def test_init_normal_empirical(self):
"""
Test if initialization by intra distance yields the desired intra distance.
"""
for intra_dist in [.1, .2, .3]:
nla = NoisyLTFArray.init_normal_empirical(
32,
1,
NoisyLTFArray.transform_id,
NoisyLTFArray.combiner_xor,
intra_dist,
approx_threshold=.01,
random_instance=RandomState(0xbeef))
self.assertTrue(
abs(tools.approx_dist(nla, nla, 10000) - intra_dist) < .02)
for intra_dist in [.1, .2, .3]:
nla = NoisyLTFArray.init_normal_empirical(
64,
4,
NoisyLTFArray.transform_id,
NoisyLTFArray.combiner_xor,
intra_dist,
approx_threshold=.1,
random_instance=RandomState(0xbeef))
self.assertTrue(
abs(tools.approx_dist(nla, nla, 10000) - intra_dist) < .15)
def test_reliability(self):
"""This method tests the test_reliability calculation."""
n = 8
k = 8
N = 2**n
transformation = LTFArray.transform_id
combiner = LTFArray.combiner_xor
instance = LTFArray(
weight_array=LTFArray.normal_weights(
n=n, k=k, random_instance=RandomState(0xA1A1)),
transform=transformation,
combiner=combiner,
)
challenges = sample_inputs(n, N, random_instance=RandomState(0xFAB1A))
reliabilities = []
for challenge in challenges:
reliabilities.append(
PropertyTest.reliability(instance, reshape(challenge, (1, n))))
# For noiseless simulations the responses are always the same hence the reliability is 0%
assert_array_equal(reliabilities, repeat(0.0, N))
noisy_instance = NoisyLTFArray(
weight_array=NoisyLTFArray.normal_weights(
n=n, k=k, random_instance=RandomState(0xA1A1)),
transform=transformation,
combiner=combiner,
sigma_noise=15.0,
random_instance=RandomState(0x5015E),
)
for challenge in challenges:
reliability = PropertyTest.reliability(noisy_instance,
reshape(challenge, (1, n)))
# For noisy simulations the responses should vary
self.assertNotEqual(reliability, 0.0)
def test_ltf_eval(self):
"""
Test ltf_eval for correct evaluation of LTFs.
"""
#random.normal(loc=0, scale=self.sigma_noise, size=(1, self.k))
weight_prng_1 = RandomState(seed=0xBADA55)
weight_prng_2 = RandomState(seed=0xBADA55)
noise_prng_1 = RandomState(seed=0xC0FFEE)
noise_prng_2 = RandomState(seed=0xC0FFEE)
N = 100 # number of random inputs per test set
for test_parameters in self.test_set:
n = test_parameters[0]
k = test_parameters[1]
mu = test_parameters[2]
sigma = test_parameters[3]
transformed_inputs = LTFArray.transform_id(
RandomState(seed=0xBAADA555).choice([-1, +1],
(N, n)), # bad ass testing
k)
ltf_array = LTFArray(
weight_array=LTFArray.normal_weights(n, k, mu, sigma,
weight_prng_1),
transform=LTFArray.transform_id,
combiner=LTFArray.combiner_xor,
)
noisy_ltf_array = NoisyLTFArray(
weight_array=LTFArray.normal_weights(
n, k, mu, sigma, weight_prng_2
), # weight_prng_2 was seeded identically to weight_prng_1
transform=LTFArray.transform_id,
combiner=LTFArray.combiner_xor,
sigma_noise=1,
random_instance=noise_prng_1,
)
evaled_ltf_array = ltf_array.ltf_eval(transformed_inputs)
assert_array_equal(
around(evaled_ltf_array + noise_prng_2.normal(
loc=0, scale=1, size=(len(evaled_ltf_array), k)),
decimals=10),
around(noisy_ltf_array.ltf_eval(transformed_inputs),
decimals=10))
def example_reliability():
"""This method shows how to use the PropertyTest.reliability function."""
