--- a/pylearn/algorithms/sandbox/DAA_inputs_groups.py	Wed Sep 16 19:18:29 2009 -0400
+++ b/pylearn/algorithms/sandbox/DAA_inputs_groups.py	Wed Sep 30 17:15:01 2009 -0400
@@ -100,6 +100,9 @@
 def softsign_act(x):
     return x/(1.0 + T.abs_(x))
 
+def arsinh_act(x):
+    return T.log(x+T.sqrt(1+x*x))
+
 # costs utils:---------------------------------------------------
 # in order to fix numerical instability of the cost and gradient calculation for the cross entropy we calculate it
 # with the following functions direclty from the activation:
@@ -128,6 +131,7 @@
     XS = T.xlogx.xlogx((target+1)/2.0) + T.xlogx.xlogx(1-(target+1)/2.0)
     return -T.mean(T.sum(XE-XS, axis=sum_axis),axis=mean_axis)
 
+
 def cross_entropy(target, output_act, act, mean_axis=0, sum_axis=1):
     if act == 'sigmoid_act':
         return sigmoid_cross_entropy(target, output_act, mean_axis, sum_axis)
@@ -205,7 +209,7 @@
         self.corruption_pattern = corruption_pattern
         self.blockgrad = blockgrad
         
-        assert hid_fn in ('sigmoid_act','tanh_act','softsign_act','tanh2_act')
+        assert hid_fn in ('sigmoid_act','tanh_act','softsign_act','tanh2_act','arsinh_act')
         self.hid_fn = eval(hid_fn)
         
         assert rec_fn in ('sigmoid_act','tanh_act','softsign_act','tanh2_act')
@@ -542,7 +546,7 @@
         self.corruption_pattern = corruption_pattern
         self.blockgrad = blockgrad
         
-        assert act_reg in ('sigmoid_act','tanh_act','softsign_act','tanh2_act')
+        assert act_reg in ('sigmoid_act','tanh_act','softsign_act','tanh2_act','arsinh_act')
         self.act_reg = eval(act_reg)
         
         print '\t**** StackedDAAig.__init__ ****'