Deleted obsolete file particle_filter.py

code/particle_filter.py

@@ -1,87 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""Copyright 2015 Roger R Labbe Jr.
-
-
-Code supporting the book
-
-Kalman and Bayesian Filters in Python
-https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
-
-
-This is licensed under an MIT license. See the LICENSE.txt file
-for more information.
-"""
-
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-
-import math
-import matplotlib.pyplot as plt
-import numpy as np
-from numpy.random import uniform
-from numpy.random import randn
-import scipy.stats
-import random
-
-
-
-if __name__ == '__main__':
-    N = 2000
-    pf = ParticleFilter(N, 100, 100)
-    #pf.particles[:,2] = np.random.randn(pf.N)*np.radians(10) + np.radians(45)
-
-    z = np.array([20, 20])
-    #pf.create_particles(mean=z, variance=40)
-
-    mu0 = np.array([0., 0.])
-    plt.plot(pf, weights=False)
-
-
-    fig = plt.gcf()
-    #fig.show()
-    #fig.canvas.draw()
-    #plt.ioff()
-
-    for x in range(10):
-
-        z[0] = x+1 + randn()*0.3
-        z[1] = x+1 + randn()*0.3
-
-
-        pf.predict((1,1), (0.2, 0.2))
-        pf.weight(z=z, var=.8)
-        neff = pf.neff()
-
-        print('neff', neff)
-        if neff < N/2 or N <= 2000:
-            pf.resample()
-        mu, var = pf.estimate()
-        if x == 0:
-            mu0 = mu
-        #print(mu - z)
-        #print(var)
-
-        plot(pf, weights=True)
-        #plt.plot(z[0], z[1], marker='v', c='r', ms=10)
-        plt.plot(x+1, x+1, marker='*', c='r', ms=10)
-        plt.scatter(mu[0], mu[1], c='g', s=100)#,
-                    #s=min(500, abs((1./np.sum(var)))*20), alpha=0.5)
-        plt.plot([0,100], [0,100])
-        plt.tight_layout()
-        plt.pause(.002)
-
-        #fig.canvas.draw()
-
-        #pf.assign_speed_by_gaussian(1, 1.5)
-        #pf.move(h=[1,1], v=1.4, t=1)
-        #pf.control(mu-mu0)
-        mu0 = mu
-
-    plt.ion()
-
-
-
-
-