问题
I have attempted to generate a triangular probability distribution in Matlab, but was not successful. I used the formula at http://en.wikipedia.org/wiki/Triangular_distribution.
n = 10000000;
a = 0.2;
b = 0.7;
c = 0.5;
u = sqrt(rand(n, 1));
x = zeros(n, 1);
for i = 1:n
U = u(i);
if U < (c-a)/(b-a)
X = a + sqrt(U*(b-a)*(c-a));
else
X = b - sqrt((1-U)*(b-a)*(b-c));
end
x(i) = X;
end
hist(x, 100);
The histogram looks like so:
Doesn't look like much of a triangle to me. What's the problem? Am I abusing rand(n)
?
回答1:
you can add up two uniform distributions, the distribution graphs convolve, and you get a triangular distribution.
easy-to-understand example: rolling two dice, each action has uniform distribution to result in a number from 1-6, combined action has triangular distribution to result in a number 2-12
edit: minimal working example:
a=randint(10000,1,10);
b=randint(10000,1,10);
c=a+b;
hist(c,max(c)-min(c)+1)
edit2: looked in your script again. It's working but you've made one mistake:
u = sqrt(rand(n, 1));
should be
u = rand(n, 1);
edit3: optimized code
n = 10000000;
a = 0.2;
b = 0.7;
c = 0.5;
u = rand(n, 1);
x = zeros(n, 1);
idx = find(u < (c-a)/(b-a));
x(idx) = a + sqrt(u(idx)*(b-a)*(c-a));
idx =setdiff(1:n,idx);
x(idx) = b - sqrt((1-u(idx))*(b-a)*(b-c));
hist(x, 100);
回答2:
This example uses the makedist() and pdf() commands.
a = 2; m = 7; b = 10;
N = 50000; % Number of samples
pd = makedist('Triangular',a,m,b); % Create probability distribution object
T = random(pd,N,1); % Generate samples from distribution
Triangular Distribution with lowerbound a = 7, mode m = 10, and upperbound b = 10.
% Plot PDF & Compare with Generated Sample
X = (a-2:.1:b+2);
figure, hold on, box on
histogram(T,'Normalization','pdf') % Note normalization-pdf option name-value pair
title([num2str(N) ' Samples'])
plot(X,pdf(pd,X),'r--','LineWidth',1.8)
legend('Empirical Density','Theoretical Density','Location','northwest')
MATLAB introduced makedist() in R2013a. Requires Stats toolbox.
Reference:
Triangular Distribution
回答3:
Change
u = sqrt(rand(n, 1));
to
u = rand(n, 1);
The nice thing about this formula is that you can distribute a sample from a general triangle distribution with a single random sample.
来源:https://stackoverflow.com/questions/9241904/generating-a-triangular-distribution-in-matlab