def run_timings_vs_num_landmarks():
np.random.seed(0)
options = simulator_options.copy()
options['num_landmarks'] = 1000
calibration = structures.Calibration.random()
trials = []
true_trajectory, measurements, spline_template, true_frame_timestamps = simulation.simulate_trajectory(
calibration, **options)
all_features = measurements.features
for n in np.linspace(10, 400, 25):
measurements.features = filter(lambda f: f.track_id < n, all_features)
try:
spline_socp.estimate_trajectory(
calibration,
measurements,
spline_template,
estimator='mixed',
feature_tolerance=options['feature_noise']*3)
trials.append((n, socp.timings['last_solve']))
except spline_socp.InsufficientObservationsError:
print 'Simulator failed to generate trajectory. Retrying...'
np.savetxt('results/timings_vs_num_landmarks.txt', trials)
def run_accuracy_vs_feature_noise():
np.random.seed(1)
calibration = structures.Calibration.random()
options = simulator_options.copy()
options['feature_noise'] = 0.
options['accel_reading_noise'] = 1e-2
true_trajectory, measurements, spline_template = simulation.simulate_trajectory(calibration, **options)
results = []
for feature_noise in np.linspace(0, 10, 25):
print 'Trying feature noise = %f' % feature_noise
noisy_measurements = copy.deepcopy(measurements)
for f in noisy_measurements.features:
f.position += np.random.randn(2) * feature_noise
try:
pos_err, vel_err, bias_err, g_err = evaluate(
calibration,
noisy_measurements,
spline_template,
'mixed',
feature_noise*3+1e-3,
true_trajectory)
results.append((feature_noise, pos_err))
except spline_socp.FeasibilityError:
pass
np.savetxt('results/accuracy_vs_feature_noise.txt', results)
def run_timings_vs_num_knots():
np.random.seed(0)
calibration = structures.Calibration.random()
trials = []
true_trajectory, measurements, spline_template, true_frame_timestamps = simulation.simulate_trajectory(
calibration, **simulator_options)
for n in np.arange(2, 21):
spline_template.knots = np.linspace(0, simulator_options['duration'], n)
try:
spline_socp.estimate_trajectory(
calibration,
measurements,
spline_template,
estimator='mixed',
feature_tolerance=simulator_options['feature_noise']*3)
trials.append((n, socp.timings['last_solve']))
except spline_socp.InsufficientObservationsError:
print 'Simulator failed to generate trajectory. Retrying...'
np.savetxt('results/timings_vs_num_knots.txt', trials)
def simulate_and_evaluate(num_trials, calibration, estimators=None, **options):
if estimators is None:
estimators = ['mixed']
trials = []
while len(trials) < num_trials:
try:
true_trajectory, measurements, spline_template = simulation.simulate_trajectory(
calibration, **options)
row = []
for estimator in estimators:
row.extend(evaluate(
calibration,
measurements,
spline_template,
estimator,
simulator_options['feature_noise']*3,
true_trajectory))
trials.append(row)
except spline_socp.FeasibilityError:
print 'Simulator failed to generate trajectory. Retrying...'
except spline_socp.InsufficientObservationsError:
print 'Simulator failed to generate trajectory. Retrying...'
return np.asarray(trials)