Extract rows from table to input into new table

Data attached.

I have the following neural network that prints a table of results. I need to extract specific rows (with all the data in the row) and combine them into a new table. The rows needed are found based on the values in the first three rows.

Example extract row with the integers: (3 5 4) or (5 4 5) (number in each column)

clear

load 'beam_designs_lhs100.mat';     % beam_designs

% Normalize beam models and responses
[beamsin, PS] = mapminmax(beam_designs(:,1:5)');
[beamsout, TS] = mapminmax(beam_designs(:,6:7)');

count = 1; % define a counter
count2 = 1;
% 3-layer network
for l1 = 3:5
    for l2 = 3:5
        for l3 = 3:5
        % Divide designs into training and test datasets
        trainin = beamsin(:,1:600);
        trainout = beamsout(:,1:600);

        testin = beamsin(:,600+1:end);
        testout = beamsout(:,600+1:end);
        
        % Create function fitting neural network
        net = fitnet([l1,l2,l3], 'trainlm');
       
        netbr = fitnet([l1,l2,l3], 'trainbr');
        
        net.divideParam.trainRatio = 1;
        net.divideParam.valRatio = 0;
        net.divideParam.testRatio = 0;
        
        % Train the NN and evaluate its performance
        [net, tr] = train(net, trainin, trainout);
        [netbr, tr] = train(netbr, trainin, trainout);

        outputsD = net(testin(1:5,:));
        outputsB = netbr(testin(1:5,:));
%         perf = perform(net, testout, outputsD);  % or use sum of squares

        % Computes the sum of squared errors and print results
        err_defD(count) = sum((testout(1,:) - outputsD(1,:)).^2);
        err_defB(count) = sum((testout(1,:) - outputsB(1,:)).^2);
        count = count+1;
        
        err_volD(count2) = sum((testout(2,:) - outputsD(2,:)).^2);
        err_volB(count2) = sum((testout(2,:) - outputsB(2,:)).^2);
        count2 = count2+1;  
    end 
  end 
end

% Table of results
            l = 3; u = 5;
            v = repmat(l:u,(u-l)+7,1);
            v = v(1:end);
        NumNeurons1stLayer = [v]';
%             l = 3; u = 5; 
%             v2 = repmat(l:u,(u-l)+1,3);
%             v2 = v(1:end);
            lu = (l:u)';  
            v2 = repmat(lu,(u-l)+1,3)
            v2 = reshape(v2', [1,27])
        NumNeurons2ndLayer = [v2]';
            lu = (l:u)';  
            v3 = repmat(lu,(u-l)+7,1);
        NumNeurons3rdLayer = v3;
        DefaultDeflectionErr = (err_defD)';
        BayesianDeflectionErr = (err_defB)';
        DefaultVolumeErr = (err_volD)';
        BayesianVolumeErr = (err_volB)';
%         BestPerfomance =
%         EpochNum = 
        T = table(NumNeurons1stLayer,NumNeurons2ndLayer,NumNeurons3rdLayer,DefaultDeflectionErr,BayesianDeflectionErr,...
                    DefaultVolumeErr, BayesianVolumeErr)

Answer 1

[Asvin Kumar]

You can do this with ismember and logical operators.

ind1 = ismember(T.Var1,[3 5]) ; 
ind2 = ismember(T.Var2,[5 4]); 
ind3 = ismember(T.Var2,[4 5]); 
T2 = T{ind1 & ind2 & ind3,:}; 

The ismember function is used to find the locations of the values in the columns.

Have a look at the following page too: https://in.mathworks.com/help/matlab/matlab_prog/access-data-in-a-table.html#ContentIndexingWithDotNotationAndLogicalValuesExample-2