Formatting of README

README

@@ -7,20 +7,22 @@ Getting started
 ---------------
 
 Running the tests:
- $ nosetests
+
+    $ nosetests
 
 Installing:
- $ python setup.py install
+
+    $ python setup.py install
 
 Example use
 -----------
 
- $ wget http://downloads.dbpedia.org/3.7/en/skos_categories_en.nt.bz2
- $ bunzip2 skos_categories_en.nt.bz2
- $ python
- >>> from rdfsim.space import Space
- >>> space = Space('skos_categories_en.nt')
- >>> space.similarity_uri(category1, category2)
+    $ wget http://downloads.dbpedia.org/3.7/en/skos_categories_en.nt.bz2
+    $ bunzip2 skos_categories_en.nt.bz2
+    $ python
+    >>> from rdfsim.space import Space
+    >>> space = Space('skos_categories_en.nt')
+    >>> space.similarity_uri(category1, category2)
 
 Constructing a vector space for the entire DBpedia SKOS category
 hierarchy (3M triples) takes a couple of minutes on a commodity laptop, 
@@ -36,16 +38,16 @@ parents(t, k) at a level k. We construct a vector for each t in a space
 where each dimension corresponds to a topic d in the hierarchy. The value
 of t on dimension d is defined as follows:
 
-t_d = \sum_{k = 0}^{max_depth} \sum_{d \in parents(t, k)} decay^k
+    t_d = \sum_{k = 0}^{max_depth} \sum_{d \in parents(t, k)} decay^k
 
 where max_depth and decay are two parameters, which can be used to influence
 how much importance we attach to ancestors that are high in the
 category hierarchy.
 
 They can be specified as follows:
 
->>> Space.max_depth = 8
->>> Space.decay = 0.9
+    >>> Space.max_depth = 8
+    >>> Space.decay = 0.9
 
 Licensing terms and authorship
 ------------------------------