Euclidean distance between two structs for nearest neighbour

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Aaron Elliott
Aaron Elliott el 9 de Nov. de 2020
Comentada: Jason Reed el 11 de Nov. de 2020
Hi all,
I am trying to do nearest neighbour between a set of images and a nearest neighbour model. However in my euclidean distance function I am getting errors suh as "matrix dimensions must agree".
As you can see i've attemped different ways but what im trying to do for these two functions is:
• Calculate the Euclidean distance between the test sample and all the training samples d(𝑠𝑎𝑚𝑝𝑙𝑒1,𝑠𝑎𝑚𝑝𝑙𝑒2)=|𝑠𝑎𝑚𝑝𝑙𝑒1−𝑠𝑎𝑚𝑝𝑙𝑒2|=√(𝑠𝑎𝑚𝑝𝑙𝑒1(1)−𝑠𝑎𝑚𝑝𝑙𝑒2(1))2+ (𝑠𝑎𝑚𝑝𝑙𝑒1(2)−𝑠𝑎𝑚𝑝𝑙𝑒2(2))2+⋯+(𝑠𝑎𝑚𝑝𝑙𝑒1(𝑛)−𝑠𝑎𝑚𝑝𝑙𝑒2(𝑛))2
• Select the closest training example
• Assign the closest training example’s label to the test image
function dEuc = EuclideanDistance(sample1,sample2)
% dEuc = sqrt(sum((sample1 - sample2).^2));
% dEuc = norm(sample1 - sample2);
% for i = length(sample1)
% for j = length(sample2)
% num = sum((sample1(:) - sample2(:)).^2);
% end
% end
% dEuc = sqrt(num);
% sample1 = repmat(sample1,1,size(sample2,2));
% dEuc = sqrt(sum((sample1(:)-sample2(:)).^2));
V = sample1 - sample2;
dEuc = sqrt(V .* V');
end
function prediction = NNTesting(testImage,modelNN)
dataset = modelNN.neighbours;
prediction = EuclideanDistance(testImage, dataset);
end

Respuestas (1)

KSSV
KSSV el 9 de Nov. de 2020
Editada: KSSV el 9 de Nov. de 2020
dEuc = sqrt(V .* V');
Replace the above with
dEuc = sqrt(sum(V.^2));
%% Demo
A = rand(100,2) ; B = rand(100,2) ;
dx = A-B ;
d = sqrt(sum(dx.^2,2)) ;
% Formula
d1 = sqrt((A(:,1)-B(:,1)).^2+(A(:,2)-B(:,2)).^2) ;
isequal(d,d1)
You have to use sum with 1 or 2 depending on your data is row major or column major.
  6 comentarios
Aaron Elliott
Aaron Elliott el 11 de Nov. de 2020
Hahaha I did, just thought to ask on here as well just have to options
Jason Reed
Jason Reed el 11 de Nov. de 2020
Ah, fair! Did he respond? I'm not exactly here because I figured this out haha

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