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)
