# pykalman

**Repository Path**: kitegit/pykalman

## Basic Information

- **Project Name**: pykalman
- **Description**: 卡尔曼滤波
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-05-31
- **Last Updated**: 2024-05-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

======================================
[pykalman](http://pykalman.github.com)
======================================

Welcome to [pykalman](http://pykalman.github.com), the dead-simple Kalman
Filter, Kalman Smoother, and EM library for Python

    >>> from pykalman import KalmanFilter
    >>> import numpy as np
    >>> kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])
    >>> measurements = np.asarray([[1,0], [0,0], [0,1]])  # 3 observations
    >>> kf = kf.em(measurements, n_iter=5)
    >>> (filtered_state_means, filtered_state_covariances) = kf.filter(measurements)
    >>> (smoothed_state_means, smoothed_state_covariances) = kf.smooth(measurements)

Also included is support for missing measurements

    >>> from numpy import ma
    >>> measurements = ma.asarray(measurements)
    >>> measurements[1] = ma.masked   # measurement at timestep 1 is unobserved
    >>> kf = kf.em(measurements, n_iter=5)
    >>> (filtered_state_means, filtered_state_covariances) = kf.filter(measurements)
    >>> (smoothed_state_means, smoothed_state_covariances) = kf.smooth(measurements)

And for the non-linear dynamics via the `UnscentedKalmanFilter`

    >>> from pykalman import UnscentedKalmanFilter
    >>> ukf = UnscentedKalmanFilter(lambda x, w: x + np.sin(w), lambda x, v: x + v, transition_covariance=0.1)
    >>> (filtered_state_means, filtered_state_covariances) = ukf.filter([0, 1, 2])
    >>> (smoothed_state_means, smoothed_state_covariances) = ukf.smooth([0, 1, 2])

And for online state estimation

    >>> for t in range(1, 3):
    ...     filtered_state_means[t], filtered_state_covariances[t] = \
    ...         kf.filter_update(filtered_state_means[t-1], filtered_state_covariances[t-1], measurements[t])

And for numerically robust "square root" filters

    >>> from pykalman.sqrt import CholeskyKalmanFilter, AdditiveUnscentedKalmanFilter
    >>> kf = CholeskyKalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])
    >>> ukf = AdditiveUnscentedKalmanFilter(lambda x, w: x + np.sin(w), lambda x, v: x + v, observation_covariance=0.1)

------------
Installation
------------

For a quick installation::

    $ easy_install pykalman

`pykalman` depends on the following modules,

* `numpy`     (for core functionality)
* `scipy`     (for core functionality)
* `Sphinx`    (for generating documentation)
* `numpydoc`  (for generating documentation)
* `nose`      (for running tests)

All of these and `pykalman` can be installed using `easy_install`

    $ easy_install numpy scipy Sphinx numpydoc nose pykalman

Alternatively, you can get the latest and greatest from
[github](https://github.com/pykalman/pykalman)::

    $ git clone git@github.com:pykalman/pykalman.git pykalman
    $ cd pykalman
    $ sudo python setup.py install


--------
Examples
--------

Examples of all of `pykalman`'s functionality can be found in the scripts in
the `examples/` folder.