# HG changeset patch
# User Xavier Glorot <glorotxa@iro.umontreal.ca>
# Date 1266441615 18000
# Node ID b84a0d009af8370c60cdbc96c5d741e2e05b2620
# Parent  0b4080394f2c084ad1f522f62195d7849e26840f
changes on pipeline mecanism: we now sample a different complexity for each transformations, this because when we use the same sampled complexity for all the modules 1/8 of the time we are close to 0 and we obtain an image very close to the source, we now save a complexity for each module in the parameters array

diff -r 0b4080394f2c -r b84a0d009af8 transformations/pipeline.py
--- a/transformations/pipeline.py	Wed Feb 17 09:29:19 2010 -0500
+++ b/transformations/pipeline.py	Wed Feb 17 16:20:15 2010 -0500
@@ -102,7 +102,7 @@
 
         self.res_data = numpy.empty((total, num_px), dtype=numpy.uint8)
         # +1 to store complexity
-        self.params = numpy.empty((total, self.num_params_stored+1))
+        self.params = numpy.empty((total, self.num_params_stored+len(self.modules)))
         self.res_labels = numpy.empty(total, dtype=numpy.int32)
 
     def run(self, img_iterator, complexity_iterator):
@@ -113,20 +113,28 @@
 
         for img_no, (img, label) in enumerate(img_iterator):
             sys.stdout.flush()
-            complexity = complexity_iterator.next()
-
+            
             global_idx = img_no
 
             img = img.reshape(img_size)
 
-            param_idx = 1
+            param_idx = 0
+            mod_idx = 0
             # store complexity along with other params
             self.params[global_idx, 0] = complexity
             for mod in self.modules:
                 # This used to be done _per batch_,
-                # ie. out of the "for img" loop                   
+                # ie. out of the "for img" loop
+                complexity = complexity_iterator.next() 
+                #better to do a complexity sampling for each transformations in order to have more variability
+                #otherwise a lot of images similar to the source are generated (i.e. when complexity is close to 0 (1/8 of the time))
+                #we need to save the complexity of each transformations and the sum of these complexity is a good indicator of the overall
+                #complexity
+                self.params[global_idx, mod_idx] = complexity
+                mod_idx += 1
+                 
                 p = mod.regenerate_parameters(complexity)
-                self.params[global_idx, param_idx:param_idx+len(p)] = p
+                self.params[global_idx, param_idx+len(self.modules):param_idx+len(p)+len(self.modules)] = p
                 param_idx += len(p)
 
                 img = mod.transform_image(img)