def test_qr():
for shape in [(4, 4), (6, 8), (8, 6)]:
tol = shape[0] * shape[1] * 100
for qtotal_A in [None, [1]]:
A = random_Array(shape, chinfo3, qtotal=qtotal_A, sort=False)
A_flat = A.to_ndarray()
for qtotal_Q in [None, [1]]:
for mode in ['reduced', 'complete']:
for qconj in [+1, -1]:
for pos in [False, True]:
print(
f"shape={shape!s} qtot_A={qtotal_A!s} qtot_Q={qtotal_Q!s}"
f"mode={mode!s} pos_diag_R={pos!s} inner_qconj={qconj:+d}"
)
Q, R = npc.qr(A,
mode=mode,
pos_diag_R=pos,
qtotal_Q=qtotal_Q,
inner_qconj=qconj)
# print(q._qdata)
Q.test_sanity()
R.test_sanity()
assert np.all(
Q.qtotal) == A.chinfo.make_valid(qtotal_Q)
assert R.legs[0].qconj == qconj
QR = npc.tensordot(Q, R, axes=1)
npt.assert_array_almost_equal_nulp(
A_flat, QR.to_ndarray(), tol)
QdaggerQ = npc.tensordot(Q.conj(), Q, axes=[0, 0])
assert npc.norm(QdaggerQ -
npc.eye_like(QdaggerQ)) < 1.e-10
def test_qr():
for shape in [(4, 4), (6, 8), (8, 6)]:
for qtotal in [None, [1]]:
print("qtotal=", qtotal, "shape =", shape)
A = random_Array(shape, chinfo3, qtotal=qtotal, sort=False)
A_flat = A.to_ndarray()
q, r = npc.qr(A, 'reduced')
print(q._qdata)
q.test_sanity()
r.test_sanity()
qr = npc.tensordot(q, r, axes=1)
npt.assert_array_almost_equal_nulp(A_flat, qr.to_ndarray(), shape[0] * shape[1] * 100)
