Documentation

rng

Control random number generation

Description

Note

To use the rng function instead of rand or randn with the 'seed', 'state', or 'twister' inputs, see the documentation on Replace Discouraged Syntaxes of rand and randn.

rng(seed) seeds the random number generator using the nonnegative integer seed so that rand, randi, and randn produce a predictable sequence of numbers.

rng('shuffle') seeds the random number generator based on the current time. Thus, rand, randi, and randn produce a different sequence of numbers after each time you call rng.

rng(seed, generator) and rng('shuffle', generator) additionally specify the type of the random number generator used by rand, randi, and randn. The generator input is one of:

• 'twister': Mersenne Twister

• 'simdTwister': SIMD-oriented Fast Mersenne Twister

• 'combRecursive': Combined Multiple Recursive

• 'philox': Philox 4x32 generator with 10 rounds

• 'threefry': Threefry 4x64 generator with 20 rounds

• 'multFibonacci': Multiplicative Lagged Fibonacci

• 'v5uniform': Legacy MATLAB® 5.0 uniform generator

• 'v5normal': Legacy MATLAB 5.0 normal generator

• 'v4': Legacy MATLAB 4.0 generator

rng('default') puts the settings of the random number generator used by rand, randi, and randn to their default values. This way, the same random numbers are produced as if you restarted MATLAB. The default settings are the Mersenne Twister with seed 0.

scurr = rng returns the current settings of the random number generator used by rand, randi, and randn. The settings are returned in a structure scurr with fields 'Type', 'Seed', and 'State'.

rng(s) restores the settings of the random number generator used by rand, randi, and randn back to the values captured previously with a command such as s = rng.

sprev = rng(...) returns the previous settings of the random number generator used by rand, randi, and randn before changing the settings.

Examples

Example 1 — Retrieve and Restore Generator Settings

Save the current generator settings in s:

s = rng;

Call rand to generate a vector of random values:

x = rand(1,5)

x =

0.8147    0.9058    0.1270    0.9134    0.6324

Restore the original generator settings by calling rng. Generate a new set of random values and verify that x and y are equal:

rng(s);
y = rand(1,5)

y =

0.8147    0.9058    0.1270    0.9134    0.6324

Example 2 — Restore Settings for Legacy Generator

Use the legacy generator.

sprev = rng(0,'v5uniform')

sprev =
Type: 'twister'
Seed: 0
State: [625x1 uint32]

x = rand

x =

0.9501

Restore the previous settings by calling rng:

rng(sprev)