# HG changeset patch
# User Olivier Delalleau <delallea@iro>
# Date 1243956710 14400
# Node ID 6117969dd37fd63ae42f9aa81a2a0c6bec8a919f
# Parent  fc85ce33b5188094358da956060ee4a8e37068ae
Added option to allow one to try and reconstruct originally missing inputs

diff -r fc85ce33b518 -r 6117969dd37f pylearn/algorithms/sandbox/DAA_inputs_groups.py
--- a/pylearn/algorithms/sandbox/DAA_inputs_groups.py	Tue Jun 02 10:14:00 2009 -0400
+++ b/pylearn/algorithms/sandbox/DAA_inputs_groups.py	Tue Jun 02 11:31:50 2009 -0400
@@ -47,7 +47,7 @@
                 in_size=None, auxin_size= None, n_hid=1,
                 regularize = False, tie_weights = False, hid_fn = 'sigmoid_act',
                 reconstruction_cost_function=cost.cross_entropy, interface = True,
-                ignore_missing=None,
+                ignore_missing=None, reconstruct_missing=False,
                 **init):
         """
         :param regularize: WRITEME
@@ -67,6 +67,8 @@
             auxilary ones (that should never contain missing values). In fact,
             in the current implementation, auxiliary inputs cannot be used when
             this option is True.
+        :param reconstruct_missing: if True, then the reconstruction cost on
+        missing inputs will be backpropagated. Otherwise, it will not.
         :todo: Default noise level for all daa levels
         """
         print '\t\t**** DAAig.__init__ ****'
@@ -88,6 +90,7 @@
         self.reconstruction_cost_function = reconstruction_cost_function
         self.interface = interface
         self.ignore_missing = ignore_missing
+        self.reconstruct_missing = reconstruct_missing
         
         assert hid_fn in ('sigmoid_act','tanh_act','softsign_act')
         self.hid_fn = eval(hid_fn)
@@ -96,7 +99,7 @@
         self.input = input
         if self.ignore_missing is not None and self.input is not None:
             no_missing = FillMissing(self.ignore_missing)(self.input)
-            self.input = no_missing[0]  # Missing values replaced by zeros.
+            self.input = no_missing[0]  # With missing values replaced.
             self.input_missing_mask = no_missing[1] # Missingness pattern.
         else:
             self.input_missing_mask = None
@@ -155,7 +158,8 @@
         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:
+        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,
@@ -343,7 +347,7 @@
                 regularize = False, tie_weights = False, hid_fn = 'sigmoid_act',
                 reconstruction_cost_function=cost.cross_entropy,
                 n_out = 2, target = None, debugmethod = False, totalupdatebool=False,
-                ignore_missing=None,
+                ignore_missing=None, reconstruct_missing=False,
                 **init):
         
         super(StackedDAAig, self).__init__()
@@ -370,6 +374,7 @@
         self.debugmethod = debugmethod
         self.totalupdatebool = totalupdatebool
         self.ignore_missing = ignore_missing
+        self.reconstruct_missing = reconstruct_missing
         
         # init for model construction
         inputprec = input
@@ -435,7 +440,8 @@
                 param = [inputprec, self.auxinput[i-offset], in_sizeprec, auxin_size[i], self.n_hid[i],\
                     False, self.tie_weights, self.hid_fn, self.reconstruction_cost_function,False]
 
-            dict_params = dict(ignore_missing = self.ignore_missing)
+            dict_params = dict(ignore_missing = self.ignore_missing,
+                    reconstruct_missing = self.reconstruct_missing)
             
             print '\tLayer init= ', i+1
             self.daaig[i] = DAAig(*param, **dict_params)