# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1268376640 18000
# Node ID 3b53ed688747fe47d7470aed9ed870abf6f7880f
# Parent  e966d5ab81034990a49fab9ef50852e61952f684
extension to kouh2008 layer have linear and softplus inputs

diff -r e966d5ab8103 -r 3b53ed688747 pylearn/shared/layers/kouh2008.py
--- a/pylearn/shared/layers/kouh2008.py	Mon Mar 08 17:56:51 2010 -0500
+++ b/pylearn/shared/layers/kouh2008.py	Fri Mar 12 01:50:40 2010 -0500
@@ -218,8 +218,14 @@
 
         #softsign's range is (-1, 1)
         # we want filter responses to span (x_low, x_high)
-        x_list = [x_low + (x_high-x_low)*(d(0.5) + d(0.5)*softsign(tensor.dot(input, f_list[i])+b_list[i]))
-                    for i in xrange(n_terms)]
+        if x_low < x_high:
+            x_list = [x_low + (x_high-x_low)*(d(0.5) + d(0.5)*softsign(tensor.dot(input, f_list[i])+b_list[i]))
+                        for i in xrange(n_terms)]
+        else:
+            if x_low == x_high:
+                x_list = [(tensor.dot(input, f_list[i])+b_list[i]) for i in xrange(n_terms)]
+            else: #x_low > x_high
+                x_list = [softplus(tensor.dot(input, f_list[i])+b_list[i]) for i in xrange(n_terms)]
 
         rval = cls.new_expbounds(rng, x_list, n_out, dtype=dtype, params=f_list + b_list,
                 p_range=p_range,