--- a/pylearn/algorithms/__init__.py	Tue Jan 13 16:00:36 2009 -0500
+++ b/pylearn/algorithms/__init__.py	Tue Jan 13 20:08:06 2009 -0500
@@ -1,5 +0,0 @@
-
-from .minimizer import make_minimizer, minimizer_factory
-from .stopper import make_stopper, stopper_factory
-from .stacker import Stacker
-from .regressor import BinRegressor

--- a/pylearn/algorithms/logistic_regression.py	Tue Jan 13 16:00:36 2009 -0500
+++ b/pylearn/algorithms/logistic_regression.py	Tue Jan 13 20:08:06 2009 -0500
@@ -8,11 +8,20 @@
 
 import numpy as N
 
-from ..datasets import make_dataset
-from .minimizer import make_minimizer
-from .stopper import make_stopper
+class LogRegN(module.FancyModule):
+    """
+    A symbolic module for performing N-class logistic regression.
+
+    Notable variables
+    -----------------
 
-class LogRegN(module.FancyModule):
+    self.input
+    self.target 
+    self.softmax
+    self.argmax
+    self.regularized_cost
+    self.unregularized_cost
+    """
 
     def __init__(self, 
             n_in=None, n_out=None,
@@ -94,8 +103,6 @@
     batchsize = 8
     verbose = 1
 
-from ..datasets import MNIST
-import sgd #TODO:  necessary to add it to factory list
 # consider pre-importing each file in algorithms, datasets (possibly with try/catch around each
 # import so that this import failure is ignored)
 
@@ -103,7 +110,7 @@
     #use stochastic gradient descent
     state.use_defaults(_fit_logreg_defaults)
 
-    dataset = make_dataset(**state.subdict(prefix='dataset_'))
+    dataset = make(state.dataset)
     train = dataset.train
     valid = dataset.valid
     test = dataset.test

--- a/pylearn/algorithms/sgd.py	Tue Jan 13 16:00:36 2009 -0500
+++ b/pylearn/algorithms/sgd.py	Tue Jan 13 20:08:06 2009 -0500
@@ -4,8 +4,6 @@
 from theano.compile import module
 from theano import tensor as T
 
-from minimizer import minimizer_factory
-
 class StochasticGradientDescent(module.FancyModule):
     """Fixed stepsize gradient descent"""
     def __init__(self, args, cost, params, gradients=None, stepsize=None):
@@ -40,8 +38,6 @@
     def _instance_initialize(self, obj):
         pass
 
-
-@minimizer_factory('sgd')
 def sgd_minimizer(stepsize=None, **args):
     def m(i,c,p,g=None):
         return StochasticGradientDescent(i, c, p, stepsize=stepsize, **args)

--- a/pylearn/algorithms/stopper.py	Tue Jan 13 16:00:36 2009 -0500
+++ b/pylearn/algorithms/stopper.py	Tue Jan 13 20:08:06 2009 -0500
@@ -57,21 +57,6 @@
                     best = (save(), stp.iter, stp.score)
         return best
 
-_stoppers = {}
-
-def stopper_factory(algo):
-    def decorator(fn):
-        if algo in _stoppers:
-            raise Exception('stopper in use', algo)
-        else:
-            _stoppers[algo] = fn
-        return fn
-    return decorator
-
-def make_stopper(algo, **kwargs):
-    return _stoppers[algo](**kwargs)
-
-
 class ICML08Stopper(Stopper):
     @staticmethod
     def icml08(ntrain, batchsize):
@@ -131,13 +116,10 @@
     #TODO: could optimize next() function. Most of what's in ICML08Stopper.next()
     #is not necessary
 
-
-@stopper_factory('icml08')
-def icml08_stopper(i_wait, v_int, min_improvement, patience, hard_limit):
+def geometric_patience(i_wait, v_int, min_improvement, patience, hard_limit):
     return ICML08Stopper(i_wait, v_int, min_improvement, patience, hard_limit)
 
-@stopper_factory('nstages')
-def nstages_stopper(hard_limit, v_int):
+def nstages(hard_limit, v_int):
     return ICML08Stopper(hard_limit, v_int, 1.0, 1.0, hard_limit)
 
 

--- a/pylearn/datasets/embeddings/parameters.py	Tue Jan 13 16:00:36 2009 -0500
+++ b/pylearn/datasets/embeddings/parameters.py	Tue Jan 13 20:08:06 2009 -0500
@@ -1,10 +1,10 @@
 """
 Locations of the embedding data files.
 """
-#WEIGHTSFILE     = "/home/fringant2/lisa/data/word_embeddings.collobert-and-weston/lm-weights.txt"
-#VOCABFILE       = "/home/fringant2/lisa/data/word_embeddings.collobert-and-weston/words.asc"
-WEIGHTSFILE     = "/home/joseph/data/word_embeddings.collobert-and-weston/lm-weights.txt"
-VOCABFILE       = "/home/joseph/data/word_embeddings.collobert-and-weston/words.asc"
+WEIGHTSFILE     = "/home/fringant2/lisa/data/word_embeddings.collobert-and-weston/lm-weights.txt"
+VOCABFILE       = "/home/fringant2/lisa/data/word_embeddings.collobert-and-weston/words.asc"
+#WEIGHTSFILE     = "/home/joseph/data/word_embeddings.collobert-and-weston/lm-weights.txt"
+#VOCABFILE       = "/home/joseph/data/word_embeddings.collobert-and-weston/words.asc"
 NUMBER_OF_WORDS = 30000
 DIMENSIONS      = 50
 UNKNOWN         = "UNKNOWN"

--- a/pylearn/dbdict/newstuff.py	Tue Jan 13 16:00:36 2009 -0500
+++ b/pylearn/dbdict/newstuff.py	Tue Jan 13 20:08:06 2009 -0500
@@ -30,18 +30,22 @@
     """
     Resolve a string of the form X.Y...Z to a python object by repeatedly using getattr, and
     __import__ to introspect objects (in this case X, then Y, etc. until finally Z is loaded).
+
     """
     symbols = name.split('.')
     builder = __import__(symbols[0])
-    for sym in symbols[1:]:
-        try:
-            builder = getattr(builder, sym)
-        except AttributeError, e:
-            if try_import:
-                __import__(builder.__name__, fromlist=[sym])
+    try:
+        for sym in symbols[1:]:
+            try:
                 builder = getattr(builder, sym)
-            else:
-                raise e
+            except AttributeError, e:
+                if try_import:
+                    __import__(builder.__name__, fromlist=[sym])
+                    builder = getattr(builder, sym)
+                else:
+                    raise e
+    except (AttributeError, ImportError), e:
+        raise type(e)('Failed to resolve compound symbol %s' % name, e)
     return builder
 
 ################################################################################