def test_cvt_attributes_04(self):
pod_basis = np.load('tests/test_datasets/pod_basis_test.npy')
mu_values = np.load('tests/test_datasets/mu_values_test.npy')
snapshots = np.load('tests/test_datasets/snapshots_test.npy')
weights = np.load('tests/test_datasets/weights_test.npy')
cvt_handler = cvt.Cvt(mu_values, snapshots, pod_basis, weights)
np.testing.assert_array_almost_equal(cvt_handler.weights, weights)
def test_cvt_attributes_09(self):
pod_basis = np.load('tests/test_datasets/pod_basis_test.npy')
mu_values = np.load('tests/test_datasets/mu_values_test.npy')
snapshots = np.load('tests/test_datasets/snapshots_test.npy')
weights = np.load('tests/test_datasets/weights_test.npy')
cvt_handler = cvt.Cvt(mu_values, snapshots, pod_basis, weights)
assert cvt_handler.dim_mu == 2
def test_cvt_compute_leave_one_out_error_02(self):
mu_values = np.load('tests/test_datasets/mu_values_test.npy')
snapshots = np.load('tests/test_datasets/snapshots_test_scalar.npy')
cvt_handler = cvt.Cvt(mu_values, snapshots)
error = cvt_handler.loo_error()
expected_error = np.array([0.718888, 0.28908 , 0.394886, 0.034921])
np.testing.assert_array_almost_equal(error, expected_error)
def test_cvt_attributes_07a(self):
pod_basis = np.load('tests/test_datasets/pod_basis_test.npy')
mu_values = np.load('tests/test_datasets/mu_values_test.npy')
snapshots = np.load('tests/test_datasets/snapshots_test.npy')
weights = np.load('tests/test_datasets/weights_test.npy')
cvt_handler = cvt.Cvt(mu_values, snapshots, pod_basis, weights)
expected_rel_error = 780.142129403
np.testing.assert_almost_equal(cvt_handler.rel_error, expected_rel_error)
def test_cvt_compute_max_error(self):
pod_basis = np.load('tests/test_datasets/pod_basis_test.npy')
mu_values = np.load('tests/test_datasets/mu_values_test.npy')
snapshots = np.load('tests/test_datasets/snapshots_test.npy')
weights = np.load('tests/test_datasets/weights_test.npy')
cvt_handler = cvt.Cvt(mu_values, snapshots, pod_basis, weights)
cvt_handler.add_new_point()
expected_value = 0.14913012395372877
np.testing.assert_almost_equal(cvt_handler.max_error, expected_value)
def test_cvt_compute_new_point(self):
pod_basis = np.load('tests/test_datasets/pod_basis_test.npy')
mu_values = np.load('tests/test_datasets/mu_values_test.npy')
snapshots = np.load('tests/test_datasets/snapshots_test.npy')
weights = np.load('tests/test_datasets/weights_test.npy')
cvt_handler = cvt.Cvt(mu_values, snapshots, pod_basis, weights)
cvt_handler.add_new_point()
expected_value = np.array([-0.29334384, -0.2312056])
np.testing.assert_array_almost_equal(cvt_handler.mu_values[:,-1], expected_value)
def test_cvt_compute_leave_one_out_error_01(self):
pod_basis = np.load('tests/test_datasets/pod_basis_test.npy')
mu_values = np.load('tests/test_datasets/mu_values_test.npy')
snapshots = np.load('tests/test_datasets/snapshots_test.npy')
weights = np.load('tests/test_datasets/weights_test.npy')
cvt_handler = cvt.Cvt(mu_values, snapshots, pod_basis, weights)
error = cvt_handler.loo_error()
expected_error = np.array([0.14913012, 0.05875263, 0.04603026, 0.07641862])
np.testing.assert_array_almost_equal(error, expected_error)
def print_info(self):
"""
This method compute and print the new parameter point for the next simulation and the maximum error
in the tesselation.
"""
self.cvt_handler = cvt.Cvt(self.mu_values, self.snapshots)
self.cvt_handler.add_new_point()
print ('Maximum error on the tassellation: ' + str(self.cvt_handler.max_error))
print ('New baricentric parameter value added to the triangulation ' + str(self.cvt_handler.mu_values[:,-1]) + '\n')
def test_cvt_compute_volume_1(self):
pod_basis = np.load('tests/test_datasets/pod_basis_test.npy')
mu_values = np.load('tests/test_datasets/mu_values_test.npy')
snapshots = np.load('tests/test_datasets/snapshots_test.npy')
weights = np.load('tests/test_datasets/weights_test.npy')
cvt_handler = cvt.Cvt(mu_values, snapshots, pod_basis, weights)
points_x = np.array([0., 1., 0.])
points_y = np.array([0., 0., 1.])
simplex_vertices = np.array([points_x, points_y])
volume = cvt_handler._compute_simplex_volume(simplex_vertices)
assert volume == 0.5
def print_info(self):
"""
This method compute and print the new parameter point for the next simulation and the maximum error
in the tesselation.
"""
weighted_snapshots = np.sqrt(self.weights) * self.snapshots.T
eigenvectors, eigenvalues, __ = np.linalg.svd(weighted_snapshots.T,
full_matrices=False)
self.pod_basis = np.transpose(
np.power(self.weights, -0.5) * eigenvectors.T)
self.cvt_handler = cvt.Cvt(self.mu_values, self.snapshots,
self.pod_basis, self.weights)
self.cvt_handler.add_new_point()
print('Maximum error on the tassellation: ' +
str(self.cvt_handler.max_error))
print('New baricentric parameter value added to the triangulation ' +
str(self.cvt_handler.mu_values[:, -1]) + '\n')
def test_cvt_attributes_07b(self):
mu_values = np.load('tests/test_datasets/mu_values_test.npy')
snapshots = np.load('tests/test_datasets/snapshots_test_scalar.npy')
cvt_handler = cvt.Cvt(mu_values, snapshots)
expected_rel_error = 36.3507969
np.testing.assert_almost_equal(cvt_handler.rel_error, expected_rel_error)
