randseed

(Removed) Generate prime numbers for use as random number seeds

Compatibility

randseed has been removed. Use rng(N) or rng('shuffle') instead. For more information, see Compatibility Considerations.

Syntax

out = randseed
out = randseed(state)
out = randseed(state,m)
out = randseed(state,m,n)
out = randseed(state,m,n,rmin)
out = randseed(state,m,n,rmin,rmax)


Description

The randseed function produces random prime numbers that work well as seeds for random source blocks or noisy channel blocks in Communications Toolbox™ software.

Note

The randseed function uses a local stream of numbers that is independent from the global stream of numbers in the MATLAB^® software. Use of this function does not affect the state of the global random number stream.

out = randseed generates a random prime number between 31 and 2¹⁷-1, using the MATLAB function rand.

out = randseed(state) generates a random prime number after setting the state of rand to the positive integer state. This syntax produces the same output for a particular value of state.

out = randseed(state,m) generates a column vector of m random primes.

out = randseed(state,m,n) generates an m-by-n matrix of random primes.

out = randseed(state,m,n,rmin) generates an m-by-n matrix of random primes between rmin and 2¹⁷-1.

out = randseed(state,m,n,rmin,rmax) generates an m-by-n matrix of random primes between rmin and rmax.

Examples

To generate a two-element sample-based row vector of random bits using the Bernoulli Random Binary Generator block, you can set Probability of a zero to [0.1 0.5] and set Initial seed to randseed(391,1,2).

To generate three streams of random data from three different blocks in a single model, you can define out = randseed(93,3) in the MATLAB workspace and then set the three blocks' Initial seed parameters to out(1), out(2), and out(3), respectively.

Compatibility Considerations

randseed function has been removed

Errors starting in R2021a

The functionality provided by randseed is no longer necessary for controlling random number generation. Instead, use rng(seed), where seed specifies a nonnegative integer seed for the random number generator, or use rng(‘shuffle’) to seed the random number generator based on the current time.