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Class: RegressionGP

Create compact Gaussian process regression model


cgprMdl = compact(gprMdl)


cgprMdl = compact(gprMdl) returns a compact version of the trained Gaussian process regression (GPR) model, gprMdl.

Input Arguments

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Gaussian process regression model, specified as a RegressionGP object.

Output Arguments

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Compact Gaussian process regression model, returned as a CompactRegressionGP object.


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Generate example training data.

rng(1) % For reproducibility
n = 100000;
X = linspace(0,1,n)';
X = [X,X.^2];
y = 1 + X*[1;2] + sin(20*X*[1;-2]) + 0.2*randn(n,1);

Train a GPR model using the subset of regressors ('sr') approximation method and predict using the subset of data ('sd') method. Use 50 points in the active set and sparse greedy matrix approximation ('sgma') method for active set selection. Because the scales of the first and second predictors are different, it is good practice to standardize the data.

gprMdl = fitrgp(X,y,'KernelFunction','squaredExponential','FitMethod',...

fitrgp accepts any combination of fitting, prediction, and active set selection methods. In some cases it might not be possible to compute the standard deviations of the predicted responses, hence the prediction intervals. See Tips. And, in some cases, using the exact method might be expensive because of the size of the training data.

Create a compact GPR object.

cgprMdl = compact(gprMdl);

Generate the test data.

n = 4000;
Xnew = linspace(0,1,n)';
Xnew = [Xnew,Xnew.^2];
ynew = 1 + Xnew*[1;2] + sin(20*Xnew*[1;-2]) + 0.2*randn(n,1);

Use the compact object to predict the response in test data and the prediction intervals.

[ypred,~,yci] = predict(cgprMdl,Xnew);

Plot the true response, predicted response, and prediction intervals.

hold on;
legend('Data','Pred','Lower 95%','Upper 95%','Location','Best');
hold off

Figure contains an axes. The axes contains 4 objects of type line. These objects represent Data, Pred, Lower 95%, Upper 95%.

Compute the mean squared error loss on the test data using the trained GPR model.

L = loss(cgprMdl,Xnew,ynew)
L = 0.0497


  • The compact object does not contain the training data that fitrgp uses in training the GPR model, gprMdl.

  • You can use the compact object to make predictions (see predict) or compute the regression error (see loss).

  • Computation of standard deviations, ysd, and prediction intervals, yint, is not supported when PredictMethod is 'bcd'.

  • If gprMdl is a CompactRegressionGP object, you cannot compute standard deviations, ysd, or prediction intervals, yint, for PredictMethod equal to 'sr' or 'fic'. To compute ysd and yint for PredictMethod equal to 'sr' or 'fic', use the full regression (RegressionGP) object.

Introduced in R2015b