def test_extract_overlapping_m(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)])
depth = width - 1
assert depth >= 0
overlap = width - step
mat_size = num_subs * step + overlap
mat_bm = gen_BandMat(mat_size, l=depth, u=depth)
target = None if rand_bool() else randn(num_subs, width, width)
if target is None:
submats = bmo.extract_overlapping_m(mat_bm, step=step)
assert submats.shape == (num_subs, width, width)
else:
bmo.extract_overlapping_m(mat_bm, step=step, target=target)
submats = target
for index in range(num_subs):
assert_allequal(
submats[index],
mat_bm.sub_matrix_view(index * step,
index * step + width).full())
def test_extract_overlapping_m(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)])
depth = width - 1
assert depth >= 0
overlap = width - step
mat_size = num_subs * step + overlap
mat_bm = gen_BandMat(mat_size, l=depth, u=depth)
target = None if rand_bool() else randn(num_subs, width, width)
if target is None:
submats = bmo.extract_overlapping_m(mat_bm, step=step)
assert submats.shape == (num_subs, width, width)
else:
bmo.extract_overlapping_m(mat_bm, step=step, target=target)
submats = target
for index in range(num_subs):
assert_allequal(
submats[index],
mat_bm.sub_matrix_view(
index * step, index * step + width
).full()
)
def test_randomize_extra_entries_bm(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_bm = gen_BandMat_simple(size)
mat_full = mat_bm.full()
th.randomize_extra_entries_bm(mat_bm)
th.assert_allequal(mat_bm.full(), mat_full)
def test_BandMat_T(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
a_bm = gen_BandMat(size)
assert_allequal(a_bm.T.full(), a_bm.full().T)
assert a_bm.T.data is a_bm.data
if size > 0:
assert np.may_share_memory(a_bm.T.data, a_bm.data)
def test_zeros(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
mat_bm = bm.zeros(l, u, size)
assert mat_bm.l == l
assert mat_bm.u == u
assert_allequal(mat_bm.full(), np.zeros((size, size)))
def test_zeros(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
mat_bm = bm.zeros(l, u, size)
assert mat_bm.l == l
assert mat_bm.u == u
assert_allequal(mat_bm.full(), np.zeros((size, size)))
def test_BandMat_T(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
a_bm = gen_BandMat(size)
assert_allequal(a_bm.T.full(), a_bm.full().T)
assert a_bm.T.data is a_bm.data
if size > 0:
assert np.may_share_memory(a_bm.T.data, a_bm.data)
def test_assert_allequal(self):
a0 = np.array([2.0, 3.0, 4.0])
a1 = np.array([2.0, 3.0, 4.0])
a2 = np.array([2.0, 3.0])
a3 = np.array([2.0, 3.0, 5.0])
a4 = np.array([[2.0, 3.0, 4.0]])
th.assert_allequal(a0, a0)
th.assert_allequal(a0, a1)
self.assertRaises(AssertionError, th.assert_allequal, a0, a2)
self.assertRaises(AssertionError, th.assert_allequal, a0, a3)
self.assertRaises(AssertionError, th.assert_allequal, a0, a4)
def test_band_ec_bm(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
a_bm = gen_BandMat(size)
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
b_bm = bm.band_ec_bm(l, u, a_bm)
b_full_good = fl.band_ec(l, u, a_bm.full())
assert_allequal(b_bm.full(), b_full_good)
assert not np.may_share_memory(b_bm.data, a_bm.data)
def test_band_e_bm(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_bm = gen_BandMat(size)
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
mat_rect = bm.band_e_bm(l, u, mat_bm)
mat_rect_good = fl.band_e(l, u, mat_bm.full())
assert_allequal(mat_rect, mat_rect_good)
assert not np.may_share_memory(mat_rect, mat_bm.data)
def test_band_c_bm(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
mat_rect = randn(l + u + 1, size)
mat_bm = bm.band_c_bm(l, u, mat_rect)
mat_full_good = fl.band_c(l, u, mat_rect)
assert_allequal(mat_bm.full(), mat_full_good)
assert not np.may_share_memory(mat_bm.data, mat_rect)
def test_band_c_bm(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
mat_rect = randn(l + u + 1, size)
mat_bm = bm.band_c_bm(l, u, mat_rect)
mat_full_good = fl.band_c(l, u, mat_rect)
assert_allequal(mat_bm.full(), mat_full_good)
assert not np.may_share_memory(mat_bm.data, mat_rect)
def test_band_e_bm(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_bm = gen_BandMat(size)
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
mat_rect = bm.band_e_bm(l, u, mat_bm)
mat_rect_good = fl.band_e(l, u, mat_bm.full())
assert_allequal(mat_rect, mat_rect_good)
assert not np.may_share_memory(mat_rect, mat_bm.data)
def test_band_ec_bm(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
a_bm = gen_BandMat(size)
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
b_bm = bm.band_ec_bm(l, u, a_bm)
b_full_good = fl.band_ec(l, u, a_bm.full())
assert_allequal(b_bm.full(), b_full_good)
assert not np.may_share_memory(b_bm.data, a_bm.data)
def test_zero_extra_entries(self, its=100):
"""Checks zero_extra_entries against its equivalent definition."""
