--- a/algorithms/tests/test_daa.py	Tue Nov 18 02:57:50 2008 -0500
+++ b/algorithms/tests/test_daa.py	Tue Nov 18 03:25:54 2008 -0500
@@ -28,7 +28,7 @@
             model.local_update[l]([[0, 1, 0, 1]])
             model.local_update[l]([[1, 0, 1, 0]])
 
-    for i in range(1):
+    for i in range(10):
         model.update([[0, 1, 0, 1]], [[1]])
         model.update([[1, 0, 1, 0]], [[0]])
     print model.classify([[0, 1, 0, 1]])
@@ -41,23 +41,31 @@
     daa = models.Stacker([(models.SigmoidXEDenoisingAA, 'hidden')] * ndaa + [(pylearn.algorithms.logistic_regression.Module_Nclass, 'pred')],
                          regularize = False)
 
-    model = daa.make([4, 20, 20, 20, 10],
+    model = daa.make([4] + [20] * ndaa + [10],
                      lr = 0.01,
                      mode = mode,
                      seed = 10)
 
-    model.layers[0].noise_level = 0.3
-    model.layers[1].noise_level = 0.3
-    model.layers[2].noise_level = 0.3
+    for l in range(ndaa): model.layers[l].noise_level = 0.3
 
-    for l in range(3):
+    instances = [([[0, 1, 0, 1]], [1]), ([[1, 0, 1, 0]], [0])]
+
+    for l in range(ndaa):
         for i in range(10):
-            model.local_update[l]([[0, 1, 0, 1]])
-            model.local_update[l]([[1, 0, 1, 0]])
+            for (input, output) in instances:
+                model.local_update[l](input)
 
-    for i in range(1):
-        model.update([[0, 1, 0, 1]], [1])
-        model.update([[1, 0, 1, 0]], [0])
+    for i in range(10):
+        for (input, output) in instances:
+#            model.update(input, output)
+            print "OLD:", 
+            print model.validate(input, output)
+            oldloss = model.update(input, output)
+            print oldloss
+            print "NEW:"
+            print model.validate(input, output)
+            print 
+
     print model.apply([[0, 1, 0, 1]])
     print model.apply([[1, 0, 1, 0]])