Hello,
i'm currently developing a gaussian process implementation for my PhD
thesis. Within this package i need to calculate and maximize the
marginal likelihood of the model given some data. This marginal
likelihood is dependent on the parameters of a covariance function which
is used. For a certain covariance function one can calculate the
derivitives and the gradients for the marginal likelihood to optimize
this function.
My Question: Can someone show me an example for the usage of
DifferentiableMultivariateRealFunction and a matching optimizer. I
understand the concept but the implementational details are hard to
find. Here is what i understood:
value: the value of the function.
gradient(): an array containing all partial derivitivs gradients for the
parameters.
partialDerivitive: Value containing only one gradient of the function
if i provide this basic implementation i should pass this to the
optimizer. Or do i have to provide an extra target function to the
optimizer as inicated by some optinmizers with the <FUNC> operator?
Thnak you
--
Dipl.-Ing. (FH) Andreas Niekler
Mitarbeiter und Promovend
Bereich Multimedia-Produktionssysteme und -technologien
Hochschule für Technik, Wirtschaft und Kultur Leipzig
Fachbereich Medien
Besucher
Gustav-Freytag-Straße 40
04277 Leipzig
Telefon: +49 0341 30 76 2378
Email: [email protected]
http://www.fbm.htwk-leipzig.de
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