Indexing with different sized vectors

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William Sheehan 2018-8-11

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编辑： dpb 2018-8-13

I currently have one vector (49x1) which contains time stamp values - i.e. the first 3 numbers of the vector idx = [1;2013;5045] are associated with specified points in time. I also have another vector (X) which has numerical values for each given point in time - i.e. if I want to the find the value at the specified time point I can just index straight into this. However, I want to find the mean of subsequent points from the initial timestamps specified above and over differing durations. For example I would like to find the mean of the 200 points after the first time stamp and the mean of 350 points after the second timestamp. Essentially I have a third vector - duration = [200;350;255] - specifying the durations for which I want to calculate.

I have currently got the function set up but only returns one value as opposed to one for each idx:

function MeanX = AvgValues(X,Idx,duration)
Duration = Idx + duration;
MeanX = mean(X(Idx:Duration,:),1,'omitnan');
end

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Stephen23 2018-8-13

编辑：Stephen23 2018-8-13

在 MATLAB Online 中打开

Note that this does not make much sense:

Output = zeros(size(length(Idx)))

length returns a scalar. The size of a scalar is 1x1. So that line is equivalent to

Output = zeros(1,1);

or even simpler:

Output = 0;

How many columns does X have?

dpb 2018-8-13

编辑：dpb 2018-8-13

在 MATLAB Online 中打开

Output = zeros(size(length(Idx)))

should be

Output = zeros(size(Idx));

and there's no need for the secondary Count indexing variable; it's the same as k so mayst as well just use it...

function Output = Val(X,Idx,Dur)
  Output = zeros(size(Idx)); 
  for k = 1:length(Idx)
    duration = Idx(k)+(Dur);
    t = Idx(k):duration;
    Output(k) = nanmean(X(t,:));
  end
end

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回答（1 个）

dpb 2018-8-11

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/414462-indexing-with-different-sized-vectors#answer_332414

编辑：dpb 2018-8-11

在 MATLAB Online 中打开

arrayfun(@(ix,n) mean(X(ix:ix+n-1),'omitnan'), Idx,Duration);

Alternatively, instead of generating the start index and count, build a grouping variable and use findgroups, splitapply or discretize...actually, iiuc, you may have the bin edges already for the latter with your indexing vector.