def test_sum_overlapping_v_chunked(self, its=50):
for it in range(its):
num_contribs = random.choice([0, 1, randint(10), randint(100)])
step = random.choice([1, randint(2), randint(10)])
width = step + random.choice([0, 1, randint(10)])
overlap = width - step
vec_size = num_contribs * step + overlap
contribs = randn(num_contribs, width)
contribs_chunks = chunk_randomly(contribs)
target = randn(vec_size)
target_orig = target.copy()
bmo.sum_overlapping_v_chunked(
contribs_chunks, width, target, step=step
)
vec_good = bmo.sum_overlapping_v(contribs, step=step)
assert_allclose(target, target_orig + vec_good)
def test_sum_overlapping_v_chunked(self, its=50):
for it in range(its):
num_contribs = random.choice([0, 1, randint(10), randint(100)])
step = random.choice([1, randint(2), randint(10)])
width = step + random.choice([0, 1, randint(10)])
overlap = width - step
vec_size = num_contribs * step + overlap
contribs = randn(num_contribs, width)
contribs_chunks = chunk_randomly(contribs)
target = randn(vec_size)
target_orig = target.copy()
bmo.sum_overlapping_v_chunked(contribs_chunks,
width,
target,
step=step)
vec_good = bmo.sum_overlapping_v(contribs, step=step)
assert_allclose(target, target_orig + vec_good)
def test_sum_overlapping_v(self, its=50):
for it in range(its):
num_contribs = random.choice([0, 1, randint(10), randint(100)])
step = random.choice([1, randint(2), randint(10)])
width = step + random.choice([0, 1, randint(10)])
overlap = width - step
vec_size = num_contribs * step + overlap
contribs = randn(num_contribs, width)
target = randn(vec_size)
target_orig = target.copy()
vec = bmo.sum_overlapping_v(contribs, step=step)
assert vec.shape == (vec_size,)
# check target-based version adds to target correctly
bmo.sum_overlapping_v(contribs, step=step, target=target)
assert_allclose(target, target_orig + vec)
if num_contribs == 0:
# check action for no contributions
assert_allequal(vec, np.zeros((overlap,)))
elif num_contribs == 1:
# check action for a single contribution
assert_allequal(vec, contribs[0])
else:
# check action under splitting list of contributions in two
split_pos = randint(num_contribs + 1)
vec_again = np.zeros((vec_size,))
bmo.sum_overlapping_v(
contribs[:split_pos],
step=step,
target=vec_again[0:(split_pos * step + overlap)]
)
bmo.sum_overlapping_v(
contribs[split_pos:],
step=step,
target=vec_again[(split_pos * step):vec_size]
)
assert_allclose(vec, vec_again)
def test_sum_overlapping_v(self, its=50):
for it in range(its):
num_contribs = random.choice([0, 1, randint(10), randint(100)])
step = random.choice([1, randint(2), randint(10)])
width = step + random.choice([0, 1, randint(10)])
overlap = width - step
vec_size = num_contribs * step + overlap
contribs = randn(num_contribs, width)
target = randn(vec_size)
target_orig = target.copy()
vec = bmo.sum_overlapping_v(contribs, step=step)
assert vec.shape == (vec_size, )
# check target-based version adds to target correctly
bmo.sum_overlapping_v(contribs, step=step, target=target)
assert_allclose(target, target_orig + vec)
if num_contribs == 0:
# check action for no contributions
assert_allequal(vec, np.zeros((overlap, )))
elif num_contribs == 1:
# check action for a single contribution
assert_allequal(vec, contribs[0])
else:
# check action under splitting list of contributions in two
split_pos = randint(num_contribs + 1)
vec_again = np.zeros((vec_size, ))
bmo.sum_overlapping_v(contribs[:split_pos],
step=step,
target=vec_again[0:(split_pos * step +
overlap)])
bmo.sum_overlapping_v(contribs[split_pos:],
step=step,
target=vec_again[(split_pos *
step):vec_size])
assert_allclose(vec, vec_again)