=============================== Installing `scikits.learn` =============================== There are different ways to get scikits.learn installed: * Install the version of scikits.learn provided by your :ref:`operating system distribution <install_by_distribution>` . This is the quickest option for those who have operating systems that distribute scikits.learn. * :ref:`Install an official release <install_official_release>`. This is the best approach for users who want a stable version number and aren't concerned about running a slightly older version of scikits.learn. * :ref:`Install the latest development version <install_bleeding_edge>`. This is best for users who want the latest-and-greatest features and aren't afraid of running brand-new code. .. _install_official_release: Installing an official release ============================== Installing from source ---------------------- Installing from source requires you to have installed numpy, setuptools, python development headers and a working C++ compiler. Under debian-like systems you can get all this by executing with root privileges:: sudo apt-get install python-dev python-numpy python-setuptools python-scipy libatlas-dev g++ Easy install ~~~~~~~~~~~~ This is usually the fastest way to install the latest stable release. If you have pip or easy_install, you can install or update with the command:: pip install -U scikits.learn or:: easy_install -U scikits.learn for easy_install. Note that you might need root privileges to run these commands. From source package ~~~~~~~~~~~~~~~~~~~ Download the package from http://sourceforge.net/projects/scikit-learn/files , unpack the sources and cd into archive. This packages uses distutils, which is the default way of installing python modules. The install command is:: python setup.py install Windows installer ----------------- You can download a windows installer from `downloads <https://sourceforge.net/projects/scikit-learn/files/>`_ in the project's web page. Note that must also have installed the packages numpy and setuptools. This package is also expected to work with python(x,y) as of 2.6.5.5. .. _install_by_distribution: Third party distributions of scikits.learn ========================================== Some third-party distributions are now providing versions of scikits.learn integrated with their package-management systems. These can make installation and upgrading much easier for users since the integration includes the ability to automatically install dependencies (numpy, scipy) that scikits.learn requires. The following is a list of linux distributions that provide their own version of scikits.learn: Debian and derivatives (Ubuntu) ------------------------------- The Debian package is named python-scikits-learn and can be install using the following commands with root privileges:: apt-get install python-scikits-learn Enthought python distribution ----------------------------- The `Enthought Python Distribution <http://www.enthought.com/products/epd.php>`_ already ships the latest version. Macports -------- The macport's package is named py26-scikits-learn and can be installed by typing the following command:: sudo port install py26-scikits-learn .. _install_bleeding_edge: Bleeding Edge ============= See section :ref:`git_repo` on how to get the development version. .. _testing: Testing ======= Testing requires having the `nose <http://somethingaboutorange.com/mrl/projects/nose/>`_ library. After installation, the package can be tested by executing from outside the source directory:: python -c "import scikits.learn as skl; skl.test()" This should give you a lot of output (and some warnings) but eventually should finish with the a text similar to:: Ran 601 tests in 27.920s OK (SKIP=2) otherwise please consider submitting a bug in the :ref:`bug_tracker` or to the :ref:`mailing_lists`. scikits.learn can also be tested without having the package installed. For this you must compile the sources inplace from the source directory:: python setup.py build_ext --inplace Test can now be run using nosetest:: nosetests scikits/learn/ If you are running the deveopment version, this is automated in the commands `make in` and `make test`. .. warning:: Because nosetest does not play well with multiprocessing on windows, this last approach is not recommended on such system.