import numpy as np
from sklearn.datasets import load_digits

digits = load_digits()
X, y = digits.data, digits.target

from sklearn.svm  import SVC

model = SVC(C=10, gamma=0.005)

from sklearn.cross_validation import cross_val_score

def print_cv_score_summary(model, X, y, cv):
    scores = cross_val_score(model, X, y, cv=cv, n_jobs=-1)
    print("mean: {:3f}, stdev: {:3f}".format(
        np.mean(scores), np.std(scores)))

from sklearn.cross_validation import KFold

cv = KFold(len(y), 5)
print_cv_score_summary(model, X, y, cv)

cv = KFold(len(y), 5, shuffle=True, random_state=0)
print_cv_score_summary(model, X, y, cv)

cv = KFold(len(y), 5, shuffle=True, random_state=1)
print_cv_score_summary(model, X, y, cv)

cv = KFold(len(y), 5, shuffle=True, random_state=2)
print_cv_score_summary(model, X, y, cv)

from sklearn.cross_validation import ShuffleSplit

cv = ShuffleSplit(len(y), n_iter=5, test_size=0.2, random_state=0)
print_cv_score_summary(model, X, y, cv)

cv = ShuffleSplit(len(y), n_iter=5, test_size=0.2, random_state=1)
print_cv_score_summary(model, X, y, cv)

cv = ShuffleSplit(len(y), n_iter=5, test_size=0.2, random_state=2)
print_cv_score_summary(model, X, y, cv)

from sklearn.cross_validation import StratifiedKFold

cv = StratifiedKFold(y, 5)
print_cv_score_summary(model, X, y, cv)