pdist — Compute pairwise distances between rows of an observation matrix.
pdist(X) returns a condensed row vector containing distances between every pair of rows in X, ordered like MATLAB's lower-left distance triangle: (2,1), (3,1), ..., (n,1), (3,2), ....
Syntax
D = pdist(X)
D = pdist(X, Distance, DistParameter)Inputs
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
X | NumericArray | Yes | — | Observation matrix with observations in rows. |
options | Any | Variadic | — | Distance metric and optional metric parameter. |
Returns
| Name | Type | Description |
|---|---|---|
D | NumericArray | Pairwise distances. |
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 pdist works
Xmust be a real numeric vector or 2-D matrix. Rows are observations and columns are variables.- The default metric is Euclidean distance.
- Supported metrics are
"euclidean","squaredeuclidean","cityblock","chebychev","minkowski","seuclidean","mahalanobis","cosine","correlation","hamming","jaccard", and"spearman". pdist(X,"minkowski",P)uses positive finite exponentP.pdist(X,"seuclidean",S)uses positive finite standard-deviation vectorS; withoutS, RunMat computes column variances fromXwith NaNs omitted from scale estimation.pdist(X,"mahalanobis",C)uses covariance matrixC; withoutC, RunMat computes the covariance fromXand inverts it.
Examples
Compute Euclidean distances
X = [0 0; 3 4; 4 0; 0 2];
D = pdist(X)Expected output:
D starts with [5 4 2], the distances from rows 2, 3, and 4 to row 1.Use a cityblock metric
D = pdist(X, "cityblock")Expected output:
D contains Manhattan distances between row pairs.Use Minkowski distance
D = pdist(X, "minkowski", 3)Expected output:
D contains pairwise L3 distances.Using pdist with coding agents
Open a RunMat example with live inputs, then ask the agent to explain how pdist changes the result.
Run a small pdist example, explain the result, then change one input and compare the output.
FAQ
Why is the output a row vector?⌄
MATLAB stores pairwise distances in condensed form to avoid duplicating the symmetric matrix. Use squareform to expand the vector.
Are custom distance functions supported?⌄
No. RunMat currently supports the standard named numeric distance metrics.
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bayesopt · classify · confusionmat · crossvalind · cvpartition · fitclinear · fitctree · fitlm · kmeans · knnsearch · lasso · lassoglm · linkage · lscov · mnrfit · optimizableVariable · pdist2 · perfcurve · predict · regress · ridge · squareform · test · training · tsne
Summary
binocdf · boxplot · cdf · cdfplot · chi2cdf · corr · corrcoef · corrcov · cov · cov2corr · dummyvar · ecdf · filloutliers · fitdist · geomean · grpstats · harmmean · icdf · isoutlier · kstest · kurtosis · lsline · mad · mode · nanmax · normalize · normcdf · norminv · normpdf · onehotdecode · onehotencode · pdf · prctile · quantile · refline · rmse · skewness · tabulate · tcdf · tiedrank · tinv · tpdf · ttest2 · wblinv
Random
binornd · bootstrp · datasample · dividerand · exprnd · gamrnd · lhsdesign · mvnrnd · normrnd · random · randsample · rng · trnd · unidrnd · unifrnd · wblrnd
Hist
Open-source implementation
Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how pdist is executed, line by line, in Rust.
- View the source for pdist 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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