daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Weights should be reset to 1/N after resampling.

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This commit is contained in:
Roger Labbe 2017-07-03 13:07:28 -07:00
parent ba61f3a96c
commit 7c5aa45019
7 changed files with 232 additions and 244 deletions
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164
12-Particle-Filters.ipynb
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animations/particle_animate.ipynb
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animations/particle_filter_anim.gif
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8
kf_book/gif_animate.py
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@ -57,4 +57,10 @@ def animate(filename, func, frames, interval, fig=None, figsize=(6.5, 6.5)):
anim = animation.FuncAnimation(fig, func, init_func=init_func,
frames=frames, interval=interval)
anim.save(filename, writer='imagemagick')
import os
basename = os.path.splitext(filename)[0]
anim.save(basename + '.mp4', writer='ffmpeg')
os.system("ffmpeg -y -i {}.mp4 {}.gif".format(basename, basename))
os.remove(basename + '.mp4')

179
kf_book/pf_internal.py
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@ -17,7 +17,6 @@ from __future__ import (absolute_import, division, print_function,
unicode_literals)
from kf_book.book_plots import figsize, end_interactive
from filterpy.monte_carlo import stratified_resample, residual_resample
import matplotlib as mpl
import matplotlib.pyplot as plt
@ -88,7 +87,7 @@ class ParticleFilter(object):
w[i] = self.weights[index]
self.particles = p
self.weights = w / np.sum(w)
self.weights.fill(1.0 / self.N)
def estimate(self):
@ -311,29 +310,27 @@ def test_pf2():
def plot_cumsum(a):
with figsize(y=2):
fig = plt.figure()
N = len(a)
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)
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)
#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([])
def plot_stratified_resample(a):
@ -346,24 +343,22 @@ def plot_stratified_resample(a):
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')
xs = np.linspace(0., 1.-1./N, N)
ax.vlines(xs, 0, 1, lw=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')
xs = np.linspace(0., 1.-1./N, N)
ax.vlines(xs, 0, 1, lw=2)
# make N subdivisions, and chose a random position within each one
b = (random(N) + range(N)) / N
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('stratified resampling')
end_interactive(fig)
# make N subdivisions, and chose a random position within each one
b = (random(N) + range(N)) / N
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('stratified resampling')
def plot_systematic_resample(a):
@ -376,24 +371,22 @@ def plot_systematic_resample(a):
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')
xs = np.linspace(0., 1.-1./N, N)
ax.vlines(xs, 0, 1, lw=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')
xs = np.linspace(0., 1.-1./N, N)
ax.vlines(xs, 0, 1, lw=2)
# make N subdivisions, and chose a random position within each one
b = (random() + np.array(range(N))) / N
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('systematic resampling')
end_interactive(fig)
# make N subdivisions, and chose a random position within each one
b = (random() + np.array(range(N))) / N
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('systematic resampling')
def plot_multinomial_resample(a):
@ -406,22 +399,20 @@ def plot_multinomial_resample(a):
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')
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)
# 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')
def plot_residual_resample(a):
@ -436,34 +427,36 @@ def plot_residual_resample(a):
[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')
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')
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)
if __name__ == '__main__':
plot_residual_resample([.1, .2, .3, .4, .2, .3, .1])
show_two_pf_plots()
#plot_residual_resample([.1, .2, .3, .4, .2, .3, .1])
#example()
#show_two_pf_plots()
a = [.1, .2, .1, .6]
#a = [.1, .2, .1, .6]
#plot_cumsum(a)
#test_pf()

2
pdf/merge_book.py
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@ -24,7 +24,7 @@ def merge_notebooks(outfile, filenames):
for fname in filenames:
with io.open(fname, 'r', encoding='utf-8') as f:
nb = nbformat.read(f, nbformat.NO_CONVERT)
remove_formatting(nb)
#remove_formatting(nb)
if not added_appendix and fname[0:8] == 'Appendix':
remove_links_add_appendix(nb)
added_appendix = True

2
pdf/run_notebooks.bat
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@ -1,7 +1,7 @@
mkdir tmp
copy ..\*.ipynb .\tmp
copy ..\*.py .\tmp
cp -r ..\code\ .\tmp\code\
cp -r ..\kf_book\ .\tmp\kf_book\
cd tmp