def test_multiple_vector_valued(self): """ For a function that returns a vector rather than a scalar, make sure Dofs.f(), Dofs.jac(), and Dofs.fd_jac() behave correctly. """ for nparams1 in range(1, 5): for nvals1 in range(1, 5): nparams2 = np.random.randint(1, 6) nparams3 = np.random.randint(1, 6) nvals2 = np.random.randint(1, 6) nvals3 = np.random.randint(1, 6) o1 = Affine(nparams=nparams1, nvals=nvals1) o2 = Affine(nparams=nparams2, nvals=nvals2) o3 = Affine(nparams=nparams3, nvals=nvals3) dofs = Dofs([o1, o2, o3], diff_method="centered") dofs.set( (np.random.rand(nparams1 + nparams2 + nparams3) - 0.5) * 4) f1 = np.matmul(o1.A, o1.x) + o1.B f2 = np.matmul(o2.A, o2.x) + o2.B f3 = np.matmul(o3.A, o3.x) + o3.B np.testing.assert_allclose(dofs.f(), np.concatenate((f1, f2, f3)), \ rtol=1e-13, atol=1e-13) true_jac = np.zeros( (nvals1 + nvals2 + nvals3, nparams1 + nparams2 + nparams3)) true_jac[0:nvals1, 0:nparams1] = o1.A true_jac[nvals1:nvals1 + nvals2, nparams1:nparams1 + nparams2] = o2.A true_jac[nvals1 + nvals2:nvals1 + nvals2 + nvals3, \ nparams1 + nparams2:nparams1 + nparams2 + nparams3] = o3.A np.testing.assert_allclose(dofs.jac(), true_jac, rtol=1e-13, atol=1e-13) np.testing.assert_allclose(dofs.fd_jac(), \ true_jac, rtol=1e-7, atol=1e-7)
def test_with_dependents(self): """ Test the case in which the original object depends on another object. """ o1 = Adder(3) o2 = Adder(4) o1.set_dofs([10, 11, 12]) o2.set_dofs([101, 102, 103, 104]) o1.depends_on = ["o2"] o1.o2 = o2 dofs = Dofs([o1.J]) np.testing.assert_allclose(dofs.x, [10, 11, 12, 101, 102, 103, 104]) self.assertEqual(dofs.all_owners, [o1, o2]) self.assertEqual(dofs.dof_owners, [o1, o1, o1, o2, o2, o2, o2]) np.testing.assert_allclose(dofs.indices, [0, 1, 2, 0, 1, 2, 3]) f = dofs.f() # f must be evaluated before we know nvals_per_func self.assertEqual(list(dofs.nvals_per_func), [1]) self.assertEqual(dofs.nvals, 1) o1.fixed = [True, False, True] o2.fixed = [False, False, True, True] del o1.depends_on o2.depends_on = ["o1"] o2.o1 = o1 dofs = Dofs([o2.J]) np.testing.assert_allclose(dofs.x, [101, 102, 11]) self.assertEqual(dofs.all_owners, [o2, o1]) self.assertEqual(dofs.dof_owners, [o2, o2, o1]) np.testing.assert_allclose(dofs.indices, [0, 1, 1])
def test_no_dependents(self): """ Tests for an object that does not depend on other objects. """ obj = Adder(4) obj.set_dofs([101, 102, 103, 104]) dofs = Dofs([obj.J]) np.testing.assert_allclose(dofs.x, [101, 102, 103, 104]) self.assertEqual(dofs.all_owners, [obj]) self.assertEqual(dofs.dof_owners, [obj, obj, obj, obj]) np.testing.assert_allclose(dofs.indices, [0, 1, 2, 3]) dummy = dofs.f() # f must be evaluated before we know nvals_per_func self.assertEqual(list(dofs.nvals_per_func), [1]) self.assertEqual(dofs.nvals, 1) obj.fixed = [True, False, True, False] dofs = Dofs([obj.J]) np.testing.assert_allclose(dofs.x, [102, 104]) self.assertEqual(dofs.all_owners, [obj]) self.assertEqual(dofs.dof_owners, [obj, obj]) np.testing.assert_allclose(dofs.indices, [1, 3]) obj.fixed[0] = False dofs = Dofs([obj.J]) np.testing.assert_allclose(dofs.x, [101, 102, 104]) self.assertEqual(dofs.all_owners, [obj]) self.assertEqual(dofs.dof_owners, [obj, obj, obj]) np.testing.assert_allclose(dofs.indices, [0, 1, 3])
def test_failures(self): """ Verify that if ObjectiveFailure is raised during function evaluations, a vector is returned filled with the expected number. """ nvals = 3 fail_val = 1.0e8 o1 = Failer(nvals=nvals) d1 = Dofs([o1], fail=fail_val) # First eval should not fail: f = d1.f() np.testing.assert_allclose(f, np.full(nvals, 1.0)) # There should be a failure on the 2nd evaluation: f = d1.f() np.testing.assert_allclose(f, np.full(nvals, fail_val)) # Third eval should not fail: f = d1.f() np.testing.assert_allclose(f, np.full(nvals, 1.0)) # Try an example with >1 object in the dofs, and with NaN # instead of a finite value for the failure value. fail_val = np.NAN o2 = Failer(nvals=3) r2 = Rosenbrock() d2 = Dofs([o2, r2.terms], fail=fail_val) # First eval should not fail: f = d2.f() np.testing.assert_allclose(f, [1., 1., 1., -1., 0.]) # There should be a failure on the 2nd evaluation: f = d2.f() np.testing.assert_allclose(f, np.full(5, fail_val)) # Third eval should not fail: f = d2.f() np.testing.assert_allclose(f, [1., 1., 1., -1., 0.])