for it in range(its):
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_rect = randn(l + u + 1, size)
mat_rect_good = mat_rect.copy()
array_mem = get_array_mem(mat_rect)
fl.zero_extra_entries(l, u, mat_rect)
mat_rect_good[:] = fl.band_e(l, u, fl.band_c(l, u, mat_rect_good))
assert_allequal(mat_rect, mat_rect_good)
assert get_array_mem(mat_rect) == array_mem
def test_zero_extra_entries(self, its=100):
"""Checks zero_extra_entries against its equivalent definition."""
for it in range(its):
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_rect = randn(l + u + 1, size)
mat_rect_good = mat_rect.copy()
array_mem = get_array_mem(mat_rect)
fl.zero_extra_entries(l, u, mat_rect)
mat_rect_good[:] = fl.band_e(l, u, fl.band_c(l, u, mat_rect_good))
assert_allequal(mat_rect, mat_rect_good)
assert get_array_mem(mat_rect) == array_mem
def test_band_ec(self, its=100):
"""Checks band_ec against its definition and required properties."""
for it in range(its):
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_full = randn(size, size)
mat_full_new = fl.band_ec(l, u, mat_full)
mat_full_new_good = fl.band_c(l, u, fl.band_e(l, u, mat_full))
assert_allequal(mat_full_new, mat_full_new_good)
assert not np.may_share_memory(mat_full_new, mat_full)
# check idempotent
assert_allequal(fl.band_ec(l, u, mat_full_new), mat_full_new)
def test_from_full(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
mat_full = gen_BandMat(size).full()
zero_outside_band = np.all(fl.band_ec(l, u, mat_full) == mat_full)
if zero_outside_band:
mat_bm = bm.from_full(l, u, mat_full)
assert mat_bm.l == l
assert mat_bm.u == u
assert_allequal(mat_bm.full(), mat_full)
assert not np.may_share_memory(mat_bm.data, mat_full)
else:
self.assertRaises(AssertionError, bm.from_full, l, u, mat_full)
def test_from_full(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
mat_full = gen_BandMat(size).full()
zero_outside_band = np.all(fl.band_ec(l, u, mat_full) == mat_full)
if zero_outside_band:
mat_bm = bm.from_full(l, u, mat_full)
assert mat_bm.l == l
assert mat_bm.u == u
assert_allequal(mat_bm.full(), mat_full)
assert not np.may_share_memory(mat_bm.data, mat_full)
else:
self.assertRaises(AssertionError, bm.from_full, l, u, mat_full)
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_diag(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
vec = randn(size)
mat_bm = bm.diag(vec)
assert isinstance(mat_bm, bm.BandMat)
assert_allequal(mat_bm.full(), np.diag(vec))
assert mat_bm.data.base is vec
if size > 0:
assert np.may_share_memory(mat_bm.data, vec)
mat_bm = gen_BandMat(size)
vec = bm.diag(mat_bm)
assert_allequal(vec, np.diag(mat_bm.full()))
assert vec.base is mat_bm.data
if size > 0:
assert np.may_share_memory(vec, mat_bm.data)
def test_BandMat_full(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
a_bm = gen_BandMat(size)
a_full = a_bm.full()
