FIX: doctests on buildbot

doc/modules/linear_model.rst

@@ -135,10 +135,10 @@ as GridSearchCV except that it defaults to Generalized Cross-Validation
 
     >>> from scikits.learn import linear_model
     >>> clf = linear_model.RidgeCV(alphas=[0.1, 1.0, 10.0])
-    >>> clf.fit([[0, 0], [0, 0], [1, 1]], [0, .1, 1])
+    >>> clf.fit([[0, 0], [0, 0], [1, 1]], [0, .1, 1])       # doctest: +SKIP
     RidgeCV(alphas=[0.1, 1.0, 10.0], cv=None, fit_intercept=True, loss_func=None,
         normalize=False, score_func=None)
-    >>> clf.best_alpha                                      # doctest: +ELLIPSIS
+    >>> clf.best_alpha                                      # doctest: +SKIP
     0.1...
 
 .. topic:: References

scikits/learn/decomposition/nmf.py

@@ -293,7 +293,7 @@ class ProjectedGradientNMF(BaseEstimator, TransformerMixin):
     >>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
     >>> from scikits.learn.decomposition import ProjectedGradientNMF
     >>> model = ProjectedGradientNMF(n_components=2, init=0)
-    >>> model.fit(X) #doctest: +ELLIPSIS
+    >>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
     ProjectedGradientNMF(beta=1, eta=0.1,
                init=<mtrand.RandomState object at 0x...>, max_iter=200,
                n_components=2, nls_max_iter=2000, sparseness=None, tol=0.0001)
@@ -304,7 +304,7 @@ class ProjectedGradientNMF(BaseEstimator, TransformerMixin):
     0.00746...
     >>> model = ProjectedGradientNMF(n_components=2, init=0,
     ...                              sparseness='components')
-    >>> model.fit(X) #doctest: +ELLIPSIS
+    >>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
     ProjectedGradientNMF(beta=1, eta=0.1,
                init=<mtrand.RandomState object at 0x...>, max_iter=200,
                n_components=2, nls_max_iter=2000, sparseness='components',