# HG changeset patch
# User gdesjardins
# Date 1287114941 14400
# Node ID 3efd0effb2a7f08ef71409d516ecea1fd20eef76
# Parent  0f69f303ba91b4149c7d1f388c6ea385e43e74d9
small changes to mode dataset (used for tempering work)

diff -r 0f69f303ba91 -r 3efd0effb2a7 pylearn/datasets/test_modes.py
--- a/pylearn/datasets/test_modes.py	Thu Oct 14 23:54:28 2010 -0400
+++ b/pylearn/datasets/test_modes.py	Thu Oct 14 23:55:41 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))