l, u = a_bm.l, a_bm.u
# N.B. these tests are not really testing much of anything (they
# are virtually identical to the implementation of BandMat.full),
# but this is not that surprising since the lines below are kind
# of the definition of the representation used by BandMat in the
# two cases (transposed True and transposed False).
if a_bm.transposed:
assert_allequal(a_full.T, fl.band_c(u, l, a_bm.data))
else:
assert_allequal(a_full, fl.band_c(l, u, a_bm.data))
assert not np.may_share_memory(a_full, a_bm.data)
def test_diag(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
vec = randn(size)
mat_bm = bm.diag(vec)
assert isinstance(mat_bm, bm.BandMat)
assert_allequal(mat_bm.full(), np.diag(vec))
assert mat_bm.data.base is vec
if size > 0:
assert np.may_share_memory(mat_bm.data, vec)
mat_bm = gen_BandMat(size)
vec = bm.diag(mat_bm)
assert_allequal(vec, np.diag(mat_bm.full()))
assert vec.base is mat_bm.data
if size > 0:
assert np.may_share_memory(vec, mat_bm.data)
def test_BandMat_full(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
a_bm = gen_BandMat(size)
a_full = a_bm.full()
l, u = a_bm.l, a_bm.u
# N.B. these tests are not really testing much of anything (they
# are virtually identical to the implementation of BandMat.full),
# but this is not that surprising since the lines below are kind
# of the definition of the representation used by BandMat in the
# two cases (transposed True and transposed False).
if a_bm.transposed:
assert_allequal(a_full.T, fl.band_c(u, l, a_bm.data))
else:
assert_allequal(a_full, fl.band_c(l, u, a_bm.data))
assert not np.may_share_memory(a_full, a_bm.data)
def test_band_ec(self, its=100):
"""Checks band_ec against its definition and required properties."""
for it in range(its):
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_full = randn(size, size)
mat_full_new = fl.band_ec(l, u, mat_full)
mat_full_new_good = fl.band_c(l, u, fl.band_e(l, u, mat_full))
assert_allequal(mat_full_new, mat_full_new_good)
assert not np.may_share_memory(mat_full_new, mat_full)
# check idempotent
assert_allequal(
fl.band_ec(l, u, mat_full_new),
mat_full_new
)
def test_BandMat_copy(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_bm = gen_BandMat(size)
mat_full_orig = mat_bm.full().copy()
mat_bm_new = mat_bm.copy()
assert mat_bm_new.l == mat_bm.l
assert mat_bm_new.u == mat_bm.u
assert not mat_bm_new.transposed
# check that copy represents the same matrix
assert_allequal(mat_bm_new.full(), mat_full_orig)
# check that copy does not share memory with original
assert not np.may_share_memory(mat_bm_new.data, mat_bm.data)
# check that mutating the copy does not change the original
mat_bm_new.data += 1.0
assert_allequal(mat_bm.full(), mat_full_orig)
def test_BandMat_copy_exact(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_bm = gen_BandMat(size)
mat_full_orig = mat_bm.full().copy()
mat_bm_new = mat_bm.copy_exact()
assert mat_bm_new.l == mat_bm.l
assert mat_bm_new.u == mat_bm.u
assert mat_bm_new.transposed == mat_bm.transposed
# check that copy represents the same matrix
assert_allequal(mat_bm_new.full(), mat_full_orig)
# check that copy does not share memory with original
assert not np.may_share_memory(mat_bm_new.data, mat_bm.data)
# check that mutating the copy does not change the original
mat_bm_new.data += 1.0
assert_allequal(mat_bm.full(), mat_full_orig)
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_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_band_cTe(self, its=100):
"""Checks band_cTe against its definition and required properties."""
