# HG changeset patch # User pascanur # Date 1284754353 14400 # Node ID a0f178bc905277aad16a5ba41352146a56ad075c # Parent 3c2d7c5f0cf7d5ad9680386110031294ea13f452 changes during the meeting diff -r 3c2d7c5f0cf7 -r a0f178bc9052 doc/v2_planning/API_coding_style.txt --- 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 `_ + * `Google Python Style Guide + `_ * `PEP 257 -- Docstring Conventions `_ * `Numpy Docstring Standard `_ - * `Google Python Style Guide - `_ 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 \ diff -r 3c2d7c5f0cf7 -r a0f178bc9052 doc/v2_planning/coding_style.txt --- 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 diff -r 3c2d7c5f0cf7 -r a0f178bc9052 doc/v2_planning/plugin_RP.py --- 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() )