rand

Uniformly distributed random numbers

Syntax

X = rand

X = rand(n)

X = rand(sz1,...,szN)

X = rand(sz)

X = rand(___,typename)

X = rand(___,"like",p)

X = rand(s,___)

Description

X = rand returns a random scalar drawn from the uniform distribution in the interval (0,1).

X = rand(n) returns an n-by-n matrix of uniformly distributed random numbers.

X = rand(sz1,...,szN) returns an sz1-by-...-by-szN array of random numbers where sz1,...,szN indicate the size of each dimension. For example, rand(3,4) returns a 3-by-4 matrix.

X = rand(sz) returns an array of random numbers where size vector sz defines size(X). For example, rand([3 4]) returns a 3-by-4 matrix.

X = rand(___,typename) returns an array of random numbers of data type typename. The typename input can be either "single" or "double". You can use any of the input arguments in the previous syntaxes.

X = rand(___,"like",p) returns an array of random numbers like p; that is, of the same data type and complexity (real or complex) as p. You can specify either typename or "like", but not both.

X = rand(s,___) generates numbers from random number stream s instead of the default global stream. To create a stream, use RandStream. You can specify s followed by any of the input argument combinations in previous syntaxes.

Examples

Matrix of Random Numbers

Generate a 5-by-5 matrix of uniformly distributed random numbers between 0 and 1.

r = rand(5)

r = 5×5

    0.8147    0.0975    0.1576    0.1419    0.6557
    0.9058    0.2785    0.9706    0.4218    0.0357
    0.1270    0.5469    0.9572    0.9157    0.8491
    0.9134    0.9575    0.4854    0.7922    0.9340
    0.6324    0.9649    0.8003    0.9595    0.6787

Random Numbers Within Specified Interval

Generate a 10-by-1 column vector of uniformly distributed numbers in the interval (-5,5). You can generate n random numbers in the interval (a,b) with the formula r = a + (b-a).*rand(n,1).

a = -5;
b = 5;
n = 10;
r = a + (b-a).*rand(n,1)

r = 10×1

    3.1472
    4.0579
   -3.7301
    4.1338
    1.3236
   -4.0246
   -2.2150
    0.4688
    4.5751
    4.6489

Random Integers

Use the randi function (instead of rand) to generate 5 random integers from the uniform distribution between 10 and 50.

r = randi([10 50],1,5)

r = 1×5

    43    47    15    47    35

Reset Random Number Generator

Save the current state of the random number generator and create a 1-by-5 vector of random numbers.

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

r = 1×5

    0.8147    0.9058    0.1270    0.9134    0.6324

Restore the state of the random number generator to s, and then create a new 1-by-5 vector of random numbers. The values are the same as before.

rng(s);
r1 = rand(1,5)

r1 = 1×5

    0.8147    0.9058    0.1270    0.9134    0.6324

3-D Array of Random Numbers

Create a 3-by-2-by-3 array of random numbers.

X = rand([3,2,3])

X = 
X(:,:,1) =

    0.8147    0.9134
    0.9058    0.6324
    0.1270    0.0975


X(:,:,2) =

    0.2785    0.9649
    0.5469    0.1576
    0.9575    0.9706


X(:,:,3) =

    0.9572    0.1419
    0.4854    0.4218
    0.8003    0.9157

Specify Data Type of Random Numbers

Create a 1-by-4 vector of random numbers whose elements are single precision.

r = rand(1,4,"single")

r = 1x4 single row vector

    0.8147    0.9058    0.1270    0.9134

class(r)

ans = 
'single'

Size Defined by Existing Array

Create a matrix of uniformly distributed random numbers with the same size as an existing array.

A = [3 2; -2 1];
sz = size(A);
X = rand(sz)

X = 2×2

    0.8147    0.1270
    0.9058    0.9134

It is a common pattern to combine the previous two lines of code into a single line:

X = rand(size(A));

Size and Data Type Defined by Existing Array

Create a 2-by-2 matrix of single-precision random numbers.

p = single([3 2; -2 1]);

Create an array of random numbers that is the same size and data type as p.

X = rand(size(p),"like",p)

X = 2x2 single matrix

    0.8147    0.1270
    0.9058    0.9134

class(X)

ans = 
'single'

Random Complex Numbers

Generate 10 random complex numbers from the uniform distribution over a square domain with real and imaginary parts in the interval (0,1).

a = rand(10,1,"like",1i)

a = 10×1 complex

   0.8147 + 0.9058i
   0.1270 + 0.9134i
   0.6324 + 0.0975i
   0.2785 + 0.5469i
   0.9575 + 0.9649i
   0.1576 + 0.9706i
   0.9572 + 0.4854i
   0.8003 + 0.1419i
   0.4218 + 0.9157i
   0.7922 + 0.9595i

Input Arguments

n — Size of square matrix

integer value

Size of square matrix, specified as an integer value.

• If n is 0, then X is an empty matrix.

• If n is negative, then it is treated as 0.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

sz1,...,szN — Size of each dimension (as separate arguments)

integer values

Size of each dimension, specified as separate arguments of integer values.

• If the size of any dimension is 0, then X is an empty array.

• If the size of any dimension is negative, then it is treated as 0.

