regnetCorr.m

function [rmse1,rmse2] = regnetCorr(tblVelo)
% tblVelo = getStats(dxx,pData,[],tblVFMD);
% X = [tblVelo.velocity,tblVelo.md];
t2 = tblVelo(tblVelo.dmd~=0,:);

X1 = [t2.velocity];
X2 = [t2.velocity,t2.md];
T = t2.dmd;

% mdl1 = fitrnet(X1,T,'Activations','relu','LayerSizes',[44]);
% cv1 = mdl1.crossval; rmse1 = cv1.kfoldLoss^0.5;
% mdl2 = fitrnet(X2,T,'Activations','sigmoid','LayerSizes',[10]);
% cv2 = mdl2.crossval; rmse2 = cv2.kfoldLoss^0.5;
% 4;
% mdl = fitrnet(X,T,"OptimizeHyperparameters","auto", "HyperparameterOptimizationOptions",struct("AcquisitionFunctionName","expected-improvement-plus"))

err = [];
for i = 1:length(X1)
    trainX2 = X1;
    trainX2(i) = [];
    trainY2 = T;
    trainY2(i) = [];
    mdl = fitrnet(trainX2,trainY2,'Activations','sigmoid','LayerSizes',[10]);
    pred = mdl.predict(X2(i));
    err(i) = abs(T(i) - pred);
end
4;