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# rng

Control random number generation

## Syntax

rng(sd)
rng('shuffle')
rng(sd, generator)
rng('shuffle', generator)
rng('default')
scurr = rng
rng(s)
sprev = rng(...)

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

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

rng(sd, 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:

GeneratorDescription
'twister'Mersenne Twister
'combRecursive'Combined Multiple Recursive
'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 so that they produce the same random numbers as if you restarted MATLAB. In this release, 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)`