Documentation

randn

Normally distributed random numbers

Description

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X = randn returns a random scalar drawn from the standard normal distribution.

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X = randn(n) returns an n-by-n matrix of normally distributed random numbers.

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

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

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X = randn(___,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.

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X = randn(___,'like',p) returns an array of random numbers like p; that is, of the same object type as p. You can specify either typename or 'like', but not both.

Note

The 'seed', 'state', and 'twister' inputs to the randn function are not recommended. Use the rng function instead. For more information, see Replace Discouraged Syntaxes of rand and randn.

Examples

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Generate a 5-by-5 matrix of normally distributed random numbers.

r = randn(5)
r = 5×5

0.5377   -1.3077   -1.3499   -0.2050    0.6715
1.8339   -0.4336    3.0349   -0.1241   -1.2075
-2.2588    0.3426    0.7254    1.4897    0.7172
0.8622    3.5784   -0.0631    1.4090    1.6302
0.3188    2.7694    0.7147    1.4172    0.4889

Generate values from a bivariate normal distribution with specified mean vector and covariance matrix.

mu = [1 2];
sigma = [1 0.5; 0.5 2];
R = chol(sigma);
z = repmat(mu,10,1) + randn(10,2)*R
z = 10×2

1.5377    0.4831
2.8339    6.9318
-1.2588    1.8302
1.8622    2.3477
1.3188    3.1049
-0.3077    1.0750
0.5664    1.6190
1.3426    4.1420
4.5784    5.6532
3.7694    5.2595

Generate a single random complex number with normally distributed real and imaginary parts.

a = randn + 1i*randn
a = 0.5377 + 1.8339i

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

s = rng;
r = randn(1,5)
r = 1×5

0.5377    1.8339   -2.2588    0.8622    0.3188

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 = randn(1,5)
r1 = 1×5

0.5377    1.8339   -2.2588    0.8622    0.3188

Always use the rng function (rather than the rand or randn functions) to specify the settings of the random number generator. For more information, see Replace Discouraged Syntaxes of rand and randn.

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

X = randn([3,2,3])
X =
X(:,:,1) =

0.5377    0.8622
1.8339    0.3188
-2.2588   -1.3077

X(:,:,2) =

-0.4336    2.7694
0.3426   -1.3499
3.5784    3.0349

X(:,:,3) =

0.7254   -0.2050
-0.0631   -0.1241
0.7147    1.4897

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

r = randn(1,4,'single')
r = 1x4 single row vector

0.5377    1.8339   -2.2588    0.8622

class(r)
ans =
'single'

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

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

0.5377   -2.2588
1.8339    0.8622

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

X = randn(size(A));

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 = randn(size(p),'like',p)
X = 2x2 single matrix

0.5377   -2.2588
1.8339    0.8622

class(X)
ans =
'single'

If you have Parallel Computing Toolbox™, create a 1000-by-1000 distributed array of random numbers with underlying data type single. For the distributed data type, the 'like' syntax clones the underlying data type in addition to the primary data type.

p = randn(1000,'single','distributed');
Starting parallel pool (parpool) using the 'local' profile ...
connected to 6 workers.

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

X = randn(size(p),'like',p);
class(X)
ans =

distributed
classUnderlying(X)
ans =
single

Input Arguments

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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

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, randn ignores trailing dimensions with a size of 1. For example, randn(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

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, randn ignores trailing dimensions with a size of 1. For example, randn([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

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

Example: randn(5,'single')

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

Example: randn(5,'like',p)

Data Types: single | double

Tips

• The sequence of numbers produced by randn 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.