predict

Predict response of Gaussian process regression model

Syntax

ypred = predict(gprMdl,Xnew)
[ypred,ysd] = predict(gprMdl,Xnew)
[ypred,ysd,yint] = predict(gprMdl,Xnew)
[ypred,ysd,yint] = predict(gprMdl,Xnew,Name,Value)


Description

ypred = predict(gprMdl,Xnew) returns the predicted responses ypred for the full or compact Gaussian process regression (GPR) model, gprMdl, and the predictor values in Xnew.

[ypred,ysd] = predict(gprMdl,Xnew) also returns the estimated standard deviations for the new responses at the predictor values in Xnew from a trained GPR model.

[ypred,ysd,yint] = predict(gprMdl,Xnew) also returns the 95% prediction intervals, yint, for the true responses corresponding to each row of Xnew.

[ypred,ysd,yint] = predict(gprMdl,Xnew,Name,Value) also returns the prediction intervals with additional options specified by one or more Name,Value pair arguments. For example, you can specify the confidence level of the prediction interval.

Input Arguments

gprMdl — Gaussian process regression model

RegressionGP object | CompactRegressionGP object

Gaussian process regression model, specified as a RegressionGP (full) or CompactRegressionGP (compact) object.

Xnew — New values for the predictors

table | m-by-d matrix

New values for the predictors that fitrgp uses in training the GPR model, specified as a table or an m-by-d matrix. m is the number of observations and d is the number of predictor variables in the training data.

If you trained gprMdl on a table, then Xnew must be a table that contains all the predictor variables used to train gprMdl.

If you trained gprMdl on a matrix, then Xnew must be a numeric matrix with d columns.

Data Types: single | double | table

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

'Alpha' — Significance level

0.05 (default) | scalar value in the range from 0 to 1

Significance level for the prediction intervals, specified as the comma-separated pair consisting of 'Alpha' and a scalar value in the range from 0 to 1.

Example: 'Alpha',0.01 specifies 99% prediction intervals.

Data Types: single | double

Output Arguments

ypred — Predicted response values

n-by-1 vector

Predicted response values, returned as an n-by-1 vector.

ysd — Estimated standard deviation of the new response values

n-by-1 vector

Estimated standard deviation of the new response values, returned as an n-by-1 vector , where ysd(i), i = 1, 2, ..., n, contains the estimated standard deviation of the new response corresponding to the predictor values at the i^th row of Xnew from the trained GPR model.

yint — Prediction intervals for the true response values

n-by-2 matrix

Prediction intervals for the true response values corresponding to each row of Xnew, returned as an n-by-2 matrix. The first column of yint contains the lower limits and the second column contains the upper limits of the prediction intervals.

Examples

Compute Predicted Responses

Generate the sample data.

n = 10000;
rng(1) % For reproducibility
x = linspace(0.5,2.5,n)';
y = sin(10*pi.*x) ./ (2.*x)+(x-1).^4 + 1.5*rand(n,1);

Fit a GPR model using the Matern 3/2 kernel function with separate length scale for each predictor and an active set size of 100. Use the subset of regressors approximation method for parameter estimation and fully independent conditional method for prediction.

gprMdl = fitrgp(x,y,'KernelFunction','ardmatern32',...
'ActiveSetSize',100,'FitMethod','sr','PredictMethod','fic');

Compute the predictions.

[ypred,~,yci] = predict(gprMdl,x);

Plot the data along with the predictions and prediction intervals.

plot(x,y,'r.');
hold on
plot(x,ypred);
plot(x,yci(:,1),'k--');
plot(x,yci(:,2),'k--');
xlabel('x');
ylabel('y');

Compute Predictions When Data in Table

Load the sample data and store in a table.

load fisheriris
tbl = table(meas(:,1),meas(:,2),meas(:,3),meas(:,4),species,...
'VariableNames',{'meas1','meas2','meas3','meas4','species'});

Fit a GPR model using the first measurement as the response and the other variables as the predictors.

mdl = fitrgp(tbl,'meas1');

Compute the predictions and the 99% confidence intervals.

[ypred,~,yci] = predict(mdl,tbl,'Alpha',0.01);

Plot the true response and the predictions along with the prediction intervals.

figure();
plot(mdl.Y,'r.');
hold on;
plot(ypred);
plot(yci(:,1),'k:');
plot(yci(:,2),'k:');
legend('True response','GPR predictions',...
'Lower prediction limit','Upper prediction limit',...
'Location','Best');

Plot Predicted Response for Test Data

Load the sample data.

load('gprdata.mat');

The data contains training and test data. There are 500 observations in training data and 100 observations in test data. The data has 8 predictor variables. This is simulated data.

Fit a GPR model using the squared exponential kernel function with a separate length scale for each predictor. Standardize predictors in the training data. Use the exact fitting and prediction methods.

gprMdl = fitrgp(Xtrain,ytrain,'Basis','constant',...
'FitMethod','exact','PredictMethod','exact',...
'KernelFunction','ardsquaredexponential','Standardize',1);

Predict the responses for test data.

[ytestpred,~,ytestci] = predict(gprMdl,Xtest);

Plot the test response along with the predictions.

figure;
plot(ytest,'r');
hold on;
plot(ytestpred,'b');
plot(ytestci(:,1),'k:');
plot(ytestci(:,2),'k:');
legend('Actual response','GPR predictions',...
'95% lower','95% upper','Location','Best');
hold off

Tips

• You can choose the prediction method while training the GPR model using the PredictMethod name-value pair argument in fitrgp. The default prediction method is 'exact' for n ≤ 10000, where n is the number of observations in the training data, and 'bcd' (block coordinate descent), otherwise.

• 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.

Alternatives

You can use resubPredict to compute the predicted responses for the trained GPR model at the observations in the training data.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

This function fully supports tall arrays. You can use models trained on either in-memory or tall data with this function.

For more information, see Tall Arrays.

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• Use saveLearnerForCoder, loadLearnerForCoder, and codegen (MATLAB Coder) to generate code for the predict function. Save a trained model by using saveLearnerForCoder. Define an entry-point function that loads the saved model by using loadLearnerForCoder and calls the predict function. Then use codegen to generate code for the entry-point function.

• You can also generate single-precision C/C++ code for predict. For single-precision code generation, specify the name-value pair argument 'DataType','single' as an additional input to the loadLearnerForCoder function.

• This table contains notes about the arguments of predict. Arguments not included in this table are fully supported.

  Argument	Notes and Limitations
  Mdl	For the usage notes and limitations of the model object, see Code Generation of the CompactRegressionGP object.
  Xnew	For general code generation, Xnew must be a single-precision or double-precision matrix or a table containing single or double predictor variables. If you want to specify Xnew as a table, then your model must be trained using a table, and you must ensure that your entry-point function for prediction: Accepts data as arrays Creates a table from the data input arguments and specifies the variable names in the table Passes the table to predict For an example of this table workflow, see Generate Code to Classify Numeric Data in Table. For more information on using tables in code generation, see Code Generation for Tables (MATLAB Coder) and Table Limitations for Code Generation (MATLAB Coder). The number of rows, or observations, in Xnew can be a variable size, but the number of columns in Xnew must be fixed.
  Name-value pair arguments	Names in name-value pair arguments must be compile-time constants. For example, to allow a user-defined significance level in the generated code, include {coder.Constant('Alpha'),0} in the -args value of codegen (MATLAB Coder).

For more information, see Introduction to Code Generation.