How to check the kernel parameter values of a Gaussian process regression (GPR) model after training
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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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Don Mathis
il 1 Feb 2018
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
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Don Mathis
il 2 Feb 2018
That's described in the section on the KernelParameters argument: https://www.mathworks.com/help/stats/fitrgp.html?searchHighlight=fitrgp&s_tid=doc_srchtitle#input_argument_namevalue_d119e335756
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