def main():
""" Main function. """
# Get problem parameters
worker_manager = SyntheticWorkerManager(NUM_WORKERS,
time_distro='halfnormal')
_, func_caller, mutation_op, init_points, init_vals = get_opt_problem(
NN_TYPE)
print('Best init value: %0.5f' % (max(init_vals)))
for method in METHODS:
print(
'Method: %s =========================================================='
% (method))
worker_manager.reset()
# Iterate through each method
if method in ['GA', 'randGA']:
is_rand = method == 'randGA'
options, reporter = get_options_and_reporter(
method, init_points, init_vals)
ga_optimiser.ga_optimise_from_args(func_caller,
worker_manager,
CAPITAL,
'asy',
mutation_op,
is_rand=is_rand,
options=options,
reporter=reporter)
# Wrap up method
print('')
def _ga_maximise(self,
obj,
num_evals,
mutation_op,
init_pool,
init_pool_vals=None,
expects_inputs_to_be_iterable=True):
""" Maximise with genetic algorithms.
if expects_inputs_as_list is True it means the function expects the inputs to
be iterable by default.
"""
# Prep necessary variables
obj, options, reporter, mutation_op = self._get_ga_optimiser_args(
obj, num_evals, mutation_op, init_pool, init_pool_vals,
expects_inputs_to_be_iterable)
worker_manager = SyntheticWorkerManager(1, time_distro='const')
func_caller = FunctionCaller(obj, self)
opt_val, opt_pt, _ = ga_optimise_from_args(func_caller,
worker_manager,
num_evals,
'asy',
mutation_op,
options=options,
reporter=reporter)
return opt_val, opt_pt
def test_optimisation_asynchronous(self):
""" Test optimisation. """
self.report('Testing GA Optimiser with four workers asynchronously.')
worker_manager = SyntheticWorkerManager(4, time_distro='halfnormal')
func_caller = FunctionCaller(mlp_syn_func1, self.nn_domain)
options, options_clone, reporter, _, mutation_op = self._get_optimiser_args('mlp')
opt_val, opt_pt, history = ga_optimiser.ga_optimise_from_args(
func_caller, worker_manager, 20, 'asy', mutation_op,
options=options, reporter=reporter)
self._test_optimiser_results(opt_val, opt_pt, history, options, options_clone)
self.report('')
def test_ga_optimisation_single(self):
""" Test optimisation. """
self.report('Testing GA Optimiser with just one worker.')
worker_manager = SyntheticWorkerManager(1, time_distro='const')
func_caller = FunctionCaller(cnn_syn_func1, self.nn_domain)
options, options_clone, reporter, _, mutation_op = self._get_optimiser_args('cnn')
opt_val, opt_pt, history = ga_optimiser.ga_optimise_from_args(
func_caller, worker_manager, 20, 'asy', mutation_op,
options=options, reporter=reporter)
self._test_optimiser_results(opt_val, opt_pt, history, options, options_clone)
self.report('')