def test_CompositionOperator_direct3(self): ig = self.ig data = self.data G = GradientOperator(domain_geometry=ig) Id1 = 2 * IdentityOperator(ig) Id2 = IdentityOperator(ig) d = CompositionOperator(G, Id2) out1 = G.direct(data) d_out = d.direct(data) d1 = Id2.direct(data) d2 = G.direct(d1) numpy.testing.assert_array_almost_equal( d2.get_item(0).as_array(), d_out.get_item(0).as_array()) numpy.testing.assert_array_almost_equal( d2.get_item(1).as_array(), d_out.get_item(1).as_array()) G2Id = G.compose(2 * Id2) d2g = G2Id.direct(data) numpy.testing.assert_array_almost_equal( d2g.get_item(0).as_array(), 2 * d_out.get_item(0).as_array()) numpy.testing.assert_array_almost_equal( d2g.get_item(1).as_array(), 2 * d_out.get_item(1).as_array())
def test_NestedBlockDataContainer2(self): M, N = 2, 3 ig = ImageGeometry(voxel_num_x=M, voxel_num_y=N) ag = ig u = ig.allocate(1) op1 = GradientOperator(ig) op2 = IdentityOperator(ig, ag) operator = BlockOperator(op1, op2, shape=(2, 1)) d1 = op1.direct(u) d2 = op2.direct(u) d = operator.direct(u) dd = operator.domain_geometry() ww = operator.range_geometry() c1 = d + d c2 = 2 * d c3 = d / (d + 0.0001) c5 = d.get_item(0).power(2).sum()
def test_PowerMethod(self): print("test_BlockOperator") N, M = 200, 300 niter = 10 ig = ImageGeometry(N, M) Id = IdentityOperator(ig) G = GradientOperator(ig) uid = Id.domain_geometry().allocate(ImageGeometry.RANDOM, seed=1) a = LinearOperator.PowerMethod(Id, niter, uid) #b = LinearOperator.PowerMethodNonsquare(Id, niter, uid) b = LinearOperator.PowerMethod(Id, niter) print("Edo impl", a[0]) print("None impl", b[0]) #self.assertAlmostEqual(a[0], b[0]) self.assertNumpyArrayAlmostEqual(a[0], b[0], decimal=6) a = LinearOperator.PowerMethod(G, niter, uid) b = LinearOperator.PowerMethod(G, niter) #b = LinearOperator.PowerMethodNonsquare(G, niter, uid) print("Edo impl", a[0]) #print ("old impl", b[0]) self.assertNumpyArrayAlmostEqual(a[0], b[0], decimal=2)
def test_IdentityOperator(self): ig = ImageGeometry(10, 20, 30) img = ig.allocate() print(img.shape, ig.shape) self.assertTrue(img.shape == (30, 20, 10)) self.assertEqual(img.sum(), 0) Id = IdentityOperator(ig) y = Id.direct(img) numpy.testing.assert_array_equal(y.as_array(), img.as_array())
def test_IdentityOperator(self): print("test_IdentityOperator") ig = ImageGeometry(10, 20, 30) img = ig.allocate() # img.fill(numpy.ones((30,20,10))) self.assertTrue(img.shape == (30, 20, 10)) #self.assertEqual(img.sum(), 2*float(10*20*30)) self.assertEqual(img.sum(), 0.) Id = IdentityOperator(ig) y = Id.direct(img) numpy.testing.assert_array_equal(y.as_array(), img.as_array())
def test_SumOperator(self): # numpy.random.seed(1) ig = self.ig data = self.data Id1 = 2 * IdentityOperator(ig) Id2 = IdentityOperator(ig) c = SumOperator(Id1, Id2) out = c.direct(data) numpy.testing.assert_array_almost_equal(out.as_array(), 3 * data.as_array())
def test_CompositionOperator_adjoint5(self): ig = self.ig data = self.data G = GradientOperator(domain_geometry=ig) Id1 = 3 * IdentityOperator(ig) Id = Id1 - IdentityOperator(ig) d = G.compose(Id) da = d.direct(data) out1 = G.adjoint(da) out2 = d.adjoint(da) numpy.testing.assert_array_almost_equal(out2.as_array(), 2 * out1.as_array())
