Description:
kalmankit 1.7.8
The Kalman filter is an optimal estimation algorithm: it estimates the true
state of a signal given that this signal is noisy and/or incomplete. This
package provides a multidimensional implementation of:
Standard Kalman Filter: if the noises are drawn from a gaussian
distribution and the underlying system is governed by linear equations, the
filter will output the best possible estimate of the signal's true state.
Extended Kalman Filter: can deal with nonlinear systems, but it does not
guarantee the optimal estimate. It works by linearizing the function locally
using the Jacobian matrix.
Installation
Normal user
pip install kalmankit
Developer
git clone https://github.com/Xylambda/kalmankit.git
pip install -e kalmankit/. -r kalmankit/requirements-dev.txt
Tests
To run tests you must install the library as a developer.
cd kalmankit/
pytest -v tests/
Usage
The library provides 3 examples of usage:
Moving Average
Market Beta estimation
Extended Kalman Filter
A requirements-example.txt is provided to install the needed dependencies to
run the examples.
References
Matlab - Understanding Kalman Filters
Bilgin's Blog - Kalman filter for dummies
Greg Welch, Gary Bishop - An Introduction to the Kalman Filter
Simo Särkkä - Bayesian filtering and Smoothing. Cambridge University Press.
Cite
If you've used this library for your projects please cite it:
@misc{alejandro2021kalmankit,
title={kalmankit - Multidimensional implementation of Kalman Filter algorithms},
author={Alejandro Pérez-Sanjuán},
year={2021},
howpublished={\url{https://github.com/Xylambda/kalmankit}},
}
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
There are no reviews.
