--- a/pylearn/algorithms/exponential_mean.py	Tue Jun 02 22:26:04 2009 -0400
+++ b/pylearn/algorithms/exponential_mean.py	Tue Jun 02 22:26:29 2009 -0400
@@ -14,6 +14,12 @@
 
     :math:`self.curval = (1.0 - (1.0/max_denom)) * self.old_curval + (1.0/max_denom) * x`
 
+
+    The symbolic buffer containing the running mean is called `old_curval`.  (This has a value
+    in the ModuleInstance).
+
+    The symbolic variable for the updated running mean is called `curval`.
+
     """
 
     max_denom = None

--- a/pylearn/algorithms/sgd.py	Tue Jun 02 22:26:04 2009 -0400
+++ b/pylearn/algorithms/sgd.py	Tue Jun 02 22:26:29 2009 -0400
@@ -4,10 +4,16 @@
 import theano
 
 class StochasticGradientDescent(theano.Module):
-    """Fixed stepsize gradient descent"""
+    """Fixed stepsize gradient descent
+
+    Methods for gradient descent are:
+    - step(arg_vals) which returns None and updates the params
+    - step_cost(arg_vals) which returns the cost value, and updates the params
+    
+    """
     def __init__(self, args, cost, params, 
                  gradients=None, stepsize=None, 
-                 updates=None, auxout=None):
+                 updates=None, auxout=None, methods=True):
         """
         :param stepsize: the step to take in (negative) gradient direction
         :type stepsize: None, scalar value, or scalar TensorVariable
@@ -15,8 +21,9 @@
         :param updates: extra symbolic updates to make when evating either step or step_cost
         (these override the gradients if necessary)
         :type updatess: dict Variable -> Variable
-        :type auxout: auxiliary outputs, list containing output symbols to 
+        :param auxout: auxiliary outputs, list containing output symbols to 
                       compute at the same time as cost (for efficiency)
+        :param methods: Should this module define the step and step_cost methods?
         """
         super(StochasticGradientDescent, self).__init__()
         self.stepsize_init = None
@@ -38,13 +45,19 @@
         if updates is not None:
             self._updates.update(updates)
 
-        auxout = auxout if auxout else []
-        self.step = theano.Method(
-                args, auxout,
-                updates=self._updates)
-        self.step_cost = theano.Method(
-                args, [cost]+auxout,
-                updates=self._updates)
+        if methods:
+            if auxout is None:
+                self.step = theano.Method(args, [], updates=self._updates)
+                self.step_cost = theano.Method(args, cost, updates=self._updates)
+            else:
+                # step cost always returns a list if auxout
+                self.step = theano.Method(
+                        args, [] + auxout,
+                        updates=self._updates)
+                self.step_cost = theano.Method(
+                        args, [cost]+auxout,
+                        updates=self._updates)
+
 
     updates = property(lambda self: self._updates.copy())
 

--- a/pylearn/datasets/norb_small.py	Tue Jun 02 22:26:04 2009 -0400
+++ b/pylearn/datasets/norb_small.py	Tue Jun 02 22:26:29 2009 -0400
@@ -63,11 +63,14 @@
         test = {}
         train['dat'] = os.path.join(dirpath, 'smallnorb-5x46789x9x18x6x2x96x96-training-dat.mat')
         train['cat'] = os.path.join(dirpath, 'smallnorb-5x46789x9x18x6x2x96x96-training-cat.mat')
+        train['info'] = os.path.join(dirpath, 'smallnorb-5x46789x9x18x6x2x96x96-training-info.mat')
         test['dat']  = os.path.join(dirpath, 'smallnorb-5x01235x9x18x6x2x96x96-testing-dat.mat')
         test['cat']  = os.path.join(dirpath, 'smallnorb-5x01235x9x18x6x2x96x96-testing-cat.mat')
+        test['info']  = os.path.join(dirpath, 'smallnorb-5x01235x9x18x6x2x96x96-testing-info.mat')
     path = Paths()
 
