def random_prod_state_tenpy(L, a_model):
product_state = []
#the numpy mps used to compare
psi_compare = []
sz = 2. * np.random.randint(0, 2, size=L) - 1.0
for i in range(L):
psi_compare.append(np.zeros((2, 1, 1)))
if sz[i] > 0:
product_state += ["up"]
psi_compare[-1][0, 0, 0] = 1
else:
product_state += ["down"]
psi_compare[-1][1, 0, 0] = 1
psi = MPS.from_product_state(a_model.lat.mps_sites(),
product_state,
bc=a_model.lat.bc_MPS,
form='B')
psi_converted = []
for i in range(L):
site = psi.sites[i]
perm = site.perm
B_tmp = psi.get_B(i).transpose(['p', 'vL', 'vR']).to_ndarray()
B = B_tmp[inverse_permutation(perm), :, :]
B = B[::-1, :, :]
psi_converted.append(B)
return psi
def get_site_op_flat(site, op):
"""Like ``site.get_op(op)``, but return a flat numpy array and revert permutation from charges.
site.perm should store the permutation compared to "conserve=None", so we can use that to
convert to the "standard" flat form with conserve=None.
"""
op = site.get_op(op).to_ndarray()
iperm = inverse_permutation(site.perm)
return op[np.ix_(iperm, iperm)]
def test_npc_svd():
for m, n in [(1, 1), (1, 10), (10, 1), (10, 10), (10, 20)]:
print("m, n = ", m, n)
tol_NULP = max(20 * max(m, n)**3, 1000)
for i in range(1000):
A = random_Array((m, n), chinfo3, sort=True)
if A.stored_blocks > 0:
break
Aflat = A.to_ndarray()
Sonly = npc.svd(A, compute_uv=False)
U, S, VH = npc.svd(A, full_matrices=False, compute_uv=True)
assert (U.shape[1] == S.shape[0] == VH.shape[0])
U.test_sanity()
VH.test_sanity()
npt.assert_array_almost_equal_nulp(Sonly, S, tol_NULP)
recalc = npc.tensordot(U.scale_axis(S, axis=-1), VH, axes=1)
npt.assert_array_almost_equal_nulp(recalc.to_ndarray(), Aflat,
tol_NULP)
# compare with flat SVD
Uflat, Sflat, VHflat = np.linalg.svd(Aflat, False, True)
perm = np.argsort(-S) # sort descending
print(S[perm])
iperm = inverse_permutation(perm)
for i in range(len(Sflat)):
if i not in iperm: # dopped it in npc.svd()
assert (Sflat[i] < EPS * 10)
Sflat = Sflat[iperm]
npt.assert_array_almost_equal_nulp(Sonly, Sflat, tol_NULP)
# comparing U and Uflat is hard: U columns can change by a phase...
print("with full_matrices")
Ufull, Sfull, VHfull = npc.svd(A, full_matrices=True, compute_uv=True)
Ufull.test_sanity()
VHfull.test_sanity()
npt.assert_array_almost_equal_nulp(Sfull, S, tol_NULP)
print("for trivial charges")
A = npc.Array.from_func(np.random.random, [lcTr, lcTr.conj()],
shape_kw='size')
Aflat = A.to_ndarray()
U, S, VH = npc.svd(A)
recalc = npc.tensordot(U.scale_axis(S, axis=-1), VH, axes=1)
tol_NULP = max(20 * max(A.shape)**3, 1000)
npt.assert_array_almost_equal_nulp(recalc.to_ndarray(), Aflat, tol_NULP)
