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 Kommentare
Jan
am 27 Nov. 2022
"It is direct when using two for loop" - then it would be useful, if you post this solution, which would uniquely define, what you want to achieve.
"it fails" - for me it fails due to the unfound function "ran()". Should this be "rand()"? Post the error message, if you mention an error.
Jingyu
am 27 Nov. 2022
Jingyu
am 28 Nov. 2022
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
am 5 Dez. 2022
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