Fix indentation

Author: zaxtax

doc/CONTRIBUTING.md renamed to CONTRIBUTING.md

@@ -9,29 +9,29 @@ The preferred way to contribute to scikit-learn is to fork the
 [main repository](http://github.com/scikit-learn/scikit-learn/) on
 GitHub:
 
-    1. Fork the [project repository](http://github.com/scikit-learn/scikit-learn):
-       click on the 'Fork' button near the top of the page. This creates
-       a copy of the code under your account on the GitHub server.
+1. Fork the [project repository](http://github.com/scikit-learn/scikit-learn):
+   click on the 'Fork' button near the top of the page. This creates
+   a copy of the code under your account on the GitHub server.
 
-    2. Clone this copy to your local disk:
+2. Clone this copy to your local disk:
 
-           $ git clone [email protected]:YourLogin/scikit-learn.git
+	$ git clone [email protected]:YourLogin/scikit-learn.git
 
-    3. Create a branch to hold your changes:
+3. Create a branch to hold your changes:
 
-           $ git checkout -b my-feature
+	$ git checkout -b my-feature
 
-       and start making changes. Never work in the ``master`` branch!
+   and start making changes. Never work in the ``master`` branch!
 
-    4. Work on this copy, on your computer, using Git to do the version
-       control. When you're done editing, do:
+4. Work on this copy, on your computer, using Git to do the version
+   control. When you're done editing, do:
 
-           $ git add modified_files
-           $ git commit
+	$ git add modified_files
+	$ git commit
 
-       to record your changes in Git, then push them to GitHub with:
+   to record your changes in Git, then push them to GitHub with:
 
-           $ git push -u origin my-feature
+	$ git push -u origin my-feature
 
 Finally, go to the web page of the your fork of the scikit-learn repo,
 and click 'Pull request' to send your changes to the maintainers for
@@ -44,53 +44,52 @@ on the web.)
 It is recommended to check that your contribution complies with the
 following rules before submitting a pull request:
 
-    -  All public methods should have informative docstrings with sample
-       usage presented as doctests when appropriate.
+-  All public methods should have informative docstrings with sample
+   usage presented as doctests when appropriate.
 
-    -  All other tests pass when everything is rebuilt from scratch. On
-       Unix-like systems, check with (from the toplevel source folder):
+-  All other tests pass when everything is rebuilt from scratch. On
+   Unix-like systems, check with (from the toplevel source folder):
 
+	$ make
 
-           $ make
+-  When adding additional functionality, provide at least one
+   example script in the ``examples/`` folder. Have a look at other
+   examples for reference. Examples should demonstrate why the new
+   functionality is useful in practice and, if possible, compare it
+   to other methods available in scikit-learn.
 
-    -  When adding additional functionality, provide at least one
-       example script in the ``examples/`` folder. Have a look at other
-       examples for reference. Examples should demonstrate why the new
-       functionality is useful in practice and, if possible, compare it
-       to other methods available in scikit-learn.
+-  At least one paragraph of narrative documentation with links to
+   references in the literature (with PDF links when possible) and
+   the example.
 
-    -  At least one paragraph of narrative documentation with links to
-       references in the literature (with PDF links when possible) and
-       the example.
-
-       The documentation should also include expected time and space
-       complexity of the algorithm and scalability, e.g. "this algorithm
-       can scale to a large number of samples > 100000, but does not
-       scale in dimensionality: n_features is expected to be lower than
-       100".
+The documentation should also include expected time and space
+complexity of the algorithm and scalability, e.g. "this algorithm
+can scale to a large number of samples > 100000, but does not
+scale in dimensionality: n_features is expected to be lower than
+100".
 
 You can also check for common programming errors with the following
 tools:
 
-    -  Code with a good unittest coverage (at least 80%), check with:
+-  Code with a good unittest coverage (at least 80%), check with:
 
-           $ pip install nose coverage
-           $ nosetests --with-coverage path/to/tests_for_package
+	$ pip install nose coverage
+	$ nosetests --with-coverage path/to/tests_for_package
 
-    -  No pyflakes warnings, check with:
+-  No pyflakes warnings, check with:
 
            $ pip install pyflakes
            $ pyflakes path/to/module.py
 
-    -  No PEP8 warnings, check with:
+-  No PEP8 warnings, check with:
 
-           $ pip install pep8
-           $ pep8 path/to/module.py
+	$ pip install pep8
+	$ pep8 path/to/module.py
 
-    -  AutoPEP8 can help you fix some of the easy redundant errors:
+-  AutoPEP8 can help you fix some of the easy redundant errors:
 
-           $ pip install autopep8
-           $ autopep8 path/to/pep8.py
+	$ pip install autopep8
+	$ autopep8 path/to/pep8.py
 
 Bonus points for contributions that include a performance analysis with
 a benchmark script and profiling output (please report on the mailing