def test_invalid_find_critical_values():
dga = DiffusionGA('just_for_test')
fit_arr = [
[[1,2,4], [1,2,4]],
[[2,6,8], [1,2,4]],
[[1,2,4], [8,7,5]]
]
fit_arr = numpy.array(fit_arr)
with pytest.raises(ValueError):
dga._find_critical_values(fit_arr)
def test_valid_find_critical_values(optim, arr, rmin, rmax):
ga = RealGA(fitness_test_sin_func, optim=optim)
dga = DiffusionGA(ga)
arr = numpy.array(arr)
coords_best, coords_worst = dga._find_critical_values(arr)
if optim == 'min':
assert arr[coords_best] == rmin
assert arr[coords_worst] == rmax
else:
assert arr[coords_best] == rmax
assert arr[coords_worst] == rmin
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_get_neighbour(fit_list, row, column):
ga = RealGA(fitness_test_sin_func)
dga = DiffusionGA(ga)
dga._chrom_arr = numpy.array(list(range(9))).reshape((3, 3))
dga._fitness_arr = numpy.array(fit_list).reshape(3, 3)
shape = dga._chrom_arr.shape
selected_chromosome = dga._get_neighbour(row, column)
indices = numpy.where(dga._chrom_arr == selected_chromosome)
coords = (indices[0][0], indices[1][0])
valid_indices = [((row - 1) % shape[0], column),
((row + 1) % shape[0], column),
(row, (column - 1) % shape[1]),
(row, (column + 1) % shape[1])]
assert coords in valid_indices
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]
def test_get_population():
ga = DiffusionGA('just_for_test')
arr = numpy.empty(4)
for i in range(4):
arr[i] = i
ga._fitness_arr = numpy.empty(2)
ga._fitness_arr[0] = arr[0]
ga._fitness_arr[1] = arr[1]
ga._chrom_arr = numpy.empty(2)
ga._chrom_arr[0] = arr[2]
ga._chrom_arr[1] = arr[3]
assert (ga.population[0] == arr[2:]).all()
assert (ga.population[1] == arr[:2]).all()
def test_valid_init_population(optim):
"""
This test includes testing of the following parts: *init_population()*,
*_init_diffusion_model()*, *_construct_diffusion_model()* and *best_solution*.
"""
ga = RealGA(fitness_test_linear_func, optim=optim)
dga = DiffusionGA(ga)
array_side = 3
population = list(range(array_side * array_side))
fitness = [fitness_test_linear_func(chrom) for chrom in population]
dga.init_population(population)
for elem, fit in zip(population, fitness):
assert elem in dga._chrom_arr
assert fit in dga._fitness_arr
if optim == 'min':
assert dga.best_solution[0] == min(population)
assert dga.best_solution[1] == fitness_test_linear_func(min(population))
else:
assert dga.best_solution[0] == max(population)
assert dga.best_solution[1] == fitness_test_linear_func(max(population))
def test_invalid_init_population(population):
with pytest.raises(ValueError):
DiffusionGA('just_for_test').init_population(population)
def test_invalid_run(generations):
with pytest.raises(ValueError):
DiffusionGA('just_for_test').run(generations)
def test_invalid_init_random_population(size, dim, interval, ga_inst):
with pytest.raises(ValueError):
DiffusionGA(ga_inst).init_random_population(size, dim, interval)