# Converting Numeric Matrix to Cell Array nesting columns into line (LSTM training input data)

조회 수: 3 (최근 30일)
Eric 2020년 5월 21일
댓글: Imrana 2023년 5월 15일
Please, how can I convert a Numeric Matrix (1400 lines/steps x 30 columns/features) to Cell Array (1400x1) Nx1 as required for LSTM training?
In this case, the 30 columns by line must be nested into the one cell array each line.
In outer view, cell array will be (1400x1), but when click on cell it will open as (1x30) containing the 30 features columns in one line.
Simplified example:
From (5x4) matrix A (lines: timesteps/observations, columns: input features data)
1 2 3 4
2 3 4 5
3 4 5 6
4 5 6 7
5 6 7 8
To (5x1) cell array C (Nx1 as requested by LSTM, N = steps) nesting features columns into the cell:
{1x4} => (1 2 3 4) inside line cell (1x4)
{1x4} => (2 3 4 5) inside line cell (1x4)
{1x4} => (3 4 5 6) inside line cell (1x4)
{1x4} => (4 5 6 7) inside line cell (1x4)
{1x4} => (5 6 7 8) inside line cell (1x4)
I can build it manually from an empty cell array by entering in each line cell and pasting data (30 columns) into them, but my data has 1400 lines.
The label (output) categorical vector (5x1 example) for the LSTM is ready (1400x1 real) and contains binary classification (0, 1) for each line as expected responses.
Thanks!

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### 채택된 답변

Ameer Hamza 2020년 5월 21일
Use mat2cell(). Here is an example
A = [ ...
1 2 3 4
2 3 4 5
3 4 5 6
4 5 6 7
5 6 7 8];
B = mat2cell(A, ones(size(A,1),1), size(A,2));
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Eric 2020년 5월 22일
Wow... thank you! this is perfect and now the LSTM training works with no errors.
Just before you wrote this solution, I got your previous solution and applied cell transposing to it and works as well. B = mat2cell(A, ones(size(A,1),1), size(A,2)); D = cellfun(@transpose,B,'UniformOutput',false);
But I prefer your last solution since it's straight to the point. I'll will go on using your last solution.
B = mat2cell(A.', size(A,2), ones(size(A,1),1)).';
I'll send you a private message (thru your profile page) regarding the field I'm trying using LSTM to predict, maybe your are interested trying too. You are so smart..
Thank you very much!
Imrana 2023년 5월 15일
Hello Eric,
I am delaing with 2 feature time series, where I have to give one as endogenous or exogenous because the lagged value of one time series correlates with current value of 2nd one. I am applying LSTM but it is taking both series as input, while defining LSTM, I need to give
numFeatures = 2;
numResponses = 1;
but I am unable to train the network. I am attaching the code can you tell me how one can tackle this issue. where I am making mistake?
data = data_p2;
% Use the first 30 time steps for training the model and the rest of the data to test the model
trainingData = data(:,1:30);
testData = data(:,31:end);
figure
subplot(2,1,1);
plot(1:30,trainingData(1,:),31:120,testData(1,:))
title("X1")
legend("Training Data", "Test Data")
subplot(2,1,2)
plot(1:30,trainingData(2,:),31:120,testData(2,:))
title("X2")
legend("Training Data", "Test Data")
%% Prepare training data
%in this case, the model will take one time step as input and provide the next time step as output.
%Prepare the data into this by creating a 'output' (y) that is one time step ahead of the 'input' (x)
XTrain = trainingData(:,1:end-1);
YTrain = trainingData(:,2:end);
%% define model
numFeatures = 2;
numResponses = 1;
numHiddenUnits1 = 50;
numHiddenUnits2 = 50;
layers = [
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits1,'OutputMode','sequence' )
dropoutLayer(0.2)
lstmLayer(numHiddenUnits2,'OutputMode','last')
dropoutLayer(0.2)
fullyConnectedLayer(numResponses)
regressionLayer
];
%% train model
'MaxEpochs',400, ...
'MiniBatchSize', 32,...
'InitialLearnRate',0.005, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',125, ...
'LearnRateDropFactor',0.2, ...
'Verbose',0, ...
'Plots','training-progress');
net = trainNetwork(XTrain,YTrain,layers,options);
%% Run Inference: Predict the Future
net = resetState(net);
%The last of the predictions from inputs 'before' is the first of predictions beyond the training data
YPred = YBefore(:,end);
numTimeStepsTest = 90;
for i = 2:numTimeStepsTest
XThis = YPred(:,i-1);
YPred(:,i) = YNext;
end
% Plot
figure
%tiledlayout("flow")
%nexttile
subplot(2,1,1)
plot(1:30,trainingData(1,:),31:120,testData(1,:),1:30,YBefore(1,:),31:120,YPred(1,:))
title("x1")
legend("Training Data", "Test Data", "Prediction", "Forecast")
%nexttile
subplot(2,1,2)
plot(1:30,trainingData(2,:),31:120,testData(2,:),1:30,YBefore(2,:),31:120,YPred(2,:))
title("x2")
legend("Training Data", "Test Data", "Prediction", "Forecast")

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