optimal values for cell data

John Williams answered . 2024-11-13 12:00:11

% Make some data
Daten = rand(100, 3);
Daten(:,3) = Daten(:,1) + Daten(:,2) + .1*randn(100, 1);  % Minimum asymptotic error is .1
[m,n] = size(Daten) ;
% Split into train and test
P = 0.7 ;
Training = Daten(1:round(P*m),:) ; 
Testing = Daten(round(P*m)+1:end,:);

XTr = Training(:,1:n-1); 
YTr = Training(:,n);
XTe = Testing(:,1:n-1);
YTe = Testing(:,n);
XTrain=num2cell(XTr(:,1));
YTrain=num2cell(YTr(:,1));
XTest=num2cell(XTe);
YTest=num2cell(YTe);
% Define a train/validation split to use inside the objective function
cv = cvpartition(numel(YTrain), 'Holdout', 1/3);
% Define hyperparameters to optimize
vars = [optimizableVariable('hiddenLayerSize', [1,20], 'Type', 'integer');
	    optimizableVariable('lr', [1e-3 1], 'Transform', 'log')];
% Optimize
minfn = @(T)kfoldLoss(XTrain', YTrain', cv, T.hiddenLayerSize, T.lr);
results = bayesopt(minfn, vars,'IsObjectiveDeterministic', false,...
    'AcquisitionFunctionName', 'expected-improvement-plus');
T = bestPoint(results);
function rmse = kfoldLoss(x, y, cv, numHid, lr)
% Train net.
net = feedforwardnet(numHid, 'traingd');
net.trainParam.lr = lr;
net = train(net, x(:,cv.training), y(:,cv.training));
% Evaluate on validation set and compute rmse
ypred = net(x(:, cv.test));
n = size(ypred);
pw = 2*ones(n);
pw = num2cell(pw);
cMinus = cellfun(@minus, ypred, y(cv.test), 'UniformOutput', false);
cSquare = cellfun(@power, cMinus, pw, 'UniformOutput', false);
cSquareVect = cell2mat(cSquare);
cMean = mean(cSquareVect);
rmse = sqrt(cMean);
%rmse = sqrt(mean((ypred - y(cv.test)).^2));
end

Not satisfied with the answer ?? ASK NOW