--- a/pylearn/algorithms/logistic_regression.py	Thu Nov 13 17:01:44 2008 -0500
+++ b/pylearn/algorithms/logistic_regression.py	Thu Nov 13 17:54:56 2008 -0500
@@ -72,11 +72,15 @@
         raise NotImplementedError()
     else:
         rval = LogRegN(n_in=n_in, n_out=n_out, l1=l1, l2=l2)
+        print 'RVAL input target', rval.input, rval.target
         rval.minimizer = minimizer([rval.input, rval.target], rval.regularized_cost,
                 rval.params)
-        return rval.make()
+        return rval.make(mode='FAST_RUN')
 
 #TODO: grouping parameters by prefix does not play well with providing defaults. Think...
+#FIX : Guillaume suggested a convention: plugin handlers (dataset_factory, minimizer_factory,
+#      etc.) should never provide default arguments for parameters, and accept **kwargs to catch
+#      irrelevant parameters.
 class _fit_logreg_defaults(object):
     minimizer_algo = 'dummy'
     #minimizer_lr = 0.001

--- a/pylearn/algorithms/rbm.py	Thu Nov 13 17:01:44 2008 -0500
+++ b/pylearn/algorithms/rbm.py	Thu Nov 13 17:54:56 2008 -0500
@@ -1,13 +1,17 @@
 import sys, copy
 import theano
 from theano import tensor as T
-from theano.tensor import nnet
+from theano.tensor.nnet import sigmoid
 from theano.compile import module
 from theano import printing, pprint
 from theano import compile
 
 import numpy as N
 
+from ..datasets import make_dataset
+from .minimizer import make_minimizer
+from .stopper import make_stopper
+
 class RBM(module.FancyModule):
 
     # is it really necessary to pass ALL of these ? - GD
@@ -15,6 +19,7 @@
             nvis=None, nhid=None,
             input=None,
             w=None, hidb=None, visb=None):
+        super(RBM, self).__init__()
        
         # symbolic theano stuff
         # what about multidimensional inputs/outputs ? do they have to be 
@@ -24,11 +29,11 @@
         self.hidb = hidb if hidb is not None else module.Member(T.dvector())
 
         # 1-step Markov chain
-        self.hid = T.sigmoid(T.dot(self.w,self.input) + self.hidb)
+        self.hid = sigmoid(T.dot(self.w,self.input) + self.hidb)
         self.hid_sample = self.hid #TODO: sample!
-        self.vis = T.sigmoid(T.dot(self.w.T, self.hid) + self.visb)
+        self.vis = sigmoid(T.dot(self.w.T, self.hid) + self.visb)
         self.vis_sample = self.vis #TODO: sample!
-        self.neg_hid = T.sigmoid(T.dot(self.w, self.vis) + self.hidb)
+        self.neg_hid = sigmoid(T.dot(self.w, self.vis) + self.hidb)
 
         # cd1 updates:
         self.params = [self.w, self.visb, self.hidb]
@@ -44,3 +49,26 @@
 
 def RBM_cd():
     pass;
+
+def train_rbm(state, channel=lambda *args, **kwargs:None):
+    dataset = make_dataset(**state.subdict(prefix='dataset_'))
+    train = dataset.train
+
+    rbm_module = RBM(
+            nvis=train.x.shape[1],
+            nhid=state.size_hidden)
+
+    batchsize = state.batchsize
+    verbose = state.verbose
+    iter = [0]
+
+    while iter[0] != state.max_iters:
+        for j in xrange(0,len(train.x)-batchsize+1,batchsize):
+            rbm.cd1(train.x[j:j+batchsize])
+            if verbose > 1:
+                print 'estimated train cost...'
+            if iter[0] == state.max_iters:
+                break
+            else:
+                iter[0] += 1
+

--- a/pylearn/algorithms/sgd.py	Thu Nov 13 17:01:44 2008 -0500
+++ b/pylearn/algorithms/sgd.py	Thu Nov 13 17:54:56 2008 -0500
@@ -15,13 +15,12 @@
         self.gparams = T.grad(cost, self.params) if gradients is None else gradients
 
         self.updates = dict((p, p - self.lr * g) for p, g in zip(self.params, self.gparams))
-        self.args = args
 
         self.step = module.Method(
-                self.args, None,
+                args, [],
                 updates=self.updates)
         self.step_cost = module.Method(
-                self.args, cost,
+                args, cost,
                 updates=self.updates)
 
     #no initialization is done here.

--- a/pylearn/dbdict/tools.py	Thu Nov 13 17:01:44 2008 -0500
+++ b/pylearn/dbdict/tools.py	Thu Nov 13 17:54:56 2008 -0500
@@ -4,6 +4,7 @@
 
 MODULE = 'dbdict_module'
 SYMBOL = 'dbdict_symbol'
+PREIMPORT = 'dbdict_preimport'
 
 def dummy_channel(*args, **kwargs):
     return None
@@ -52,6 +53,10 @@
     dbdict_module_name = getattr(state,MODULE)
     dbdict_symbol = getattr(state, SYMBOL)
 
+    preimport_list = getattr(state, PREIMPORT, "").split()
+    for preimport in preimport_list:
+        __import__(preimport, fromlist=[None], level=0)
+
     try:
         dbdict_module = __import__(dbdict_module_name, fromlist=[None], level=0)
         dbdict_fn = getattr(dbdict_module, dbdict_symbol)