inverse distance weighting on matrix

hi

I have Below matrix that distanse from 17 point in image

How i can calculate inverse distance weighting on this matrix?

how weighting on distance that have minim distanse for each row?

Answer 1

[Image Analyst]

This 17-by-17 matrix looks like it was made by pdist2(). And you say it is the distance of every point to every other point. If you want 1 over the distance, just do

inverseDistances = 1 ./ Dist;

To get the min value of inverseDistances, do

minsPerRow = min(inverseDistances, [], 2);

To get the min distance of the original Dist, do

dist2 = Dist; % Initialize
dist2(dist2 == 0) = inf; % Trick to get it to ignore zero distances (point to itself)
minsPerRow = min(dist2, [], 2);

[jenifer Ask]

thank you Dear image Analyst

how weighting on distance that have minim distanse for each row (such as currenct point to three other points that located in closet distanse

[Image Analyst]

You can call sort on each row.

s = load('Dis_all.mat')
m = 1 ./ s.Dist
[rows, columns] = size(m)
[sortedDistances, sortedIndexes] = sort(m, 2)

sortedIndexes are the indexes in the original, unsorted array. The first 3 columns of that are the indexes of the three other points that are closest to that point. So

sortedIndexes =
    17    16     7    15    14    11    13    12     8    10     5     9     6     4     3     2     1
    17    16     7    15    14    11    13    12     8    10     5     9     6     4     3     1     2
    17    16     7    15    14    11     8    13    12    10     1     2     5     9     6     4     3
    17    16     7    15    14    11     8    13    12    10     1     2     5     9     6     3     4
    17    16     7    15    14     1    11    13     2    12     9     8     6    10     4     3     5
    17    16     7     8    11    15    14    13    12    10     1     5     2     3     4     9     6
    17    16     1     9     2     6     4     3    15    14     5    12    13    10    11     8     7
    17    16     1     2     9     6    15    14     7     4     3    13    12     5    11    10     8
    17    16     7     8    11    15    14    13    12    10     1     5     2     3     4     6     9
    17    16     7     1     2     9     6     3     4    15    14     8     5    11    13    12    10
    17    16     1     2     7     9     6     3     4     5     8    15    14    10    12    13    11
    17    16     7     1     2     6     9     3     4     8     5    15    14    10    11    13    12
    17    16     7     1     2     6     9     3     4     5     8    15    14    10    11    12    13
    17     7     1    16     2     6     3     4     9     8     5    10    11    12    13    15    14
    17     7     1    16     2     6     3     4     9     8     5    10    11    12    13    14    15
     1     2     7     3     4     6     5     9     8    10    11    12    13    14    15    17    16
     1     2     3     6     4     9     7     5     8    10    11    12    13    14    15    16    17
     

You can see that for point 1 (represented by row 1), point 17 is closest, followed by point 16, followed by point 7, with point 2 being farthest away (actually closest in actual distance but farthest in inverse distance).

As another example, for point 16 (represented by row 16) point 1 is closest, followed by point 2, followed by point 7, and point 17 is farthest away.

Why do you want inverse weighting rather than just on the actual distances directly?

[jenifer Ask]

Yes, thank you