--- a/doc/v2_planning/API_coding_style.txt	Fri Sep 17 12:02:14 2010 -0400
+++ b/doc/v2_planning/API_coding_style.txt	Fri Sep 17 16:12:33 2010 -0400
@@ -26,12 +26,12 @@
 The four main documents describing our Python coding guidelines are:
     * `PEP 8 -- Style Guide for Python Code
       <http://www.python.org/dev/peps/pep-0008>`_
+    * `Google Python Style Guide
+      <http://google-styleguide.googlecode.com/svn/trunk/pyguide.html>`_
     * `PEP 257 -- Docstring Conventions
       <http://www.python.org/dev/peps/pep-0257>`_
     * `Numpy Docstring Standard
       <http://projects.scipy.org/numpy/wiki/CodingStyleGuidelines#docstring-standard>`_
-    * `Google Python Style Guide
-      <http://google-styleguide.googlecode.com/svn/trunk/pyguide.html>`_
 
 
 However, there are a few points mentioned in those documents that we decided
@@ -45,7 +45,8 @@
       .. code-block:: python
 
         # Good.
-        """This is a multi-line docstring.
+        """
+        This is a multi-line docstring.
 
         Which means it has more than one line.
         """
@@ -148,6 +149,7 @@
         if (cond_1 and
             cond_2 and
             cond_3):
+
             ... 
         # Bad.
         if cond_1 and \

--- a/doc/v2_planning/coding_style.txt	Fri Sep 17 12:02:14 2010 -0400
+++ b/doc/v2_planning/coding_style.txt	Fri Sep 17 16:12:33 2010 -0400
@@ -54,7 +54,7 @@
         - You cannot use a **kw argument in your constructor for your own
           selfish purpose.
         - I have no clue whether one could do this with multiple inheritance.
-        - More?
+        - Pb if super class adds an argument that has same name as a child class.
       Question: Should we encourage this in Pylearn?
 
       JB: +0.5

--- a/doc/v2_planning/plugin_RP.py	Fri Sep 17 12:02:14 2010 -0400
+++ b/doc/v2_planning/plugin_RP.py	Fri Sep 17 16:12:33 2010 -0400
@@ -105,9 +105,12 @@
         cPickle.dump(model.parameters(), open('best_params.pkl','wb'))
 
 
-# Create the dependency graph describing what does what 
+# Create the dependency graph describing what does what
+train_data.act( on = sched.begin(), when = once() )
+train_data.act( on = Event('batch'),
+train_data.act( on = train_model.done(), when = always())
 train_model.act(on = train_data.batch(), when = always())
-validate_model.act(on = train_model.done(), when = every(n=10000)) 
+validate_model.act(on = train_model.done(), when = every(n=10000))
 early_stopper.act(on = validate_model.error(), when = always())
 print_error.act( on = train_model.error(), when = always() )
 print_error.act( on = train_data.eod(), when = always() )