def test_catch_combos_too_large(self):
N = int(1e6)
k_choices = 4
num_params = 2
input_sample = np.random.random_sample((N, num_params))
strategy = BruteForce()
with raises(ValueError):
strategy.find_most_distant(input_sample, N, num_params, k_choices)
def test_combo_from_find_most_distant(self, setup_input):
'''
Tests whether the correct combination is picked from the fixture drawn
from Saltelli et al. 2008, in the solution to exercise 3a,
Chapter 3, page 134.
'''
sample_inputs = setup_input
N = 6
num_params = 2
k_choices = 4
strategy = BruteForce()
scores = strategy.find_most_distant(sample_inputs, N, num_params,
k_choices)
output = strategy.find_maximum(scores, N, k_choices)
expected = [0, 2, 3, 5] # trajectories 1, 3, 4, 6
assert_equal(output, expected)
def test_scores_from_find_most_distant(self, setup_input):
'''
Checks whether array of scores from (6 4) is correct.
Data is derived from Saltelli et al. 2008,
in the solution to exercise 3a, Chapter 3, page 134.
'''
sample_inputs = setup_input
N = 6
num_params = 2
k_choices = 4
strategy = BruteForce()
output = strategy.find_most_distant(sample_inputs, N, num_params,
k_choices)
expected = np.array([15.022, 13.871, 14.815, 14.582, 16.178, 14.912,
15.055, 16.410, 15.685, 16.098, 14.049, 15.146,
14.333, 14.807, 14.825],
dtype=np.float32)
assert_allclose(output, expected, rtol=1e-1, atol=1e-2)