--- a/algorithms/logistic_regression.py	Wed Oct 29 02:08:56 2008 -0400
+++ b/algorithms/logistic_regression.py	Wed Oct 29 03:29:18 2008 -0400
@@ -18,6 +18,8 @@
         self.b = N.zeros(n_out)
         self.lr = 0.01
         self.__hide__ = ['params']
+        self.input_dimension = n_in
+        self.output_dimension = n_out
 
 class Module_Nclass(module.FancyModule):
     InstanceType = LogRegInstanceType
@@ -34,22 +36,35 @@
 
         self.params = [p for p in [self.w, self.b] if p.owner is None]
 
-        xent, output = nnet.crossentropy_softmax_1hot(
-                T.dot(self.x, self.w) + self.b, self.targ)
+        linear_output = T.dot(self.x, self.w) + self.b
+
+        (xent, softmax, max_pr, argmax) = nnet.crossentropy_softmax_max_and_argmax_1hot(
+                linear_output, self.targ)
         sum_xent = T.sum(xent)
 
-        self.output = output
+        self.softmax = softmax
+        self.argmax = argmax 
+        self.max_pr = max_pr
         self.sum_xent = sum_xent
 
+        # Softmax being computed directly.
+        softmax_unsupervised = nnet.softmax(linear_output)
+        self.softmax_unsupervised = softmax_unsupervised
+
         #compatibility with current implementation of stacker/daa or something
         #TODO: remove this, make a wrapper
-        self.cost = sum_xent
+        self.cost = self.sum_xent
         self.input = self.x
+        # TODO: I want to make output = linear_output.
+        self.output = self.softmax_unsupervised
 
         #define the apply method
-        self.pred = T.argmax(T.dot(self.input, self.w) + self.b, axis=1)
+        self.pred = T.argmax(linear_output, axis=1)
         self.apply = module.Method([self.input], self.pred)
 
+        self.validate = module.Method([self.input, self.targ], [self.cost, self.argmax, self.max_pr])
+        self.softmax_output = module.Method([self.input], self.softmax_unsupervised)
+
         if self.params:
             gparams = T.grad(sum_xent, self.params)