How to divide the data using "divideblock" for training and testing , excluding validation dataset in NAR network for time series prediction.

Question

anurag kulshrestha 2017-6-1

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/342853-how-to-divide-the-data-using-divideblock-for-training-and-testing-excluding-validation-dataset-i

编辑： Kazi Tamaddun 2018-5-17

I am using NAR network for time series prediction. I have to divide my datset of 133 time points into training and tetsing dataset uisng "divideblock". I want first 106 points for training and the rest for testing. I am getting the error "Attempted to access valInd(0); index must be a positive integer or logical.

Error in divideblock>divide_indices testInd = (1:numTest)+valInd(end);

Error in divideblock [out1,out2,out3] = divide_indices(in1,params);".

Here is my code:

targetSeries = tonndata(untitled,false,false);

feedbackDelays = 1:1; hiddenLayerSize = 10; net = narnet(feedbackDelays,hiddenLayerSize);

net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; [inputs,inputStates,layerStates,targets] = preparets(net,{},{},targetSeries);

net.divideFcn = 'divideblock'; [trainInd,valInd,testInd] = divideblock(133,0.8,0,0.2)

net.divideMode = 'time'; % Divide up every value net.divideParam.trainRatio = 80/100; net.divideParam.valRatio = 0/100; net.divideParam.testRatio = 20/100;

net.trainFcn = 'trainlm'; % Levenberg-Marquardt

net.performFcn = 'mse'; % Mean squared error

net.plotFcns = {'plotperform','plottrainstate','plotresponse','ploterrcorr', 'plotinerrcorr'};

[net,tr] = train(net,inputs,targets,inputStates,layerStates);

       outputs = net(inputs,inputStates,layerStates);
       errors = gsubtract(targets,outputs);
       performance = perform(net,targets,outputs)
       trainTargets = gmultiply(targets,tr.trainMask);
       valTargets = gmultiply(targets,tr.valMask);
       testTargets = gmultiply(targets,tr.testMask);
       trainPerformance = perform(net,trainTargets,outputs)
       valPerformance = perform(net,valTargets,outputs)
       testPerformance = perform(net,testTargets,outputs)

view(net)

netc = closeloop(net); [xc,xic,aic,tc] = preparets(netc,{},{},targetSeries); yc = netc(xc,xic,aic); perfc = perform(net,tc,yc)

              nets = removedelay(net);
              [xs,xis,ais,ts] = preparets(nets,{},{},targetSeries);
              ys = nets(xs,xis,ais);
              closedLoopPerformance = perform(net,tc,yc)