How to remove all NaN and Inf values when calculate the mean?
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I just computed a 167*50 double matrix (Log_ret) mean, with a lot of NaN and +-Inf. I've seen a solution from another question, which is really good. But when I got the extractedData, all the data turn into one column, which means that I got a 6798*1 double variable. So, how can I keep the reult still in their previous column without moving in to one column. (I mean numbers in column 2 after removing all NaN and Inf still in the column2 and column 33 after removing all NaN and Inf still in the column 33 etc.)
Thanks in advance!
mask = (Log_ret ~= 0) & isfinite(Log_ret)
extractedData = Log_ret(mask)
4 个评论
Star Strider
about 3 hours 前
Perhaps this —
Log_ret = [ 2 3 Inf;
NaN 5 7]
Log_ret(~isfinite(Log_ret)) = NaN
Log_ret = fillmissing(Log_ret, 'nearest')
Mean_Log_Ret = mean(Log_ret)
.
采纳的回答
Matt J
2024-11-20,15:08
编辑:Matt J
2024-11-20,15:24
If you're saying you want to take the column-wise mean, ignoring zeros and non-finite values, then you could do,
exclude = (Log_ret == 0) | ~isfinite(Log_ret) ;
extractedData=Log_ret;
extractedData(exclude)=nan;
columnMeans = mean(extractedData,1,'omitnan'); %the result
In other words, you shouldn't approach it by trying to remove NaNs and Infs. You should approach it by converting all values you want to exclude to NaNs and using the omitnan flag, where appropriate.
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Image Analyst
about 3 hours 前
You might also consider using interpolation with interp1. Just identify the non inf and nan locations and then interpolate the whole thing to replace them with the interpolated values from either side of the stretch of nans and infs.
Matt J
about 3 hours 前
Thanks, that's a really good point. I forget to say that my purpose in calculating the column mean is to use the mean of each column to repalce the NaN and Inf values in ecah column. Then the later calculation won't be affact.
@Amy But is your original question answered? If so, please Accept-click the answer.
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Image Analyst
about 1 hour 前
编辑:Image Analyst
about 1 hour 前
@Amy You might try interpolating the values, for example:
% Create data with good values and inf and nan values.
v = [10, nan, 30, 40, nan, nan, inf, nan, nan, 90];
% Replace nans and infs with interpolated values.
badIndexes = isnan(v) | isinf(v) % Find bad value locations
y = v(~badIndexes) % Extract only the good values.
xFull = 1 : length(v) % Interpolate over the whole vector.
vInterpolated = interp1(find(~badIndexes), y, xFull); % Do the interpolation
vInterpolated =
Columns 1 through 4
10 20 30 40
Columns 5 through 8
48.3333333333333 56.6666666666667 65 73.3333333333333
Columns 9 through 10
81.6666666666667 90
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