# HG changeset patch
# User Joseph Turian <turian@iro.umontreal.ca>
# Date 1243373983 14400
# Node ID e915f5c9bb21775d5110f059da727075f3b9e310
# Parent  df3aef87d8d2adbbe1dc28e66c7ba92f0d8e9012
Added more descriptive comments to crossentropy and KL divergence.

diff -r df3aef87d8d2 -r e915f5c9bb21 pylearn/algorithms/cost.py
--- a/pylearn/algorithms/cost.py	Mon May 25 23:13:56 2009 -0400
+++ b/pylearn/algorithms/cost.py	Tue May 26 17:39:43 2009 -0400
@@ -17,6 +17,8 @@
 
 def cross_entropy(target, output, mean_axis=0, sum_axis=1):
     """
+    This is the cross-entropy over a binomial event, in which each dimension
+    is an independent binomial trial.
     @todo: This is essentially duplicated as nnet_ops.binary_crossentropy
     @warning: OUTPUT and TARGET are reversed in nnet_ops.binary_crossentropy
     """
@@ -25,6 +27,8 @@
 
 def KL_divergence(target, output):
     """
+    This is a KL divergence over a binomial event, in which each dimension
+    is an independent binomial trial.
     @note: We do not compute the mean, because if target and output have
     different shapes then the result will be garbled.
     """