Thread Subject: How to generate a random data?

I want to generate a random sequence. For example(1,2,3,4).The probability distributing of each element is (20%, 20%, 20%, 40%). How to realize it in Matlab?

"Rui Yang" <rui.yang@mathworks.com> wrote in message <ef5ede4.-1@webcrossing.raydaftYaTP>...
> I want to generate a random sequence. For example(1,2,3,4).The probability distributing of each element is (20%, 20%, 20%, 40%). How to realize it in Matlab?



===============
Hi,

A simple but not wise way is:

x = rand(1,1000);
y = 4*ones(1,1000);
y(find(x<-0.6)) = 1;
y(find(x>-0.6 || x < -0.2)) = 2;
y(find(x>-0.2 || x < 0.2)) = 3;

HTH

Yang

"Yang Zhang" <zhyang99@hotmail.com> wrote in message <f8bujg$6n6$1@fred.mathworks.com>...
> "Rui Yang" <rui.yang@mathworks.com> wrote in message <ef5ede4.-1@webcrossing.raydaftYaTP>...
> > I want to generate a random sequence. For example(1,2,3,4).The probability distributing of each element is (20%, 20%, 20%, 40%). How to realize it in Matlab?
>
>
>
> ===============
> Hi,
>
> A simple but not wise way is:
>
> x = rand(1,1000);
> y = 4*ones(1,1000);
> y(find(x<-0.6)) = 1;
> y(find(x>-0.6 || x < -0.2)) = 2;
> y(find(x>-0.2 || x < 0.2)) = 3;
>
> HTH
>
> Yang

Thanks, It is a good way to get it.

It think this function is faster:

function states = randomstate(probs, M, N)
%RANDOMSTATE Randomly choose a state from given discrete probabilites.
% X = RANDOMSTATE(PROBS) gives a random state from the discrete
% probability distribuation given in vector PROBS.
%
% X = RANDOMSTATE(PROBS, M, N) returns an N-by-M-matrix of random states.
%
% For example, RANDOMSTATE([0.1 0.5 0.4]) is 1 with a probability of
% 0.1, 2 with a probability of 0.5 and 3 with a probability of 0.4
%
% Note: The probability vector may contain weight values that are not
% normalized to one.
%
% Markus Buehren

nOfStates = length(probs);
% sumProbs = sum(probs);

if nargin == 1
M = 1;
N = 1;
elseif nargin == 2
N = M;
end

% Find states
if M == 1 && N == 1

sumProbs = sum(probs);
number = rand(1) * sumProbs;
threshold = 0;
for k = 1:nOfStates
threshold = threshold + probs(k);
if number <= threshold
states = k;
return
end
end
states = nOfStates;

% this version is slower:
%index = cumsum(probs);
%states = find(rand(1) * index(end) < index, 1);

else

sumProbs = sum(probs);
numbers = rand(M,N) * sumProbs;
states = ones(M,N);
threshold = 0;
for k = 1:nOfStates-1
threshold = threshold + probs(k);
states(numbers >= threshold) = k+1;
end

end