def test_PDHG_strongly_convex_gamma_g(self): ig = ImageGeometry(3,3) data = ig.allocate('random') f = L2NormSquared(b=data) g = L2NormSquared() operator = IdentityOperator(ig) # sigma, tau sigma = 1.0 tau = 1.0 pdhg = PDHG(f=f, g=g, operator=operator, sigma = sigma, tau=tau, max_iteration=5, gamma_g=0.5) pdhg.run(1, verbose=0) self.assertAlmostEquals(pdhg.theta, 1.0/ np.sqrt(1 + 2 * pdhg.gamma_g * tau)) self.assertAlmostEquals(pdhg.tau, tau * pdhg.theta) self.assertAlmostEquals(pdhg.sigma, sigma / pdhg.theta) pdhg.run(4, verbose=0) self.assertNotEqual(pdhg.sigma, sigma) self.assertNotEqual(pdhg.tau, tau) # check negative strongly convex constant with self.assertRaises(ValueError): pdhg = PDHG(f=f, g=g, operator=operator, sigma = sigma, tau=tau, max_iteration=5, gamma_g=-0.5) # check strongly convex constant not a number with self.assertRaises(ValueError): pdhg = PDHG(f=f, g=g, operator=operator, sigma = sigma, tau=tau, max_iteration=5, gamma_g="-0.5")
def test_CGLS(self): print("Test CGLS") #ig = ImageGeometry(124,153,154) ig = ImageGeometry(10, 2) numpy.random.seed(2) initial = ig.allocate(0.) b = ig.allocate('random') # b = initial.copy() # fill with random numbers # b.fill(numpy.random.random(initial.shape)) # b = ig.allocate() # bdata = numpy.reshape(numpy.asarray([i for i in range(20)]), (2,10)) # b.fill(bdata) identity = IdentityOperator(ig) alg = CGLS(initial=initial, operator=identity, data=b) alg.max_iteration = 200 alg.run(20, verbose=0) self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array()) alg = CGLS(initial=initial, operator=identity, data=b, max_iteration=200, update_objective_interval=2) self.assertTrue(alg.max_iteration == 200) self.assertTrue(alg.update_objective_interval == 2) alg.run(20, verbose=0) self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
def setUp(self, *args, **kwargs): M, N, K = 3, 4, 5 self.ig = ImageGeometry(M, N, K) self.x = self.ig.allocate('random', seed=1) self.b = self.ig.allocate('random', seed=2) self.eta = self.ig.allocate(0.1) self.operator = IdentityOperator(self.ig) scalar = 0.25 self.f1 = L2NormSquared() self.f2 = L1Norm() self.f3 = scalar * L2NormSquared() self.f4 = scalar * L1Norm() self.f5 = scalar * L2NormSquared(b=self.b) self.f6 = scalar * L1Norm(b=self.b) self.f7 = ZeroFunction() self.f8 = 5 * ConstantFunction(10) self.f9 = LeastSquares(self.operator, self.b, c=scalar) self.f10 = 0.5 * KullbackLeibler(b=self.b, eta=self.eta) self.f11 = KullbackLeibler(b=self.b, eta=self.eta) self.f12 = 10 self.list1 = [self.f1, self.f2, self.f3, self.f4, self.f5, \ self.f6, self.f7, self.f8, self.f9, self.f10, self.f11, self.f12]
def test_FISTA_Norm2Sq(self): print("Test FISTA Norm2Sq") ig = ImageGeometry(127, 139, 149) b = ig.allocate(ImageGeometry.RANDOM) # fill with random numbers initial = ig.allocate(ImageGeometry.RANDOM) identity = IdentityOperator(ig) #### it seems FISTA does not work with Nowm2Sq norm2sq = LeastSquares(identity, b) #norm2sq.L = 2 * norm2sq.c * identity.norm()**2 #norm2sq = OperatorCompositionFunction(L2NormSquared(b=b), identity) opt = {'tol': 1e-4, 'memopt': False} if debug_print: print("initial objective", norm2sq(initial)) alg = FISTA(initial=initial, f=norm2sq, g=ZeroFunction()) alg.max_iteration = 2 alg.run(20, verbose=0) self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array()) alg = FISTA(initial=initial, f=norm2sq, g=ZeroFunction(), max_iteration=2, update_objective_interval=3) self.assertTrue(alg.max_iteration == 2) self.assertTrue(alg.update_objective_interval == 3) alg.run(20, verbose=0) self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
