Minor changes suggested by Fotis Jannidis

requirements.txt

@@ -11,3 +11,4 @@ scipy>=0.13.3
 sphinxcontrib-bibtex>=0.3.1
 sphinxcontrib-tikz>=0.4.1
 statsmodels>=0.6.0
+sphinx-rtd-theme>=0.1.6

source/conf.py

@@ -41,7 +41,7 @@
     'IPython.sphinxext.ipython_console_highlighting',
     'matplotlib.sphinxext.only_directives',
     'sphinxcontrib.tikz',
-    'sphinxcontrib.bibtex'
+    'sphinxcontrib.bibtex',
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -112,9 +112,8 @@
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
-# html_theme = 'sphinxdoc'
+html_theme = "nature"
 
-html_theme = 'sphinxdoc'
 #html_theme_path = ['./themes/']
 
 # Theme options are theme-specific and customize the look and feel of a theme

source/topic_model_mallet.rst

@@ -490,8 +490,4 @@ to be associated more strongly with Austen's novels than with Brontë's.
 
 .. FOOTNOTES
 
-.. [#fnmapreduce] Those familiar with
-    `MapReduce <https://en.wikipedia.org/wiki/MapReduce>`_ may recognize the pattern of
-    splitting a dataset into smaller pieces and then (re)ordering them.
-
-
+.. [#fnmapreduce] Those familiar with `MapReduce <https://en.wikipedia.org/wiki/MapReduce>`_ may recognize the pattern of splitting a dataset into smaller pieces and then (re)ordering them.

source/working_with_text.rst

@@ -54,10 +54,10 @@ parameter. Other important parameters include:
 - ``min_df`` (default ``1``) remove terms from the vocabulary that occur in
   fewer than ``min_df`` documents (in a large corpus this may be set to
   ``15`` or higher to eliminate very rare words)
-- ``vocabulary`` ignore words that do not appear in the provided list of words 
+- ``vocabulary`` ignore words that do not appear in the provided list of words
 - ``strip_accents`` remove accents
 - ``token_pattern`` (default ``u'(?u)\b\w\w+\b'``) regular expression
-  identifying tokens–by default words that consist of a single character 
+  identifying tokens–by default words that consist of a single character
   (e.g., 'a', '2') are ignored, setting ``token_pattern`` to ``'(?u)\b\w+\b'``
   will include these tokens
 - ``tokenizer`` (default unused) use a custom function for tokenizing
@@ -88,7 +88,7 @@ into a NumPy array, as an array supports a greater variety of operations than
 a list.
 
 .. ipython:: python
-    
+
     # for reference, note the current class of `dtm`
     type(dtm)
     dtm = dtm.toarray()  # convert to a regular array
@@ -195,7 +195,7 @@ avail ourselves of the ``scikit-learn`` function ``euclidean_distances``.
         for j in range(n):
             x, y = dtm[i, :], dtm[j, :]
             dist[i, j] = np.sqrt(np.sum((x - y)**2))
-    
+
     from sklearn.metrics.pairwise import euclidean_distances
     dist = euclidean_distances(dtm)
 
@@ -360,7 +360,7 @@ produces a hierarchical clustering of texts via the following procedure:
 #. Start with each text in its own cluster
 
 #. Until only a single cluster remains,
-   
+
    - Find the closest clusters and merge them. The distance between two clusters
      is the change in the sum of squared distances when they are merged.
 
@@ -372,17 +372,19 @@ this algorithm and returns a tree of cluster-merges. The hierarchy of clusters
 can be visualized using ``scipy.cluster.hierarchy.dendrogram``.
 
 .. ipython:: python
-    
+
     from scipy.cluster.hierarchy import ward, dendrogram
 
     linkage_matrix = ward(dist)
-    
+
     # match dendrogram to that returned by R's hclust()
-    dendrogram(linkage_matrix, orientation="right", labels=names);
+    dendrogram(linkage_matrix, orientation="right", labels=names)
 
-    @savefig plot_getting_started_ward_dendrogram.png width=7in
     plt.tight_layout()  # fixes margins
 
+    @savefig plot_getting_started_ward_dendrogram.png width=7in
+    plt.show()
+
 For those familiar with R, the procedure is performed as follows:
 
 .. code-block:: r
@@ -409,7 +411,7 @@ Exercises
     text1 = "Indeed, she had a rather kindly disposition."
     text2 = "The real evils, indeed, of Emma's situation were the power of having rather too much her own way, and a disposition to think a little too well of herself;"
     text3 = "The Jaccard distance is a way of measuring the distance from one set to another set."
-   
+
 3. Using the document-term matrix just created, calculate the Euclidean
    distance, `Jaccard distance <http://en.wikipedia.org/wiki/Jaccard_index>`_,
    and cosine distance between each pair of documents. Make sure to calculate