def plot_dog_track(xs, dog, measurement_var, process_var):
N = len(xs)
bp.plot_track(dog)
bp.plot_measurements(xs, label='Sensor')
bp.set_labels('variance = {}, process variance = {}'.format(
measurement_var, process_var), 'time', 'pos')
plt.ylim([0, N])
bp.show_legend()
plt.show()
def plot_dog_track(xs, dog, measurement_var, process_var):
N = len(xs)
bp.plot_track(dog)
bp.plot_measurements(xs, label='Sensor')
bp.set_labels('variance = {}, process variance = {}'.format(
measurement_var, process_var), 'time', 'pos')
plt.ylim([0, N])
bp.show_legend()
plt.show()
def plot_gh_results(weights, estimates, predictions, actual, time_step=0):
n = len(weights)
if time_step > 0:
rng = range(1, n+1)
else:
rng = range(n, n+1)
xs = range(n+1)
book_plots.plot_measurements(xs[1:], weights, color='k', lines=False)
book_plots.plot_filter(xs, estimates, marker='o', label='Estimates')
book_plots.plot_track(xs[1:], predictions, c='r', marker='v', label='Predictions')
plt.plot([xs[0], xs[-1]], actual, c='k', lw=1, label='Actual')
plt.legend(loc=4)
book_plots.set_labels(x='day', y='weight (lbs)')
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
def plot_gh_results(weights, estimates, predictions, time_step=0):
n = len(weights)
if time_step > 0:
rng = range(1, n+1)
else:
rng = range(n, n+1)
xs = range(n+1)
pred, = book_plots.plot_track(xs[1:], predictions, c='r', marker='v')
scale, = book_plots.plot_measurements(xs[1:], weights, color='k', lines=False)
est, = book_plots.plot_filter(xs, estimates, marker='o')
plt.legend([scale, est, pred], ['Measurement', 'Estimates', 'Predictions'], loc=4)
book_plots.set_labels(x='day', y='weight (lbs)')
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
def plot_gh_results(weights, estimates, predictions, time_step=0):
n = len(weights)
if time_step > 0:
rng = range(1, n+1)
else:
rng = range(n, n+1)
xs = range(n+1)
pred, = book_plots.plot_track(xs[1:], predictions, c='r', marker='v')
scale, = book_plots.plot_measurements(xs[1:], weights, color='k', lines=False)
est, = book_plots.plot_filter(xs, estimates, marker='o')
plt.legend([scale, est, pred], ['Measurement', 'Estimates', 'Predictions'], loc=4)
book_plots.set_labels(x='day', y='weight (lbs)')
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
def plot_gh_results(weights, estimates, predictions, time_step=0):
plt.figure(figsize=(9, 4))
n = len(weights)
if time_step > 0:
rng = range(1, n + 1)
else:
rng = range(n, n + 1)
act, = book_plots.plot_track([0, n], [160, 160 + n], c='k')
plt.gcf().canvas.draw()
for i in rng:
xs = list(range(i + 1))
pred, = book_plots.plot_track(xs[1:],
predictions[:i],
c='r',
marker='v')
plt.xlim([-1, n + 1])
plt.ylim([156.0, 173])
plt.gcf().canvas.draw()
time.sleep(time_step)
scale, = book_plots.plot_measurements(xs[1:],
weights[:i],
color='k',
lines=False)
plt.xlim([-1, n + 1])
plt.ylim([156.0, 173])
plt.gcf().canvas.draw()
time.sleep(time_step)
est, = book_plots.plot_filter(xs[:i + 1],
estimates[:i + 1],
marker='o')
plt.xlim([-1, n + 1])
plt.ylim([156.0, 173])
plt.gcf().canvas.draw()
time.sleep(time_step)
plt.legend(
[act, scale, est, pred],
['Actual Weight', 'Measurement', 'Estimates', 'Predictions'],
loc=4)
book_plots.set_labels(x='day', y='weight (lbs)')
plt.xlim([-1, n + 1])
plt.ylim([156.0, 173])
def plot_gh_results(weights, estimates, predictions, time_step=0):
n = len(weights)
if time_step > 0:
rng = range(1, n+1)
else:
rng = range(n, n+1)
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
act, = book_plots.plot_track([0, n], [160, 160+n], c='k')
plt.gcf().canvas.draw()
for i in rng:
xs = list(range(i+1))
#plt.cla()
pred, = book_plots.plot_track(xs[1:], predictions[:i], c='r', marker='v')
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
plt.gcf().canvas.draw()
time.sleep(time_step)
scale, = book_plots.plot_measurements(xs[1:], weights[:i], color='k', lines=False)
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
plt.gcf().canvas.draw()
time.sleep(time_step)
est, = book_plots.plot_filter(xs[:i+1], estimates[:i+1], marker='o')
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
plt.gcf().canvas.draw()
time.sleep(time_step)
plt.legend([act, scale, est, pred], ['Actual Weight', 'Measurement', 'Estimates', 'Predictions'], loc=4)
book_plots.set_labels(x='day', y='weight (lbs)')
x = gaussian(0., 1000.) # initial state
process_model = gaussian(1., process_var)
N = 12
zs = distancia(distance_std, N)
ps = []
estimates = []
priors = np.zeros((N, 2))
for i, z in enumerate(zs):
prior = predict(x, process_model)
priors[i] = prior
x = update(prior, gaussian(z, distance_std**2))
# save for latter plotting
estimates.append(x.mean)
ps.append(x.var)
# plot the filter output and the variance
book_plots.plot_measurements(zs)
book_plots.plot_filter(estimates, var=np.array(ps))
book_plots.plot_predictions(priors[:, 0])
book_plots.show_legend()
book_plots.set_labels(x='Tempo (s)', y='Posições')
plt.show()
plt.figure()
plt.plot(ps)
plt.title('Variância')
print('Variance converges to {:.3f}'.format(ps[-1]))