我知道以前有人问过这种事情，但我尝试过的都没有帮助。
我从github为一个项目借用了一个函数，但它在matlab中，我的项目在python中，所以我想把输出数据保存为csv，这样我就可以把它输入回python中。
我实际上只使用了大约4个小时的matlab，所以这可能是一个愚蠢的问题。。。
我尝试了一系列不同的保存为csv的方法，但都不起作用。不过他们不会抛出错误，只是文件不会显示。
这是我的最新尝试：
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
我不确定我是指错了东西，还是把writematrix(dataImputed,['M.csv'])放错了地方。
我也尝试过这类路线：
% 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')
dlmwrite(filename,M)
csvwrite('filename.csv',variable2,0,2)
这就是目前我的工作区的样子
试试这个：writematrix(dataImputed,'M.csv','WriteMode','append')
如果它仍然不起作用，与我们共享AlzScSK.csv将很有帮助。