def test_random_generator(self, fix_optimization_object_kwargs,
fix_optimization_object_kwargs_with_seed):
"""Test if random_generator works, and if given it a seed, ensure
random_generator return the same randomize numbers for each
constructed object."""
opt_object = Optimization(**fix_optimization_object_kwargs)
is_different = False
for i in range(100):
rand_val_1 = opt_object.random_generator.random()
rand_val_2 = opt_object.random_generator.random()
is_different |= rand_val_1 != rand_val_2
assert is_different
opt_object_custom_seed_1 = \
Optimization(**fix_optimization_object_kwargs_with_seed)
rand_val_3 = opt_object_custom_seed_1.random_generator.random()
rand_val_4 = opt_object_custom_seed_1.random_generator.random()
opt_object_custom_seed_2 = \
Optimization(**fix_optimization_object_kwargs_with_seed)
rand_val_5 = opt_object_custom_seed_2.random_generator.random()
rand_val_6 = opt_object_custom_seed_2.random_generator.random()
assert rand_val_3 == rand_val_5 and rand_val_4 == rand_val_6
def test___repr__(self, fix_optimization_object_kwargs,
fix_optimization_constraint_1):
"""Test __repr__ function."""
opt_object = Optimization(**fix_optimization_object_kwargs)
opt_object.add_constraint(fix_optimization_constraint_1)
print_string = opt_object.__repr__()
print_string.find("x[0] ** 4 + x[0] ** 3 - 3 * (x[0] ** 2) + 1")
print_string.find("x0: (-3, 3)")
print_string.find("x[0]**4 + x[0]**3 - 2*(x[0] ** 2) + 2 >= 0")
def test_check_constraints(self, fix_optimization_object_kwargs,
fix_optimization_constraint_1,
fix_optimization_constraint_2):
"""
Test check constraints function work.
Success cases:
- Boundaries check work
- Constraints check work
"""
opt_object = Optimization(**fix_optimization_object_kwargs)
opt_object.add_constraint(fix_optimization_constraint_1)
opt_object.add_constraint(fix_optimization_constraint_2)
# Boundaries fail check
assert not opt_object.check_constraints([-4])
# Constraint 1 fail check
assert not opt_object.check_constraints([-1.6])
# Constraint 2 fail check
assert not opt_object.check_constraints([0.0])
# Satisfy all check
assert opt_object.check_constraints([-1.8])
assert opt_object.check_constraints([0.9])
def test___init__(self, fix_optimization_object_kwargs):
"""Test __init__() constructor function."""
opt_object = Optimization(**fix_optimization_object_kwargs)
assert opt_object.func == \
fix_optimization_object_kwargs['optimizing_function']
assert opt_object.no_dimensions == \
fix_optimization_object_kwargs['no_dimensions']
assert opt_object.boundaries == \
fix_optimization_object_kwargs['boundaries']
assert opt_object.find_max == \
fix_optimization_object_kwargs['find_max']
def run(self, **kwargs):
no_particles = kwargs.get('no_particles', 10)
no_iterations = kwargs.get('no_iterations', 20)
c_1 = kwargs.get('c_1', 2.0)
c_2 = kwargs.get('c_2', 2.0)
opt_obj = Optimization(optimizing_function=self._optimizing_function,
boundaries=[(0, self.max_particles),
(0, self.max_iteration),
(0, self.max_learning_factor),
(0, self.max_learning_factor)],
no_dimensions=4,
find_max=False)
opt_obj.add_constraint(self._constraint_function)
pso = PSO(optimization_object=opt_obj,
no_particles=no_particles,
no_iteration_steps=no_iterations,
c_1=c_1,
c_2=c_2,
verbose=True)
ms = MultipleSolving(algorithm_obj=pso,
no_tries=kwargs.get('no_tries', 5))
max_thread = kwargs.get('max_thread', 0)
ms.run(pool=Pool(max_thread if max_thread else None))
return ms
def test_add_constraints(self, fix_optimization_object_kwargs,
fix_optimization_constraint_1,
fix_optimization_constraint_2):
"""Test if add_constraint() work."""
opt_object = Optimization(**fix_optimization_object_kwargs)
opt_object.add_constraint(fix_optimization_constraint_1)
assert opt_object.constraints.__len__() == 1
opt_object.add_constraint(fix_optimization_constraint_2)
assert opt_object.constraints.__len__() == 2
x[2]: 2nd year gaz
x[3]: 2nd year gaz
"""
return x[0] * 40 + x[1] * 50
def constraint(x):
return (x[0] + x[1] <= 50) & \
(x[0] * 3 + x[1] * 4 <= 180) & \
(x[1] <= 40)
optimization = Optimization(
optimizing_function=optimizing_function,
boundaries=[(0, 100), (0, 100)],
no_dimensions=2,
find_max=True
)
optimization.add_constraint(constraint)
pso = PSO(
optimization_object=optimization,
no_particles=10,
no_iteration_steps=20,
c_1=2.0,
c_2=2.0
)
ms = MultipleSolving(pso, 20)
ms.run()
def fix_optimization_object():
kwargs = fix_optimization_object_kwargs()
opt_object = Optimization(**kwargs)
opt_object.add_constraint(fix_optimization_constraint_1())
opt_object.add_constraint(fix_optimization_constraint_2())
return opt_object
year_2 = ProducePeriod(
resources=[50, 180, 40],
predicted_prices=[40, 50],
profit_calculator=profit_calculator_1st,
last_period=year_1
)
year_2.add_consumer(consumer_1st_n)
year_2.add_consumer(consumer_1st_h)
year_2.add_consumer(consumer_1st_ch)
optimization_2_years = Optimization(
optimizing_function=year_2.calculate_profit,
boundaries=[
(5, 100.0),
(5, 100.0),
(5, 100.0),
(5, 100.0),
],
no_dimensions=4,
find_max=True
)
optimization_2_years.add_constraint(year_2.check_consumption)
pso_1st = PSO(
optimization_object=optimization_2_years,
no_particles=40,
no_iteration_steps=20,
c_1=2.0,
c_2=2.0
)
print("Result for 1st case:")