def test_ctm_admm_adjoint_run():
scen, control = test_ctm.small_scenario()
set_cells = []
set_ramps = []
adjoint = ctm_admm.AdmmCTMAdjoint(scen, set_cells, set_ramps)
u_star = AcceleratedGradient.ramp_metering().solve(adjoint,
ctm_admm.AdmmCTMAdjoint.no_control(scen, set_cells))
state = adjoint.fs(u_star)
#state.plot()
assert_almost_equal(adjoint.j(state, u_star), 7.7, delta=.1)
set_cells = [0, 1]
set_ramps = [0, 1]
adjoint = ctm_admm.AdmmCTMAdjoint(scen, set_cells, set_ramps)
u_star = AcceleratedGradient.ramp_metering().solve(adjoint,
ctm_admm.AdmmCTMAdjoint.no_control(scen, set_cells))
state = adjoint.fs(u_star)
#state.plot()
assert_almost_equal(adjoint.j(state, u_star), 2.0, delta=0.01)
set_cells = []
set_ramps = [0, 1]
adjoint = ctm_admm.AdmmCTMAdjoint(scen, set_cells, set_ramps)
u_star = AcceleratedGradient.ramp_metering().solve(adjoint, ctm_admm.AdmmCTMAdjoint.no_control(scen, set_cells))
state = adjoint.fs(u_star)
assert_almost_equal(adjoint.j(state, u_star), 7.7, delta=0.05)
#state.plot()
u_0 = u_star.copy()
u_0[10:] = 0.0
state_0 = adjoint.fs(u_0)
def test_large(self):
scen = large_network()
#scen, _ = small_scenario()
print scen
print scen.simulate().density
print scen.ttt(scen.simulate())
scen.simulate().plot()
AcceleratedGradient.ramp_metering().solve(CTMAdjoint(scen), scen.no_control().flatten())
def test_interpretation(self):
scen, control = small_scenario()
dummy_links = [0]
control = numpy.array(
[
1.0,
0.5,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.9,
0.9,
0.9,
0.0,
0,
0,
0,
0,
0,
0,
]
)
dummy = DummyControl(dummy_links, scen)
dummy.alpha = 0.0
self.assertAlmostEqual(7.7, dummy.eval(control))
dummy.alpha = 5.0
self.assertAlmostEqual(7.7, dummy.eval(control))
control[20] = 0.0
self.assertAlmostEqual(7.7 + dummy.alpha * 0.9 ** 2, dummy.eval(control))
control[20] = 0.9
grad = dummy.grad(control)
assert_array_almost_equal(grad[20:], numpy.zeros(10))
control[1] = 0.5
print dummy.grad(control)
control[20] = 0.5
grad = dummy.grad(control)
self.assertAlmostEqual(grad[20], -dummy.alpha * 2 * (0.4))
control[20] = 1.1
grad = dummy.grad(control)
self.assertAlmostEqual(grad[20], dummy.alpha * 2 * (0.2))
control[20] = 0.9
print AcceleratedGradient.ramp_metering().solve(dummy, control)
def test_smoother():
beta_op.beta = 20.0
beta_op.factor = 1.000
beta_op.beta_admm_factor = 1.000
beta_op.inner_iters = 30
beta = 1.0
n = 6
t = 10
fw = CTMFreeway.standard(n)
ic = InitialConditions.blank(n)
bc = BoundaryConditions.blank(n, t)
ic.mainline[:] = 1.0
ic.queue[0] = 0.5
ic.queue[[3]] = 0.5
bc.demand[0, :] = 0.5
bc.demand[[1, 4], :2] = 0.3
scen = Scenario(fw, ic, bc)
print scen.ttt(scen.simulate())
scen.simulate().plot()
# IpOptSolver(n_iters=100).solve(CTMAdjoint(scen), scen.no_control().flatten())
# u_star = AcceleratedGradient(n_iters=40, beta=50.0, lb=0.0, ub=1.0).solve(CTMAdjoint(scen),
# scen.no_control().transpose().flatten())
#
#
# print scen.ttt(scen.simulate(u_star))
# scen.simulate(u_star).plot()
sln = CtmAdjointAdmm.from_full_scenario(scen, 3, beta=beta, n_iters=100).solve()
u_star = sln.construct_control()
sln.density_summary()
print "###########"
print CTMAdjoint(scen).j(scen.simulate(u_star), u_star)
print scen.ttt(scen.simulate(u_star))
u_star = AcceleratedGradient(n_iters=20, beta=20.0, lb=0.0, ub=1.0).solve(
CTMAdjoint(scen), u_star.transpose().flatten()
)
print scen.ttt(scen.simulate(u_star))
def test_full_run():
scen, control = small_scenario()
adjoint = CTMAdjoint(scen)
u_star = AcceleratedGradient.ramp_metering().solve(adjoint, control.ravel())
assert_almost_equal(scen.ttt(CTMSimulator().simulate(scen, u_star)), 7.7, 2)