def test_FISTA_catch_Lipschitz(self): if debug_print: print("Test FISTA catch Lipschitz") ig = ImageGeometry(127, 139, 149) initial = ImageData(geometry=ig) 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) #### it seems FISTA does not work with Nowm2Sq norm2sq = LeastSquares(identity, b) if debug_print: print('Lipschitz', norm2sq.L) # norm2sq.L = None #norm2sq.L = 2 * norm2sq.c * identity.norm()**2 #norm2sq = OperatorCompositionFunction(L2NormSquared(b=b), identity) opt = {'tol': 1e-4, 'memopt': False} if debug_print: print("initial objective", norm2sq(initial)) try: alg = FISTA(initial=initial, f=L1Norm(), g=ZeroFunction()) self.assertTrue(False) except ValueError as ve: print(ve) self.assertTrue(True)
def test_ScaledBlockOperatorScalarList(self): ig = [ ImageGeometry(2,3) , \ #ImageGeometry(10,20,30) , \ ImageGeometry(2,3 ) ] x = [g.allocate() for g in ig] ops = [IdentityOperator(g) for g in ig] # test limit as non Scaled scalar = numpy.asarray([1 for _ in x]) k = BlockOperator(*ops) K = scalar * k val = 1 X = BlockDataContainer(*x) + val Y = K.T.direct(X) self.assertTrue(Y.shape == (1, 1)) zero = numpy.zeros(X.get_item(0).shape) xx = numpy.asarray([val for _ in x]) numpy.testing.assert_array_equal( Y.get_item(0).as_array(), (scalar * xx).sum() + zero) scalar = numpy.asarray([i + 1 for i, el in enumerate(x)]) #scalar = numpy.asarray([6,0]) k = BlockOperator(*ops) K = scalar * k X = BlockDataContainer(*x) + val Y = K.T.direct(X) self.assertTrue(Y.shape == (1, 1)) zero = numpy.zeros(X.get_item(0).shape) xx = numpy.asarray([val for _ in x]) numpy.testing.assert_array_equal( Y.get_item(0).as_array(), (scalar * xx).sum() + zero)
def test_Function(self): numpy.random.seed(10) N = 3 ig = ImageGeometry(N, N) ag = ig op1 = GradientOperator(ig) op2 = IdentityOperator(ig, ag) # Form Composite Operator operator = BlockOperator(op1, op2, shape=(2, 1)) # Create functions noisy_data = ag.allocate(ImageGeometry.RANDOM) d = ag.allocate(ImageGeometry.RANDOM) alpha = 0.5 # scaled function g = alpha * L2NormSquared(b=noisy_data) # Compare call of g a2 = alpha * (d - noisy_data).power(2).sum() #print(a2, g(d)) self.assertEqual(a2, g(d)) # Compare convex conjugate of g a3 = 0.5 * d.squared_norm() + d.dot(noisy_data) self.assertAlmostEqual(a3, g.convex_conjugate(d), places=7)
def test_ScaledBlockOperatorSingleScalar(self): ig = [ ImageGeometry(10,20,30) , \ ImageGeometry(10,20,30) , \ ImageGeometry(10,20,30) ] x = [g.allocate() for g in ig] ops = [IdentityOperator(g) for g in ig] val = 1 # test limit as non Scaled scalar = 1 k = BlockOperator(*ops) K = scalar * k X = BlockDataContainer(*x) + val Y = K.T.direct(X) self.assertTrue(Y.shape == (1, 1)) zero = numpy.zeros(X.get_item(0).shape) xx = numpy.asarray([val for _ in x]) numpy.testing.assert_array_equal( Y.get_item(0).as_array(), ((scalar * xx).sum() + zero)) scalar = 0.5 k = BlockOperator(*ops) K = scalar * k X = BlockDataContainer(*x) + 1 Y = K.T.direct(X) self.assertTrue(Y.shape == (1, 1)) zero = numpy.zeros(X.get_item(0).shape) numpy.testing.assert_array_equal( Y.get_item(0).as_array(), scalar * (len(x) + zero))
