Comparing Two Data to find best match
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Hello,
I have 2 sets of data, Y and X. Y data is a single column and X contains multiple columns. X & Y have different lengths. Each row in X (columns C-L) is a sample on its on. In other words, all the variables in each row of X (columns C-L) belongs to one sample. As you can see in the attached file, there are 12 Y data and hence I want to extract the corresponding X data (12 of them) that will match well with Y. So I want to take the first value in Y, perform some sort of matching analysis all rows of X to see which one give the least error of matching or some sort of distance. After A value in X has been found that matches well with the first value in Y, either by distance or by some other methods, I want that X row to be replaced with NaN so that it wont be selected again. The procedure will be repeated on all the remaining Y values. I also want the indices or row number of the each of the selected X data to that of the Y so that I can extract them latter. Any suggestions?
Thank you!
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Aghamarsh Varanasi
el 20 de Mzo. de 2020
Hi,
I understand that you are trying to process the data x and y. Let us consider the data is stored in list y and matrix x. And we also have a function that does the matching analysis and returns the best possible row in x, that matches the value in y.
The following algorithm may help you
% Data is extracted into lists x and y
selectedRows = zeros(length(y));
for ii = 1:length(y)
yElement = y(ii);
% Function that returns the selected row in data x
% by performing matching analysis
xRow = getBestMachingRow(yElement,x);
% replace xRow with nan
x(xRow,:) = nan;
% store the row
selectedRows(ii) = xRow;
end
2 comentarios
Aghamarsh Varanasi
el 26 de Mzo. de 2020
Editada: Aghamarsh Varanasi
el 26 de Mzo. de 2020
getBestMachingRow() is the function that will do the computation to get the best row for a value in y. This function is to be defined according to the requirement. As you where not certain about the matching analysis, I left it as an empty function.
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