Matlab Saving the Output of a Function (preferably as a csv)

I know this kind of thing has been asked before but nothing I have tried has helped.

I've borrowed a function from github for a project but it's in matlab and my project is in python so I want to save the output data as a csv so I can input it back into python.

I've literally only been using matlab for about 4 hrs so this could be a stupid question...

I've tried an array of different methods for saving as a csv but none of them have worked. They don't throw errors though, the files just don't show up.

This is my latest attempt:

clear;clc;

fileName = 'AlzScSK.csv';
csvData = importdata(fileName);

rawData = csvData.data;
dataMV = rawData;
scaledMV = dataMV

K = 6;

function [dataImputed dataImputedWeighted] = NSkNNDataHM(dataMV)
% Function to impute missing values in a dataset using NSkNN. scaledMV is 
% the autoscale dataset with the missing values and K is the # of nearest 
% neighbors to use to impute the data. filteredMV is the missing value 
% dataset after filtering, but before autoscaling.
% NSkNNData_HM does not skip neighbors that have NaN values in the same 
% location as the metabolite being imputed. Instead, it replaces these NaN 
% values with the half minimum value of that metabolite.

numCol = size(scaledMV,2);

for col = 1:numCol
    rowMV{col} = find(isnan(scaledMV(:,col))); % Finds the row # of every missing value in each column
end

counter = 1;
for targetCol = 1:numCol % i is the target sample
    for neighborCol = 1:numCol % Calculate the Euclidean distances between the target sample (i) and the other samples (j)
        MVRowsRemoved = scaledMV;
        rowsToRemove = union(rowMV{targetCol},rowMV{neighborCol}); % Ignore NaNs when calculating distances
        MVRowsRemoved(rowsToRemove,:) = []; % Remove rows in target sample that have missing values
        numMetInCalc = size(MVRowsRemoved,1); % # of metabolites used in calculation of metabolites in order to weight distance
        % Divide by numMetInCalc to avoid scenarios where distances 
        % calculated with only a few metabolites are weighted more heavily 
        % over distances that are close to the same distance, but used more 
        % metabolites in the calculation.
        distance = pdist2(MVRowsRemoved(:,targetCol)',MVRowsRemoved(:,neighborCol)')/sqrt(numMetInCalc);  
        %distance = pdist2(MVRowsRemoved(:,targetCol)',MVRowsRemoved(:,neighborCol)');
        distIdx(counter,:) = [targetCol distance neighborCol];
        counter = counter+1;
    end
end

% Remove rows that calculated the Euclidean distance between a sample and
% itself.
sameSample = find(distIdx(:,1)==distIdx(:,3));
distIdx(sameSample,:) = [];
distIdxSorted = sortrows(distIdx);

minValperRow = min(scaledMV,[],2); % Finds minimum of each metabolite

% Implement NSkNN with half minimum replacement
dataImputed = scaledMV;
dataImputedWeighted = scaledMV;
for targetCol = 1:numCol
    numMV = size(rowMV{targetCol},1); % # of missing values in a column
    firstNNIdx = (targetCol-1)*(numCol-1)+1; % Column index of the first nearest neighbor of target sample (i) in distIdxSorted
    for MVidx = 1:numMV % For each missing value in the target sample...
        tempDataMV = scaledMV;
        NN = distIdxSorted(firstNNIdx:firstNNIdx+K-1,3); % Column #s of the k nearest neighbors
        DistanceNN = distIdxSorted(firstNNIdx:firstNNIdx+K-1,2); % Distances of k nearest neighbors
        idxNaNinCol = find(isnan(tempDataMV(rowMV{targetCol}(MVidx),NN))); % Finds missing values that are the same metabolite as the target metabolite to be imputed
        if isempty(idxNaNinCol)~=1 % If NaN values found...
            % If there are NaN values in the nearest neighbor
            % metabolite that is the same as the target metabolite to
            % be imputed, replace with half min value of the target
            % metabolite

            tempDataMV(rowMV{targetCol}(MVidx),NN(idxNaNinCol)) = (minValperRow(rowMV{targetCol}(MVidx)) - avgMV(rowMV{targetCol}(MVidx))/stddevMV(rowMV{targetCol}(MVidx)))/2;           
        end
        % Imputed data is weighted by the inverse of the distance
        WeightMultiplier = (1./DistanceNN')/sum(1./DistanceNN);
        dataImputedWeighted(rowMV{targetCol}(MVidx),targetCol) = sum(tempDataMV(rowMV{targetCol}(MVidx),NN).*WeightMultiplier);
        % Not weighted
        dataImputed(rowMV{targetCol}(MVidx),targetCol) = mean(tempDataMV(rowMV{targetCol}(MVidx),NN));
        writematrix(dataImputed,['M.csv'])
    end
end
end

I'm not sure if I'm maybe referring to the wrong thing or if I've put the

writematrix(dataImputed,['M.csv'])

in the wrong place.

I've also tried these sorts of routes:

dlmwrite(filename,M)

csvwrite('filename.csv',variable2,0,2)

% Convert cell to a table and use first row as variable names
T = cell2table(c(2:end,:),'VariableNames',c(1,:))
 
% Write the table to a CSV file
writetable(T,'myDataFile.csv')

I've also tried specifying the file path ect but it makes no difference

[Walter Roberson]

you define a function NSkNNDataHM but you do not call the function.

[Image Analyst]

You forgot to attach your CSV file, and your script never calls the function NSkNNDataHM().

If you have any more questions, then attach your data and code to read it in with the paperclip icon after you read this:

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