pdist2 — Compute pairwise distances between two sets of observations.
pdist2(X,Y) returns a size(X,1) by size(Y,1) matrix of distances between rows of X and rows of Y.
Syntax
D = pdist2(X, Y)
D = pdist2(X, Y, Distance, DistParameter, Name, Value)Inputs
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
X | NumericArray | Yes | — | Observation matrix with observations in rows. |
Y | NumericArray | Yes | — | Second observation matrix with observations in rows. |
options | Any | Variadic | — | Distance metric, metric parameter, or Smallest/Largest selection options. |
Returns
| Name | Type | Description |
|---|---|---|
D | NumericArray | Pairwise distances. |
D | NumericArray | Selected pairwise distances. |
I | NumericArray | One-based row indices from X for selected distances. |
Returned values from pdist2 depend on how many outputs the caller requests.
Errors
| Identifier | When | Message |
|---|---|---|
RunMat:distance:InvalidArgument | Inputs, dimensions, metrics, metric parameters, or selection options are malformed. | distance helper: invalid argument |
RunMat:distance:Internal | RunMat cannot allocate or construct a distance output. | distance helper: internal error |
How pdist2 works
XandYmust be real numeric vectors or 2-D matrices with the same number of columns.- The default metric is Euclidean distance.
- Supported metrics match
pdist:"euclidean","squaredeuclidean","cityblock","chebychev","minkowski","seuclidean","mahalanobis","cosine","correlation","hamming","jaccard", and"spearman". pdist2(X,Y,"minkowski",P)uses positive finite exponentP.pdist2(X,Y,"seuclidean",S)accepts a standard-deviation vector and defaults to scale estimated fromX.pdist2(X,Y,"mahalanobis",C)accepts a symmetric positive definite covariance matrix and defaults to covariance estimated fromX."Smallest",Kand"Largest",Kreturn the K smallest or largest distances per row ofY; when K exceeds the number of rows in X, all X rows are returned.[D,I] = pdist2(...)also returns one-based row indices fromXfor the selected distances.- Typed-integer observation data, distance parameters, and
CacheSizevalues are checked RunMat floating-boundary extensions. TypedSmallestandLargestcounts are separate structural extensions decoded directly from native storage.
Examples
Compute distances between two sets
X = [0 0; 2 0];
Y = [1 0; 3 0];
D = pdist2(X,Y)Expected output:
D is a 2-by-2 distance matrix.Use squared Euclidean distance
D = pdist2(X,Y,"squaredeuclidean")Expected output:
D contains squared distances.Keep nearest distances per query row
[D,I] = pdist2(X,Y,"euclidean","Smallest",1)Expected output:
D is a 1-by-size(Y,1) matrix of nearest distances; I contains row indices from X.Using pdist2 with coding agents
Open a RunMat example with live inputs, then ask the agent to explain how pdist2 changes the result.
Run a small pdist2 example, explain the result, then change one input and compare the output.
FAQ
Does Smallest return indices?⌄
Yes. Request [D,I] to receive one-based row indices from X, matching MATLAB's nearest-distance output form.
Are custom distance functions supported?⌄
No. RunMat currently supports the standard named numeric distance metrics.
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Hist
Open-source implementation
Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how pdist2 is executed, line by line, in Rust.
- View the source for pdist2 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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