8305200ff0
due to running them. In the process of adding a bunch of new .py scripts to support the book. Most important is the DiscreteBayes1D.py class, which implements a Discrete Bayesian filter in a generalized way.
59 lines
1.3 KiB
Python
59 lines
1.3 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Tue Apr 22 11:38:49 2014
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@author: rlabbe
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"""
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from __future__ import division, print_function
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import math
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import matplotlib.pyplot as plt
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import numpy.random as random
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class gaussian(object):
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def __init__ (self,m,s):
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self.mu = float(m)
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self.sigma = float(s)
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def __add__ (a,b):
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return gaussian (a.mu + b.mu, a.sigma + b.sigma)
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def __mul__ (a,b):
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m = (a.sigma*b.mu + b.sigma*a.mu) / (a.sigma + b.sigma)
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s = 1. / (1./a.sigma + 1./b.sigma)
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return gaussian (m,s)
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def __call__(self,x):
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return math.exp (-0.5 * (x-self.mu)**2 / self.sigma) / \
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math.sqrt(2.*math.pi*self.sigma)
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def __str__(self):
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return "(%f, %f)" %(self.mu, self.sigma)
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def __getitem__ (self,index):
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""" maybe silly, allows you to access obect as a tuple:
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a = gaussian(3,4)
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print (tuple(a))
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"""
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if index == 0: return self.mu
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elif index == 1: return self.sigma
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else: raise StopIteration
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class KF1D(object):
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def __init__ (self, pos, sigma):
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self.estimate = gaussian(pos,sigma)
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def update(self, Z,var):
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self.estimate = self.estimate * gaussian (Z,var)
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def predict(self, U, var):
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self.estimate = self.estimate + gaussian (U,var)
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#varying_error_kf( noise_factor=100)
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