def attack_rmse(attack, num_colluders):
num_legit_sensors = total_sensors - num_colluders
legitimate_readings = readings_generator.readings(biases[:num_legit_sensors], variances[:num_legit_sensors],
num_times, true_value)
return rms_error(robust_aggregate.estimate(attack(legitimate_readings,
true_value,
num_colluders,
colluder_bias),
reciprocal), [true_value(t) for t in range(num_times)])
def test_iterfilter(num_honest,
num_skewing,
num_avg,
num_times,
repetitions,
randseed=None):
if randseed:
random.seed(randseed)
num_pre_avg = num_honest + num_skewing
recip_rms = []
expo_rms = []
for r in range(repetitions):
print(r)
honest_data = [[
random.gauss(TRUTH, VARIANCE) for j in range(num_times)
] for i in range(num_honest)]
skewed_data = [[
random.gauss(COLLUSION_VALUE, VARIANCE) for j in range(num_times)
] for i in range(num_skewing)]
uncolluded_data = honest_data + skewed_data
sneaky_data = [[
sum([uncolluded_data[j][i]
for j in range(num_pre_avg)]) / num_pre_avg
for i in range(num_times)
]]
final_data = uncolluded_data + sneaky_data
recip_rms += [
rms_error(estimate(final_data, reciprocal), [TRUTH] * num_times)
]
expo_rms += [
rms_error(estimate(final_data, exponential), [TRUTH] * num_times)
]
recip_rms_bayes = stats.bayes_mvs(recip_rms)
expo_rms_bayes = stats.bayes_mvs(expo_rms)
return (recip_rms_bayes[0], expo_rms_bayes[0])
if __name__ == '__main__':
truth = 0
num_times = 100
num_honest = 20
honest_variance = 1
honest_bias = 0
honest_bias_variance = 0.1
honest_variances = [honest_variance] * num_honest
honest_biases = [
random.gauss(honest_bias, honest_bias_variance)
for i in range(num_honest)
]
honest_readings = readings_generator.readings(honest_biases,
honest_variances, num_times,
lambda t: truth).tolist()
num_influenced = 5
influenced_value = 1000
influenced_readings = num_influenced * [[truth] * (num_times - 1) +
[influenced_value]]
readings = honest_readings + influenced_readings
estimates = robust_aggregate.estimate(readings,
robust_aggregate.reciprocal)
error = rms_error(estimates, [truth] * len(readings)) / len(readings)
print(error)
if __name__ == '__main__':
repeats = 1000
variance = 1
bias = 0
truth = 0
times = 10
num_sensors = 10
variances = [variance] * num_sensors
biases = [bias] * num_sensors
time_errors = [[] for t in range(times)]
def truth_fn(t):
return truth
for i in range(repeats):
print ('{}/{}'.format(i, repeats))
readings = readings_generator.readings(biases, variances, times, truth_fn)
estimate = robust_aggregate.estimate(readings, exponential)
for t in range(times):
time_errors[t] += [estimate[t]]
mvs = [bayes_mvs(t) for t in time_errors]
pp.errorbar(range(times), [m[0][0] for m in mvs], yerr=[m[0][0]-m[0][1][0] for m in mvs])
pp.show()
def estimate(readings):
return robust_aggregate.estimate(readings, robust_aggregate.reciprocal)