randn — Generate standard normal random numbers in MATLAB and RunMat.

randn draws pseudorandom samples from the standard normal distribution (μ = 0, σ = 1). Size forms and 'like' behavior follow MATLAB semantics.

Syntax

A = randn()
A = randn(n)
A = randn(size_vector)
A = randn(m, n, ...)
A = randn(prototype)
A = randn(..., typename)
A = randn(..., "like", prototype)

Inputs

Returns

Errors

How randn works

• randn() returns a scalar double drawn from 𝒩(0, 1).
• randn(n) returns an n × n dense double matrix.
• randn(m, n, ...) accepts an arbitrary number of dimension arguments.
• randn(sz) accepts a size vector (row or column) and returns an array with shape sz.
• randn(A) or randn(___, 'like', A) matches both the shape and residency of A, including GPU tensors and complex prototypes.
• Complex prototypes yield complex Gaussian samples with independent 𝒩(0, 1) real and imaginary parts.
• Class specifiers currently support 'double'; other classes (e.g., 'single') emit descriptive errors until native representations land.

Does RunMat run randn on the GPU?

When the output or 'like' prototype lives on the GPU, RunMat calls into the active acceleration provider via random_normal / random_normal_like. Providers without these hooks fall back to host generation followed by a single upload, ensuring the resulting tensor still resides on device even if samples were produced on the CPU.

GPU memory and residency

You usually do not need to call gpuArray explicitly in RunMat. The fusion planner keeps results on the GPU when downstream work benefits from device residency. However, for MATLAB compatibility—and when you want deterministic control—you can still use gpuArray to seed GPU execution manually.

MathWorks MATLAB lacks an integrated fusion planner and ships GPU acceleration as a separate toolbox, so MATLAB users move data manually. RunMat automates this to streamline accelerated workflows.

Examples

Drawing a single standard normal variate

rng(0);
z = randn()

Expected output:

z = 1.8179

Creating a matrix of Gaussian noise

rng(0);
E = randn(2, 3)

Expected output:

E =
    1.8179    0.3895    0.9838
   -1.1645    0.4175    0.1386

Specifying dimensions with a size vector

rng(0);
shape = [2 2 2];
T = randn(shape)

Expected output:

T(:, :, 1) =
    1.8179    0.3895
   -1.1645    0.4175

T(:, :, 2) =
    0.9838   -1.1226
    0.1386    2.7430

Matching an existing gpuArray prototype

rng(0);
G = gpuArray.zeros(512, 512);
noise = randn('like', G);
stats = [mean(gather(noise(:))) std(gather(noise(:)))]

Expected output:

size(noise)
ans =
       512   512

stats =
   -0.0009    0.9986

Generating complex Gaussian samples

rng(0);
z = randn(3, 1, 'like', 1 + 1i)

Expected output:

z =
   1.8179 - 1.1645i
   0.3895 + 0.4175i
   0.9838 + 0.1386i

Producing reproducible noise for Monte Carlo tests

rng(0);
samples = randn(1, 5)

Expected output:

samples = [1.8179  -1.1645   0.3895   0.4175   0.9838]

Using randn with coding agents

Open a RunMat example with live inputs, then ask the agent to explain how randn changes the result.

FAQ

Related Array functions

Open-source implementation

Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how randn is executed, line by line, in Rust.

• View the source for randn in Rust on GitHub
• Learn how the RunMat runtime works
• Found a bug? Open an issue with a minimal reproduction.

About RunMat

RunMat is an open-source runtime that executes MATLAB-syntax code blazing on any GPU. It is licensed under the Apache 2.0 license.

• RunMat automatically optimizes your math for GPU execution on Apple, Nvidia, and AMD hardware. No code changes needed. Simulations that took hours now take minutes.
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