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I need to make a set of 2000 random numbers between 0 and 1, with a mean of 0.5, with most of the numbers being either high or low (bimodal distrubution with peaks at 0 and 1)

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meepimmaduck
meepimmaduck on 23 Feb 2016
Commented: Amy Wong on 13 Dec 2017
Basically what the question says. It should look something like this
Though the left side doesn't need to have a higher peak or anything.
Flat distributions are easy - but how do I go about making a vector with 2000 numbers with this type of distribution?

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Accepted Answer

Roger Stafford
Roger Stafford on 24 Feb 2016
Edited: Roger Stafford on 24 Feb 2016
t = 2*rand(1,2000)-1;
x = (sign(t).*sqrt(abs(t))+1)/2;
hist(x,20)
This should approximate your two linear distribution segments. If you use a much higher value than 2000, say 2000000, it will approximate it much more closely. This does not get the spike at the left end. I'll leave that refinement to you.
Added note: You can test the accuracy of the above by replacing the 'rand' call with 'linspace', since the latter will be exactly uniformly distributed.
t = 2*linspace(0,1,2000000)-1;
x = (sign(t).*sqrt(abs(t))+1)/2;
hist(x,20)

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meepimmaduck
meepimmaduck on 24 Feb 2016
Both of the answers I got were perfect, and I'm a tad disappointed in myself for not being able to think of either of them! Must have been tired. I only chose this one as the accepted answer over the other because it's the one I ended up using. Thanks very much.
Amy Wong
Amy Wong on 13 Dec 2017
If I have a set of data and I want to plot a histogram with multiples of 0.1 which is the x-axis in the image. So, if I use your answer, for the hist what do I change to?
x = (sign(COE).*sqrt(abs(COE))+1)/2;
hist(x,??)

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More Answers (1)

jgg
jgg on 24 Feb 2016
m = randi([0,1],2000,1);
r = abs(randn(2000,1));
r = r - min(r);
r = r./max(r);
num = m;
num(m==0) = 0 + r(m==0);
num(m==1) = 1 - r(m==1);
Something like this should do it, since you don`t really care how they are generated. Basically, generate your integers (0,1) then just add some noise. This is truncated Gaussian noise.

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