n = 8
k = 8
transformation = NoisyLTFArray.transform_id
combiner = NoisyLTFArray.combiner_xor
weights = NoisyLTFArray.normal_weights(n=n, k=k)
instance = NoisyLTFArray(
weight_array=weights,
transform=transformation,
combiner=combiner,
sigma_noise=NoisyLTFArray.sigma_noise_from_random_weights(n, 0.5))
challenge = array([-1, 1, 1, 1, -1, 1, 1, 1])
reliability = PropertyTest.reliability(instance,
reshape(challenge, (1, n)))
print('The reliability is {}.'.format(reliability))
def test_mv_experiments(self):
experiments = []
for i in range(5):
n = 8
logger_name = 'test_mv_exp{0}'.format(i)
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+i,
desired_stability=0.95,
overall_desired_stability=0.8,
minimum_vote_count=1,
iterations=2,
bias=False
)
experiments.append(experiment)
experimenter = Experimenter('test_mv_experimenter', experiments)
experimenter.run()
def test_multiprocessing_logs(self):
"""
This test checks for the predicted amount for result.
"""
experiments = []
n = 28
for i in range(n):
log_name = 'test_multiprocessing_logs{0}'.format(i)
lr16_4_1 = ExperimentLogisticRegression(log_name, 8, 2, 2 ** 8, 0xbeef, 0xbeef,
LTFArray.transform_id,
LTFArray.combiner_xor)
experiments.append(lr16_4_1)
for i in range(n):
log_name = 'test_multiprocessing_logs{0}'.format(i)
experiment = ExperimentMajorityVoteFindVotes(
log_name=log_name,
n=8,
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 + i,
desired_stability=0.95,
overall_desired_stability=0.8,
minimum_vote_count=1,
iterations=2,
bias=False
)
experiments.append(experiment)
experimenter = Experimenter('test_multiprocessing_logs', experiments)
experimenter.run()
def line_count(file_object):
"""
:param file_object:
:return: number of lines
"""
count = 0
while file_object.readline() != '':
count = count + 1
return count
paths = list(glob.glob('*.log'))
# Check if the number of lines is greater than zero
for log_path in paths:
exp_log_file = open(log_path, 'r')
self.assertGreater(line_count(exp_log_file), 0, 'The experiment log is empty.')
exp_log_file.close()
# Check if the number of results is correct
log_file = open('test_multiprocessing_logs.log', 'r')
self.assertEqual(line_count(log_file), n*2, 'Unexpected number of results')
log_file.close()
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(
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=0.56)
experiment.execute(logger.queue, logger.logger_name)
self.assertGreaterEqual(experiment.result_overall_stab,
experiment.overall_desired_stability,
'No vote_count was found.')
def test_reliability_set(self):
"""This method tests the reliability_statistic calculation."""
n = 8
k = 3
N = 2**n
measurements = 10
transformation = LTFArray.transform_id
combiner = LTFArray.combiner_xor
instances = []
instance_count = 3
for i in range(instance_count):
instance = LTFArray(
weight_array=LTFArray.normal_weights(
n=n, k=k, random_instance=RandomState(0xA1A1 + i)),
transform=transformation,
combiner=combiner,
)
instances.append(instance)
challenges = sample_inputs(n, N, random_instance=RandomState(0xFAB0))
reliability_set = PropertyTest.reliability_set(
instances, challenges, measurements=measurements)
# The result is an array like with N * k entries.
self.assertEqual(len(reliability_set), N * instance_count)
# For noiseless simulations the all reliabilities must be 0%
assert_array_equal(reliability_set, repeat(0.0, N * instance_count))
noisy_instances = []
for i in range(instance_count):
noisy_instance = NoisyLTFArray(
weight_array=NoisyLTFArray.normal_weights(
n=n, k=k, random_instance=RandomState(0xA1A1 + i)),
transform=transformation,
combiner=combiner,
sigma_noise=0.5,
random_instance=RandomState(0x5015C + i),
)
noisy_instances.append(noisy_instance)
noisy_reliability_set = PropertyTest.reliability_set(
noisy_instances, challenges, measurements=measurements)
# For a noisy simulation the mean reliability must differ from zero
self.assertNotEqual(mean(noisy_reliability_set), 0.0)
def example_uniqueness():
"""
This method shows the function which can be used to calculate the uniqueness of a set of simulation instances.
