Question about preparing data for closed loop NarX
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Good, and I posted the problem that I have made, I return it to post to indicate that I have not yet found a solution to this problem and have if someone can help me. The boot files are twofold:
-One contains the inputs and consists of 2272 rows by 13 columns -the other file is composed of 2272 rows by 1 column.
The code already imported the split data for direct use.
I think the problem is that the network has been trained with dimensions and to create a new input data and targets, I do not meet those dimensions and still gives me error.
The code you posted I think it works correctly because I tested with some data in compliance with the network size and predicts well.
I will try to put the code used as clear as possible to see if you can give me some idea. I continued to test and I get the same error.
if true
% code
% PREDICTION %
xlsread p1; %matrix 1136x13
xlsread p2; %matrix 1136x13
xlsread t1; %matrix 1136x1
xlsread t2; %matrix 1136x1
p1=p1'; %matrix 13x1136
p2=p2'; %matrix 13X1136
t1=t1'; %matrix 1X1136
t2=t2'; %matrix 1x1136
nputSeries = tonndata(p1,true,false); %cell 1x1136
targetSeries = tonndata(t1,true,false); %cell 1x1136
% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:1;
feedbackDelays = 1:1;
hiddenLayerSize = 16;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
[inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% Train the Network
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
% Test the Network
outputs = net(inputs,inputStates,layerStates);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs);
% Closed Loop Network
netc = closeloop(net);
netc.name = [net.name ' - Closed Loop'];
NumberOfPredictions = 3;
% Creating a new input series
newInputSeries = p2(1:NumberOfPredictions); %here I built the array must be
%dimensioned 13x3
newInputSeries = [cell2mat(inputSeries(end-1:end) ) (newInputSeries)]; %
%matrix 13x5
newInputSeries = num2cell(newInputSeries); %cell 13x5
%Creating a new target
newTargetSet = nan(size(newInputSeries)); %matrix 13x5
newTargetSet = num2cell(newTargetSet );
newTargetSet (1:1) = targetSeries(end:end); %cell 13x5
[xc,xic,aic,tc] = preparets(netc,newInputSeries,{},newTargetSet);
I tried to apply to normalize newInputSeries tonndata newTargetSet and out and in both cases a 1x5 matrix. Applying preparets out well but I simulate values for prediction makes me the following error:
"Error using network/sim (line 130) Layer state sizes does not match net.layers{:}.size."
If someone comes to how to fix it, or where it can refer to appreciate it.
Thank you very much
8 Commenti
Shashank Prasanna
il 8 Feb 2013
FRANCISCO, I have already shared a perfectly working prediction code in your previous post. At this point all you have to do is make sure that your inputs are in the same form as the one in the working example. From you are error message it is extremely clear that your inputs are not as expected by the function. Even if you feel that the documentation is unclear the ONLY thing you have to do is to make sure that the dimensions are in order compared to the working example.
Risposta accettata
Greg Heath
il 9 Feb 2013
0. I USED YOUR CODE ON THE POLLUTION_DATASET
1. VARIABLES ON DIFFERENT SCALES;. SHOULD NORMALIZE
2. PLOT INPUT AND OUPUTS
3. NARXNET INPUTS ARE NOT OPTIMAL. FIND SIGNIFICANT AUTO
AND CROSSCORRELATIONS. VARY H IF NEEDED
4.DO NOT USE THE DEFAULT DIVIDERAND; IT DESTROYS CORRELATIONS
5. INITIALIZE THE RNG SO THAT RUNS CAN BE DUPLICATED
6. NORMALIZE THE MSE WITH THE AVERAGE TARGET VARIANCE AND
OBTAIN THE COEFFICIENT OF DETERMINATION R^2 (See Wikipedia)
7. CAN OBTAIN INDIVIDUAL TRN/VAL/TST STATS FROM TR
==> 8. WHEN CREATING NEW SERIES, USE MATRIX INDEXING INSTEAD OF
VECTOR INDEXING
9. TRY THE 8-INPUT/3-OUTPUT POLLUTION_DATASET. I GET R2 = 0.72 on
all of the data (508 timesteps) using your parameter settings (1:1, 1:1, 16,
0.7 / 0.15 / 0.15 ) on 356 timesteps.
10. Will next implement some of the above suggestions.
Hope this helps.
Thank you for formally accepting my answer
Greg
3 Commenti
Greg Heath
il 10 Feb 2013
Modificato: Greg Heath
il 10 Feb 2013
1. Run your original data through netc.
2. When you create the new series, use matrix indexing instead of vector indexing.
3. Put cell series, not matrix series into preparets.
4. If you still have problems, we can compare numbers on the pollution_dataset.
5. Note that the pollution data set has 3 outputs.
6. Don't forget to accept my answer (;>)
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