,
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`pypi <https://pypi.python.org/pypi/scikit-learn>`_,
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, unpack the sources and cd into the source directory.
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this packages uses distutils, which is the default way of installing
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windows
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first, you need to install `numpy <http://numpy.scipy.org/>`_ and `scipy
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first, you need to install `numpy <http://www.numpy.org/>`_ and `scipy
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<http://www.scipy.org/>`_ from their own official installers.
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wheel packages (.whl files) for scikit-learn from `pypi
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<https://pypi.python.org/pypi/scikit-learn/>`_ can be installed with the `pip
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<http://pip.readthedocs.org/en/latest/installing.html>`_ utility.
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<https://pip.readthedocs.org/en/stable/installing/>`_ utility.
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open a console and type the following to install or upgrade scikit-learn to the
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latest stable release::
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for 32-bit python it is possible use the standalone installers for
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`microsoft visual c++ express 2008 <http://go.microsoft.com/?linkid=7729279>`_
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for python 2 or
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`microsoft visual c++ express 2010 <http://go.microsoft.com/?linkid=9709949>`_
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or python 3.
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for python 2 or microsoft visual c++ express 2010 for python 3.
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once installed you should be able to build scikit-learn without any
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particular configuration by running the following command in the scikit-learn

doc/developers/contributing.rst

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This project is a community effort, and everyone is welcome to
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contribute.
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The project is hosted on http://github.com/scikit-learn/scikit-learn
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The project is hosted on https://github.com/scikit-learn/scikit-learn
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Scikit-learn is somewhat :ref:`selective <selectiveness>` when it comes to
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adding new algorithms, and the best way to contribute and to help the project
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In case you experience issues using this package, do not hesitate to submit a
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ticket to the
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`Bug Tracker <http://github.com/scikit-learn/scikit-learn/issues>`_. You are
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`Bug Tracker <https://github.com/scikit-learn/scikit-learn/issues>`_. You are
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also welcome to post feature requests or pull requests.
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==========================
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We use `Git <http://git-scm.com/>`_ for version control and
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`GitHub <https://github.com/>`_ for hosting our main repository.
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You can check out the latest sources with the command::
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The preferred way to contribute to scikit-learn is to fork the `main
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repository <http://github.com/scikit-learn/scikit-learn/>`__ on GitHub,
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repository <https://github.com/scikit-learn/scikit-learn/>`__ on GitHub,
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then submit a "pull request" (PR):
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1. `Create an account <https://github.com/signup/free>`_ on
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1. `Create an account <https://github.com/join>`_ on
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GitHub if you do not already have one.
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2. Fork the `project repository
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<http://github.com/scikit-learn/scikit-learn>`__: click on the 'Fork'
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<https://github.com/scikit-learn/scikit-learn>`__: click on the 'Fork'
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button near the top of the page. This creates a copy of the code under your
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account on the GitHub server.
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For building the documentation, you will need `sphinx
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<http://sphinx.pocoo.org/>`_,
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`matplotlib <http://matplotlib.sourceforge.net/>`_ and
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<http://sphinx-doc.org/>`_,
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`matplotlib <http://matplotlib.org>`_ and
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**When you are writing documentation**, it is important to keep a good
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Testing and improving test coverage
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High-quality `unit testing <http://en.wikipedia.org/wiki/Unit_testing>`_
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High-quality `unit testing <https://en.wikipedia.org/wiki/Unit_testing>`_
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is a corner-stone of the scikit-learn development process. For this
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package. The tests are functions appropriately named, located in `tests`
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.. note:: **Workflow to improve test coverage**
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<http://pypi.python.org/pypi/coverage>`_ package in addition to nose.
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<https://pypi.python.org/pypi/coverage>`_ package in addition to nose.
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numbers that are not tested.
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Uniformly formatted code makes it easier to share code ownership. The
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scikit-learn project tries to closely follow the official Python guidelines
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detailed in `PEP8 <http://www.python.org/dev/peps/pep-0008/>`_ that
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detailed in `PEP8 <https://www.python.org/dev/peps/pep-0008>`_ that
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* **Please don't use** ``import *`` **in any case**. It is considered harmful
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<http://docs.python.org/howto/doanddont.html#from-module-import>`_.
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<https://docs.python.org/2/howto/doanddont.html#from-module-import>`_.
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doc/developers/performance.rst

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Numpy array methods**. The goal is to avoid the CPU wasting time in the
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model. It's generally a good idea to consider NumPy and SciPy performance tips:
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http://scipy.github.io/old-wiki/pages/PerformanceTips
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You can analyze in detail the memory usage of any Python code with the help of
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`memory_profiler <https://pypi.python.org/pypi/memory_profiler>`_. First,
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- https://www.youtube.com/watch?v=gMvkiQ-gOW8
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- https://pypi.python.org/pypi/yep
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- http://fa.bianp.net/blog/2011/a-profiler-for-python-extensions
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does not seem to work correctly at the time of writing. This
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<https://code.google.com/p/google-perftools/issues/detail?id=326>`_.
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<https://github.com/gperftools/gperftools>`_.
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.. _warm-restarts:

doc/developers/utilities.rst

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by directly calling the BLAS
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``nrm2`` function. This is more stable than ``scipy.linalg.norm``. See
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<http://fseoane.net/blog/2011/computing-the-vector-norm/>`_ for a discussion.
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<http://fa.bianp.net/blog/2011/computing-the-vector-norm>`_ for a discussion.
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- :func:`extmath.fast_logdet`: efficiently compute the log of the determinant
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doc/install.rst

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`Canopy
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<http://www.enthought.com/products/canopy>`_ and `Anaconda
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<https://store.continuum.io/cshop/anaconda/>`_ both ship a recent
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<https://www.enthought.com/products/canopy>`_ and `Anaconda
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<https://www.continuum.io/downloads>`_ both ship a recent
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Python(x,y) for Windows
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The `Python(x,y) <https://code.google.com/p/pythonxy/>`_ project distributes
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The `Python(x,y) <https://python-xy.github.io>`_ project distributes
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For installation instructions for particular operating systems or for compiling

doc/modules/clustering.rst

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* `Comparing Partitions
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<http://www.springerlink.com/content/x64124718341j1j0/>`_
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<http://link.springer.com/article/10.1007%2FBF01908075>`_
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L. Hubert and P. Arabie, Journal of Classification 1985
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* `Wikipedia entry for the adjusted Rand index
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* Vinh, Epps, and Bailey, (2009). "Information theoretic measures
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for clusterings comparison". Proceedings of the 26th Annual International
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Conference on Machine Learning - ICML '09.
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`doi:10.1145/1553374.1553511 <http://dx.doi.org/10.1145/1553374.1553511>`_.
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`doi:10.1145/1553374.1553511 <https://dl.acm.org/citation.cfm?doid=1553374.1553511>`_.
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ISBN 9781605585161.
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* Vinh, Epps, and Bailey, (2010). Information Theoretic Measures for

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