• Beyond the second dimension, rand ignores trailing dimensions with a size of 1. For example, rand(3,1,1,1) produces a 3-by-1 vector of random numbers.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

sz — Size of each dimension (as a row vector)

integer values

Size of each dimension, specified as a row vector of integer values. Each element of this vector indicates the size of the corresponding dimension:

• If the size of any dimension is 0, then X is an empty array.

• If the size of any dimension is negative, then it is treated as 0.

• Beyond the second dimension, rand ignores trailing dimensions with a size of 1. For example, rand([3 1 1 1]) produces a 3-by-1 vector of random numbers.

Example: sz = [2 3 4] creates a 2-by-3-by-4 array.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

typename — Data type (class) to create

"double" (default) | "single"

Data type (class) to create, specified as "double", "single", or the name of another class that provides rand support.

Example: rand(5,"single")

p — Prototype of array to create

numeric array

Prototype of array to create, specified as a numeric array.

Example: rand(5,"like",p)

Data Types: single | double
Complex Number Support: Yes

s — Random number stream

RandStream object

Random number stream, specified as a RandStream object.

Example: s = RandStream("dsfmt19937"); rand(s,[3 1])

More About

Pseudorandom Number Generator

The underlying number generator for rand is a pseudorandom number generator, which creates a deterministic sequence of numbers that appear random. These numbers are predictable if the seed and the deterministic algorithm of the generator are known. While not truly random, the generated numbers pass various statistical tests of randomness, satisfying the independent and identically distributed (i.i.d.) condition, and justifying the name pseudorandom.

Tips

• The sequence of numbers produced by rand is determined by the internal settings of the uniform pseudorandom number generator that underlies rand, randi, and randn. You can control that shared random number generator using rng.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• The data type (class) must be a built-in MATLAB^® numeric type. For other classes, the static rand method is not invoked. For example, rand(sz,'myclass') does not invoke myclass.rand(sz).

• Size arguments must have a fixed size.

• See Variable-Sizing Restrictions for Code Generation of Toolbox Functions (MATLAB Coder).

  • If extrinsic calls are enabled and rand is not called from inside a parfor loop, generated MEX files use the same random number state as MATLAB in serial code. Otherwise, the generated MEX code and standalone code maintain their own random number state that is initialized to the same state as MATLAB.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The rand function supports GPU array input with these usage notes and limitations:

• You can specify typename as 'gpuArray'. If you specify typename as 'gpuArray', the default underlying type of the array is double.

  To create a GPU array with underlying type datatype, specify the underlying type as an additional argument before typename. For example, X = rand(3,datatype,'gpuArray') creates a 3-by-3 GPU array of random numbers with underlying type datatype.

  You can specify the underlying type datatype as one of these options:

  • 'double'

  • 'single'

• You can also specify the numeric variable p as a gpuArray.

  If you specify p as a gpuArray, the underlying type of the returned array is the same as p.

• To use the stream syntax, rand(s,___), on a GPU, s must be a parallel.gpu.RandStream (Parallel Computing Toolbox) object.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

Usage notes and limitations:

• The stream syntax rand(s,___) is not supported for codistributed or distributed arrays.

• You can specify typename as 'codistributed' or 'distributed'. If you specify typename as 'codistributed' or 'distributed', the default underlying type of the returned array is double.

  To create a distributed or codistributed array with underlying type datatype, specify the underlying type as an additional argument before typename. For example, X = rand(3,datatype,'distributed') creates a 3-by-3 distributed matrix of random numbers with underlying type datatype.

  You can specify the underlying type datatype as one of these options:

  • 'double'

  • 'single'

• You can also specify p as a codistributed or distributed array.

  If you specify p as a codistributed or distributed array, the underlying type of the returned array is the same as p.

• For additional codistributed syntaxes, see rand (codistributed) (Parallel Computing Toolbox).

For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a

R2022a: Match complexity with "like", and use "like" with RandStream object

The "like" input supports both real and complex prototype arrays. For example:

r = rand(2,2,"like",1i)

r =

   0.8147 + 0.9058i   0.6324 + 0.0975i
   0.1270 + 0.9134i   0.2785 + 0.5469i

All syntaxes support this feature. Also, you can now use "like" with a RandStream object as the first input of rand.

R2014a: Match data type of an existing variable with 'like'

To generate random numbers with the same data type as an existing variable, use the syntax rand(__,'like',p). For example:

A = single(pi);
r = rand(4,4,'like',A);
class(r)

ans = 
single

This feature is not available when passing a RandStream object as the first input to rand.

R2013b: Non-integer size inputs are not supported

Specifying a dimension that is not an integer causes an error. Use floor to convert non-integer size inputs to integers.

R2008b: 'seed', 'state', and 'twister' inputs are not recommended

There are no plans to remove these inputs, which control the random number generator that underlies rand, randi and randn. However, the rng function is recommended instead for these reasons:

• The 'seed' and 'state' generators are flawed.

• The terms 'seed' and 'state' are misleading names for the generators. 'seed' refers to the MATLAB v4 generator, not the seed initialization value. 'state' refers to the v5 generators, not the internal state of the generator.

• These three inputs unnecessarily use different generators for rand and randn.

For information on updating your code, see Replace Discouraged Syntaxes of rand and randn.