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Guillaume
on 12 Mar 2020

Be aware that the following creates a temporary matrix of size numel(A) x numel(B), so if both vectors are very large you may run out of memory:

%for row vectors:

%example data

A = [1,4,2,6,9,0,4.2,5.6];

B = [0.1,4.1,3];

assert(isrow(A) && isrow(B), 'Inputs must be row vectors');

[~, groupA] = min(abs(A - B.'), [], 1)

%for column vectors:

%demo data

A = A.'; B = B.';

assert(iscolumn(A) && iscolumn(B), 'Inputs must be column vectors')

[~, groupA] = min(abs(A.' - B), [], 1)

I'm not sure how this should be applied to your example datasheet. Looking at what it contains, you should import that excel file in a table, in which case you should store the group as another variable of the table so you can then use aggregation functions such as groupsummary.

Guillaume
on 17 Mar 2020

A(groupnumber == 1)

is all the elements of group 1. I'm not sure what you're planning to do afterwards. Be aware that creating a matrix for each group is not a good approach. There are plenty of matlab functions that will let you apply the same algorithm to all the groups at once, as long as everything is kept together in one matrix.

For example, if you want to calculate the mean of each group in A:

groupmean = groupsummary(A(:), groupnumber(:), 'mean'); %calculate the mean of each group of A

Guillaume
on 17 Mar 2020

As I've said, this is typically a bad idea. I was asking about what you want to do after that. It is very likely that whatever it is, it will be much easier if you don't do this.

Now if you really insist:

group = accumarray(a(:), groupnumber(:), [], @(v) {v});

or

group = splitapply(@(v) {v}, a(:), groupnumber(:));

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