-    def __init__(self, ntrain=19440, nvalid=4860, ntest=24300, 
+    def __init__(self, ntrain=19440, nvalid=4860, ntest=24300,
+               valid_variant=None,
                downsample_amt=1, seed=1, normalize=False,
                mode='stereo', dtype='int8'):
 
@@ -83,11 +86,26 @@
         self.dtype = dtype
 
         rng = numpy.random.RandomState(seed)
-        self.indices = rng.permutation(self.nsamples)
-        self.itr  = self.indices[0:ntrain]
-        self.ival = self.indices[ntrain:ntrain+nvalid]
+        if valid_variant is None:
+            # The validation set is just a random subset of training
+            self.indices = rng.permutation(self.nsamples)
+            self.itr  = self.indices[0:ntrain]
+            self.ival = self.indices[ntrain:ntrain+nvalid]
+        elif valid_variant in (4,6,7,8,9):
+            # The validation set consists in an instance of each category
+            # In order to know which indices correspond to which instance,
+            # we need to load the 'info' files.
+            train_info = read(open(train['info']))
+
+            ordered_itrain = numpy.nonzero(train_info[:,0] != valid_variant)[0]
+            ordered_ivalid = numpy.nonzero(train_info[:,0] == valid_variant)[0]
+
+            # TODO: randomize
+            self.itr = ordered_itrain
+            self.ival = ordered_ivalid
+
         self.current = None
- 
+
     def load(self, dataset='train'):
 
         if dataset == 'train' or dataset=='valid':
@@ -99,7 +117,7 @@
                 print 'need to reload from train file'
                 dat, cat  = load_file(self.path.train, self.normalize,
                                       self.downsample_amt, self.dtype)
-                
+
                 x = dat[self.itr,...].reshape(self.ntrain,-1)
                 y = cat[self.itr]
                 self.dat1 = Dataset.Obj(x=x, y=y) # training
@@ -126,7 +144,7 @@
                 x = dat.reshape(self.nsamples,-1)
                 y = cat
                 self.dat1 = Dataset.Obj(x=x, y=y)
-                
+
                 del dat, cat, x, y
 
             rval = self.dat1

--- a/pylearn/external/wrap_libsvm.py	Tue Jun 02 22:26:04 2009 -0400
+++ b/pylearn/external/wrap_libsvm.py	Tue Jun 02 22:26:29 2009 -0400
@@ -66,7 +66,7 @@
 
 svm_problem = libsvm.svm_problem
 svm_parameter = libsvm.svm_parameter
-RBF = libsvm.svm_RBF
+RBF = libsvm.RBF
 
 
 ####################################
@@ -159,7 +159,7 @@
 def train_rbf_model(train_X, train_Y, C, gamma):
     param = libsvm.svm_parameter(C=C, kernel_type=libsvm.RBF, gamma=gamma)
     problem = libsvm.svm_problem(train_Y, train_X)
-    model libsvm.svm_model(problem, param)
+    model = svm_model(problem, param)
 
     #save_filename = state.save_filename
     #model.save(save_filename)
@@ -181,7 +181,7 @@
         train_set=None,
         svm_param=dict(kernel='RBF', C=C, gamma=g),
         save_filename='model_RBF_C%f_G%f.libsvm')
-            for C in C_grid,
+            for C in C_grid
             for g in gamma_grid]
 
     # will return quickly if jobs have already run

--- a/pylearn/io/image_tiling.py	Tue Jun 02 22:26:04 2009 -0400
+++ b/pylearn/io/image_tiling.py	Tue Jun 02 22:26:29 2009 -0400
@@ -5,10 +5,10 @@
 import numpy
 from PIL import Image
 
-def scale_to_unit_interval(ndar):
+def scale_to_unit_interval(ndar,eps=1e-8):
     ndar = ndar.copy()
     ndar -= ndar.min()
-    ndar *= 1.0 / ndar.max()
+    ndar *= 1.0 / (ndar.max()+eps)
     return ndar
 
 def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0,0),