"""
n = 8
k = 1
instance_count = 3
transformation = NoisyLTFArray.transform_id
combiner = NoisyLTFArray.combiner_xor
weights = NoisyLTFArray.normal_weights(n=n, k=k)
instances = [
NoisyLTFArray(
weight_array=weights,
transform=transformation,
combiner=combiner,
sigma_noise=NoisyLTFArray.sigma_noise_from_random_weights(
n, weights)) for _ in range(instance_count)
]
challenge = array([-1, 1, 1, 1, -1, 1, 1, 1])
uniqueness = PropertyTest.uniqueness(instances, reshape(challenge, (1, n)))
print('The uniqueness is {}.'.format(uniqueness))
def test_bias_influence_array(self):
"""
This method tests the influence of the bias array. The results should be different.
"""
n = 8
k = 4
mu = 1
sigma = 0.5
challenges = tools.all_inputs(n)
weight_array = NoisyLTFArray.normal_weights(
n, k, mu=mu, sigma=sigma, random_instance=RandomState(0xBADA556))
bias_array = NoisyLTFArray.normal_weights(
1,
k,
mu=mu,
sigma=sigma * 2,
random_instance=RandomState(0xBADAFF1))
biased_ltf_array = NoisyLTFArray(
weight_array=weight_array,
transform=NoisyLTFArray.transform_id,
combiner=NoisyLTFArray.combiner_xor,
sigma_noise=sigma,
bias=bias_array,
)
ltf_array = NoisyLTFArray(
weight_array=weight_array,
transform=NoisyLTFArray.transform_id,
combiner=NoisyLTFArray.combiner_xor,
sigma_noise=sigma,
bias=None,
)
self.assertEqual(ltf_array.weight_array.shape,
biased_ltf_array.weight_array.shape)
bias_array = biased_ltf_array.weight_array[:, -1]
bias_array_compared = [
bias == bias_array[0] for bias in bias_array[1:]
]
# the bias values should be different for this test. It is possible that they are all equal but this chance is
# low.
self.assertFalse(array(bias_array_compared).all())
biased_responses = biased_ltf_array.eval(challenges)
responses = ltf_array.eval(challenges)
# The arithmetic mean of the res
self.assertFalse(array_equal(biased_responses, responses))
def example_reliability_statistic():
"""This method shows hot to use the PropertyTest.reliability_statistic."""
n = 8
k = 1
N = 2**n
instance_count = 3
measurements = 100
transformation = NoisyLTFArray.transform_id
combiner = NoisyLTFArray.combiner_xor
weights = NoisyLTFArray.normal_weights(n=n, k=k)
instances = [
NoisyLTFArray(
weight_array=weights,
transform=transformation,
combiner=combiner,
sigma_noise=NoisyLTFArray.sigma_noise_from_random_weights(n, 0.5))
for _ in range(instance_count)
]
challenges = array(list(sample_inputs(n, N)))
property_test = PropertyTest(instances)
reliability_statistic = property_test.reliability_statistic(
challenges, measurements=measurements)
print('The reliability statistic is {}.'.format(reliability_statistic))
def __init__(self, n: int, k: int, seed: int = None, transform=None, noisiness: float = 0, noise_seed: int = None):
random_instance = RandomState(seed=seed) if seed is not None else RandomState()
super().__init__(
weight_array=self.normal_weights(n=n, k=k, random_instance=random_instance),
transform=transform or LTFArray.transform_atf,
combiner=LTFArray.combiner_xor,
sigma_noise=NoisyLTFArray.sigma_noise_from_random_weights(
n=n,
sigma_weight=1,
noisiness=noisiness,
),
random_instance=RandomState(seed=noise_seed) if noise_seed else RandomState()
)
self.n = n
self.k = k
def example_uniqueness_statistic():
"""This method shows the uniqueness statistic function."""
