def test_FISTA_catch_Lipschitz(self):
print ("Test FISTA catch Lipschitz")
ig = ImageGeometry(127,139,149)
x_init = ImageData(geometry=ig)
x_init = ig.allocate()
b = x_init.copy()
# fill with random numbers
b.fill(numpy.random.random(x_init.shape))
x_init = ig.allocate(ImageGeometry.RANDOM)
identity = Identity(ig)
#### it seems FISTA does not work with Nowm2Sq
norm2sq = LeastSquares(identity, b)
print ('Lipschitz', norm2sq.L)
norm2sq.L = None
#norm2sq.L = 2 * norm2sq.c * identity.norm()**2
#norm2sq = FunctionOperatorComposition(L2NormSquared(b=b), identity)
opt = {'tol': 1e-4, 'memopt':False}
print ("initial objective", norm2sq(x_init))
try:
alg = FISTA(x_init=x_init, f=norm2sq, g=ZeroFunction())
self.assertTrue(False)
except ValueError as ve:
print (ve)
self.assertTrue(True)
def test_CGLS(self):
print ("Test CGLS")
ig = ImageGeometry(124,153,154)
x_init = ImageData(geometry=ig)
b = x_init.copy()
# fill with random numbers
b.fill(numpy.random.random(x_init.shape))
identity = TomoIdentity(geometry=ig)
alg = CGLS(x_init=x_init, operator=identity, data=b)
alg.max_iteration = 1
alg.run(20, verbose=True)
self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
def test_GradientDescent(self):
print ("Test GradientDescent")
ig = ImageGeometry(12,13,14)
x_init = ImageData(geometry=ig)
b = x_init.copy()
# fill with random numbers
b.fill(numpy.random.random(x_init.shape))
identity = TomoIdentity(geometry=ig)
norm2sq = Norm2sq(identity, b)
alg = GradientDescent(x_init=x_init,
objective_function=norm2sq,
rate=0.3)
alg.max_iteration = 20
alg.run(20, verbose=True)
self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
def test_FISTA(self):
print ("Test FISTA")
ig = ImageGeometry(127,139,149)
x_init = ImageData(geometry=ig)
b = x_init.copy()
# fill with random numbers
b.fill(numpy.random.random(x_init.shape))
x_init = ImageData(geometry=ig)
x_init.fill(numpy.random.random(x_init.shape))
identity = TomoIdentity(geometry=ig)
norm2sq = Norm2sq(identity, b)
opt = {'tol': 1e-4, 'memopt':False}
alg = FISTA(x_init=x_init, f=norm2sq, g=None, opt=opt)
alg.max_iteration = 2
alg.run(20, verbose=True)
self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
alg.run(20, verbose=True)
self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())