--- a/gradient_learner.py	Wed Apr 09 18:27:13 2008 -0400
+++ b/gradient_learner.py	Fri Apr 11 11:14:54 2008 -0400
@@ -26,7 +26,7 @@
     It is assumed that all the inputs are provided in the training set (as dataset fields
     with the corresponding name), but not necessarily when using the learned function.
     """
-    def __init__(self, inputs, parameters, outputs, example_wise_cost, regularization_term,
+    def __init__(self, inputs, parameters, outputs, example_wise_cost, regularization_term=astensor(0.0),
                  regularization_coefficient = astensor(1.0)):
         self.inputs = inputs
         self.outputs = outputs
@@ -48,13 +48,24 @@
     def use(self,input_dataset,output_fields=None,copy_inputs=True):
         # obtain the function that maps the desired inputs to desired outputs
         input_fields = input_dataset.fieldNames()
+        # map names of input fields to Theano tensors in self.inputs
+        input_variables = ???
         if output_fields is None: output_fields = [output.name for output in outputs]
         # handle special case of inputs that are directly copied into outputs
-        
+        # map names of output fields to Theano tensors in self.outputs
+        output_variables = ???
         use_function_key = input_fields+output_fields
         if not self.use_functions.has_key(use_function_key):
-            self.use_function[use_function_key]=Function(input_fields,output_fields)
+            self.use_function[use_function_key]=Function(input_variables,output_variables)
         use_function = self.use_functions[use_function_key]
         # return a dataset that computes the outputs
         return input_dataset.applyFunction(use_function,input_fields,output_fields,copy_inputs,compute_now=True)
     
+
+class StochasticGradientDescent(object):
+    def update_parameters(self):
+        
+class StochasticGradientLearner(GradientLearner,StochasticGradientDescent):
+    def __init__(self,inputs, parameters, outputs, example_wise_cost, regularization_term=astensor(0.0),
+                 regularization_coefficient = astensor(1.0),)
+    def update()