Matrix moving mean with overflow average

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Albert Zurita 2022-11-13

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https://ww2.mathworks.cn/matlabcentral/answers/1849978-matrix-moving-mean-with-overflow-average

评论： Albert Zurita 2022-11-15

I am wondering if there is a (simply) way when using movmean for controlling row overflow and for specifying a rectangular window instead of a square window. The idea would be to keep averaging the patch corresponding to each number in red until the end of row is found, and simply jump to the next row. Thanks!

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Answer 1

Matt J 2022-11-13

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https://ww2.mathworks.cn/matlabcentral/answers/1849978-matrix-moving-mean-with-overflow-average#answer_1098643

编辑：Matt J 2022-11-13

在 MATLAB Online 中打开

Here's one way. I assumed here you want the same wrap-around to occur in the lower-right corner of the matrix as well.

A=reshape(1:24,[],4)'
A = 4×6
     1     2     3     4     5     6
     7     8     9    10    11    12
    13    14    15    16    17    18
    19    20    21    22    23    24
B=[A,circshift(A(:,1:2),-1)  ]
B = 4×8
     1     2     3     4     5     6     7     8
     7     8     9    10    11    12    13    14
    13    14    15    16    17    18    19    20
    19    20    21    22    23    24     1     2
slidingMeans=conv2(B,ones(3)/9,'valid')
slidingMeans = 2×6
    8.0000    9.0000   10.0000   11.0000   12.0000   13.0000
   14.0000   15.0000   16.0000   17.0000   15.3333   13.6667

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Matt J 2022-11-15

在 MATLAB Online 中打开

win = [3 5]; % rows, cols
A=reshape(1:24,[],4)'
A = 4×6
     1     2     3     4     5     6
     7     8     9    10    11    12
    13    14    15    16    17    18
    19    20    21    22    23    24
B=[A,circshift(A(:,1:win(2)-1),-1)];
B(end,end-win(2)+2:end)=nan
B = 4×10
     1     2     3     4     5     6     7     8     9    10
     7     8     9    10    11    12    13    14    15    16
    13    14    15    16    17    18    19    20    21    22
    19    20    21    22    23    24   NaN   NaN   NaN   NaN
slidingMeans=conv2(B,ones(win)/prod(win),'valid' )
slidingMeans = 2×6
    9.0000   10.0000   11.0000   12.0000   13.0000   14.0000
   15.0000   16.0000       NaN       NaN       NaN       NaN