Help vectorising for loop for kernel density
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I am having a devil of a time figuring out how to vectorise the following for loopp
dens = NaN(length(y),1);
for i=1:length(y)
dens(i) = (1/(n*h))*sum(kernel((Y-y(i))/h));
end
Where Y has 1000 points and y has 50 points, i think perhaps it could be done by subtracting each element in y from Y such that i get a 1000 by 50 matrix. And then i can sum across columns to get the result perhaps ? but that would requre some fairly large matrices for changed dimensions and i also dont know how to do it
any help will be greatly appreciated
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Matt Fig
2012년 11월 24일
편집: Matt Fig
2012년 11월 24일
With these values for Y and y:
Y = rand(10,1)*10;
y = rand(5,1)*10;
This gives the same result as your FOR loop:
D = (1/(n*h))*sum(kernel(bsxfun(@minus,Y.',y)/h),2);
Here is the code I used to check the equality of the two approaches, in case it helps you:
Y = rand(1000,1)*10;
y = rand(50,1)*10;
dens = NaN(length(y),1);
n = length(Y);
h = .15;
kernel = @(z) exp((-z.^2)./2)./sqrt(2*pi);
for i=1:length(y)
dens(i) = (1/(n*h))*sum(kernel((Y-y(i))/h));
end
D = (1/(n*h))*sum(kernel(bsxfun(@minus,Y.',y)/h),2);
isequal(dens,D) % Check for equality.
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