def test_extract_overlapping_v(self, its=50):
for it in range(its):
num_subs = random.choice([0, 1, randint(10), randint(100)])
step = random.choice([1, randint(1, 10)])
width = step + random.choice([0, 1, randint(10)])
overlap = width - step
vec_size = num_subs * step + overlap
vec = randn(vec_size)
target = None if rand_bool() else randn(num_subs, width)
if target is None:
subvectors = bmo.extract_overlapping_v(vec, width, step=step)
assert subvectors.shape == (num_subs, width)
else:
bmo.extract_overlapping_v(vec, width, step=step, target=target)
subvectors = target
for index in range(num_subs):
assert_allequal(subvectors[index],
vec[(index * step):(index * step + width)])
def test_extract_overlapping_v(self, its=50):
for it in range(its):
num_subs = random.choice([0, 1, randint(10), randint(100)])
step = random.choice([1, randint(1, 10)])
width = step + random.choice([0, 1, randint(10)])
overlap = width - step
vec_size = num_subs * step + overlap
vec = randn(vec_size)
target = None if rand_bool() else randn(num_subs, width)
if target is None:
subvectors = bmo.extract_overlapping_v(vec, width, step=step)
assert subvectors.shape == (num_subs, width)
else:
bmo.extract_overlapping_v(vec, width, step=step, target=target)
subvectors = target
for index in range(num_subs):
assert_allequal(
subvectors[index],
vec[(index * step):(index * step + width)]
)
def test_extract_overlapping_v_chunked(self, its=50):
for it in range(its):
num_subs = random.choice([0, 1, randint(10), randint(100)])
step = random.choice([1, randint(1, 10)])
width = step + random.choice([0, 1, randint(10)])
overlap = width - step
vec_size = num_subs * step + overlap
chunk_size = random.choice([1, randint(1, 10), randint(1, 10)])
vec = randn(vec_size)
indices_remaining = set(range(num_subs))
subvectors_all = np.empty((num_subs, width))
for start, end, subvectors in bmo.extract_overlapping_v_chunked(
vec, width, chunk_size, step=step):
assert end >= start + 1
for index in range(start, end):
assert index in indices_remaining
indices_remaining.remove(index)
subvectors_all[start:end] = subvectors
subvectors_good = bmo.extract_overlapping_v(vec, width, step=step)
assert_allclose(subvectors_all, subvectors_good)
def test_extract_overlapping_v_chunked(self, its=50):
for it in range(its):
num_subs = random.choice([0, 1, randint(10), randint(100)])
step = random.choice([1, randint(1, 10)])
width = step + random.choice([0, 1, randint(10)])
overlap = width - step
vec_size = num_subs * step + overlap
chunk_size = random.choice([1, randint(1, 10), randint(1, 10)])
vec = randn(vec_size)
indices_remaining = set(range(num_subs))
subvectors_all = np.empty((num_subs, width))
for start, end, subvectors in bmo.extract_overlapping_v_chunked(
vec, width, chunk_size, step=step
):
assert end >= start + 1
for index in range(start, end):
assert index in indices_remaining
indices_remaining.remove(index)
subvectors_all[start:end] = subvectors
subvectors_good = bmo.extract_overlapping_v(vec, width, step=step)
assert_allclose(subvectors_all, subvectors_good)
