ENH don't use mutable defaults in RidgeCV.

Author: amueller

sklearn/linear_model/ridge.py

@@ -636,7 +636,7 @@ class _RidgeGCV(LinearModel):
     http://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf
     """
 
-    def __init__(self, alphas=[0.1, 1.0, 10.0],
+    def __init__(self, alphas=(0.1, 1.0, 10.0),
                  fit_intercept=True, normalize=False,
                  scoring=None, copy_X=True,
                  gcv_mode=None, store_cv_values=False):
@@ -816,7 +816,7 @@ def identity_estimator():
 
 
 class _BaseRidgeCV(LinearModel):
-    def __init__(self, alphas=np.array([0.1, 1.0, 10.0]),
+    def __init__(self, alphas=(0.1, 1.0, 10.0),
                  fit_intercept=True, normalize=False, scoring=None,
                  cv=None, gcv_mode=None,
                  store_cv_values=False):
@@ -1026,7 +1026,7 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
     a one-versus-all approach. Concretely, this is implemented by taking
     advantage of the multi-variate response support in Ridge.
     """
-    def __init__(self, alphas=np.array([0.1, 1.0, 10.0]), fit_intercept=True,
+    def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True,
                  normalize=False, scoring=None, cv=None, class_weight=None):
         super(RidgeClassifierCV, self).__init__(
             alphas=alphas, fit_intercept=fit_intercept, normalize=normalize,