for it in range(its):
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_rect = randn(l + u + 1, size)
mat_rect_new = fl.band_cTe(l, u, mat_rect)
mat_rect_new_good = fl.band_e(u, l, fl.band_c(l, u, mat_rect).T)
assert_allequal(mat_rect_new, mat_rect_new_good)
assert not np.may_share_memory(mat_rect_new, mat_rect)
# check a property to do with doing band_cTe twice
assert_allequal(
fl.band_cTe(u, l, mat_rect_new),
fl.band_ce(l, u, mat_rect)
)
# check version that uses pre-specified target
mat_rect_new2 = np.empty((l + u + 1, size))
array_mem = get_array_mem(mat_rect, mat_rect_new2)
ret = fl.band_cTe(l, u, mat_rect, target_rect=mat_rect_new2)
self.assertIsNone(ret)
assert_allequal(mat_rect_new2, mat_rect_new)
assert get_array_mem(mat_rect, mat_rect_new2) == array_mem
def test_band_cTe(self, its=100):
"""Checks band_cTe against its definition and required properties."""
for it in range(its):
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_rect = randn(l + u + 1, size)
mat_rect_new = fl.band_cTe(l, u, mat_rect)
mat_rect_new_good = fl.band_e(u, l, fl.band_c(l, u, mat_rect).T)
assert_allequal(mat_rect_new, mat_rect_new_good)
assert not np.may_share_memory(mat_rect_new, mat_rect)
# check a property to do with doing band_cTe twice
assert_allequal(
fl.band_cTe(u, l, mat_rect_new),
fl.band_ce(l, u, mat_rect)
)
# check version that uses pre-specified target
mat_rect_new2 = np.empty((l + u + 1, size))
array_mem = get_array_mem(mat_rect, mat_rect_new2)
ret = fl.band_cTe(l, u, mat_rect, target_rect=mat_rect_new2)
self.assertIsNone(ret)
assert_allequal(mat_rect_new2, mat_rect_new)
assert get_array_mem(mat_rect, mat_rect_new2) == array_mem
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_band_e_basis(self, its=100):
"""Checks band_e behaves correctly on canonical basis matrices."""
for it in range(its):
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
# size >= 1 (there are no canonical basis matrices if size == 0)
size = random.choice([1, 2, randint(1, 10), randint(1, 100)])
# pick a random canonical basis matrix
k = randint(size)
j = randint(size)
mat_full = np.zeros((size, size))
mat_full[k, j] = 1.0
mat_rect = fl.band_e(l, u, mat_full)
i = k - j
mat_rect_good = np.zeros((l + u + 1, size))
if -u <= i <= l:
mat_rect_good[u + i, j] = 1.0
assert_allequal(mat_rect, mat_rect_good)
def test_band_c_basis(self, its=100):
"""Checks band_c behaves correctly on canonical basis matrices."""
for it in range(its):
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
# size >= 1 (there are no canonical basis matrices if size == 0)
size = random.choice([1, 2, randint(1, 10), randint(1, 100)])
# pick a random canonical basis matrix
i = randint(-u, l + 1)
j = randint(size)
mat_rect = np.zeros((l + u + 1, size))
mat_rect[u + i, j] = 1.0
mat_full = fl.band_c(l, u, mat_rect)
k = i + j
mat_full_good = np.zeros((size, size))
if 0 <= k < size:
mat_full_good[k, j] = 1.0
assert_allequal(mat_full, mat_full_good)
def test_band_c_basis(self, its=100):
"""Checks band_c behaves correctly on canonical basis matrices."""
for it in range(its):
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
# size >= 1 (there are no canonical basis matrices if size == 0)
size = random.choice([1, 2, randint(1, 10), randint(1, 100)])
# pick a random canonical basis matrix
i = randint(-u, l + 1)
j = randint(size)
mat_rect = np.zeros((l + u + 1, size))
mat_rect[u + i, j] = 1.0
mat_full = fl.band_c(l, u, mat_rect)
k = i + j
mat_full_good = np.zeros((size, size))
if 0 <= k < size:
mat_full_good[k, j] = 1.0
assert_allequal(mat_full, mat_full_good)
def test_band_e_basis(self, its=100):
"""Checks band_e behaves correctly on canonical basis matrices."""
