Example code for trainedmodel.predictfcn

Is there a way to have matlab generate a script showing the steps the predictfcn of a trained regression model uses to generate predictions? The steps executed when yfit=trainedmodel.predictfcn(x)is called? PCA steps, if included in the model, should be shown also.

For example, a GPR Rational Quadratic model:

trainedModelGPR_RQ.RegressionGP=

RegressionGP

PredictorNames: {1×58 cell}

ResponseName: 'Y'

CategoricalPredictors: []

ResponseTransform: 'none'

NumObservations: 1487

KernelFunction: 'RationalQuadratic'

KernelInformation: [1×1 struct]

BasisFunction: 'Constant'

Beta: 74.872929476342

Sigma: 0.276734525120447

PredictorLocation: [58×1 double]

PredictorScale: [58×1 double]

Alpha: [1487×1 double]

ActiveSetVectors: [1487×58 double]

PredictMethod: 'Exact'

ActiveSetSize: 1487

FitMethod: 'Exact'

ActiveSetMethod: 'Random'

IsActiveSetVector: [1487×1 logical]

LogLikelihood: -6383.13344737248

ActiveSetHistory: []

BCDInformation: []

with

trainedModelGPR_RQ.RegressionGP.KernelInformation.KernelParameters =

1.83516539103131

0.288843383795902

32.3071857452573

where

trainedModelGPR_RQ.RegressionGP.KernelInformation.KernelParameterNames =

3×1 cell array

{'SigmaL' }

{'AlphaRQ'}

{'SigmaF' }

For a new set of predictors, Xnew having a size of m x 58, how are the predictions for Xnew computed (manually, ie not using predict) given informaiton in the model the above? Looking for something in the form of:

ypred=A*f(Xnew)-B, where A and B are some combination of values stored in the trained model and f may be a PCA step on Xnew, if part of the model.

For GPR Rational Quadratic, ignoring any PCA, what are

A and B?