observation_cov=observation_cov,
explosion_scale=EXPLOSION_SCALE,
contamination_probability=CONTAMINATION,
seed=SIMULATOR_SEED)
vanilla_bpf_data, student_bpf_data, symmetric_robust_data, asymmetric_robust_data = [], [], [], []
for _ in trange(runs):
simulator, vanilla_bpf, student_bpfs, s_robust_bpfs, a_robust_bpf = experiment_step(
simulator)
vanilla_bpf_data.append(
compute_mse_and_coverage(simulator, vanilla_bpf))
student_bpf_data.append([
compute_mse_and_coverage(simulator, t_bpf)
for t_bpf in student_bpfs
])
symmetric_robust_data.append([
compute_mse_and_coverage(simulator, robust_bpf)
for robust_bpf in s_robust_bpfs
])
asymmetric_robust_data.append(
compute_mse_and_coverage(simulator, a_robust_bpf))
return np.array(vanilla_bpf_data), np.array(student_bpf_data),\
np.array(symmetric_robust_data), np.array(asymmetric_robust_data)
if __name__ == '__main__':
results = run(NUM_RUNS)
pickle_save(
f'./results/constant-velocity/asymmetric_noise/student_t_comparison.pk',
results)
final_time=FINAL_TIME,
time_step=TIME_STEP,
observation_std=NOISE_STD,
process_std=process_std,
contamination_probability=contamination,
degrees_of_freedom=1.01,
seed=SIMULATOR_SEED
)
vanilla_bpf_data, student_bpf_data, robust_bpf_data = [], [], []
robust_predictive_data = []
for _ in trange(runs):
simulator, vanilla_bpf, student_bpf, robust_bpfs = experiment_step(simulator)
vanilla_bpf_data.append(compute_mse_and_coverage(simulator, vanilla_bpf))
student_bpf_data.append(compute_mse_and_coverage(simulator, student_bpf))
robust_bpf_data.append([compute_mse_and_coverage(simulator, robust_bpf) for robust_bpf in robust_bpfs])
robust_predictive_data.append([compute_predictive_score(simulator, robust_bpf) for robust_bpf in robust_bpfs])
return (np.array(vanilla_bpf_data), np.array(student_bpf_data), np.array(robust_bpf_data)), \
np.array(robust_predictive_data)
if __name__ == '__main__':
for contamination in CONTAMINATION:
results, predictive_results = run(NUM_RUNS, contamination)
pickle_save(f'./results/tan/impulsive_noise_with_finite_mean/beta-sweep-contamination-{contamination}.pk', results)
pickle_save(
f'./results/tan/impulsive_noise_with_finite_mean/beta-predictive-sweep-contamination-{contamination}.pk',
predictive_results
)
coverage = np.sum(upper * lower) / simulator.X.shape[0]
scores.append([mse, coverage])
return scores
def run(runs, contamination):
observation_cov = NOISE_VAR * np.eye(2)
simulator = ConstantVelocityModel(
final_time=FINAL_TIME,
time_step=TIME_STEP,
observation_cov=observation_cov,
explosion_scale=EXPLOSION_SCALE,
contamination_probability=contamination,
seed=SIMULATOR_SEED
)
vanilla_bpf_data, robust_bpf_data, kalman_data = [], [], []
for _ in trange(runs):
simulator, kalman, vanilla_bpf, robust_bpfs = experiment_step(simulator)
kalman_data.append(compute_mse_and_coverage(simulator, kalman))
vanilla_bpf_data.append(compute_mse_and_coverage(simulator, vanilla_bpf))
robust_bpf_data.append([compute_mse_and_coverage(simulator, robust_bpf) for robust_bpf in robust_bpfs])
return np.array(kalman_data), np.array(vanilla_bpf_data), np.array(robust_bpf_data)
if __name__ == '__main__':
for contamination in CONTAMINATION:
results = run(NUM_RUNS, contamination)
pickle_save(f'./results/constant-velocity/impulsive_noise/beta-sweep-contamination-{contamination}.pk', results)
X, Y, test_series = get_data()
kalman_data = kalman_results(X, Y, test_series)
vanilla_bpf_data, robust_bpf_data = [], []
robust_predictive_data = []
for _ in trange(runs):
vanilla_smoother, robust_smoothers, vanilla_bpf, robust_bpfs = experiment_step(
X, Y)
vanilla_bpf_data.append(
compute_mse_and_coverage(test_series, vanilla_smoother))
robust_bpf_data.append([
compute_mse_and_coverage(test_series, robust_smoother)
for robust_smoother in robust_smoothers
])
robust_predictive_data.append([
compute_predictive_score(Y, robust_bpf)
for robust_bpf in robust_bpfs
])
return (np.array(kalman_data), np.array(vanilla_bpf_data),
np.array(robust_bpf_data)), np.array(robust_predictive_data)
if __name__ == '__main__':
results, predictive_results = run(NUM_RUNS)
pickle_save(f'./results/air_quality/beta-sweep.pk', results)
pickle_save(f'./results/air_quality/beta-predictive-sweep.pk',
predictive_results)