Vectorization of while loop
显示 更早的评论
Hi all,
I have a small function, which generates random numbers in a specific interval. The function tests the generated values in respect to a certain bound and recalculates the random numbers again, if the bound is violated. I have to call this function several times (in this case 60 000 times), so this takes a lot of time (3/4 of the total time). I have read about vectorizing the code to improve performance. But I am totally helpless with this task. Your help would be greatly appreciated.
However, if someone has an idea to rewrite the while loop in a different way that would be great, as well.
Michi
Code:
meanValue = 10;
devValue = 1;
numValues = 10000;
presumption = 1;
for i = 1:60000
arrayValues = RandomNums(meanValue, devValue, numValues, presumption);
end
function arrayValues = RandomNums (meanValue, devValue, numValues, presumption)
%% create rand nums
% normally distributed with meanValue +/- devValue
numValues = round(numValues);
arrayValues = devValue*randn(numValues,1) + meanValue;
% check confidence-limes for each value and create new one if outside
for idx = 1:numel(arrayValues)
while (arrayValues(idx) > (meanValue + presumption*devValue) || arrayValues(idx) < (meanValue - presumption*devValue))
arrayValues(idx) = devValue*randn(1,1,'double') + meanValue;
end
end
end
Performance:
2 个评论
Bruno Luong
2020-7-15
You should look for "truncated gaussian" distribution, and posts how to generate them.
elchico
2020-7-15
Hi Bruno,
thank you for your reply. I've tried that, but as far as I understand, it does not make it better. Maybe, I have missed /missunderstood something?
Thanks again!
Code with Comparison:
meanValue = 10;
devValue = 1;
numValues = 10000;
presumption = 1;
for i = 1:10
arrayValues = RandomNums(meanValue, devValue, numValues, presumption);
a = ["arrayValues1",num2str(i)];
disp(a)
pause(0.00001);
end
for ii = 1:10
arrayValues2 = RandomNums2(meanValue, devValue, numValues, presumption);
a = ["arrayValues2",num2str(ii)];
disp(a)
pause(0.00001);
end
function arrayValues = RandomNums (meanValue, devValue, numValues, presumption)
%% create rand nums
% normally distributed with meanValue +/- devValue
numValues = round(numValues);
arrayValues = devValue*randn(numValues,1) + meanValue;
% check confidence-limes for each value and create new one if outside
for idx = 1:numel(arrayValues)
while (arrayValues(idx) > (meanValue + presumption*devValue) || arrayValues(idx) < (meanValue - presumption*devValue))
arrayValues(idx) = devValue*randn(1,1,'double') + meanValue;
end
end
end
function arrayValues2 = RandomNums2 (meanValue, devValue, numValues, presumption)
%% create rand nums
% normally distributed with meanValue +/- devValue
numValues = round(numValues);
pd = makedist('Normal','mu',meanValue,'sigma',devValue);
confidence = presumption*devValue;
t = truncate(pd,meanValue - confidence,meanValue + confidence);
arrayValues2 = random(t,numValues);
end
Performance:
采纳的回答
Bruno Luong
2020-7-16
meanValue = 10;
devValue = 1;
numValues = 1000000;
presumption = 2;
tic
% Function from here https://www.mathworks.com/matlabcentral/fileexchange/23832-truncated-gaussian
arrayValues = meanValue + TruncatedGaussian(-devValue, presumption*[-1 1], [1 numValues]);
toc % Elapsed time is 0.055511 seconds for one billions random numbers.
% Check histogram
hist(arrayValues,100)
Histogram obtained
3 个评论
elchico
2020-7-16
Bruno Luong
2020-10-3
It mainly because it uses different method (non rejection) and inverse error function.
elchico
2020-10-3
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