def test__prior__samples_sample_priors(self):
model = af.PriorModel(af.m.MockClassx4)
model.one = af.UniformPrior(lower_limit=0.099, upper_limit=0.101)
model.two = af.UniformPrior(lower_limit=0.199, upper_limit=0.201)
model.three = af.UniformPrior(lower_limit=0.299, upper_limit=0.301)
model.four = af.UniformPrior(lower_limit=0.399, upper_limit=0.401)
initializer = af.InitializerPrior()
unit_parameter_lists, parameter_lists, figure_of_merit_list = initializer.samples_from_model(
total_points=2, model=model, fitness_function=MockFitness())
assert 0.0 < unit_parameter_lists[0][0] < 1.0
assert 0.0 < unit_parameter_lists[1][0] < 1.0
assert 0.0 < unit_parameter_lists[0][1] < 1.0
assert 0.0 < unit_parameter_lists[1][1] < 1.0
assert 0.0 < unit_parameter_lists[0][2] < 1.0
assert 0.0 < unit_parameter_lists[1][2] < 1.0
assert 0.0 < unit_parameter_lists[0][3] < 1.0
assert 0.0 < unit_parameter_lists[1][3] < 1.0
assert 0.099 < parameter_lists[0][0] < 0.101
assert 0.099 < parameter_lists[1][0] < 0.101
assert 0.199 < parameter_lists[0][1] < 0.201
assert 0.199 < parameter_lists[1][1] < 0.201
assert 0.299 < parameter_lists[0][2] < 0.301
assert 0.299 < parameter_lists[1][2] < 0.301
assert 0.399 < parameter_lists[0][3] < 0.401
assert 0.399 < parameter_lists[1][3] < 0.401
assert figure_of_merit_list == [1.0, 1.0]
def test__samples_in_test_model(self):
model = af.PriorModel(af.m.MockClassx4)
model.one = af.UniformPrior(lower_limit=0.099, upper_limit=0.101)
model.two = af.UniformPrior(lower_limit=0.199, upper_limit=0.201)
model.three = af.UniformPrior(lower_limit=0.299, upper_limit=0.301)
model.four = af.UniformPrior(lower_limit=0.399, upper_limit=0.401)
initializer = af.InitializerPrior()
unit_parameter_lists, parameter_lists, figure_of_merit_list = initializer.samples_in_test_mode(
total_points=2,
model=model,
)
assert 0.0 < unit_parameter_lists[0][0] < 1.0
assert 0.0 < unit_parameter_lists[1][0] < 1.0
assert 0.0 < unit_parameter_lists[0][1] < 1.0
assert 0.0 < unit_parameter_lists[1][1] < 1.0
assert 0.0 < unit_parameter_lists[0][2] < 1.0
assert 0.0 < unit_parameter_lists[1][2] < 1.0
assert 0.0 < unit_parameter_lists[0][3] < 1.0
assert 0.0 < unit_parameter_lists[1][3] < 1.0
assert 0.099 < parameter_lists[0][0] < 0.101
assert 0.099 < parameter_lists[1][0] < 0.101
assert 0.199 < parameter_lists[0][1] < 0.201
assert 0.199 < parameter_lists[1][1] < 0.201
assert 0.299 < parameter_lists[0][2] < 0.301
assert 0.299 < parameter_lists[1][2] < 0.301
assert 0.399 < parameter_lists[0][3] < 0.401
assert 0.399 < parameter_lists[1][3] < 0.401
assert figure_of_merit_list == [-1.0e99, -1.0e99]
https://pyswarms.readthedocs.io/en/latest/index.html
**PyAutoFit** extends *PySwarms* by allowing runs to be terminated and resumed from the point of termination, as well
as providing different options for the initial distribution of particles.
"""
# %%
pso = af.PySwarmsLocal(
n_particles=100,
iters=1000,
cognitive=0.5,
social=0.3,
inertia=0.9,
ftol=-np.inf,
initializer=af.InitializerPrior(),
number_of_cores=1,
paths=af.Paths(folders=["examples", "complex"]),
)
result = pso.fit(model=model, analysis=analysis)
# %%
"""
__Result__
The result object returned by PSO is again very similar in structure to previous results.
"""
# %%
instance = result.max_log_likelihood_instance