diff --git a/scikits/learn/bayes/bayes.py b/scikits/learn/bayes/regression.py
similarity index 100%
rename from scikits/learn/bayes/bayes.py
rename to scikits/learn/bayes/regression.py
diff --git a/scikits/learn/bayes/tests/test_bayes.py b/scikits/learn/bayes/tests/test_bayes.py
index c65152a8c294dca2bd3f35b61f62bfb024df9851..74bde12c428785eff51bd4ffd82e4206e124a90d 100644
--- a/scikits/learn/bayes/tests/test_bayes.py
+++ b/scikits/learn/bayes/tests/test_bayes.py
@@ -1,5 +1,5 @@
 import numpy as np
-from scikits.learn.bayes.bayes import *
+from scikits.learn.bayes.regression import *
 from numpy.testing import assert_array_almost_equal
 from  scikits.learn.datasets.samples_generator import linear,nonlinear
 
diff --git a/scikits/learn/datasets/samples_generator/linear.py b/scikits/learn/datasets/samples_generator/linear.py
index 966bcd84b72a190ecca56e6ac710cafe30f7d652..8492442812b5ea06b7459fb89ad8b3e7d3daac35 100755
--- a/scikits/learn/datasets/samples_generator/linear.py
+++ b/scikits/learn/datasets/samples_generator/linear.py
@@ -6,7 +6,7 @@ def sparse_uncorrelated(nb_samples=100, nb_features=10):
     Function creating simulated data with sparse uncorrelated design.
     (cf.Celeux et al. 2009,  Bayesian regularization in regression)
     X = NR.normal(0,1)
-    Y = NR.normal(X[:,2]+2*X[:,3]-2*X[:,6]-1.5*X[:,7])
+    Y = NR.normal(X[:,0]+2*X[:,1]-2*X[:,2]-1.5*X[:,3])
     The number of features is at least 10.
 
     Parameters
@@ -14,7 +14,7 @@ def sparse_uncorrelated(nb_samples=100, nb_features=10):
     nb_samples : int
                  number of samples (defaut is 100).
     nb_features : int
-                  number of features (defaut is 10).
+                  number of features (defaut is 5).
 
     Returns
     -------
@@ -22,6 +22,6 @@ def sparse_uncorrelated(nb_samples=100, nb_features=10):
     Y : numpy array of shape (nb_samples) for labels
     """
     X = nr.normal(loc=0, scale=1, size=(nb_samples, nb_features))
-    Y = nr.normal(loc=X[:, 2] + 2 * X[:, 3] - 2 * X[:,6] - 1.5 * X[:, 7],
+    Y = nr.normal(loc=X[:, 0] + 2 * X[:, 1] - 2 * X[:,2] - 1.5 * X[:, 3],
                   scale = np.ones(nb_samples))
     return X, Y