for it in range(its):
l = random.choice([0, 1, randint(0, 10)])
u = random.choice([0, 1, randint(0, 10)])
# size >= 1 (there are no canonical basis matrices if size == 0)
size = random.choice([1, 2, randint(1, 10), randint(1, 100)])
# pick a random canonical basis matrix
k = randint(size)
j = randint(size)
mat_full = np.zeros((size, size))
mat_full[k, j] = 1.0
mat_rect = fl.band_e(l, u, mat_full)
i = k - j
mat_rect_good = np.zeros((l + u + 1, size))
if -u <= i <= l:
mat_rect_good[u + i, j] = 1.0
assert_allequal(mat_rect, mat_rect_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_BandMat_equiv(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_bm = gen_BandMat(size)
l_new = random.choice([None, 0, 1, randint(0, 10)])
u_new = random.choice([None, 0, 1, randint(0, 10)])
transposed_new = random.choice([None, True, False])
zero_extra = rand_bool()
l_new_value = mat_bm.l if l_new is None else l_new
u_new_value = mat_bm.u if u_new is None else u_new
transposed_new_value = (mat_bm.transposed if transposed_new is None
else transposed_new)
if l_new_value < mat_bm.l or u_new_value < mat_bm.u:
self.assertRaises(AssertionError,
mat_bm.equiv,
l_new=l_new,
u_new=u_new,
transposed_new=transposed_new,
zero_extra=zero_extra)
else:
mat_bm_new = mat_bm.equiv(l_new=l_new,
u_new=u_new,
transposed_new=transposed_new,
zero_extra=zero_extra)
assert mat_bm_new.l == l_new_value
assert mat_bm_new.u == u_new_value
assert mat_bm_new.transposed == transposed_new_value
assert_allequal(mat_bm_new.full(), mat_bm.full())
assert not np.may_share_memory(mat_bm_new.data, mat_bm.data)
if zero_extra:
mat_new_data_good = (fl.band_e(
u_new_value, l_new_value,
mat_bm.full().T)) if mat_bm_new.transposed else (
fl.band_e(l_new_value, u_new_value, mat_bm.full()))
assert_allequal(mat_bm_new.data, mat_new_data_good)
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_BandMat_equiv(self, its=50):
for it in range(its):
size = random.choice([0, 1, randint(0, 10), randint(0, 100)])
mat_bm = gen_BandMat(size)
l_new = random.choice([None, 0, 1, randint(0, 10)])
u_new = random.choice([None, 0, 1, randint(0, 10)])
transposed_new = random.choice([None, True, False])
zero_extra = rand_bool()
l_new_value = mat_bm.l if l_new is None else l_new
u_new_value = mat_bm.u if u_new is None else u_new
transposed_new_value = (mat_bm.transposed if transposed_new is None
else transposed_new)
if l_new_value < mat_bm.l or u_new_value < mat_bm.u:
self.assertRaises(AssertionError,
mat_bm.equiv,
l_new=l_new, u_new=u_new,
transposed_new=transposed_new,
zero_extra=zero_extra)
else:
mat_bm_new = mat_bm.equiv(l_new=l_new, u_new=u_new,
transposed_new=transposed_new,
zero_extra=zero_extra)
assert mat_bm_new.l == l_new_value
assert mat_bm_new.u == u_new_value
assert mat_bm_new.transposed == transposed_new_value
assert_allequal(mat_bm_new.full(), mat_bm.full())
assert not np.may_share_memory(mat_bm_new.data, mat_bm.data)
if zero_extra:
mat_new_data_good = (
fl.band_e(u_new_value, l_new_value, mat_bm.full().T)
) if mat_bm_new.transposed else (
fl.band_e(l_new_value, u_new_value, mat_bm.full())
)
assert_allequal(mat_bm_new.data, mat_new_data_good)
