def plot_estimate_chart_3():
with figsize(y=2.5):
plt.figure()
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->',
ec='r', lw=3, shrinkA=6, shrinkB=5))
ax.annotate('', xy=[1,159], xytext=[1,164.2],
arrowprops=dict(arrowstyle='-',
ec='k', lw=3, shrinkA=8, shrinkB=8))
est_y = (158 + .4*(164.2-158))
plt.scatter ([0,1], [158.0,est_y], c='k',s=128)
plt.scatter ([1], [164.2], c='b',s=128)
plt.scatter ([1], [159], c='r', s=128)
plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
plt.text (0, 159.8, "last estimate ($\hat{x}_{t-1}$)", ha='left', va='top',fontsize=18)
plt.text (0.95, est_y, "estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')
ax.yaxis.grid(True, which="major", linestyle='dotted')
plt.ylim(157, 164.5)
def plot_residual_resample(a):
N = len(a)
a_norm = np.asarray(a) / np.sum(a)
cumsum = np.cumsum(a_norm)
cumsum = np.insert(cumsum, 0, 0)
cmap = mpl.colors.ListedColormap([[0., .4, 1.],
[0., .8, 1.],
[1., .8, 0.],
[1., .4, 0.]]*(int(N/4) + 1))
with figsize(y=2):
fig = plt.figure()
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
indexes = residual_resample(a_norm)
bins = np.bincount(indexes)
for i in range(1, N):
n = bins[i-1] # number particles in this sample
if n > 0:
b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('residual resampling')
end_interactive(fig)
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_cumsum(a):
with figsize(y=2):
fig = plt.figure()
N = len(a)
cmap = mpl.colors.ListedColormap([[0., .4, 1.],
[0., .8, 1.],
[1., .8, 0.],
[1., .4, 0.]]*(int(N/4) + 1))
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
cumsum = np.insert(cumsum, 0, 0)
#fig = plt.figure(figsize=(6,3))
fig=plt.gcf()
ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
if N > 10:
bar.set_ticks([])
end_interactive(fig)
def plot_multinomial_resample(a):
N = len(a)
cmap = mpl.colors.ListedColormap([[0., .4, 1.],
[0., .8, 1.],
[1., .8, 0.],
[1., .4, 0.]]*(int(N/4) + 1))
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
cumsum = np.insert(cumsum, 0, 0)
with figsize(y=2):
fig = plt.figure()
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
# make N subdivisions, and chose a random position within each one
b = random(N)
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('multinomial resampling')
end_interactive(fig)
def plot_estimate_chart_3():
with figsize(y=2.5):
plt.figure()
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->',
ec='r', lw=3, shrinkA=6, shrinkB=5))
ax.annotate('', xy=[1,159], xytext=[1,164.2],
arrowprops=dict(arrowstyle='-',
ec='k', lw=3, shrinkA=8, shrinkB=8))
est_y = (158 + .4*(164.2-158))
plt.scatter ([0,1], [158.0,est_y], c='k',s=128)
plt.scatter ([1], [164.2], c='b',s=128)
plt.scatter ([1], [159], c='r', s=128)
plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
plt.text (0, 159.8, "last estimate ($\hat{x}_{t-1}$)", ha='left', va='top',fontsize=18)
plt.text (0.95, est_y, "estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')
ax.yaxis.grid(True, which="major", linestyle='dotted')
plt.ylim(157, 164.5)
def plot_hypothesis2():
with book_plots.figsize(y=2.5):
plt.figure()
plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 10], [169, 170.5], color='g', ls='--')
plt.xlim(0, 11)
plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
def plot_hypothesis2():
with book_plots.figsize(y=2.5):
plt.figure()
plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 10], [169, 170.5], color='g', ls='--')
plt.xlim(0, 11)
plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
plt.grid(False)
def plot_estimate_chart_1():
with figsize(y=2.5):
plt.figure()
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->', ec='r',shrinkA=6, lw=3,shrinkB=5))
plt.scatter ([0], [158], c='b')
plt.scatter ([1], [159], c='r')
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')
ax.yaxis.grid(True, which="major", linestyle='dotted')
plt.tight_layout()
def plot_estimate_chart_1():
with figsize(y=2.5):
plt.figure()
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->', ec='r',shrinkA=6, lw=3,shrinkB=5))
plt.scatter ([0], [158], c='b')
plt.scatter ([1], [159], c='r')
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')
ax.yaxis.grid(True, which="major", linestyle='dotted')
plt.tight_layout()
def plot_hypothesis3():
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()
plt.errorbar(range(1, 13), weights,
xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)
plt.xlim(0, 13)
plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
def plot_hypothesis1():
with figsize(y=3.5):
plt.figure()
plt.errorbar([1, 2, 3], [170, 161, 169],
xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 3], [180, 160], color='g', ls='--')
plt.plot([1, 3], [170, 170], color='g', ls='--')
plt.plot([1, 3], [160, 175], color='g', ls='--')
plt.plot([1, 2, 3], [180, 152, 179], color='g', ls='--')
plt.xlim(0,4)
plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
plt.tight_layout()
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_hypothesis3():
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()
plt.errorbar(range(1, 13), weights,
xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)
plt.xlim(0, 13)
plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
plt.grid(False)
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_hypothesis1():
with figsize(y=3.5):
plt.figure()
plt.errorbar([1, 2, 3], [170, 161, 169],
xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 3], [180, 160], color='g', ls='--')
plt.plot([1, 3], [170, 170], color='g', ls='--')
plt.plot([1, 3], [160, 175], color='g', ls='--')
plt.plot([1, 2, 3], [180, 152, 179], color='g', ls='--')
plt.xlim(0,4)
plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
plt.grid(False)
plt.tight_layout()
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.bar(x,prior,width,label='Previsão')
if do_print:
print()
print('final position is', robot.pos)
index = np.argmax(posterior)
print('''Estimated position is {} with '''
'''confidence {:.4f}%:'''.format(
index, posterior[index]*100))
return m
index = [1,2]
index.extend([1,2,3,4]*3)
#index = [1,2,3,4]*3
with figsize(y=5.5):
j=1
flag = False
for indice,i in enumerate(index):
random.seed(3)
if i == 1:
plt.figure()
elif indice == 14:
break
if indice == 0 or indice == 1:
a = plt.subplot(1,2,i)
else:
a = plt.subplot(2,2,i)
m = train_filter(indice, kernel=[.05,.1, .7, .1 ,.05],
sensor_accuracy=.7,
dx = 5
count = 50
x0 = 5
dt = 1
h = 0.01
#re-seed the generation
np.random.seed(100)
vector = gen_data(x0=x0, dx=dx, count=count, noise_factor=noise_factor)
data = range(3 * len(vector))
data = np.reshape(data, (3, len(vector)))
for i in range(len(g)):
data[i] = filter(data=vector, x0=0., dx=5., dt=1., g=g[i], h=0.01)
with book_plots.figsize(y=4):
book_plots.plot_measurements(vector, color='k')
book_plots.plot_filter(data[0], label='g=0.1', marker='s', c='C0')
book_plots.plot_filter(data[1], label='g=0.4', marker='v', c='C1')
book_plots.plot_filter(data[2], label='g=0.8', c='C2')
plt.legend(loc=4)
book_plots.set_limits([20, 40], [50, 250])
plt.grid()
plt.show()
zs = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
for i in range(50):
zs.append(14)
data1 = filter(data=zs, x0=4., dx=1., dt=1., g=0.1, h=0.01)
data2 = filter(data=zs, x0=4., dx=1., dt=1., g=0.5, h=0.01)
