# HG changeset patch
# User Olivier Delalleau <delallea@iro>
# Date 1243968043 14400
# Node ID 2a30f22ef7ffe4835e41fe139bb40e8078b40f92
# Parent  84d22b7d835aa2626b9a10f2f780fe62cc41454b
Fixed bug introduced in previous change meant to improve numerical stability

diff -r 84d22b7d835a -r 2a30f22ef7ff pylearn/algorithms/sandbox/DAA_inputs_groups.py
--- a/pylearn/algorithms/sandbox/DAA_inputs_groups.py	Tue Jun 02 13:51:22 2009 -0400
+++ b/pylearn/algorithms/sandbox/DAA_inputs_groups.py	Tue Jun 02 14:40:43 2009 -0400
@@ -195,13 +195,7 @@
         container.hidden = self.hid_fn(container.hidden_activation)
         self.define_propdown(container, idx_list , auxinput)
         container.rec = self.hid_fn(container.rec_activation)
-        if (self.ignore_missing is not None and self.input is not None and not
-                self.reconstruct_missing):
-            # Apply mask to gradient to ensure we do not backpropagate on the
-            # cost computed on missing inputs (that were replaced with zeros).
-            container.rec = mask_gradient(container.rec,
-                    self.input_missing_mask)
-        
+       
     def define_propup(self, container, input, idx_list, auxinput):
         if self.input is not None:
             container.hidden_activation = self.filter_up(input, self.wenc, self.benc)
@@ -226,7 +220,14 @@
                 container.rec_activation = rec_activation1
             else:
                 container.rec_activation = rec_activation2
-        
+
+        if (self.ignore_missing is not None and self.input is not None and not
+                self.reconstruct_missing):
+            # Apply mask to gradient to ensure we do not backpropagate on the
+            # cost computed on missing inputs (that have been imputed).
+            container.rec_activation = mask_gradient(container.rec_activation,
+                    self.input_missing_mask)
+  
     def filter_up(self, vis, w, b=None):
         out = T.dot(vis, w)
         return out + b if b else out