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, 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)
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, pred],
['Actual Weight', 'Measurement', 'Predictions'],
loc=4)
book_plots.set_labels(x='day', y='weight (lbs)')
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)
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, pred], ['Actual Weight', 'Measurement', 'Predictions'], loc=4)
book_plots.set_labels(x='day', y='weight (lbs)')
#"I know you would never set
#both g and h to zero as that takes a special kind of genius that only I
#possess, but I promise that if you are not careful you will set them lower
#than they should be."
#
#
# Tracking train
#
#
#from numpy.random import randn
def compute_new_position(pos, vel, dt=1):
""" dt is the time delta in seconds."""
return pos + (vel * dt)
def measure_position(pos):
return pos + randn()*500
def gen_train_data(pos, vel, count):
zs = []
for t in range(count):
pos = compute_new_position(pos, vel)
zs.append(measure_position(pos))
return np.asarray(zs)
pos, vel = 23*1000, 15
zs = gen_train_data(pos, vel, 100)
plt.plot(zs / 1000.) # convert to km
book_plots.set_labels('Train Position', 'time(sec)', 'km')
# ослабление h может помочь проверять отследить ускорение
show()
