开源软件名称： pykalman/pykalman开源软件地址： https://github.com/pykalman/pykalman开源编程语言：
Python
98.2%
开源软件介绍： pykalman Welcome to pykalman , 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)
For a quick installation::
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 ::
$ git clone [email protected] :pykalman/pykalman.git pykalman
$ cd pykalman
$ sudo python setup.py install
Examples of all of pykalman's functionality can be found in the scripts in
the examples/ folder.
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