def plot_g_h_results(measurements, filtered_data,
title='', z_label='Measurements',
**kwargs):
book_plots.plot_filter(filtered_data, **kwargs)
book_plots.plot_measurements(measurements, label=z_label)
plt.legend(loc=4)
plt.title(title)
plt.gca().set_xlim(left=0,right=len(measurements))
return
import time
if not interactive:
book_plots.plot_filter(filtered_data, **kwargs)
book_plots.plot_measurements(measurements, label=z_label)
book_plots.show_legend()
plt.title(title)
plt.gca().set_xlim(left=0,right=len(measurements))
else:
for i in range(2, len(measurements)):
book_plots.plot_filter(filtered_data, **kwargs)
book_plots.plot_measurements(measurements, label=z_label)
book_plots.show_legend()
plt.title(title)
plt.gca().set_xlim(left=0,right=len(measurements))
plt.gca().canvas.draw()
time.sleep(0.5)
def plot_g_h_results(measurements,
filtered_data,
title='',
z_label='Measurements',
**kwargs):
book_plots.plot_filter(filtered_data, **kwargs)
book_plots.plot_measurements(measurements, label=z_label)
plt.legend(loc=4)
plt.title(title)
plt.gca().set_xlim(left=0, right=len(measurements))
return
import time
if not interactive:
book_plots.plot_filter(filtered_data, **kwargs)
book_plots.plot_measurements(measurements, label=z_label)
book_plots.show_legend()
plt.title(title)
plt.gca().set_xlim(left=0, right=len(measurements))
else:
for i in range(2, len(measurements)):
book_plots.plot_filter(filtered_data, **kwargs)
book_plots.plot_measurements(measurements, label=z_label)
book_plots.show_legend()
plt.title(title)
plt.gca().set_xlim(left=0, right=len(measurements))
plt.gca().canvas.draw()
time.sleep(0.5)
def plot_hypothesis5():
weights = [
158.0, 164.2, 160.3, 159.9, 162.1, 164.6, 169.6, 167.4, 166.4, 171.0,
171.2, 172.6
]
xs = range(1, len(weights) + 1)
line = np.poly1d(np.polyfit(xs, weights, 1))
with figsize(y=2.5):
plt.figure()
plt.errorbar(range(1, 13),
weights,
label='weights',
yerr=5,
fmt='o',
capthick=2,
capsize=10)
plt.plot(xs, line(xs), c='r', label='hypothesis')
plt.xlim(0, 13)
plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
book_plots.show_legend()
plt.grid(False)
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_hypothesis4():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
with book_plots.figsize(y=2.5):
plt.figure()
ave = np.sum(weights) / len(weights)
plt.errorbar(range(1,13), weights, label='weights',
yerr=6, fmt='o', capthick=2, capsize=10)
plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')
plt.xlim(0, 13)
plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
book_plots.show_legend()
def plot_hypothesis4():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
with book_plots.figsize(y=2.5):
plt.figure()
ave = np.sum(weights) / len(weights)
plt.errorbar(range(1,13), weights, label='weights',
yerr=6, fmt='o', capthick=2, capsize=10)
plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')
plt.xlim(0, 13)
plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
book_plots.show_legend()
plt.grid(False)
def plot_hypothesis5():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
xs = range(1, len(weights)+1)
line = np.poly1d(np.polyfit(xs, weights, 1))
with figsize(y=2.5):
plt.figure()
plt.errorbar(range(1, 13), weights, label='weights',
yerr=5, fmt='o', capthick=2, capsize=10)
plt.plot (xs, line(xs), c='r', label='hypothesis')
plt.xlim(0, 13)
plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
book_plots.show_legend()
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]))