This is the Linux app named LWPR whose latest release can be downloaded as lwpr-1.2.6.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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LWPR
DESCRIPTION:
Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite:[1] Sethu Vijayakumar, Aaron D'Souza and Stefan Schaal, Incremental Online Learning in High Dimensions, Neural Computation, vol. 17, no. 12, pp. 2602-2634 (2005).
[2] Stefan Klanke, Sethu Vijayakumar and Stefan Schaal, A Library for Locally Weighted Projection Regression, Journal of Machine Learning Research (JMLR), vol. 9, pp. 623--626 (2008).
More details and usage guidelines on the code website.
Programming Language
C, MATLAB, Python
This is an application that can also be fetched from https://sourceforge.net/projects/lwpr/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.