Source code for filterpy.discrete_bayes.discrete_bayes

# -*- coding: utf-8 -*-
"""Copyright 2015 Roger R Labbe Jr.

FilterPy library.

Documentation at:

Supporting book at:

This is licensed under an MIT license. See the readme.MD file
for more information.

from __future__ import (absolute_import, division, print_function,

import numpy as np
from scipy.ndimage.filters import convolve
from scipy.ndimage.interpolation import shift

[docs]def normalize(pdf): """Normalize distribution `pdf` in-place so it sums to 1.0. Returns pdf for convienence, so you can write things like: >>> kernel = normalize(randn(7)) Parameters ---------- pdf : ndarray discrete distribution that needs to be converted to a pdf. Converted in-place, i.e., this is modified. Returns ------- pdf : ndarray The converted pdf. """ pdf /= sum(np.asarray(pdf, dtype=float)) return pdf
[docs]def update(likelihood, prior): """ Computes the posterior of a discrete random variable given a discrete likelihood and prior. In a typical application the likelihood will be the likelihood of a measurement matching your current environment, and the prior comes from discrete_bayes.predict(). Parameters ---------- likelihood : ndarray, dtype=flaot array of likelihood values prior : ndarray, dtype=flaot prior pdf. Returns ------- posterior : ndarray, dtype=float Returns array representing the posterior. Examples -------- .. code-block:: Python # self driving car. Sensor returns values that can be equated to positions # on the road. A real likelihood compuation would be much more complicated # than this example. likelihood = np.ones(len(road)) likelihood[road==z] *= scale_factor prior = predict(posterior, velocity, kernel) posterior = update(likelihood, prior) """ posterior = prior * likelihood return normalize(posterior)
[docs]def predict(pdf, offset, kernel, mode='wrap', cval=0.): """ Performs the discrete Bayes filter prediction step, generating the prior. `pdf` is a discrete probability distribution expressing our initial belief. `offset` is an integer specifying how much we want to move to the right (negative values means move to the left) We assume there is some noise in that offset, which we express in `kernel`. For example, if offset=3 and kernel=[.1, .7., .2], that means we think there is a 70% chance of moving right by 3, a 10% chance of moving 2 spaces, and a 20% chance of moving by 4. It returns the resulting distribution. If `mode='wrap'`, then the probability distribution is wrapped around the array. If `mode='constant'`, or any other value the pdf is shifted, with `cval` used to fill in missing elements. Examples -------- .. code-block:: Python belief = [.05, .05, .05, .05, .55, .05, .05, .05, .05, .05] prior = predict(belief, offset=2, kernel=[.1, .8, .1]) """ if mode == 'wrap': return convolve(np.roll(pdf, offset), kernel, mode='wrap') return convolve(shift(pdf, offset, cval=cval), kernel, cval=cval, mode='constant')