Implements linear regression and Sparse Spectrum Gaussian Process Regression (SSGPR) (Random Fourier Features).
Requires numpy and matplotlib.
Run python regression.py to see SSGPR test output on the famous cross 2D example from the LWPR paper [1].
[1] S. Vijayakumar, A. D’Souza, and S. Schaal, “Incremental Online Learning in High Dimensions,” Neural Computation, vol. 17, no. 12, pp. 2602–2634, Dec. 2005, doi: 10.1162/089976605774320557.