n = 8
k = 1
N = 2**n
instance_count = 11
measurements = 1
transformation = NoisyLTFArray.transform_id
combiner = NoisyLTFArray.combiner_xor
weights = NoisyLTFArray.normal_weights(n=n, k=k)
instances = [
NoisyLTFArray(
weight_array=weights,
transform=transformation,
combiner=combiner,
sigma_noise=NoisyLTFArray.sigma_noise_from_random_weights(
n, weights)) for _ in range(instance_count)
]
challenges = array(list(sample_inputs(n, N)))
property_test = PropertyTest(instances)
uniqueness_statistic = property_test.uniqueness_statistic(
challenges, measurements=measurements)
print('The uniqueness statistic is {}.'.format(uniqueness_statistic))
def create_noisy_ltf_arrays(cls, n=8, k=1, instance_count=10, transformation=LTFArray.transform_id,
combiner=LTFArray.combiner_xor, bias=None, mu=0, sigma=1, weight_random_seed=0x123,
sigma_noise=0.5, noise_random_seed=0x321):
"""
This function can be used to create a list of NoisyLTFArray.
:param n: int
Number of stages of the PUF
:param k: int
Number different LTFArrays
:param instance_count: int
Number of simulations to be instantiated.
:param transformation: A function: array of int with shape(N,k,n), int number of PUFs k -> shape(N,k,n)
The function transforms input challenges in order to increase resistance against attacks.
:param combiner: A function: array of int with shape(N,k,n) -> array of in with shape(N)
The functions combines the outputs of k PUFs to one bit results,
in oder to increase resistance against attacks.
:param bias: None, float or a two dimensional array of float with shape (k, 1)
This bias value or array of bias values will be appended to the weight_array.
Use a single value if you want the same bias for all weight_vectors.
:param mu: float
Mean (“centre”) of the stage weight distribution of the PUF instance simulation.
:param sigma: float
Standard deviation of the stage weight distribution of the PUF instance simulation.
:param weight_random_seed: int
The seed which is used to initialize the pseudo-random number generator
which is used to generate the stage weights for the arbiter PUF simulation.
:param sigma_noise: float
Standard deviation of the noise distribution.
:param noise_random_seed: int
The seed which is used to initialize the pseudo-random number generator
which is used to generate the noise for the arbiter PUF simulation.
:return: list of pypuf.simulation.arbiter_based.ltfarray.NoisyLTFArray
"""
instances = []
for seed_offset in range(instance_count):
weight_array = LTFArray.normal_weights(n, k, mu, sigma,
random_instance=RandomState(weight_random_seed + seed_offset))
instances.append(
NoisyLTFArray(
weight_array=weight_array,
transform=transformation,
combiner=combiner,
sigma_noise=sigma_noise,
random_instance=RandomState(noise_random_seed + seed_offset),
bias=bias,
)
)
return instances
def test_reliability_statistic(self):
"""This method tests the reliability statistic of an instance set."""
