def test_sum_overlapping_m(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 = max(step, 1) + random.choice([0, 1, randint(10)])
depth = width - 1
assert depth >= 0
overlap = width - step
mat_size = num_contribs * step + overlap
contribs = randn(num_contribs, width, width)
target_bm = gen_BandMat(mat_size, l=depth, u=depth)
target_bm_orig = target_bm.copy()
mat_bm = bmo.sum_overlapping_m(contribs, step=step)
assert mat_bm.size == mat_size
assert mat_bm.l == mat_bm.u == depth
# check target-based version adds to target_bm correctly
bmo.sum_overlapping_m(contribs, step=step, target_bm=target_bm)
assert_allclose(*cc(target_bm, target_bm_orig + mat_bm))
if num_contribs == 0:
# check action for no contributions
assert_allequal(mat_bm.full(), np.zeros((overlap, overlap)))
elif num_contribs == 1:
# check action for a single contribution
assert_allequal(mat_bm.full(), contribs[0])
else:
# check action under splitting list of contributions in two
split_pos = randint(num_contribs + 1)
mat_bm_again = bm.zeros(depth, depth, mat_size)
bmo.sum_overlapping_m(
contribs[:split_pos],
step=step,
target_bm=mat_bm_again.sub_matrix_view(
0, split_pos * step + overlap
)
)
bmo.sum_overlapping_m(
contribs[split_pos:],
step=step,
target_bm=mat_bm_again.sub_matrix_view(
split_pos * step, mat_size
)
)
assert_allclose(*cc(mat_bm, mat_bm_again))
def test_sum_overlapping_m_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 = max(step, 1) + random.choice([0, 1, randint(10)])
depth = width - 1
assert depth >= 0
overlap = width - step
mat_size = num_contribs * step + overlap
contribs = randn(num_contribs, width, width)
contribs_chunks = chunk_randomly(contribs)
target_bm = gen_BandMat(mat_size, l=depth, u=depth)
target_bm_orig = target_bm.copy()
bmo.sum_overlapping_m_chunked(
contribs_chunks, target_bm, step=step
)
mat_bm_good = bmo.sum_overlapping_m(contribs, step=step)
assert_allclose(*cc(target_bm, target_bm_orig + mat_bm_good))
def test_sum_overlapping_m_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 = max(step, 1) + random.choice([0, 1, randint(10)])
depth = width - 1
assert depth >= 0
overlap = width - step
mat_size = num_contribs * step + overlap
contribs = randn(num_contribs, width, width)
contribs_chunks = chunk_randomly(contribs)
target_bm = gen_BandMat(mat_size, l=depth, u=depth)
target_bm_orig = target_bm.copy()
bmo.sum_overlapping_m_chunked(contribs_chunks,
target_bm,
step=step)
mat_bm_good = bmo.sum_overlapping_m(contribs, step=step)
assert_allclose(*cc(target_bm, target_bm_orig + mat_bm_good))
def test_sum_overlapping_m(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 = max(step, 1) + random.choice([0, 1, randint(10)])
depth = width - 1
assert depth >= 0
overlap = width - step
mat_size = num_contribs * step + overlap
contribs = randn(num_contribs, width, width)
target_bm = gen_BandMat(mat_size, l=depth, u=depth)
target_bm_orig = target_bm.copy()
mat_bm = bmo.sum_overlapping_m(contribs, step=step)
assert mat_bm.size == mat_size
assert mat_bm.l == mat_bm.u == depth
# check target-based version adds to target_bm correctly
bmo.sum_overlapping_m(contribs, step=step, target_bm=target_bm)
assert_allclose(*cc(target_bm, target_bm_orig + mat_bm))
if num_contribs == 0:
# check action for no contributions
assert_allequal(mat_bm.full(), np.zeros((overlap, overlap)))
elif num_contribs == 1:
# check action for a single contribution
assert_allequal(mat_bm.full(), contribs[0])
else:
# check action under splitting list of contributions in two
split_pos = randint(num_contribs + 1)
mat_bm_again = bm.zeros(depth, depth, mat_size)
bmo.sum_overlapping_m(contribs[:split_pos],
step=step,
target_bm=mat_bm_again.sub_matrix_view(
0, split_pos * step + overlap))
bmo.sum_overlapping_m(contribs[split_pos:],
step=step,
target_bm=mat_bm_again.sub_matrix_view(
split_pos * step, mat_size))
assert_allclose(*cc(mat_bm, mat_bm_again))
