def test_subsample_indexes_notsame(self): np.random.seed(1234567890) indexes = boot.subsample_indexes(np.arange(0, 50), 1000, -1) # Test to make sure that subsamples are not all the same. # In theory, this test could fail even with correct code, but in # practice the probability is too low to care, and the test is useful. np.testing.assert_(not np.all(indexes[0] == indexes[1:]))
def test_subsample_indexes_notsame(self): np.random.seed(1234567890) indexes = boot.subsample_indexes(np.arange(0,50), 1000, -1) # Test to make sure that subsamples are not all the same. # In theory, this test could fail even with correct code, but in # practice the probability is too low to care, and the test is useful. np.testing.assert_(not np.all(indexes[0]==indexes[1:]))
def test_subsample_indexes(self): indexes = boot.subsample_indexes(self.data, 1000, 0.5) # Each sample when sorted must contain len(self.data)/2 unique numbers (eg, be entirely unique) for x in indexes: np.testing.assert_(len(np.unique(x)) == len(self.data) / 2)
def test_subsample_indexes(self): indexes = boot.subsample_indexes(self.data, 1000, 0.5) # Each sample when sorted must contain len(self.data)/2 unique numbers (eg, be entirely unique) for x in indexes: np.testing.assert_(len(np.unique(x)) == len(self.data)/2)