How to vectorize the evaluation of a kernel function.

4 vues (au cours des 30 derniers jours)
Jingyu
Jingyu le 27 Nov 2022
Commenté : Jingyu le 5 Déc 2022
I have a kernal function which is defined for . And now I have to compute a matrix for m points and n points , where K is given by . It is direct when using two for loop. But how can I vectorize the evaluation? For example, I tried
k_fun = @(x, y) 1 / norm(x - y);
d = 2; % Make d = 1 if you want it runs correctly.
m = 100;
n = 100;
x_points = rand(m, d);
y_points = rand(n, d);
% The following code is the two for loop version.
K = zeros(m, n);
for i = 1 : m
for j = 1 : n
K(i, j) = k_fun(x_points(i,:), y_points(j, :));
end
end
% The folloing code works when d = 1, but when d > 1 it failes.
K = k_fun(x_points, y_points');
% When d > 1, the error is "Arrays have incompatible sizes for this
% operation."
When , it gives the result I want, But for , it failes. How can I improve it?
  6 commentaires
Jan
Jan le 5 Déc 2022
@Jingyu: "I have told you the code will occur error" - yes, you did. Please insert the error message also in future questions.
While your code is vectorized already, you let the readers guess, what you want to achieve. All we know, is that your kernal function is "special" and the not working code.
Jingyu
Jingyu le 5 Déc 2022
@Jan Thanks for your advice. I have changed the code.

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Réponse acceptée

Matt J
Matt J le 27 Nov 2022
K=1./pdist2(x_points,y_points);
  14 commentaires
Torsten
Torsten le 28 Nov 2022
Does it mean I must write two for loops?
Yes.
Jingyu
Jingyu le 28 Nov 2022
@Torsten Thanks for your answer!

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