# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1305730362 14400
# Node ID a57f4839a9d859beb5f08b24e2f66af610dcc79e
# Parent  91a475ca9b6d586373d940e5f27af9074820cd08# Parent  c41fdf8c35b80218f170ffb00b20a2704321ca3a
merge

diff -r c41fdf8c35b8 -r a57f4839a9d8 pylearn/dataset_ops/protocol.py
--- a/pylearn/dataset_ops/protocol.py	Wed Apr 27 11:34:36 2011 -0400
+++ b/pylearn/dataset_ops/protocol.py	Wed May 18 10:52:42 2011 -0400
@@ -3,6 +3,7 @@
 """
 
 __docformat__  = "restructuredtext_en"
+import numpy
 import theano
 
 class Dataset(theano.Op):
@@ -119,6 +120,6 @@
         except:
             x = self.x_ = self.fn(*self.fn_args)
         if idx.ndim == 0:
-            z[0] = x[int(idx)]
+            z[0] = numpy.asarray(x[int(idx)]) # asarray is important for memmaps
         else:
-            z[0] = x[idx]
+            z[0] = numpy.asarray(x[idx]) # asarray is important for memmaps
diff -r c41fdf8c35b8 -r a57f4839a9d8 pylearn/datasets/icml07.py
--- a/pylearn/datasets/icml07.py	Wed Apr 27 11:34:36 2011 -0400
+++ b/pylearn/datasets/icml07.py	Wed May 18 10:52:42 2011 -0400
@@ -3,6 +3,7 @@
 import os, sys
 import numpy
 
+from config import get_filepath_in_roots
 from pylearn.io.amat import AMat
 
 class DatasetLoader(object):
@@ -68,7 +69,9 @@
         assert numpy.all(labels < self.n_classes)
         return inputs, labels
 
-def icml07_loaders(new_version=True, rootdir='.'):
+def icml07_loaders(new_version=True, rootdir=None):
+    if rootdir is None:
+        rootdir = get_filepath_in_roots('icml07data_twiki')
     rval = dict(
         mnist_basic=DatasetLoader(
             http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/mnist.zip',
diff -r c41fdf8c35b8 -r a57f4839a9d8 pylearn/gd/sgd.py
--- a/pylearn/gd/sgd.py	Wed Apr 27 11:34:36 2011 -0400
+++ b/pylearn/gd/sgd.py	Wed May 18 10:52:42 2011 -0400
@@ -1,6 +1,6 @@
 """A stochastic gradient descent minimizer.
 """
-
+import numpy
 import theano
 
 def sgd_updates(params, grads, stepsizes):
@@ -35,11 +35,11 @@
         momentum = [momentum for p in params]
     if len(params) != len(grads):
         raise ValueError('params and grads have different lens')
-    headings = [theano.shared(p.get_value(borrow=False)*0) for p in params]
+    headings = [theano.shared(numpy.zeros_like(p.get_value(borrow=True))) for p in params]
     updates = []
     for s, p, gp, m, h in zip(stepsizes, params, grads, momentum, headings):
         updates.append((p, p + s * h))
-        updates.append((h, m*h - (1-m)*gp))
+        updates.append((h, m*h - (1.0-m)*gp))
     return updates
 
 
diff -r c41fdf8c35b8 -r a57f4839a9d8 pylearn/io/image_tiling.py
--- a/pylearn/io/image_tiling.py	Wed Apr 27 11:34:36 2011 -0400
+++ b/pylearn/io/image_tiling.py	Wed May 18 10:52:42 2011 -0400
@@ -100,6 +100,10 @@
         H, W = img_shape
         Hs, Ws = tile_spacing
 
+        out_scaling = 1
+        if output_pixel_vals and str(X.dtype).startswith('float'):
+            out_scaling = 255
+
         out_array = numpy.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype)
         for tile_row in xrange(tile_shape[0]):
             for tile_col in xrange(tile_shape[1]):
@@ -121,7 +125,7 @@
                         tile_row * (H+Hs):tile_row*(H+Hs)+H,
                         tile_col * (W+Ws):tile_col*(W+Ws)+W
                         ] \
-                        = this_img * (255 if output_pixel_vals else 1)
+                        = this_img * out_scaling
         return out_array