def test_ctm_adjoint():
scen, control = small_scenario()
state = CTMSimulator().simulate(scen, control)
hx = CTMAdjoint(scen).hx(state, control)
hu = CTMAdjoint(scen).hu(state, control)
assert_array_equal(hx, np.tril(hx))
u = np.ones(control.flatten().shape) * 0.01
adjoint = CTMAdjoint(scen)
assert_array_almost_equal(adjoint.grad(u), adjoint.grad_fd(u), 3)
def test_ttt():
scen, control = small_scenario()
state = CTMSimulator().simulate(scen, control)
adjoint = CTMAdjoint(scen)
jx_test = adjoint.jx(state, control)
jx = np.zeros(scen.N * scen.T * 8)
for i in range(scen.N):
for t in range(scen.T):
jx[t * scen.N * 8 + i] = 1.0
jx[t * scen.N * 8 + scen.N + i] = 1.0
assert_array_equal(jx, jx_test)
ju = adjoint.ju(state, control)
assert_array_equal(ju, np.zeros(scen.N * scen.T))
def test_fs_controlled():
beta_op.beta = 1.0
scen, control = small_scenario()
ca = CTMAdjoint(scen)
for _ in range(5):
c = rand(scen.N * scen.T, 1).flatten()
assert_array_almost_equal(ca.grad(c), ca.grad_fd(c))
# fs = CTMSimulator().simulate(scen, control)
ca = ControlledCtmAdjoint(True, scen)
control = ca.construct_control(ca.no_control())
fs = ca.fs(control)
# assert_array_equal(fs.density, [[.9, 1.0, .9, .1, 0, 0, 0, 0, 0, 0, 0], [0, 1.0, .9, .9, .1, 0, 0, 0, 0, 0, 0]])
assert_array_almost_equal(fs.density, [[0.9, 1.0, 0.9, 0.1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]])
control[1][:] += 2.0
fs = ca.fs(control)
assert_array_almost_equal(
fs.density, np.array([[0.9, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8], [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0]])
)
def test_simplest():
scen, control = smallest_possible()
u = control.flatten()
adjoint = CTMAdjoint(scen)
assert_array_almost_equal(adjoint.grad(u), adjoint.grad_fd(u))
