How to vectorize the evaluation of a kernel function.

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Jingyu
Jingyu am 27 Nov. 2022
Kommentiert: Jingyu am 5 Dez. 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 Kommentare
Jan
Jan am 5 Dez. 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 am 5 Dez. 2022
@Jan Thanks for your advice. I have changed the code.

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Akzeptierte Antwort

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

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