--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pylearn/datasets/caltech.py	Thu Oct 14 23:55:55 2010 -0400
@@ -0,0 +1,46 @@
+"""
+Various routines to load/access MNIST data.
+"""
+
+import os
+import numpy
+
+from pylearn.io.pmat import PMat
+from pylearn.datasets.config import data_root # config
+from pylearn.datasets.dataset import Dataset
+
+def caltech_silhouette():
+
+    rval = Dataset()
+
+    
+    path = os.path.join(data_root(), 'caltech_silhouettes')
+
+    rval.train = Dataset.Obj(x=numpy.load(os.path.join(path,'train_data.npy')),
+                             y=numpy.load(os.path.join(path,'train_labels.npy')))
+    rval.valid = Dataset.Obj(x=numpy.load(os.path.join(path,'val_data.npy')),
+                             y=numpy.load(os.path.join(path,'val_labels.npy')))
+    rval.test  = Dataset.Obj(x=numpy.load(os.path.join(path,'test_data.npy')),
+                             y=numpy.load(os.path.join(path,'test_labels.npy')))
+
+    rval.n_classes = 101
+    rval.img_shape = (28,28)
+
+    return rval
+
+def caltech_silhouette2():
+
+    rval = Dataset()
+
+    from scipy import io 
+    path = '/data/lisa6/desjagui/caltech101_silhouettes_28_split1.mat'
+
+    data = io.loadmat(open(path,'r'))
+
+    rval.train = Dataset.Obj(x=data['train_data'], y=data['train_labels'])
+    rval.valid = Dataset.Obj(x=data['val_data'],   y=data['val_labels'])
+    rval.test  = Dataset.Obj(x=data['test_data'],  y=data['test_labels'])
+    rval.n_classes = 101
+    rval.img_shape = (28,28)
+
+    return rval

--- a/pylearn/datasets/test_modes.py	Thu Oct 14 13:33:33 2010 -0400
+++ b/pylearn/datasets/test_modes.py	Thu Oct 14 23:55:55 2010 -0400
@@ -99,7 +99,8 @@
 
     def __init__(self, n_modes, img_shape, seed=238904, 
                  min_p=1e-4, max_p=1e-1,
-                 min_w=0., max_w=1.):
+                 min_w=0., max_w=1.,
+                 w = None, p = None):
 
         self.n_modes = n_modes
         self.img_shape = img_shape
@@ -107,9 +108,14 @@
         self.img_size = numpy.prod(img_shape)
 
         # generate random p, w values
-        self.p = min_p + self.rng.rand(n_modes) * (max_p - min_p)
-        w = min_w + self.rng.rand(n_modes) * (max_w - min_w)
+        if p is None:
+            p = min_p + self.rng.rand(n_modes) * (max_p - min_p)
+        self.p = p
+
+        if w is None:
+            w = min_w + self.rng.rand(n_modes) * (max_w - min_w)
         self.w = w / numpy.sum(w)
+
         self.sort_w_idx = numpy.argsort(self.w)
 
         self.modes = self.rng.randint(0,2,size=(n_modes,self.img_size))