def test_two_to_n_block():
oa_gen = OrthogonalArrayGenerator(num_values=3,
degree=2,
dtype=DEFAULT_NDARRAY_TYPE)
ttn = oa_gen.two_to_n_block(repeat_factor=4)
ttn_expected = np.array([
[2, 2, 2, 2],
[2, 2, 2, 2],
[2, 2, 2, 2],
[3, 3, 3, 3],
[3, 3, 3, 3],
[3, 3, 3, 3],
],
dtype=DEFAULT_NDARRAY_TYPE)
assert np.array_equal(ttn, ttn_expected)
def build_configurations(self) -> np.ndarray:
oa_gen = OrthogonalArrayGenerator(num_values=self.square_size,
degree=self.coverage_degree,
dtype=self.ndarray_type)
final_grid = None
basic_capacity = self.group_repeat_factor[self.num_rounds - 1] \
* self.round_group_size[self.num_rounds - 1]
for round_num in range(0, self.num_rounds):
extra_parameters_grid = None
repeat_factor = basic_capacity // (
self.group_repeat_factor[round_num] *
self.round_group_size[round_num])
if round_num == 0:
add_grid = oa_gen.generate_oa()
elif self.round_group_size[round_num] == self.square_size:
add_grid = oa_gen.reduced_array(
self.group_repeat_factor[round_num])
extra_parameters_grid = oa_gen.extra_parameter_block(
len(final_grid), self.extra_parms_per_round[round_num])
elif self.round_group_size[round_num] == self.square_size + 1:
add_grid = oa_gen.basic_array(
self.group_repeat_factor[round_num])
else:
raise ValueError("Error in internal construction")
if final_grid is not None:
if extra_parameters_grid is not None:
final_grid = np.hstack([final_grid, extra_parameters_grid])
else:
final_grid = extra_parameters_grid
round_grid = np.tile(add_grid, reps=repeat_factor)
for prior_round in range(1, round_num):
num_groups = self.extra_parms_per_round[prior_round] // (
self.extra_parms_repeat_factor[prior_round][round_num] *
self.round_group_size[prior_round])
add_grid = oa_gen.reduced_array(
self.extra_parms_repeat_factor[prior_round][prior_round])
round_grid = np.hstack(
[round_grid,
np.tile(add_grid, reps=num_groups)])
two_to_n_block = oa_gen.two_to_n_block(
self.extra_parms_per_round[round_num])
if two_to_n_block is not None:
round_grid = np.hstack([round_grid, two_to_n_block])
if final_grid is not None:
final_grid = np.vstack([final_grid, round_grid])
else:
final_grid = round_grid
offset = len(final_grid[0]) - self.num_parms
over_sized_grid = \
oa_gen.oversized_parameter(
self.max_num_values,
self.max_index + offset,
len(final_grid[0]),
self.second_max_num_values)
if over_sized_grid is not None:
final_grid = np.vstack([final_grid, over_sized_grid])
config_grid = self.adjust_values(
final_grid[:, offset:],
self.square_size - self.second_max_num_values + 1)
return config_grid