n = 8
k = 1
N = 2**n
instance_count = 2
measurements = 100
transformation = LTFArray.transform_id
combiner = LTFArray.combiner_xor
instances = [
LTFArray(weight_array=LTFArray.normal_weights(
n=n, k=k, random_instance=RandomState(0xA1A1 + i)),
transform=transformation,
combiner=combiner) for i in range(instance_count)
]
challenges = sample_inputs(n, N, random_instance=RandomState(0xFAB10))
property_test = PropertyTest(instances)
reliability_statistic = property_test.reliability_statistic(
challenges, measurements=measurements)
# For an noiseless set of simulations the reliability must be 0%
for key, value in reliability_statistic.items():
if key == 'sv':
self.assertEqual(value, 0.0, '{}'.format(key))
elif key == 'samples':
self.assertEqual(len(value), instance_count * N,
'{}'.format(key))
else:
self.assertEqual(value, 0.0, '{}'.format(key))
noisy_instances = [
NoisyLTFArray(
weight_array=LTFArray.normal_weights(
n=n, k=k, random_instance=RandomState(0xA1A1 + i)),
transform=transformation,
combiner=combiner,
sigma_noise=0.5,
random_instance=RandomState(0xCABE),
) for i in range(instance_count)
]
noisy_property_test = PropertyTest(noisy_instances)
noisy_reliability_statistic = noisy_property_test.reliability_statistic(
challenges, measurements=measurements)
self.assertNotEqual(noisy_reliability_statistic['mean'], 0.0)
def run(self):
random = RandomState(seed=self.parameters.seed)
sigma_noise = NoisyLTFArray.sigma_noise_from_random_weights(
self.parameters.n, 1, self.parameters.sigma_noise_ratio)
weights = LTFArray.normal_weights(self.parameters.n,
self.parameters.k,
random_instance=random)
instance_mv = SimulationMajorityLTFArray(
weights,
LTFArray.transform_atf,
LTFArray.combiner_xor,
sigma_noise,
random_instance_noise=random,
vote_count=self.parameters.vote_count)
self.stability = approx_stabilities(instance_mv, self.parameters.N,
self.parameters.samples, random)
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()
def stability_figure_data(n, k, vote_count, sigma_noise_ratio, num, reps, random):
"""
Returns a list of stabilities for randomly chosen challenges of randomly chosen MV XOR Arbiter PUF.
:param n: Length of arbiter chains
:param k: Number of arbiter chains
:param vote_count: Number of votes for each chain
:param sigma_noise_ratio: sigma_noise to sigma_model ratio of the arbiter chains
:param num: number of challenges to compute stability for
:param reps: number of samples per challenge to base the stability computation on
:param random: random seed for all PRNG used here
"""
sigma_noise = NoisyLTFArray.sigma_noise_from_random_weights(n, 1, sigma_noise_ratio)
weights = LTFArray.normal_weights(n, k, random_instance=random)
instance_mv = SimulationMajorityLTFArray(weights,
LTFArray.transform_atf,
LTFArray.combiner_xor,
sigma_noise,
random_instance_noise=random,
vote_count=vote_count)
stabilities = tools.approx_stabilities(instance_mv, num, reps, random)
print('{' + ','.join(map(str, stabilities)) + '}')
def test_bias_influence_value(self):
"""
This method tests the influence of the bias value. The results should be different.
"""
n = 8
k = 4
mu = 1
sigma = 0.5
challenges = array(list(tools.all_inputs(n)))
weight_array = NoisyLTFArray.normal_weights(n, k, mu=mu, sigma=sigma, random_instance=RandomState(0xBADA556))
bias_value = 2.5
biased_ltf_array = NoisyLTFArray(
weight_array=weight_array,
transform=NoisyLTFArray.transform_id,
combiner=NoisyLTFArray.combiner_xor,
sigma_noise=sigma,
bias=bias_value,
)
ltf_array = NoisyLTFArray(
weight_array=weight_array,
transform=NoisyLTFArray.transform_id,
combiner=NoisyLTFArray.combiner_xor,
sigma_noise=sigma,
bias=None,
)
# the second dimension of the weight_array plus one must be the number of elements in biased weight_array
self.assertEqual(shape(ltf_array.weight_array)[1]+1, shape(biased_ltf_array.weight_array)[1])
bias_array = biased_ltf_array.weight_array[:, -1]
bias_array_compared = [bias == bias_array[0] for bias in bias_array]
