--- a/dataset.py	Fri May 02 18:19:35 2008 -0400
+++ b/dataset.py	Fri May 02 18:36:47 2008 -0400
@@ -27,29 +27,29 @@
     feasible or not recommanded on streams).
 
     To iterate over examples, there are several possibilities:
-    * for example in dataset([field1, field2,field3, ...]):
-    * for val1,val2,val3 in dataset([field1, field2,field3]):
-    * for minibatch in dataset.minibatches([field1, field2, ...],minibatch_size=N):
-    * for mini1,mini2,mini3 in dataset.minibatches([field1, field2, field3], minibatch_size=N):
-    * for example in dataset:
+     - for example in dataset([field1, field2,field3, ...]):
+     - for val1,val2,val3 in dataset([field1, field2,field3]):
+     - for minibatch in dataset.minibatches([field1, field2, ...],minibatch_size=N):
+     - for mini1,mini2,mini3 in dataset.minibatches([field1, field2, field3], minibatch_size=N):
+     - for example in dataset::
         print example['x']
-    * for x,y,z in dataset:
-    Each of these is documented below. All of these iterators are expected
-    to provide, in addition to the usual 'next()' method, a 'next_index()' method
-    which returns a non-negative integer pointing to the position of the next
-    example that will be returned by 'next()' (or of the first example in the
-    next minibatch returned). This is important because these iterators
-    can wrap around the dataset in order to do multiple passes through it,
-    in possibly unregular ways if the minibatch size is not a divisor of the
-    dataset length.
+     - for x,y,z in dataset:
+     Each of these is documented below. All of these iterators are expected
+     to provide, in addition to the usual 'next()' method, a 'next_index()' method
+     which returns a non-negative integer pointing to the position of the next
+     example that will be returned by 'next()' (or of the first example in the
+     next minibatch returned). This is important because these iterators
+     can wrap around the dataset in order to do multiple passes through it,
+     in possibly unregular ways if the minibatch size is not a divisor of the
+     dataset length.
     
     To iterate over fields, one can do
-    * for field in dataset.fields():
+     - for field in dataset.fields():
          for field_value in field: # iterate over the values associated to that field for all the dataset examples
-    * for field in dataset(field1,field2,...).fields() to select a subset of fields
-    * for field in dataset.fields(field1,field2,...) to select a subset of fields
+     - for field in dataset(field1,field2,...).fields() to select a subset of fields
+     - for field in dataset.fields(field1,field2,...) to select a subset of fields
     and each of these fields is iterable over the examples:
-    * for field_examples in dataset.fields():
+     - for field_examples in dataset.fields():
         for example_value in field_examples:
            ...
     but when the dataset is a stream (unbounded length), it is not recommanded to do 
@@ -57,7 +57,7 @@
     an unsynchronized ways. Hence the fields() method is illegal for streams, by default.
     The result of fields() is a DataSetFields object, which iterates over fields,
     and whose elements are iterable over examples. A DataSetFields object can
-    be turned back into a DataSet with its examples() method:
+    be turned back into a DataSet with its examples() method::
       dataset2 = dataset1.fields().examples()
     and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1).
     
@@ -72,20 +72,20 @@
     of examples) can be extracted. These operations are not supported
     by default in the case of streams.
 
-    * dataset[:n] returns a dataset with the n first examples.
+     - dataset[:n] returns a dataset with the n first examples.
 
-    * dataset[i1:i2:s] returns a dataset with the examples i1,i1+s,...i2-s.
+     - dataset[i1:i2:s] returns a dataset with the examples i1,i1+s,...i2-s.
 
-    * dataset[i] returns an Example.
+     - dataset[i] returns an Example.
 
-    * dataset[[i1,i2,...in]] returns a dataset with examples i1,i2,...in.
+     - dataset[[i1,i2,...in]] returns a dataset with examples i1,i2,...in.
 
-    * dataset[fieldname] an iterable over the values of the field fieldname across
-      the dataset (the iterable is obtained by default by calling valuesVStack
-      over the values for individual examples).
+     - dataset[fieldname] an iterable over the values of the field fieldname across
+     the dataset (the iterable is obtained by default by calling valuesVStack
+     over the values for individual examples).
 
-    * dataset.<property> returns the value of a property associated with
-      the name <property>. The following properties should be supported:
+     - dataset.<property> returns the value of a property associated with
+     the name <property>. The following properties should be supported:
           - 'description': a textual description or name for the dataset
           - 'fieldtypes': a list of types (one per field)
 
@@ -94,12 +94,12 @@
     the following operations (with the same basic semantics as numpy.hstack
     and numpy.vstack):
 
-    * dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3])
+     - dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3])
 
     creates a new dataset whose list of fields is the concatenation of the list of
     fields of the argument datasets. This only works if they all have the same length.
 
-    * dataset1 & dataset2 & dataset3 == dataset.vstack([dataset1,dataset2,dataset3])
+     - dataset1 & dataset2 & dataset3 == dataset.vstack([dataset1,dataset2,dataset3])
 
     creates a new dataset that concatenates the examples from the argument datasets
     (and whose length is the sum of the length of the argument datasets). This only
@@ -116,15 +116,15 @@
     when key is a string (or more specifically, neither an integer, a slice, nor a list).
     
     A DataSet sub-class should always redefine the following methods:
-      * __len__ if it is not a stream
-      * fieldNames
-      * minibatches_nowrap (called by DataSet.minibatches())
-      * valuesHStack
-      * valuesVStack
+       - __len__ if it is not a stream
+       - fieldNames
+       - minibatches_nowrap (called by DataSet.minibatches())
+       - valuesHStack
+       - valuesVStack
     For efficiency of implementation, a sub-class might also want to redefine
-      * hasFields
-      * __getitem__ may not be feasible with some streams
-      * __iter__
+       - hasFields
+       - __getitem__ may not be feasible with some streams
+       - __iter__
     """
 
     def __init__(self,description=None,fieldtypes=None):

--- a/filetensor.py	Fri May 02 18:19:35 2008 -0400
+++ b/filetensor.py	Fri May 02 18:36:47 2008 -0400
@@ -1,18 +1,18 @@
 """
 Read and write the matrix file format described at
-http://www.cs.nyu.edu/~ylclab/data/norb-v1.0/index.html
+U{http://www.cs.nyu.edu/~ylclab/data/norb-v1.0/index.html}
 
 The format is for dense tensors:
 
-    magic number indicating type and endianness - 4bytes
-    rank of tensor - int32
-    dimensions - int32, int32, int32, ...
-    <data>
+    - magic number indicating type and endianness - 4bytes
+    - rank of tensor - int32
+    - dimensions - int32, int32, int32, ...
+    - <data>
 
 The number of dimensions and rank is slightly tricky: 
-    for scalar: rank=0, dimensions = [1, 1, 1]
-    for vector: rank=1, dimensions = [?, 1, 1]
-    for matrix: rank=2, dimensions = [?, ?, 1]
+    - for scalar: rank=0, dimensions = [1, 1, 1]
+    - for vector: rank=1, dimensions = [?, 1, 1]
+    - for matrix: rank=2, dimensions = [?, ?, 1]
 
 For rank >= 3, the number of dimensions matches the rank exactly.