Gaussian Process Regression (GPR)
1. This code is based on the GPML toolbox V4.2.
2. Provided two demos (multiple input single output & multiple input multiple output).
3. Use feval(@ function name) to see the number of hyperparameters in a function. For example:
K > > feval (@ covRQiso)
Ans =
'(1 + 1 + 1)'
It shows that the covariance function covRQiso requires 3 hyperparameters. Therefore, 3
hyperparameters need to be initialized when using the optimization function minimize. The meaning
and range of each hyperparameter are explained in detail in the description of each function.
4. Different likelihood functions have different inference function requirements, which can be seen in
detail ./gpml-matlab-v4.2-2018-06-11/doc/index.html or ./gpml-matlab-v4.2-2018-06-
11/doc/manual.PDF.
Compatibilidad con la versión de MATLAB
Compatibilidad con las plataformas
Windows macOS LinuxCategorías
- AI and Statistics > Statistics and Machine Learning Toolbox > Regression > Gaussian Process Regression >
Etiquetas
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Descubra Live Editor
Cree scripts con código, salida y texto formateado en un documento ejecutable.
func
gpml-matlab-v4.2-2018-06-11
gpml-matlab-v4.2-2018-06-11/cov
gpml-matlab-v4.2-2018-06-11/doc
gpml-matlab-v4.2-2018-06-11/inf
gpml-matlab-v4.2-2018-06-11/lik
gpml-matlab-v4.2-2018-06-11/mean
gpml-matlab-v4.2-2018-06-11/prior
gpml-matlab-v4.2-2018-06-11/util
gpml-matlab-v4.2-2018-06-11/util/minfunc
gpml-matlab-v4.2-2018-06-11/util/minfunc/mex
gpml-matlab-v4.2-2018-06-11/util/sparseinv
No se pueden descargar versiones que utilicen la rama predeterminada de GitHub
Versión | Publicado | Notas de la versión | |
---|---|---|---|
1.0.0 |
|