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)
