matrix, value, comparing

Question

Nicle Davidson 2021-11-11

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1584189-matrix-value-comparing

评论： Star Strider 2021-11-12

I would like to find out if each line with its six values in matrix U has closer values to the line of the six values in matrix MA or to the line of values in matrix TA? (by the way these are time values)so if I find f.ex. line one in U is more like the line in MA, so it means that the happening is the same as happening in MA.

MA =

0 4.4710 8.8828 13.6977 17.7027 21.7184

TA =

0 5.0490 9.3733 13.6038 17.2513 20.5759

and

U =

0 3.4874 8.3723 12.8400 15.7461 21.0089

0 3.6776 8.4321 12.9606 15.7762 21.0557

0 5.5318 8.2832 12.8565 17.5710 20.9515

0 5.6120 8.4264 12.8430 17.5558 20.9401

0 5.6243 10.0483 12.8954 17.7006 22.3821

0 5.5102 10.0276 12.8021 17.5814 22.2412

0 3.1558 7.6719 12.2234 15.1850 19.9813

0 3.1794 7.6655 12.3352 15.4489 20.0210

0 4.7771 7.5656 12.2135 17.4471 19.8625

0 4.7598 7.5804 12.2065 17.5210 20.0173

Is it possible? how can one do this?

*PS: I have heard that knnsearch might could help, but have no idea how it does. maybe someone could show?

https://se.mathworks.com/help/stats/exhaustivesearcher.knnsearch.html

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Answer 1

Star Strider 2021-11-11

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1584189-matrix-value-comparing#answer_829234

在 MATLAB Online 中打开

One option is pdist2 —

MA = [0     4.4710     8.8828     13.6977     17.7027     21.7184];
TA = [0     5.0490     9.3733     13.6038     17.2513     20.5759];
U = [0 3.4874 8.3723 12.8400 15.7461 21.0089
    0 3.6776 8.4321 12.9606 15.7762 21.0557
    0 5.5318 8.2832 12.8565 17.5710 20.9515
    0 5.6120 8.4264 12.8430 17.5558 20.9401
    0 5.6243 10.0483 12.8954 17.7006 22.3821
    0 5.5102 10.0276 12.8021 17.5814 22.2412
    0 3.1558 7.6719 12.2234 15.1850 19.9813
    0 3.1794 7.6655 12.3352 15.4489 20.0210
    0 4.7771 7.5656 12.2135 17.4471 19.8625
    0 4.7598 7.5804 12.2065 17.5210 20.0173];
[DMA,IMA] = pdist2(U,MA, 'euclidean', 'Smallest',1)
DMA = 1.6727
IMA = 3
[DTA,ITA] = pdist2(U,TA, 'euclidean', 'Smallest',1)
DTA = 1.4205
ITA = 4

So here the thiird row of ‘U’ is closest to ‘MA’ and the fourth row closeest to ‘TA’.

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Star Strider 2021-11-12

在 MATLAB Online 中打开

Yes, it does.

That said, this seems to be a moving target.

Try this —

MA = [0     4.4710     8.8828     13.6977     17.7027     21.7184];
TA = [0     5.0490     9.3733     13.6038     17.2513     20.5759];
U = [0 3.4874 8.3723 12.8400 15.7461 21.0089
    0 3.6776 8.4321 12.9606 15.7762 21.0557
    0 5.5318 8.2832 12.8565 17.5710 20.9515
    0 5.6120 8.4264 12.8430 17.5558 20.9401
    0 5.6243 10.0483 12.8954 17.7006 22.3821
    0 5.5102 10.0276 12.8021 17.5814 22.2412
    0 3.1558 7.6719 12.2234 15.1850 19.9813
    0 3.1794 7.6655 12.3352 15.4489 20.0210
    0 4.7771 7.5656 12.2135 17.4471 19.8625
    0 4.7598 7.5804 12.2065 17.5210 20.0173];
Out = matrixVectorDist(U,[MA;TA]);
vectorName = {'MA';'TA'};
DesiredResult = table(Out(:,1),Out(:,2),Out(:,3),vectorName(Out(:,3)), 'VariableNames',{'U(row #)','Distance','Vector #','VectorName'})
DesiredResult = 10×4 table
    U(row #)    Distance    Vector #    VectorName
    ________    ________    ________    __________

        1        2.5091        1          {'MA'}  
        2        2.3509        1          {'MA'}  
        3         1.491        2          {'TA'}  
        4        1.4205        2          {'TA'}  
        5        1.9423        1          {'MA'}  
        6        1.8656        1          {'MA'}  
        7        3.6066        2          {'TA'}  
        8        3.4026        2          {'TA'}  
        9        2.4129        2          {'TA'}  
       10        2.3739        2          {'TA'}  
function [distc,idx] = matrixVectorDist(M,v)
% M = Argument Matrix
% v = Comparison Vector Or Vectors
    for k = 1:size(M,1)
        dst(:,k) = sqrt(sum((M(k,:)-v).^2,2));          % Calculate Distances
        [closest,idx] = min(dst(:,k));                  % Find Closest To Elements Of 'v'
        distc(k,:) = [k closest idx];                   % Return Closest & 'v' Index
    end
end

The ‘matrixVectorDist’ function is not robust, since it was not designed to be. It is simply designed to apply to this particular problem, and while it will work for similar problems, it is not equivalent to pdist2.

.

Nicle Davidson 2021-11-12

@Star Strider you are a star!

Star Strider 2021-11-12

Thank you!!!

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