def test_init_random_population_real(optim, type):
size, dim, interval = 9, 1, (-5, 5)
if type == 'real':
ga = RealGA(fitness_test_linear_func, optim=optim)
dga = DiffusionGA(ga)
dga.init_random_population(size, dim, interval)
else:
ga = BinaryGA(test_bin_data, fitness_test_func, optim=optim)
dga = DiffusionGA(ga)
dga.init_random_population(size)
assert dga.population[0].size == size
assert dga.population[1].size == size
shape = dga.population[0].shape
if optim == 'max':
for row in range(shape[0]):
for column in range(shape[1]):
assert dga.population[1][row][column] <= dga.best_solution[1]
else:
for row in range(shape[0]):
for column in range(shape[1]):
assert dga.population[1][row][column] >= dga.best_solution[1]
def test_valid_run(optim):
"""
"Run" function is the same for both types of diffusion GA: RealGA and Binary GA and thus,
it is not necessary to test it twice.
"""
ga = RealGA(fitness_test_sin_func, optim=optim)
dga = DiffusionGA(ga)
dga.init_random_population(11, 1, (-5, 5)) # size is 11 but actually wil be 9 (truncated square root from 11)
init_best = dga.best_solution
generations = 10
fitness_progress = dga.run(generations)
assert len(fitness_progress) == generations + 1
if optim == 'min':
assert init_best[1] >= dga.best_solution[1]
else:
assert init_best[1] <= dga.best_solution[1]