def test_design_jacobian(self):
J = GrandMeanDesign(number_of_observations=6).design_jacobian(
currentstate=None)
self.assertEqual(SPARSEFORMAT, J.getformat())
numpy.testing.assert_almost_equal(
J.todense(), [[1.0], [1.0], [1.0], [1.0], [1.0], [1.0]])
def test_init(self):
obs = TestCombinationElement.SimulatedObservationStructure()
spde = SphereMeshSPDE(0)
design = CombinationDesign(
[GrandMeanDesign(2), LocalDesign(obs, spde)])
self.assertEqual(2, len(design.designlist))
self.assertTrue(isinstance(design.designlist[0], GrandMeanDesign))
self.assertTrue(isinstance(design.designlist[1], LocalDesign))
def test_element_states(self):
obs = TestCombinationElement.SimulatedObservationStructure()
spde = SphereMeshSPDE(0)
design = CombinationDesign(
[GrandMeanDesign(obs),
LocalDesign(obs, spde)])
states = design.element_states(
numpy.array([
270.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0,
12.0
]))
self.assertTrue(isinstance(states, list))
self.assertEqual(2, len(states))
numpy.testing.assert_equal(states[0], [270.0])
numpy.testing.assert_equal(
states[1],
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0])
def test_design_jacobian(self):
obs = TestCombinationElement.SimulatedObservationStructure()
spde = SphereMeshSPDE(1)
design = CombinationDesign(
[GrandMeanDesign(2), LocalDesign(obs, spde)])
# Make a state vector full of a test value 561 which should never actually get accessed
state_vector = numpy.tile(561.0, (47, 1))
J = design.design_jacobian(state_vector)
# A-matrix for comparison
A = spde.build_A(obs.location_polar_coordinates())
# Should have ones at start for global mean
J_expected = numpy.hstack([numpy.array([[1.0], [1.0]]), A.todense()])
# Check as exepcted
self.assertEqual(SPARSEFORMAT, J.getformat())
numpy.testing.assert_equal(J_expected, J.todense())
def test_design_function(self):
obs = TestCombinationElement.SimulatedObservationStructure()
spde = SphereMeshSPDE(1)
design = CombinationDesign(
[GrandMeanDesign(2), LocalDesign(obs, spde)])
A = spde.build_A(obs.location_polar_coordinates())
observation_indices, vertex_indices = A.sorted_indices().nonzero()
# Make a state vector full of a test value 561 which should never actually get accessed
state_vector = numpy.tile(561.0, (47, 1))
# Set the grand mean to 20.0
state_vector[0, 0] = 20.0
# Choose some numbers for observations of interest
state_vector[(vertex_indices[0:3] + 1), 0] = 43.5
state_vector[(vertex_indices[3:6] + 1), 0] = -27.0
# Should end up with (20.0 + 43.5) and (20.0 - 27.0)
y = design.design_function(state_vector)
numpy.testing.assert_almost_equal(y, [[63.5], [-7.0]])
def test_design_function(self):
y = GrandMeanDesign(number_of_observations=3).design_function(
currentstate=numpy.array([[27.9]]))
numpy.testing.assert_almost_equal(y, [[27.9], [27.9], [27.9]])
def test_design_matrix(self):
A = GrandMeanDesign(number_of_observations=7).design_matrix()
self.assertEqual(SPARSEFORMAT, A.getformat())
numpy.testing.assert_almost_equal(
A.todense(), [[1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]])
def test_isnonlinear(self):
self.assertFalse(
GrandMeanDesign(number_of_observations=3).isnonlinear())
def test_design_number_of_state_parameters(self):
self.assertEqual(
1,
GrandMeanDesign(
number_of_observations=3).design_number_of_state_parameters())