Euclidean distance between two structs for nearest neighbour

2 Ansichten (letzte 30 Tage)
Aaron Elliott
Aaron Elliott am 9 Nov. 2020
Kommentiert: Jason Reed am 11 Nov. 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

Antworten (1)

KSSV
KSSV am 9 Nov. 2020
Bearbeitet: KSSV am 9 Nov. 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 Kommentare
Aaron Elliott
Aaron Elliott am 11 Nov. 2020
Hahaha I did, just thought to ask on here as well just have to options
Jason Reed
Jason Reed am 11 Nov. 2020
Ah, fair! Did he respond? I'm not exactly here because I figured this out haha

Melden Sie sich an, um zu kommentieren.

Kategorien

Mehr zu Pattern Recognition and Classification finden Sie in Help Center und File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by