def testStdNfoldNormalizedAccuracies(self):
# Same test considering normalized accuracies
cm = ConfusionMatrix()
# Fold 0 with normalized acc = 0%
cm = self.populateFold(cm, 0, 1, 0, 1, fold=0)
# Fold 1 with normalized acc = 10%
cm = self.populateFold(cm, 4, 16, 0, 10, fold=1)
# Fold 2 with normalized acc = 20%
cm = self.populateFold(cm, 2, 8, 2, 8, fold=2)
# resulting accuracy should be the average
nfolds = 3.
values = [-10. * -10., 0., 10. * 10.] # each fold contribution to the
# std after substractig the mean
analitic_std = sqrt(sum(values) / nfolds)
self.assertEqual(cm.stdNfold(normalizedAccuracies=True), analitic_std)
# Also make sure that this test does not work without the normalization
self.assertNotEqual(cm.stdNfold(normalizedAccuracies=False),
analitic_std)
def testStdNfold(self):
cm = ConfusionMatrix()
# Fold 0 with acc = 0%
cm = self.populateFold(cm, 0, 1, 0, 1, fold=0)
# Fold 1 with acc = 10%
cm = self.populateFold(cm, 1, 9, 1, 9, fold=1)
# Fold 2 with acc = 20%
cm = self.populateFold(cm, 2, 8, 2, 8, fold=2)
nfolds = 3.
values = [-10. * -10., 0., 10. * 10.] # each fold contribution to the
# std after substractig the mean
analitic_std = sqrt(sum(values) / nfolds)
self.assertEqual(cm.stdNfold(), analitic_std)
