--- a/dataset.py	Tue Jun 03 13:22:45 2008 -0400
+++ b/dataset.py	Tue Jun 03 13:23:28 2008 -0400
@@ -1186,34 +1186,6 @@
 
       return CacheIteratorIter(self)
 
-#       class CachedDataSetIterator(object):
-#           def __init__(self,dataset,fieldnames):#,minibatch_size,n_batches,offset):
-# #              if fieldnames is None: fieldnames = dataset.fieldNames()
-#   # store the resulting minibatch in a lookup-list of values
-#               self.minibatch = LookupList(fieldnames,[0]*len(fieldnames))
-#               self.dataset=dataset
-# #              self.minibatch_size=minibatch_size
-# #              assert offset>=0 and offset<len(dataset.data)
-# #              assert offset+minibatch_size<=len(dataset.data)
-#               self.current=0
-#               self.columns = [self.dataset.fields_columns[f] 
-#                               for f in self.minibatch._names]
-#               self.l = len(self.dataset)
-#           def __iter__(self):
-#               return self
-#           def next(self):
-#               #@todo: we suppose that we need to stop only when minibatch_size == 1.
-#               # Otherwise, MinibatchWrapAroundIterator do it.
-#               if self.current>=self.l:
-#                   raise StopIteration
-#               sub_data =  self.dataset.data[self.current]
-#               self.minibatch._values = [sub_data[c] for c in self.columns]
-              
-#               self.current+=self.minibatch_size
-#               return self.minibatch
-
-#         return CachedDataSetIterator(self,self.fieldNames())#,1,0,0)
-    
 class ApplyFunctionDataSet(DataSet):
   """
   A L{DataSet} that contains as fields the results of applying a