--- a/pylearn/algorithms/minimizer.py	Fri Aug 20 09:27:59 2010 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,36 +0,0 @@
-"""Define the interface and factory for gradient-based minimizers.
-"""
-import theano
-
-class DummyMinimizer(theano.Module):
-    """ The idea of a minimizer is that it provides an `step` function that will
-    eventually converge toward (maybe realize?) the minimum of a cost function.
-
-    The step_cost function takes a step and returns the cost associated with either
-    the current or previous parameter values (return whichever is easiest to compute, it's
-    meant for user feedback.)
-
-    """
-    def __init__(self, args, cost, parameters, gradients=None):
-        super(DummyMinimizer, self).__init__()
-
-    def _instance_step(self, obj, *args):
-        """Move the parameters toward the minimum of a cost
-
-        :param args: The arguments here should be values for the Variables that were in the
-        `args` argument to the constructor.
-
-        :Return:  None
-        """
-        pass
-
-    def _instance_step_cost(self, obj, *args):
-        """Move the parameters toward the minimum of a cost, and compute the cost
-
-        :param args: The arguments here should be values for the Variables that were in the
-        `args` argument to the constructor.
-
-        :Return:  The current cost value.
-        """
-        pass
-