improved docs for purging addtional matrices

README.rst

@@ -146,16 +146,21 @@ The document-topic distributions should be retrived as: ``doc_topic = model.tran
     top topic: 1 Document: company, comic, series, case, executive
     top topic: 3 Document: son, scene, charge, episode, attack
 
-Save the model for production or for running later:
+Optionally, reduce the model by purging additional matrices:
 
 .. code-block:: python
 
-    >>> from six.moves import cPickle as pickle
-    >>> # Uncomment next step if you want to lighten the model object
+    >>> # Next step will lighten the model object
     >>> # This step will delete some matrices inside the model.
     >>> # you will be able to use model.transform(X) the same way as earlier.
     >>> # you wont be able to use model.fit_transform(X_new)
-    >>> # model.purge_extra_matrices()
+    >>> model.purge_extra_matrices()
+
+Save the model for production or for running later:
+
+.. code-block:: python
+
+    >>> from six.moves import cPickle as pickle
     >>> with open('guidedlda_model.pickle', 'wb') as file_handle:
     >>>     pickle.dump(model, file_handle)
     >>> # load the model for prediction

docs/index.rst

@@ -155,16 +155,21 @@ The document-topic distributions should be retrived as: ``doc_topic = model.tran
     top topic: 1 Document: company, comic, series, case, executive
     top topic: 3 Document: son, scene, charge, episode, attack
 
-Save the model for production or for running later:
+Optionally, reduce the model by purging additional matrices:
 
 .. code-block:: python
 
-    >>> from six.moves import cPickle as pickle
-    >>> # Uncomment next step if you want to lighten the model object
+    >>> # Next step will lighten the model object
     >>> # This step will delete some matrices inside the model.
     >>> # you will be able to use model.transform(X) the same way as earlier.
     >>> # you wont be able to use model.fit_transform(X_new)
-    >>> # model.purge_extra_matrices()
+    >>> model.purge_extra_matrices()
+
+Save the model for production or for running later:
+
+.. code-block:: python
+
+    >>> from six.moves import cPickle as pickle
     >>> with open('guidedlda_model.pickle', 'wb') as file_handle:
     >>>     pickle.dump(model, file_handle)
     >>> # load the model for prediction