# HG changeset patch
# User Joseph Turian <turian@iro.umontreal.ca>
# Date 1229723211 18000
# Node ID 3f9ec536f2c166ba4e4b495cfdbea6ecf8ceae30
# Parent  f8d29730f1462146733c2a0d55fef9515cd18e48
Two minor fixes.

diff -r f8d29730f146 -r 3f9ec536f2c1 pylearn/algorithms/cost.py
--- a/pylearn/algorithms/cost.py	Wed Dec 17 18:16:30 2008 -0500
+++ b/pylearn/algorithms/cost.py	Fri Dec 19 16:46:51 2008 -0500
@@ -10,7 +10,6 @@
 """
 
 import theano.tensor as T
-from xlogx import xlogx
 
 def quadratic(target, output, axis=1):
     return T.mean(T.sqr(target - output), axis=axis)
@@ -28,5 +27,5 @@
     different shapes then the result will be garbled.
     """
     return -(target * T.log(output) + (1 - target) * T.log(1 - output)) \
-            + (xlogx(target) + xlogx(1 - target))
+            + (T.xlogx(target) + T.xlogx(1 - target))
 #    return cross_entropy(target, output, axis) - cross_entropy(target, target, axis)
diff -r f8d29730f146 -r 3f9ec536f2c1 pylearn/algorithms/logistic_regression.py
--- a/pylearn/algorithms/logistic_regression.py	Wed Dec 17 18:16:30 2008 -0500
+++ b/pylearn/algorithms/logistic_regression.py	Fri Dec 19 16:46:51 2008 -0500
@@ -48,7 +48,7 @@
         else:
             # TODO: when above is fixed, remove this hack (need an argmax
             # which is independent of targets)
-            self.argmax_standalone = T.argmax(self.linear_output);
+            self.argmax_standalone = T.argmax(self.linear_output)
             (self._xent, self.softmax, self._max_pr, self.argmax) =\
                     nnet.crossentropy_softmax_max_and_argmax_1hot(
                     self.linear_output, self.target)