compact

Class: RegressionGP

Create compact Gaussian process regression model

Syntax

cgprMdl = compact(gprMdl)


Description

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

Input Arguments

gprMdl — Gaussian process regression model

RegressionGP object

Gaussian process regression model, specified as a RegressionGP object.

Output Arguments

cgprMdl — Compact Gaussian process regression model

CompactRegressionGP object

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

Examples

Compute Predictions and Regression Loss for Test Data

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',...
'sr','PredictMethod','sd','Basis','none','ActiveSetSize',50,...
'ActiveSetMethod','sgma','Standardize',1,'KernelParameters',[1;1]);

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.

figure;
plot(ynew,'r');
hold on;
plot(ypred,'b')
plot(yci(:,1),'k--');
plot(yci(:,2),'k--');
legend('Data','Pred','Lower 95%','Upper 95%','Location','Best');
xlabel('x');
ylabel('y');
hold off

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

L = loss(cgprMdl,Xnew,ynew)

L = 0.0497

Tips

• 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.