smoothing with Gaussian Kernel for loop problem
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im having a probelm with smoothing the data "ys" using gaussian kernel function everytime i run the for loop i recieve uhat = ys and kerf = 0 0 0 0 0 ... anyone can help me?
ns =length(ys);
nv =length(tv);
lambda = 0.05;
tv = (1:1:1000)';
for i = 1 : ns
k=(tv-tv(i));
kerf=exp(-k.*k/2*(lambda^2))/(sqrt(2*pi)*lambda);
uhat(i)=sum(kerf.*ys(i))/sum(kerf);
end
采纳的回答
Mathieu NOE
2022-11-18
hello
try this
lambda = 0.05;
tv = (1:1:1000)';
nv =length(tv);
ys = sin(4*pi*tv./max(tv))+0.25*rand(nv,1);% dummy data
for i = 1 : nv
k=(tv-tv(i));
kerf=exp(-k.*k/2*(lambda^2))/(sqrt(2*pi)*lambda);
uhat(i)=sum(kerf(:).*ys(:))/sum(kerf);
end
plot(tv,ys,tv,uhat);
8 个评论
KHOULOUD DABOUSSI
2022-11-18
编辑：KHOULOUD DABOUSSI
2022-11-18
Mathieu NOE
2022-11-21
hello again
try this code :
load('test_b.mat')
lambda = cv;
for i = 1 : numel(ts)
k=(ts-ts(i));
kerf=exp(-k.*k/2*(lambda^2))/(sqrt(2*pi)*lambda);
uhat(i)=sum(kerf(:).*ys(:))/sum(kerf);
end
plot(ts,ys,ts,uhat);
Mathieu NOE
2022-11-21
FYI, I made a comparison with matlab smoothdata function
with a gaussian window (length = 10 samples) you get pretty much the same result (the two traces are quite perfectly overlaid)
load('test_b.mat')
lambda = cv;
for i = 1 : numel(ts)
k=(ts-ts(i));
kerf=exp(-k.*k/2*(lambda^2))/(sqrt(2*pi)*lambda);
uhat(i)=sum(kerf(:).*ys(:))/sum(kerf);
end
% comparison with smoothdata function
ysm = smoothdata(ys,'gaussian',10);
plot(ts,ys,ts,uhat,ts,ysm);
legend('raw','exp smoothing','gaussian smoothing');
Mathieu NOE
2022-11-21
well, this is no surprise as both codes implement a gausian window !! :)
KHOULOUD DABOUSSI
2022-11-21
Mathieu NOE
2022-11-21
as always, my pleasure !
KHOULOUD DABOUSSI
2022-11-21
Mathieu NOE
2022-11-22
hello again
well, statistics are not my field of expertise
there are some publications that describe some methods for optimal tuning of lambda
but then i's up to you to code that as a matlab function (would basically be your own version of ksdensity)
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