# Efficient moving average of scattered data

15 vues (au cours des 30 derniers jours)
Chad Greene le 28 Juin 2016
I have some scattered data and I'd like to take something similar to a moving average, where I average all values with in some radius of each point. I can do this with a loop, but I'd like a more efficient approach. Any ideas?
Here's a working example I'd like to make more efficient:
x = randi(100,45,1) + 20+3*randn(45,1) ;
y = 15*sind(x) + randn(size(x)) + 3;
figure
plot(x,y,'bo')
ymean = NaN(size(x));
for k = 1:length(x)
% Indicies of all points within specified radius:
% Mean of y values within radius:
ymean(k) = mean(y(ind));
end
hold on
plot(x,ymean,'ks')
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Walter Roberson le 28 Juin 2016
When I read the title I thought you might mean "sparse", and was thinking about how I might do an efficient moving average on sparse data.

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### Réponse acceptée

Chad Greene le 30 Juin 2016
I turned this into a generalized function called scatstat1, which is on the file exchange here.
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Chad Greene le 30 Juin 2016
And a 2D version called scatstat2.

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### Plus de réponses (2)

Chris Turnes le 9 Mar 2017
If you can upgrade to R2017a, this functionality can now be achieved through the 'SamplePoints' name-value pair in the moving statistics. For your example, you would do something like movmean(y, 2*radius, 'SamplePoints', x); (though you'd need to sort your x values first).
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Walter Roberson le 28 Juin 2016
pdist() to get all of the distances simultaneously. Compare to the radius. Store the resulting mask. Multiply the mask by repmat() of the y value, and sum along a dimension. sum the mask along the same dimension and divide the value sum by that count. Result should be the moving average.
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Walter Roberson le 30 Juin 2016
Modifié(e) : Walter Roberson le 30 Juin 2016
I wonder if looping pdist2() would be efficient? Eh, it probably just adds unnecessary overhead to a simple Euclidean calculation.
Chad Greene le 1 Juil 2016
Also adds a Stats Toolbox dependency. I'll have to keep pdist in mind for future applications though. Thanks for the suggestion!

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