DOC: updating doc for StratifiedKFold + ellipsis in svm support

agramfort · GaelVaroquaux · commit c4b9b00709a5 · 2011-02-24T22:18:07.000+01:00
diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst
@@ -162,13 +162,13 @@ Example of stratified 2-fold:
 >>> from scikits.learn.cross_val import StratifiedKFold
 >>> X = [[0., 0.], [1., 1.], [-1., -1.], [2., 2.], [3., 3.], [4., 4.], [0., 1.]]
 >>> Y = [0, 0, 0, 1, 1, 1, 0]
->>> loo = StratifiedKFold(Y, 2)
->>> print loo
+>>> skf = StratifiedKFold(Y, 2)
+>>> print skf
 scikits.learn.cross_val.StratifiedKFold(labels=[0 0 0 1 1 1 0], k=2)
->>> for train, test in loo: print train,test
-[False False  True False  True  True  True] [ True  True False  True False False False]
-[ True  True False  True False False False] [False False  True False  True  True  True]
- 
+>>> for train, test in skf: print train, test
+[False  True False False  True False  True] [ True False  True  True False  True False]
+[ True False  True  True False  True False] [False  True False False  True False  True]
+
 
 
 
diff --git a/doc/modules/svm.rst b/doc/modules/svm.rst
@@ -86,13 +86,13 @@ SVMs perform classification as a function of some subset of the
 training data, called the support vectors. These vectors can be
 accessed in member `support_`:
 
-    >>> clf.support_
-    array([0, 1])
+    >>> clf.support_ #doctest: +ELLIPSIS
+    array([0, 1]...)
 
 Member `n_support_` holds the number of support vectors for each class:
 
-    >>> clf.n_support_
-    array([1, 1])
+    >>> clf.n_support_ #doctest: +ELLIPSIS
+    array([1, 1]...)
 
 
 .. topic:: Examples:
