# Extracting neighbouring cells in a cell array

2 vues (au cours des 30 derniers jours)
OK le 1 Juil 2024
Commenté : Voss le 1 Juil 2024
This is a follow up to this question. I have cell array and a list of cells of interests (given as a matrix, whose columns correspond to the indices of cells). I want to access and concatenate the entries of all cells located +/- 1 the cell of interest.
For example, as suggested in the accepted answer to the references question, I construct the cell array A as follows
B = [1,2,2,1,1; 2,1,2,1,2];
V = 1:size(B,2);
A = accumarray(B.',V(:),[],@(m){m.'})
A = 2x2 cell array
{[4]} {[1 5]} {[2]} {[ 3]}
Now I have the matrix C with the cells of interest
C=[1 2; 1 2]
C = 2x2
1 2 1 2
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I have 2 neighbourhoods that I want to explore
NbhInd1=[1 1 2; 1 2 1];
NbhInd2=[1 2 2; 2 2 1];
In the end, I want to be able to get two arrays of neighbourhoods (can be sorted or unsorted)
Nbh1=[4 1 5 2]
Nbh1 = 1x4
4 1 5 2
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Nbh2=[1 5 3 2]
Nbh2 = 1x4
1 5 3 2
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I have 3 problems:
1. (Same as in the referenced question): I don't know how to convert an array into a proper index to refer to the correct cell of A. I.e. A{[1,1]} or A ([1,1]) is not the same as A{1,1}, and I need to the latter.
2. How to automate the construction of indices of the neighbourhoods. In principle, I can use combinations() but it gives too many indices. Also, I'm not sure how to automatically easily convert [1,1] into table2array(combinations(1:2,1:2)), i.e. splitting an array into its coordinates and manipulating separately
3. The true array A has high dimensionality (e.g. size(A)=repmat(9,[1,10])), so I'd like to minimize the number of loops.
##### 3 commentairesAfficher 1 commentaire plus ancienMasquer 1 commentaire plus ancien
Stephen23 le 1 Juil 2024
Modifié(e) : Stephen23 le 1 Juil 2024
"I want to access and concatenate the entries of all cells located +/- 1 the cell of interest."
How many times do you need to perform this operation? With how many indices? Your data arrays are large, so if you want an efficient approach you might need to think outside the square, perhaps based on interpolation or convolution or something of that ilk. Another option might be to use some image processing tools.
OK le 1 Juil 2024
@Stephen23 good question, thanks for raising this. The story is that I have a large set S of points (in principle, can be arbitrarily large, for now 10^6) and I have another set S' of points (~|S'|=1000) for which I want to study a certain property P, i.e. for each x in S', I want to know whether P(x)==true.
To compute P(x), I need to understand how x is positioned with respect to the its neighbours in S (e.g. all points in S that lie within some distance d from x). Which means that I need to select the neighbourhood of x in S. For now I am extracting the neighbourhoods using Euclidean distance, but this is pretty slow.
My hope is that by first putting S on a grid and then restricting the search space to the neighbouring cells instead of the entire S should speed up the process.

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### Réponse acceptée

Voss le 1 Juil 2024
B = [1,2,2,1,1; 2,1,2,1,2];
V = 1:size(B,2);
A = accumarray(B.',V(:),[],@(m){m.'})
A = 2x2 cell array
{[4]} {[1 5]} {[2]} {[ 3]}
C=[1 2; 1 2]
C = 2x2
1 2 1 2
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<mw-icon class=""></mw-icon>
% construct NbhInd (a cell array of neighborhood index matrices) from C
N = size(C,2);
NbhInd = cell(1,N);
sizA = size(A);
NsA = numel(sizA);
assert(size(C,1) == NsA)
offsets = [zeros(1,NsA); eye(NsA); -eye(NsA)];
for ii = 1:N
NbhInd{ii} = unique(min(sizA,max(1,C(:,ii).'+offsets)),'rows').';
end
NbhInd{:}
ans = 2x3
1 1 2 1 2 1
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ans = 2x3
1 2 2 2 1 2
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% take elements from A within each neighborhood
Nbh = cell(1,N);
for ii = 1:N
M = size(NbhInd{ii},2);
idx = num2cell(NbhInd{ii});
temp = cell(1,M);
for jj = 1:M
temp{jj} = A{idx{:,jj}};
end
Nbh{ii} = [temp{:}];
end
Nbh
Nbh = 1x2 cell array
{[4 1 5 2]} {[1 5 2 3]}
##### 3 commentairesAfficher 1 commentaire plus ancienMasquer 1 commentaire plus ancien
OK le 1 Juil 2024
Thank you!
Voss le 1 Juil 2024
You're welcome!

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