# the bias values should be equal for this test.
self.assertTrue(array(list(bias_array_compared)).all())
biased_responses = biased_ltf_array.eval(challenges)
responses = ltf_array.eval(challenges)
# The arithmetic mean of the res
self.assertFalse(array_equal(biased_responses, responses))
def __init__(self,
log_name,
n,
k,
challenge_count,
seed_instance,
seed_instance_noise,
transformation,
combiner,
mu,
sigma,
sigma_noise_ratio,
seed_challenges,
desired_stability,
overall_desired_stability,
minimum_vote_count,
iterations,
bias=False):
"""
:param log_name: string
The prefix of the self.progress_logger.
:param n: int
The number of stages of the PUF.
:param k: int
The number different LTFArrays of the SimulationMajorityLTFArray.
:param challenge_count: int
The number of challenges which are used to evaluate the PUF.
:param seed_instance: int
The seed which is used to initialize the pseudo-random number generator
which is used to generate the stage weights for the arbiter PUF simulation.
:param seed_instance_noise: int
The random seed which is used to initialize the pseudo-random number
generator which is used to generate the noise for the arbiter PUF simulation.
:param transformation: A function: array of int with shape(N,k,n), int number of PUFs k -> shape(N,k,n)
The function transforms input challenges in order to increase resistance against attacks.
:param combiner: A function: array of int with shape(N,k,n) -> array of in with shape(N)
The functions combines the outputs of k PUFs to one bit results,
in oder to increase resistance against attacks.
:param mu: float
Mean (“centre”) of the stage weight distribution of the PUF instance simulation.
:param sigma: float
Standard deviation of the stage weight distribution of the PUF instance simulation.
:param sigma_noise_ratio: float
The noisiness factor which is used to scale sigma_noise. The value sigma_noise
is the standard deviation of the stage weight distribution of the PUF instance
simulation.
:param seed_challenges: int
The seed which is used to initialize the pseudo-random number generator which
is used to generate challenges.
:param desired_stability: float
The number which is used to decide whether a PUF is stable or not.
:param overall_desired_stability: float
The relative frequency of challenges which are greater equal than the
desired_stability.
:param minimum_vote_count: int
That is the minimum number of votes which are used to evaluate the SimulationMajorityLTFArray
instance.
:param iterations: int
The number of evaluations of the SimulationMajorityLTFArray instance which are used
to check the desired_stability.
:param bias: boolean
The value is used to turn on input/output distort of the PUF instance simulation.
"""
super().__init__(log_name='%s.0x%x_0_%i_%i_%i_%s_%s' % (
log_name,
seed_instance,
n,
k,
challenge_count,
transformation.__name__,
combiner.__name__,
), )
self.n = n
self.k = k
self.N = challenge_count
self.seed_instance = seed_instance
self.seed_instance_noise = seed_instance_noise
self.seed_challenges = seed_challenges
self.transformation = transformation
self.combiner = combiner
self.mu = mu
self.sigma = sigma
self.sigma_noise = NoisyLTFArray.sigma_noise_from_random_weights(
n, 1, sigma_noise_ratio)
self.bias = bias
self.desired_stability = desired_stability
self.overall_desired_stability = overall_desired_stability
self.minimum_vote_count = minimum_vote_count
self.maximum_vote_count = 0 # Upper bound for binary search calculated in run()
self.vote_count = 0 # That is calculated during run()
self.result_overall_stab = 0.0
self.result_vote_count = 0
self.iterations = iterations
self.overall_stab = 0.0 # That is the overall_stab for vote_count calculated during run()
def prepare(self):
self.sigma_noise = NoisyLTFArray.sigma_noise_from_random_weights(
self.parameters.challenge_count, 1, self.parameters.sigma_noise_ratio)