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_PowerMethod(self): # 2x2 real matrix, dominant eigenvalue = 2 M1 = numpy.array([[1, 0], [1, 2]], dtype=float) M1op = MatrixOperator(M1) res1 = M1op.PowerMethod(M1op, 100) numpy.testing.assert_almost_equal(res1, 2., decimal=4) # Test with the norm res2 = M1op.norm() numpy.testing.assert_almost_equal(res1, res2, decimal=4) # 2x3 real matrix, dominant eigenvalue = 4.711479432297657 M1 = numpy.array([[1., 0., 3], [1, 2., 3]]) M1op = MatrixOperator(M1) res1 = M1op.PowerMethod(M1op, 100) numpy.testing.assert_almost_equal(res1, 4.711479432297657, decimal=4) # 2x3 complex matrix, (real eigenvalues), dominant eigenvalue = 5.417602365823937 M1 = numpy.array([[2, 1j, 0], [2j, 5j, 0]]) M1op = MatrixOperator(M1) res1 = M1op.PowerMethod(M1op, 100) numpy.testing.assert_almost_equal(res1, 5.417602365823937, decimal=4) # 3x3 complex matrix, (real+complex eigenvalue), dominant eigenvalue = 3.1624439599276974 M1 = numpy.array([[2, 0, 0], [1, 2j, 1j], [3, 3 - 1j, 3]]) M1op = MatrixOperator(M1) res1 = M1op.PowerMethod(M1op, 100) numpy.testing.assert_almost_equal(res1, 3.1624439599276974, decimal=4) # Gradient Operator (float) ig = ImageGeometry(30, 30) Grad = GradientOperator(ig) res1 = Grad.PowerMethod(Grad, 500, tolerance=1e-6) numpy.testing.assert_almost_equal(res1, numpy.sqrt(8), decimal=2) # Gradient Operator (complex) ig = ImageGeometry(30, 30, dtype=numpy.complex) Grad = GradientOperator(ig) res1 = Grad.PowerMethod(Grad, 500, tolerance=1e-6) numpy.testing.assert_almost_equal(res1, numpy.sqrt(8), decimal=2) # Identity Operator Id = IdentityOperator(ig) res1 = Id.PowerMethod(Id, 100) numpy.testing.assert_almost_equal(res1, 1.0, decimal=4)
def test_ScaledOperator(self): print("test_ScaledOperator") ig = ImageGeometry(10, 20, 30) img = ig.allocate() scalar = 0.5 sid = scalar * IdentityOperator(ig) numpy.testing.assert_array_equal(scalar * img.as_array(), sid.direct(img).as_array())
def test_CompositionOperator_adjoint7(self): ig = self.ig data = self.data G = GradientOperator(domain_geometry=ig) Id1 = 2 * IdentityOperator(ig) Id2 = IdentityOperator(ig) d = CompositionOperator(G, Id1, Id2) out1 = G.direct(data) out2 = G.adjoint(out1) d_out = d.adjoint(out1) numpy.testing.assert_array_almost_equal(d_out.as_array(), 2 * out2.as_array()) numpy.testing.assert_array_almost_equal(d_out.as_array(), 2 * out2.as_array())
def test_CompositionOperator_direct1(self): ig = self.ig data = self.data G = GradientOperator(domain_geometry=ig) Id1 = 2 * IdentityOperator(ig) Id2 = IdentityOperator(ig) d = CompositionOperator(G, Id2) out1 = G.direct(data) out2 = d.direct(data) numpy.testing.assert_array_almost_equal( out2.get_item(0).as_array(), out1.get_item(0).as_array()) numpy.testing.assert_array_almost_equal( out2.get_item(1).as_array(), out1.get_item(1).as_array())