def test_mixed_vector_valued(self): """ For a mixture of functions that return a scalar vs return a vector, make sure Dofs.f(), Dofs.jac(), and Dofs.fd_jac() behave correctly. """ for nparams1 in range(1, 5): for nvals1 in range(1, 5): nparams2 = np.random.randint(1, 6) nparams3 = np.random.randint(1, 6) nvals2 = np.random.randint(1, 6) nvals3 = np.random.randint(1, 6) o1 = Affine(nparams=nparams1, nvals=nvals1) o2 = Affine(nparams=nparams2, nvals=nvals2) o3 = Affine(nparams=nparams3, nvals=nvals3) a1 = Adder(n=2) a2 = Adder(n=3) dofs = Dofs([o1, o2, a1, o3, a2], diff_method="centered") dofs.set( (np.random.rand(nparams1 + nparams2 + nparams3 + 5) - 0.5) * 4) f1 = np.matmul(o1.A, o1.x) + o1.B f2 = np.matmul(o2.A, o2.x) + o2.B f3 = np.array([a1.f]) f4 = np.matmul(o3.A, o3.x) + o3.B f5 = np.array([a2.f]) np.testing.assert_allclose(dofs.f(), np.concatenate((f1, f2, f3, f4, f5)), \ rtol=1e-13, atol=1e-13) true_jac = np.zeros((nvals1 + nvals2 + nvals3 + 2, nparams1 + nparams2 + nparams3 + 5)) true_jac[0:nvals1, 0:nparams1] = o1.A true_jac[nvals1:nvals1 + nvals2, nparams1:nparams1 + nparams2] = o2.A true_jac[nvals1 + nvals2:nvals1 + nvals2 + 1, \ nparams1 + nparams2:nparams1 + nparams2 + 2] = np.ones(2) true_jac[nvals1 + nvals2 + 1:nvals1 + nvals2 + 1 + nvals3, \ nparams1 + nparams2 + 2:nparams1 + nparams2 + 2 + nparams3] = o3.A true_jac[nvals1 + nvals2 + 1 + nvals3:nvals1 + nvals2 + nvals3 + 2, \ nparams1 + nparams2 + nparams3 + 2:nparams1 + nparams2 + nparams3 + 5] = np.ones(3) np.testing.assert_allclose(dofs.jac(), true_jac, rtol=1e-13, atol=1e-13) np.testing.assert_allclose(dofs.fd_jac(), \ true_jac, rtol=1e-7, atol=1e-7)
def test_vector_valued(self): """ For a function that returns a vector rather than a scalar, make sure Dofs.f(), Dofs.jac(), and Dofs.fd_jac() behave correctly. """ for nparams in range(1, 5): for nvals in range(1, 5): o = Affine(nparams=nparams, nvals=nvals) o.set_dofs((np.random.rand(nparams) - 0.5) * 4) dofs = Dofs([o], diff_method="centered") np.testing.assert_allclose(dofs.f(), np.matmul(o.A, o.x) + o.B, \ rtol=1e-13, atol=1e-13) np.testing.assert_allclose(dofs.jac(), o.A, rtol=1e-13, atol=1e-13) np.testing.assert_allclose(dofs.fd_jac(), \ o.A, rtol=1e-7, atol=1e-7)