def test_pinv_Z_alt():
## Test pseudo-inverse computation of a complex double precision distributed matrix
m, n = 7, 4
gA = np.random.standard_normal((m, n)).astype(np.float64)
gA = gA + 1.0J * np.random.standard_normal((m, n)).astype(np.float64)
gA = np.dot(gA, gA.T.conj())
assert np.linalg.matrix_rank(gA) < gA.shape[0] # no full rank
gA = np.asfortranarray(gA)
m, n = gA.shape
dA = core.DistributedMatrix.from_global_array(gA, rank=0)
pinvA = rt.pinv(dA)
gpinvA = pinvA.to_global_array()
gpinvA = gpinvA[:n, :]
if rank == 0:
assert not allclose(gA, np.dot(gA, np.dot(gpinvA, gA)))
assert not allclose(gpinvA, np.dot(gpinvA, np.dot(gA, gpinvA)))
spinvA = la.pinv(gA)
assert allclose(gA, np.dot(gA, np.dot(spinvA, gA)))
assert allclose(spinvA, np.dot(spinvA, np.dot(gA, spinvA)))
assert not allclose(gpinvA, la.pinv(gA)) # compare with scipy result
def test_pinv_Z_alt():
## Test pseudo-inverse computation of a complex double precision distributed matrix
m, n = 7, 4
gA = np.random.standard_normal((m, n)).astype(np.float64)
gA = gA + 1.0J * np.random.standard_normal((m, n)).astype(np.float64)
gA = np.dot(gA, gA.T.conj())
assert np.linalg.matrix_rank(gA) < gA.shape[0] # no full rank
gA = np.asfortranarray(gA)
m, n = gA.shape
dA = core.DistributedMatrix.from_global_array(gA, rank=0)
pinvA = rt.pinv(dA)
gpinvA = pinvA.to_global_array()
gpinvA = gpinvA[:n, :]
if rank == 0:
assert not allclose(gA, np.dot(gA, np.dot(gpinvA, gA)))
assert not allclose(gpinvA, np.dot(gpinvA, np.dot(gA, gpinvA)))
spinvA = la.pinv(gA)
assert allclose(gA, np.dot(gA, np.dot(spinvA, gA)))
assert allclose(spinvA, np.dot(spinvA, np.dot(gA, spinvA)))
assert not allclose(gpinvA, la.pinv(gA)) # compare with scipy result
def test_pinv_D():
## Test pseudo-inverse computation of a real double precision distributed matrix
m, n = 9, 23
gA = np.random.standard_normal((m, n)).astype(np.float64)
gA = np.asfortranarray(gA)
dA = core.DistributedMatrix.from_global_array(gA, rank=0)
pinvA = rt.pinv(dA)
gpinvA = pinvA.to_global_array()
gpinvA = gpinvA[:n, :]
if rank == 0:
assert allclose(gA, np.dot(gA, np.dot(gpinvA, gA)))
assert allclose(gpinvA, np.dot(gpinvA, np.dot(gA, gpinvA)))
assert allclose(gpinvA, la.pinv(gA)) # compare with scipy result
if m == n:
assert allclose(gpinvA, la.inv(gA)) # compare with scipy result
def test_pinv_D():
## Test pseudo-inverse computation of a real double precision distributed matrix
m, n = 9, 23
gA = np.random.standard_normal((m, n)).astype(np.float64)
gA = np.asfortranarray(gA)
dA = core.DistributedMatrix.from_global_array(gA, rank=0)
pinvA = rt.pinv(dA)
gpinvA = pinvA.to_global_array()
gpinvA = gpinvA[:n, :]
if rank == 0:
assert allclose(gA, np.dot(gA, np.dot(gpinvA, gA)))
assert allclose(gpinvA, np.dot(gpinvA, np.dot(gA, gpinvA)))
assert allclose(gpinvA, la.pinv(gA)) # compare with scipy result
if m == n:
assert allclose(gpinvA, la.inv(gA)) # compare with scipy result
