Gaussian process regression with derivative constraints and predictions.
copied from cf-staging / gptoolsGaussian processes with arbitrary derivative constraints and predictions.
gptools is a Python package that provides a convenient, powerful, and extensible
implementation of Gaussian process regression (GPR). Central to gptool's
implementation is support for derivatives and their variances. Furthermore, the
implementation supports the incorporation of arbitrary linearly transformed
quantities into the GP.
Developed and tested using Python 2.7 and scipy 0.14.0. May work with other versions, but it has not been tested under such configurations.
If you find this software useful, please be sure to cite it:
M.A. Chilenski et al. 2015 Nucl. Fusion 55 023012
http://stacks.iop.org/0029-5515/55/023012
