ismember — Test membership of elements or rows in MATLAB and RunMat.

ismember(A, B) returns membership flags for elements (or rows) of A in B, with optional first-match indices. Ordering and comparison behavior follow MATLAB semantics.

Syntax

tf = ismember(A, B)
tf = ismember(A, B, option...)
[tf, loc] = ismember(A, B)
[tf, loc] = ismember(A, B, option...)

Inputs

Returns

Returned values from ismember depend on how many outputs the caller requests.

Errors

How ismember works

• The first output tf has the same shape as A (or size(A,1) × 1 when using 'rows').
• The optional second output loc contains one-based indices into B, with 0 for values that are not found.
• Duplicate values in A return the index of the first occurrence in B every time they match.
• NaN values are treated as identical so they match other NaN entries in B.
• Character arrays follow column-major linear indexing, mirroring MATLAB.
• The 'rows' option compares complete rows; inputs must agree on the number of columns.
• Legacy flags ('legacy', 'R2012a') are deliberately unsupported in RunMat.

Does RunMat run ismember on the GPU?

When either input is a GPU tensor, RunMat first checks whether the active acceleration provider exposes a custom ismember hook. Until providers implement that hook, the runtime transparently gathers GPU operands to host memory, performs the membership lookup using the CPU implementation, and returns host-resident outputs so results exactly match MATLAB.

GPU memory and residency

Most code does not need to call gpuArray explicitly. The native auto-offload planner keeps track of residency and recognises that ismember is a sink: the operation produces logical outputs and one-based indices that currently live on the host. If an acceleration provider exposes a full ismember hook in the future, the planner can keep data on the device automatically. Until then, manual gpuArray / gather calls only serve to mirror MATLAB workflows; RunMat already performs the necessary transfers when it detects that tensors reside on the GPU.

Examples

Checking membership of numeric vectors

A = [5 7 2 7];
B = [7 9 5];
[tf, loc] = ismember(A, B)

Expected output:

tf =
     1     1     0     1
loc =
     3     1     0     1

Finding row membership in a matrix

A = [1 2; 3 4; 1 2];
B = [3 4; 5 6; 1 2];
[tf, loc] = ismember(A, B, 'rows')

Expected output:

tf =
     1
     1
     1
loc =
     3
     1
     3

Locating values and retrieving the index

values = [10 20 30];
set = [30 10 40];
[tf, loc] = ismember(values, set)

Expected output:

tf =
     1     0     1
loc =
     2     0     1

Testing characters against a set

chars = ['r','u'; 'n','m'];
set = ['m','a'; 'r','u'];
[tf, loc] = ismember(chars, set)

Expected output:

tf =
     1     1
     0     0
loc =
     3     1
     0     0

Working with string arrays

A = ["apple" "pear" "banana"];
B = ["pear" "orange" "apple"];
[tf, loc] = ismember(A, B)

Expected output:

tf =
  1×3 logical array
   1   1   0
loc =
  1×3 double
   3   1   0

Using ismember with gpuArray inputs

G = gpuArray([1 4 2 4]);
H = gpuArray([4 5]);
[tf, loc] = ismember(G, H)

Expected output:

tf =
     0     1     0     1
loc =
     0     1     0     1

Using ismember with coding agents

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

FAQ

Related Array functions

Open-source implementation

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

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

About RunMat

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