How to check the kernel parameter values of a Gaussian process regression (GPR) model after training

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Shoubo 2018-1-31

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https://ww2.mathworks.cn/matlabcentral/answers/379994-how-to-check-the-kernel-parameter-values-of-a-gaussian-process-regression-gpr-model-after-training

评论： Shoubo 2018-2-2

I am trying to train a GPR model with the length scale of the kernel function in certain range. Therefore, I did the following ( follow this example ):

params = hyperparameters('fitrgp',X,y);
params(4).Range = [1,10];
gprMdl = fitrgp(X, y, 'OptimizeHyperparameters', params);

However, after training, I found that the kernel parameters in the trained model is empty. This confuses me a lot and I tried different ranges of kernel parameters and found that this restriction on its range actually worked. So I was wondering how I could obtain the exact value of kernel parameters from the trained GPR model. Thanks in advance!

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采纳的回答

Don Mathis 2018-2-1

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https://ww2.mathworks.cn/matlabcentral/answers/379994-how-to-check-the-kernel-parameter-values-of-a-gaussian-process-regression-gpr-model-after-training#answer_302957

在 MATLAB Online 中打开

You can see the kernel parameters like this:

gprMdl.KernelInformation.KernelParameters

By default, params(4) (KernelScale) is not optimized. To optimize it, you'll need to set the Optimize field:

params = hyperparameters('fitrgp',X,y);
params(4).Range = [1,10];
params(4).Optimize = true;
gprMdl = fitrgp(X, y, 'OptimizeHyperparameters', params);
gprMdl.KernelInformation.KernelParameters