def test_sum_distances():
'''
Tests whether the combinations are summed correctly.
'''
sample_inputs = setup()
dist_matr = compute_distance_matrix(sample_inputs, 6, 2, local_optimization=True)
indices = (1,3,2)
distance = sum_distances(indices, dist_matr)
expected = 10.47
assert_allclose(distance, expected, rtol=1e-2)
def test_sum_distances():
'''
Tests whether the combinations are summed correctly.
'''
sample_inputs = setup()
dist_matr = compute_distance_matrix(sample_inputs,
6,
2,
local_optimization=True)
indices = (1, 3, 2)
distance = sum_distances(indices, dist_matr)
expected = 10.47
assert_allclose(distance, expected, rtol=1e-2)
def test_compute_distance_matrix():
'''
Tests that a distance matrix is computed correctly
for an input of six trajectories and two parameters
'''
sample_inputs = setup()
output = compute_distance_matrix(sample_inputs, 6, 2)
expected = np.zeros((6, 6), dtype=np.float32)
expected[1, :] = [5.50, 0, 0, 0, 0, 0]
expected[2, :] = [6.18, 5.31, 0, 0, 0, 0]
expected[3, :] = [6.89, 6.18, 6.57, 0, 0, 0]
expected[4, :] = [6.18, 5.31, 5.41, 5.5, 0, 0]
expected[5, :] = [7.52, 5.99, 5.52, 7.31, 5.77, 0]
assert_allclose(output, expected, rtol=1e-2)
def test_compute_distance_matrix():
'''
Tests that a distance matrix is computed correctly
for an input of six trajectories and two parameters
'''
sample_inputs = setup()
output = compute_distance_matrix(sample_inputs, 6, 2)
expected = np.zeros((6, 6), dtype=np.float32)
expected[1, :] = [5.50, 0, 0, 0, 0, 0]
expected[2, :] = [6.18, 5.31, 0, 0, 0, 0]
expected[3, :] = [6.89, 6.18, 6.57, 0, 0, 0]
expected[4, :] = [6.18, 5.31, 5.41, 5.5, 0, 0]
expected[5, :] = [7.52, 5.99, 5.52, 7.31, 5.77, 0]
assert_allclose(output, expected, rtol=1e-2)
def test_compute_distance_matrix_local():
'''
Tests that a distance matrix is computed correctly for the local distance optimization.
The only change is that the local method needs the upper triangle of
the distance matrix instead of the lower one.
This is for an input of six trajectories and two parameters
'''
sample_inputs = setup()
output = compute_distance_matrix(sample_inputs, 6, 2, local_optimization=True)
expected = np.zeros((6, 6), dtype=np.float32)
expected[0, :] = [0, 5.50, 6.18, 6.89, 6.18, 7.52]
expected[1, :] = [5.50, 0, 5.31, 6.18, 5.31, 5.99]
expected[2, :] = [6.18, 5.31, 0, 6.57, 5.41, 5.52]
expected[3, :] = [6.89, 6.18, 6.57, 0, 5.50, 7.31]
expected[4, :] = [6.18, 5.31, 5.41, 5.5, 0, 5.77]
expected[5, :] = [7.52, 5.99, 5.52, 7.31, 5.77, 0 ]
assert_allclose(output, expected, rtol=1e-2)
def test_compute_distance_matrix_local():
'''
Tests that a distance matrix is computed correctly for the local distance optimization.
The only change is that the local method needs the upper triangle of
the distance matrix instead of the lower one.
This is for an input of six trajectories and two parameters
'''
sample_inputs = setup()
output = compute_distance_matrix(sample_inputs,
6,
2,
local_optimization=True)
expected = np.zeros((6, 6), dtype=np.float32)
expected[0, :] = [0, 5.50, 6.18, 6.89, 6.18, 7.52]
expected[1, :] = [5.50, 0, 5.31, 6.18, 5.31, 5.99]
expected[2, :] = [6.18, 5.31, 0, 6.57, 5.41, 5.52]
expected[3, :] = [6.89, 6.18, 6.57, 0, 5.50, 7.31]
expected[4, :] = [6.18, 5.31, 5.41, 5.5, 0, 5.77]
expected[5, :] = [7.52, 5.99, 5.52, 7.31, 5.77, 0]
assert_allclose(output, expected, rtol=1e-2)