def test_compare_with_PDHG(self): # Load an image from the CIL gallery. data = dataexample.SHAPES.get() ig = data.geometry # Add gaussian noise noisy_data = applynoise.gaussian(data, seed=10, var=0.005) # TV regularisation parameter alpha = 1 # fidelity = 0.5 * L2NormSquared(b=noisy_data) # fidelity = L1Norm(b=noisy_data) fidelity = KullbackLeibler(b=noisy_data, use_numba=False) # Setup and run the PDHG algorithm F = BlockFunction(alpha * MixedL21Norm(), fidelity) G = ZeroFunction() K = BlockOperator(GradientOperator(ig), IdentityOperator(ig)) # Compute operator Norm normK = K.norm() # Primal & dual stepsizes sigma = 1. / normK tau = 1. / normK pdhg = PDHG(f=F, g=G, operator=K, tau=tau, sigma=sigma, max_iteration=100, update_objective_interval=10) pdhg.run(verbose=0) sigma = 1 tau = sigma / normK**2 admm = LADMM(f=G, g=F, operator=K, tau=tau, sigma=sigma, max_iteration=100, update_objective_interval=10) admm.run(verbose=0) from cil.utilities.quality_measures import psnr if debug_print: print("PSNR", psnr(admm.solution, pdhg.solution)) np.testing.assert_almost_equal(psnr(admm.solution, pdhg.solution), 84.46678222768597, decimal=4)
def test_Norm2sq_as_OperatorCompositionFunction(self): print('Test for OperatorCompositionFunction') M, N = 50, 50 ig = ImageGeometry(voxel_num_x=M, voxel_num_y=N) #numpy.random.seed(1) b = ig.allocate('random', seed=1) print('Check call with IdentityOperator operator... OK\n') operator = 3 * IdentityOperator(ig) u = ig.allocate('random', seed=50) f = 0.5 * L2NormSquared(b=b) func1 = OperatorCompositionFunction(f, operator) func2 = LeastSquares(operator, b, 0.5) print("f.L {}".format(f.L)) print("0.5*f.L {}".format((0.5 * f).L)) print("type func1 {}".format(type(func1))) print("func1.L {}".format(func1.L)) print("func2.L {}".format(func2.L)) print("operator.norm() {}".format(operator.norm())) numpy.testing.assert_almost_equal(func1(u), func2(u)) print('Check gradient with IdentityOperator operator... OK\n') tmp1 = ig.allocate() tmp2 = ig.allocate() res_gradient1 = func1.gradient(u) res_gradient2 = func2.gradient(u) func1.gradient(u, out=tmp1) func2.gradient(u, out=tmp2) self.assertNumpyArrayAlmostEqual(res_gradient1.as_array(), res_gradient2.as_array()) self.assertNumpyArrayAlmostEqual(tmp1.as_array(), tmp2.as_array()) print('Check call with MatrixOperator... OK\n') mat = np.random.randn(M, N) operator = MatrixOperator(mat) vg = VectorGeometry(N) b = vg.allocate('random') u = vg.allocate('random') func1 = OperatorCompositionFunction(0.5 * L2NormSquared(b=b), operator) func2 = LeastSquares(operator, b, 0.5) self.assertNumpyArrayAlmostEqual(func1(u), func2(u)) numpy.testing.assert_almost_equal(func1.L, func2.L)
def test_exception_initial_CGLS(self): if debug_print: print ("Test CGLS") ig = ImageGeometry(10,2) numpy.random.seed(2) initial = ig.allocate(0.) b = ig.allocate('random') identity = IdentityOperator(ig) try: alg = CGLS(initial=initial, operator=identity, data=b, x_init=initial) assert False except ValueError as ve: assert True
def test_PDHG_strongly_convex_both_fconj_and_g(self): ig = ImageGeometry(3,3) data = ig.allocate('random') f = L2NormSquared(b=data) g = L2NormSquared() operator = IdentityOperator(ig) try: pdhg = PDHG(f=f, g=g, operator=operator, max_iteration=10, gamma_g = 0.5, gamma_fconj=0.5) pdhg.run(verbose=0) except ValueError as err: print(err)
