--- a/pylearn/dataset_ops/image_patches.py	Tue Aug 24 16:00:08 2010 -0400
+++ b/pylearn/dataset_ops/image_patches.py	Tue Aug 24 16:51:53 2010 -0400
@@ -2,12 +2,16 @@
 import theano
 
 from pylearn.datasets.image_patches import  (
+        data_root,
         olshausen_field_1996_whitened_images,
         extract_random_patches)
 
 from .protocol import TensorFnDataset # protocol.py __init__.py
 from .memo import memo
 
+import scipy.io
+from pylearn.io import image_tiling
+
 @memo
 def get_dataset(N,R,C,dtype,center,unitvar):
     seed=98234
@@ -48,3 +52,60 @@
 
     return x
 
+
+
+@memo
+def ranzato_hinton_2010(path=None):
+    if path is None:
+        path = os.path.join(data_root(), 'image_patches', 'mcRBM',
+                'training_colorpatches_16x16_demo.mat')
+    dct = scipy.io.loadmat(path)
+    return dct
+def ranzato_hinton_2010_whitened_patches(path=None):
+    """Return the pca of the data, which is 10240 x 105
+    """
+    dct = ranzato_hinton_2010(path)
+    return dct['whitendata'].astype('float32')
+
+def undo_pca_filters_of_ranzato_hinton_2010(X, path=None):
+    """Return tuple (R,G,B,None) of matrices for matrix `X` of filters (one per row)
+    
+    Return value can be passed to `image_tiling.tile_raster_images`.
+    """
+    dct = ranzato_hinton_2010(path)
+    X = numpy.dot(X, dct['invpcatransf'].T)
+    return (X[:,:256], X[:,256:512], X[:,512:], None)
+
+def save_filters_of_ranzato_hinton_2010(X, fname, min_dynamic_range=1e-3, data_path=None):
+    _img = image_tiling.tile_raster_images(
+            undo_pca_filters_of_ranzato_hinton_2010(X, path=data_path),
+            img_shape=(16,16),
+            min_dynamic_range=min_dynamic_range)
+    image_tiling.save_tiled_raster_images(_img, fname)
+
+def ranzato_hinton_2010_op(s_idx,
+        split='train', 
+        dtype=theano.config.floatX, rasterized=True,
+        center=True,
+        unitvar=True):
+    N = 10240
+
+    if split != 'train':
+        raise NotImplementedError('train/test/valid splits for randomly sampled image patches?')
+
+    if not rasterized:
+        # the data is provided as PCA-sphered, so rasterizing does not make sense
+        # TODO: add a param to enable/disable 'PCA', and if disabled, then consider
+        # rasterizing or not
+        raise NotImplementedError('only pca data is provided')
+
+    if dtype != 'float32':
+        raise NotImplementedError('dtype not float32')
+
+    op = TensorFnDataset(dtype,
+            bcast=(False,), 
+            fn=ranzato_hinton_2010_whitened_patches,
+            single_shape=(105,))
+    x = op(s_idx%N)
+    return x
+