diff --git a/scikits/learn/datasets/samples_generator/linear.py b/scikits/learn/datasets/samples_generator/linear.py
index cf649ea5a06a19fd4d5c5ab0865eff8214a4ddda..966bcd84b72a190ecca56e6ac710cafe30f7d652 100755
--- a/scikits/learn/datasets/samples_generator/linear.py
+++ b/scikits/learn/datasets/samples_generator/linear.py
@@ -1,7 +1,7 @@
 import numpy as np
 import numpy.random as nr
 
-def sparse_uncorrelated(nb_samples=100,nb_features=10):
+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)
@@ -15,14 +15,13 @@ def sparse_uncorrelated(nb_samples=100,nb_features=10):
                  number of samples (defaut is 100).
     nb_features : int
                   number of features (defaut is 10).
-    
+
     Returns
     -------
-    X : numpy array of shape (nb_samples,nb_features)
-	simulated samples.
-    Y : numpy array of shape (nb_samples)
+    X : numpy array of shape (nb_samples, nb_features) for input samples
+    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],
-    scale=np.ones(nb_samples))
-    return X,Y
\ No newline at end of file
+    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],
+                  scale = np.ones(nb_samples))
+    return X, Y