Root Mean Square Value of Histogram

Question

Amy Wong 2018-1-15

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https://ww2.mathworks.cn/matlabcentral/answers/377179-root-mean-square-value-of-histogram

评论： Image Analyst 2018-1-17

采纳的回答： Rik

How do I find the root mean square value of this histogram?

The data is Histo = [1;47;56;1298;9770;1114;0;0;2]

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Answer 1

Rik 2018-1-15

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https://ww2.mathworks.cn/matlabcentral/answers/377179-root-mean-square-value-of-histogram#answer_300150

编辑：Rik 2018-1-15

在 MATLAB Online 中打开

If you don't have the original data, you can approximate it with the code below.

Histo = [1;47;56;1298;9770;1114;0;0;2]
centers=-4:4;
mean_of_squared_values=sum(Histo.*(centers(:).^2))/sum(Histo);
RMS=sqrt(mean_of_squared_values);

This works because the mean is the sum divided by the count.

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Amy Wong 2018-1-17

编辑：Amy Wong 2018-1-17

I know that the RMS is to find the Standard Deviation of the histogram. Do I use the RMS of the original data values or the RMS the bin locations?

Image Analyst 2018-1-17

I'm guessing you overlooked my answer below. We don't know which of the 3 RMS values you want. Only you know that. Maybe if you told us what you wanted to do with that information we could guide you.

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Answer 2

Image Analyst 2018-1-15

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https://ww2.mathworks.cn/matlabcentral/answers/377179-root-mean-square-value-of-histogram#answer_300207

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Amy:

I just want to make sure you realize that the RMS of the original data values is not the same as the RMS of the bin locations (what Rik gave you), and neither is the same as the RMS of the bin heights. Yo DO know that don't you? So, which do you want? I give an illustration of all 3 in the demo code below, and you can see how they're different.

r = randn(20);
h = histogram(r)
counts = h.Values'
edges = h.BinEdges'
binCenters = (edges(1:end-1) + edges(2:end))/2
mean_of_squared_values = sum(counts.*(binCenters.^2))/sum(counts);
% Compute the RMS value of the bin center locations.
rmsCenters = sqrt(mean_of_squared_values)
% Compute the RMS value of the bin heights (counts).
rmsCounts = rms(counts)
% Compute the RMS of the actual data values.
rmsValues = rms(r(:))
% They're not the same!