def test_Lipschitz(self): print('Test for OperatorCompositionFunction') M, N = 50, 50 ig = ImageGeometry(voxel_num_x=M, voxel_num_y=N) b = ig.allocate('random', seed=1) print('Check call with IdentityOperator operator... OK\n') operator = 3 * IdentityOperator(ig) u = ig.allocate('random_int', seed=50) func2 = LeastSquares(operator, b, 0.5) assert func2.L != 2 print(func2.L) func2.L = 2 assert func2.L == 2
def test_BlockOperatorLinearValidity(self): print("test_BlockOperatorLinearValidity") M, N = 3, 4 ig = ImageGeometry(M, N) arr = ig.allocate('random', seed=1) G = GradientOperator(ig) Id = IdentityOperator(ig) B = BlockOperator(G, Id) # Nx1 case u = ig.allocate('random', seed=2) w = B.range_geometry().allocate(ImageGeometry.RANDOM, seed=3) w1 = B.direct(u) u1 = B.adjoint(w) self.assertAlmostEqual((w * w1).sum(), (u1 * u).sum(), places=5)
def test_Lipschitz3(self): print('Test for test_Lipschitz3') M, N = 50, 50 ig = ImageGeometry(voxel_num_x=M, voxel_num_y=N) b = ig.allocate('random', seed=1) print('Check call with IdentityOperator operator... OK\n') operator = 3 * IdentityOperator(ig) u = ig.allocate('random_int', seed=50) # func2 = LeastSquares(operator, b, 0.5) func1 = ConstantFunction(0.3) f3 = TranslateFunction(func1, 3) assert f3.L != 2 print(f3.L) f3.L = 2 assert f3.L == 2
def test_SIRT(self): if debug_print: print("Test CGLS") #ig = ImageGeometry(124,153,154) ig = ImageGeometry(10, 2) numpy.random.seed(2) initial = ig.allocate(0.) b = ig.allocate('random') # b = initial.copy() # fill with random numbers # b.fill(numpy.random.random(initial.shape)) # b = ig.allocate() # bdata = numpy.reshape(numpy.asarray([i for i in range(20)]), (2,10)) # b.fill(bdata) identity = IdentityOperator(ig) alg = SIRT(initial=initial, operator=identity, data=b) alg.max_iteration = 200 alg.run(20, verbose=0) np.testing.assert_array_almost_equal(alg.x.as_array(), b.as_array()) alg2 = SIRT(initial=initial, operator=identity, data=b, upper=0.3) alg2.max_iteration = 200 alg2.run(20, verbose=0) # equal try: numpy.testing.assert_equal(alg2.get_output().max(), 0.3) if debug_print: print("Equal OK, returning") return except AssertionError as ae: if debug_print: print("Not equal, trying almost equal") # almost equal to 7 digits or less try: numpy.testing.assert_almost_equal(alg2.get_output().max(), 0.3) if debug_print: print("Almost Equal OK, returning") return except AssertionError as ae: if debug_print: print("Not almost equal, trying less") numpy.testing.assert_array_less(alg2.get_output().max(), 0.3)
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
def test_Lipschitz4(self): print('Test for test_Lipschitz4') M, N = 50, 50 ig = ImageGeometry(voxel_num_x=M, voxel_num_y=N) b = ig.allocate('random', seed=1) print('Check call with IdentityOperator operator... OK\n') operator = 3 * IdentityOperator(ig) u = ig.allocate('random_int', seed=50) # func2 = LeastSquares(operator, b, 0.5) func1 = ConstantFunction(0.3) f3 = func1 + 3 assert f3.L == 0 print("OK") print(f3.L) f3.L = 2 assert f3.L == 2 print("OK") assert func1.L == 0 print("OK") try: func1.L = 2 assert False except AttributeError as ve: assert True print("OK") f2 = LeastSquares(operator, b, 0.5) f4 = 2 * f2 assert f4.L == 2 * f2.L print("OK") f4.L = 10 assert f4.L != 2 * f2.L print("OK") f4 = -2 * f2 assert f4.L == 2 * f2.L