499f8d91f5
the kalman filters. It was annoying anyway - have to pack and unpack it everywhere when calling around.
79 lines
1.9 KiB
Python
79 lines
1.9 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, mean, variance):
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try:
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self.mean = float(mean)
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self.variance = float(variance)
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except:
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self.mean = mean
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self.variance = variance
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def __add__ (a, b):
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return gaussian (a.mean + b.mean, a.variance + b.variance)
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def __mul__ (a, b):
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m = (a.variance*b.mean + b.variance*a.mean) / (a.variance + b.variance)
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v = 1. / (1./a.variance + 1./b.variance)
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return gaussian (m, v)
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def __call__(self, x):
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""" Impl
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"""
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return math.exp (-0.5 * (x-self.mean)**2 / self.variance) / \
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math.sqrt(2.*math.pi*self.variance)
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def __str__(self):
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return "(%f, %f)" %(self.mean, self.sigma)
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def stddev(self):
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return math.sqrt (self.variance)
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def as_tuple(self):
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return (self.mean, self.variance)
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def __tuple__(self):
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return (self.mean, self.variance)
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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.mean
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elif index == 1: return self.variance
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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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def mul2 (a, b):
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m = (a['variance']*b['mean'] + b['variance']*a['mean']) / (a['variance'] + b['variance'])
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v = 1. / (1./a['variance'] + 1./b['variance'])
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return gaussian (m, v)
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#varying_error_kf( noise_factor=100)
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