def test_best_child_min():
"""Test best_child method for a minimization problem"""
problem = OptProb(5, OneMax(), maximize=False)
pop = np.array([[0, 0, 0, 0, 1], [1, 0, 1, 0, 1], [1, 1, 1, 1, 0],
[1, 0, 0, 0, 1], [100, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[1, 1, 1, 1, 1], [0, 0, 0, 0, -50]])
problem.set_population(pop)
x = problem.best_child()
assert np.array_equal(x, np.array([0, 0, 0, 0, -50]))
def test_eval_mate_probs():
"""Test eval_mate_probs method"""
problem = OptProb(5, OneMax(), maximize=True)
pop = np.array([[0, 0, 0, 0, 1], [1, 0, 1, 0, 1], [1, 1, 1, 1, 0],
[1, 0, 0, 0, 1], [0, 0, 0, 0, 0], [1, 1, 1, 1, 1]])
problem.set_population(pop)
problem.eval_mate_probs()
probs = np.array([0.06667, 0.2, 0.26667, 0.13333, 0, 0.33333])
assert np.allclose(problem.get_mate_probs(), probs, atol=0.00001)
def test_eval_mate_probs_all_zero():
"""Test eval_mate_probs method when all states have zero fitness"""
problem = OptProb(5, OneMax(), maximize=True)
pop = np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
problem.set_population(pop)
problem.eval_mate_probs()
probs = np.array(
[0.16667, 0.16667, 0.16667, 0.16667, 0.16667, 0.16667])
assert np.allclose(problem.get_mate_probs(), probs, atol=0.00001)
def test_set_population_min():
"""Test set_population method for a minimization problem"""
problem = OptProb(5, OneMax(), maximize=False)
pop = np.array([[0, 0, 0, 0, 1], [1, 0, 1, 0, 1], [1, 1, 1, 1, 0],
[1, 0, 0, 0, 1], [100, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[1, 1, 1, 1, 1], [0, 0, 0, 0, -50]])
pop_fit = -1.0 * np.array([1, 3, 4, 2, 100, 0, 5, -50])
problem.set_population(pop)
assert (np.array_equal(problem.get_population(), pop)
and np.array_equal(problem.get_pop_fitness(), pop_fit))
