def test_run_and_analyze(self, logger):
"""
This method run the experiment and checks if a number of votes was found in oder to satisfy an
overall desired stability.
"""
n = 8
experiment = ExperimentMajorityVoteFindVotes(
log_name=logger.logger_name,
n=n,
k=2,
challenge_count=2**8,
seed_instance=0xC0DEBA5E,
seed_instance_noise=0xdeadbeef,
transformation=LTFArray.transform_id,
combiner=LTFArray.combiner_xor,
mu=0,
sigma=1,
sigma_noise_ratio=NoisyLTFArray.sigma_noise_from_random_weights(
n, 1, .5),
seed_challenges=0xf000,
desired_stability=0.95,
overall_desired_stability=0.8,
minimum_vote_count=1,
iterations=2,
bias=None)
experiment.execute(logger.queue, logger.logger_name)
self.assertGreaterEqual(experiment.result_overall_stab,
experiment.overall_desired_stability,
'No vote_count was found.')
def test_run_and_analyze_bias_value(self, logger):
"""
This method runs the experiment with a bias value and checks if a number of votes was found in order to
satisfy an overall desired stability.
"""
n = 8
experiment = ExperimentMajorityVoteFindVotes(
progress_log_prefix=logger.logger_name,
parameters=MVParameters(n=n,
k=2,
challenge_count=2**8,
seed_instance=0xC0DEBA5E,
seed_instance_noise=0xdeadbeef,
transformation='id',
combiner='xor',
mu=0,
sigma=1,
sigma_noise_ratio=NoisyLTFArray.
sigma_noise_from_random_weights(n, 1, .5),
seed_challenges=0xf000,
desired_stability=0.95,
overall_desired_stability=0.8,
minimum_vote_count=1,
iterations=2,
bias=0.56))
experiment.execute(logger.queue, logger.logger_name)
self.assertGreaterEqual(
experiment.result.overall_stab,
experiment.parameters.overall_desired_stability,
'No vote_count was found.')
def test_run_and_analyze(self):
logger_name = 'log'
# Setup multiprocessing logging
queue = multiprocessing.Queue(-1)
listener = multiprocessing.Process(target=log_listener,
args=(
queue,
setup_logger,
logger_name,
))
listener.start()
n = 8
experiment = ExperimentMajorityVoteFindVotes(
log_name=logger_name,
n=n,
k=2,
challenge_count=2**8,
seed_instance=0xC0DEBA5E,
seed_instance_noise=0xdeadbeef,
transformation=LTFArray.transform_id,
combiner=LTFArray.combiner_xor,
mu=0,
sigma=1,
sigma_noise_ratio=NoisyLTFArray.sigma_noise_from_random_weights(
n, 1, .5),
seed_challenges=0xf000,
desired_stability=0.95,
overall_desired_stability=0.8,
minimum_vote_count=1,
iterations=2,
bias=False)
experiment.execute(queue, logger_name)
self.assertGreaterEqual(experiment.result_overall_stab,
experiment.overall_desired_stability,
'No vote_count was found.')
queue.put_nowait(None)
listener.join()