daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Extensive copy editing.

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This commit is contained in:
Roger Labbe
2015-12-09 06:31:14 -08:00
parent 7b218e7243
commit 41d8d246e0
8 changed files with 795 additions and 740 deletions
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25
code/nonlinear_plots.py
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@@ -16,6 +16,7 @@ for more information.
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from filterpy.kalman import MerweScaledSigmaPoints, unscented_transform
from filterpy.stats import multivariate_gaussian
from matplotlib import cm
import matplotlib.pyplot as plt
@@ -47,10 +48,10 @@ def plot_nonlinear_func(data, f, gaussian, num_bins=300):
#plot output
h = np.histogram(ys, num_bins, density=False)
plt.subplot(2,2,4)
plt.plot(h[0], h[1][1:], lw=4, alpha=0.5)
plt.plot(h[0], h[1][1:], lw=4, alpha=0.8)
plt.ylim(out_lims[1], out_lims[0])
plt.gca().xaxis.set_ticklabels([])
plt.title('output')
plt.title('Output')
plt.axhline(np.mean(ys), ls='--', lw=2)
plt.axhline(f(x0), lw=1)
@@ -66,16 +67,16 @@ def plot_nonlinear_func(data, f, gaussian, num_bins=300):
print(max(norm.pdf(xs)))'''
# plot transfer function
plt.subplot(2,2,3)
plt.subplot(2, 2, 3)
x = np.arange(in_lims[0], in_lims[1], 0.1)
y = f(x)
plt.plot (x,y, 'k')
plt.plot (x, y, 'k')
isct = f(x0)
plt.plot([x0, x0, in_lims[1]], [out_lims[1], isct, isct], color='r', lw=1)
plt.xlim(in_lims)
plt.ylim(out_lims)
#plt.axis('equal')
plt.title('function')
plt.title('f(x)')
# plot input
h = np.histogram(data, num_bins, density=True)
@@ -84,7 +85,7 @@ def plot_nonlinear_func(data, f, gaussian, num_bins=300):
plt.plot(h[1][1:], h[0], lw=4)
plt.xlim(in_lims)
plt.gca().yaxis.set_ticklabels([])
plt.title('input')
plt.title('Input')
plt.show()
@@ -106,11 +107,9 @@ def plot_ekf_vs_mc():
d_t = fx(data)
mean_ekf = fx(mean)
slope = dfx(mean)
std_ekf = abs(slope*std)
norm = scipy.stats.norm(mean_ekf, std_ekf)
xs = np.linspace(-3, 5, 200)
plt.plot(xs, norm.pdf(xs), lw=2, ls='--', color='b')
@@ -126,8 +125,6 @@ def plot_ekf_vs_mc():
print('EKF mean={:.2f}, std={:.2f}'.format(mean_ekf, std_ekf))
from filterpy.kalman import MerweScaledSigmaPoints, unscented_transform
def plot_ukf_vs_mc(alpha=0.001, beta=3., kappa=1.):
def fx(x):
@@ -159,12 +156,12 @@ def plot_ukf_vs_mc(alpha=0.001, beta=3., kappa=1.):
norm = scipy.stats.norm(ukf_mean, ukf_std)
xs = np.linspace(-3, 5, 200)
plt.plot(xs, norm.pdf(xs), ls='--', lw=1, color='b')
plt.hist(d_t, bins=200, normed=True, histtype='step', lw=1, color='g')
plt.plot(xs, norm.pdf(xs), ls='--', lw=2, color='b')
plt.hist(d_t, bins=200, normed=True, histtype='step', lw=2, color='g')
actual_mean = d_t.mean()
plt.axvline(actual_mean, lw=1, color='g', label='Monte Carlo')
plt.axvline(ukf_mean, lw=1, ls='--', color='b', label='UKF')
plt.axvline(actual_mean, lw=2, color='g', label='Monte Carlo')
plt.axvline(ukf_mean, lw=2, ls='--', color='b', label='UKF')
plt.legend()
plt.show()