Hi Jonathan,
You are on the right track. I agree that this can be very tedious. Figuring out the right parameters to pass to the subsequent "fits" after the first one can be fairly complex especially if non-default values are used. We have created a feature request based on your question so that we can consider delivering this functionality in the future.
For now, however, if we are using the default parameters for fitrgp, we need to be passing over, Beta, Sigma and KernelParameters to the new model. I would like to illustrate how we can update a model, using a simple API.
%%train and update
rng(0,'twister'); % For reproducibility
n = 1000;
x = linspace(-10,10,n)';
y = 1 + x*5e-2 + sin(x)./x + 0.2*randn(n,1);
gprMdl = fitrgp(x(1:500),y(1:500)); % use half of the data for training
gprMdl2 = updateGPRMdl(gprMdl, x,y); % now pass the entire data
%%predict from the older mdl
[ypred, ~, yci] = predict(gprMdl,x);
subplot(2,1,1);
title('no data, not much confidence on the right');
hold on;
plot(gprMdl.Y,'r.');
plot(ypred);
plot(yci(:,1),'k:');
plot(yci(:,2),'k:');
%%predict from the updated mdl
[ypred, ~, yci] = predict(gprMdl2,x);
subplot(2,1,2);
title('more confident prediction after adding data');
hold on;
plot(gprMdl2.Y,'r.');
plot(ypred);
plot(yci(:,1),'k:');
plot(yci(:,2),'k:');
%%api to update mdl
function newmdl = updateGPRMdl(mdl,Xtrain, Ytrain) % a very simple updating api, assumes defaults, will need to be modified for nondefaults
kernelparams = mdl.KernelInformation.KernelParameters;
inisigma = mdl.Sigma;
beta = mdl.Beta;
newmdl = fitrgp(Xtrain, Ytrain, 'FitMethod', 'none', 'Sigma', inisigma, 'Beta', beta, 'KernelParameters', kernelparams);
end
The above code should be saved as a script before running.
HTH, Nitin
