def test_gaussian_sum_no_clip_no_noise(self):
record1 = torch.tensor([2.0, 0.0])
record2 = torch.tensor([-1.0, 1.0])
query = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0)
query_result, _ = run_query(query, [record1, record2])
expected = np.asarray([1.0, 1.0])
assert_allclose(query_result, expected)
def test_gaussian_sum_with_clip_no_noise(self):
record1 = torch.tensor([-6.0, 8.0]) # Clipped to [-3.0, 4.0].
record2 = torch.tensor([4.0, -3.0]) # Not clipped.
query = gaussian_query.GaussianSumQuery(l2_norm_clip=5.0, stddev=0.0)
query_result, _ = run_query(query, [record1, record2])
expected = np.asarray([1.0, 1.0])
assert_allclose(query_result, expected)
def get_sample_state(records):
query = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0,
stddev=1.0)
global_state = query.initial_global_state()
params = query.derive_sample_params(global_state)
sample_state = query.initial_sample_state(records[0])
for record in records:
sample_state = query.accumulate_record(params, sample_state,
record)
return sample_state
def test_gaussian_sum_with_noise(self):
record1, record2 = 2.71828, 3.14159
stddev = 1.0
query = gaussian_query.GaussianSumQuery(l2_norm_clip=5.0,
stddev=stddev)
query_result, _ = run_query(query, torch.tensor([record1, record2]))
noised_sums = []
for _ in xrange(1000):
noised_sums.append(query_result)
result_stddev = np.std(noised_sums)
assert_near(result_stddev, stddev, 0.1)
def test_gaussian_sum_merge(self):
records1 = [torch.tensor([2.0, 0.0]), torch.tensor([-1.0, 1.0])]
records2 = [torch.tensor([3.0, 5.0]), torch.tensor([-1.0, 4.0])]
def get_sample_state(records):
query = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0,
stddev=1.0)
global_state = query.initial_global_state()
params = query.derive_sample_params(global_state)
sample_state = query.initial_sample_state(records[0])
for record in records:
sample_state = query.accumulate_record(params, sample_state,
record)
return sample_state
sample_state_1 = get_sample_state(records1)
sample_state_2 = get_sample_state(records2)
merged = gaussian_query.GaussianSumQuery(
10.0, 1.0).merge_sample_states(sample_state_1, sample_state_2)
expected = np.asarray([3.0, 10.0])
assert_allclose(merged, expected)