def test_GD(self):
print("Test GD")
ig = ImageGeometry(12, 13, 14)
initial = ig.allocate()
# b = initial.copy()
# fill with random numbers
# b.fill(numpy.random.random(initial.shape))
b = ig.allocate('random')
identity = IdentityOperator(ig)
norm2sq = LeastSquares(identity, b)
rate = norm2sq.L / 3.
alg = GD(initial=initial,
objective_function=norm2sq,
rate=rate,
atol=1e-9,
rtol=1e-6)
alg.max_iteration = 1000
alg.run(verbose=0)
self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
alg = GD(initial=initial,
objective_function=norm2sq,
rate=rate,
max_iteration=20,
update_objective_interval=2,
atol=1e-9,
rtol=1e-6)
alg.max_iteration = 20
self.assertTrue(alg.max_iteration == 20)
self.assertTrue(alg.update_objective_interval == 2)
alg.run(20, verbose=0)
self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
def test_GDArmijo2(self):
from cil.optimisation.functions import Rosenbrock
from cil.framework import VectorData, VectorGeometry
f = Rosenbrock(alpha=1., beta=100.)
vg = VectorGeometry(2)
x = vg.allocate('random_int', seed=2)
# x = vg.allocate('random', seed=1)
x.fill(numpy.asarray([10., -3.]))
max_iter = 10000
update_interval = 1000
alg = GD(x,
f,
max_iteration=max_iter,
update_objective_interval=update_interval,
alpha=1e6)
alg.run(verbose=0)
print(alg.get_output().as_array(), alg.step_size, alg.kmax, alg.k)
# this with 10k iterations
numpy.testing.assert_array_almost_equal(alg.get_output().as_array(),
[0.13463363, 0.01604593],
decimal=5)
def test_exception_initial_GD(self):
print("Test FISTA")
ig = ImageGeometry(127, 139, 149)
initial = ig.allocate()
b = initial.copy()
# fill with random numbers
b.fill(numpy.random.random(initial.shape))
initial = ig.allocate(ImageGeometry.RANDOM)
identity = IdentityOperator(ig)
norm2sq = OperatorCompositionFunction(L2NormSquared(b=b), identity)
opt = {'tol': 1e-4, 'memopt': False}
print("initial objective", norm2sq(initial))
try:
alg = GD(initial=initial,
objective_function=norm2sq,
x_init=initial)
assert False
except ValueError as ve:
assert True