How to vectorize the evaluation of a kernel function.

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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 Commenti
Jan
Jan il 5 Dic 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 il 5 Dic 2022
@Jan Thanks for your advice. I have changed the code.

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Matt J
Matt J il 27 Nov 2022
K=1./pdist2(x_points,y_points);
  14 Commenti
Torsten
Torsten il 28 Nov 2022
Does it mean I must write two for loops?
Yes.

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