Uniformly distributed pseudorandom integers
r = randi(s,imax,n)
r = randi(s,imax,m,n)
r =
randi(s,imax,[m,n])
r = randi(s,imax,m,n,p,...)
r
= randi(s,imax,[m,n,p,...])
r = randi(s,imax)
r = randi(s,imax,size(A))
r = randi(s,[imin,imax],...)
r = randi(...,classname)
r = randi(s,imax,n) returns an
n-by-n matrix containing pseudorandom integer values
drawn from the discrete uniform distribution on 1:imax.
randi draws those values from the random stream
s.
r = randi(s,imax,m,n) or r =
randi(s,imax,[m,n])
returns an m-by-n matrix.
r = randi(s,imax,m,n,p,...) or
r
= randi(s,imax,[m,n,p,...]) returns an
m-by-n-by-p-by-... array.
r = randi(s,imax) returns a scalar.
r = randi(s,imax,size(A)) returns an array the same size as
A.
r = randi(s,[imin,imax],...) returns an array containing integer values
drawn from the discrete uniform distribution on imin:imax.
r = randi(...,classname) returns an array of integer values of class
classname. classname does not support 64-bit
integers.
The size inputs m, n, p, ...
should be nonnegative integers. Negative integers are treated as 0.
The arrays returned by randi might contain repeated integer values.
This is sometimes referred to as sampling with replacement. To get unique integer values,
sometimes referred to as sampling without replacement, use randperm
(RandStream).
The sequence of numbers produced by randi is determined by the
internal state of the random stream s. randi uses one
uniform value from s to generate each integer value. Resetting
s to the same fixed state allows computations to be repeated. Setting the
stream to different states leads to unique computations, however, it does not improve any
statistical properties.