Distance computations (scipy.spatial.distance) — SciPy v1.15.2 Manual (original) (raw)
Function reference#
Distance matrix computation from a collection of raw observation vectors stored in a rectangular array.
| pdist(X[, metric, out]) | Pairwise distances between observations in n-dimensional space. |
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| cdist(XA, XB[, metric, out]) | Compute distance between each pair of the two collections of inputs. |
| squareform(X[, force, checks]) | Convert a vector-form distance vector to a square-form distance matrix, and vice-versa. |
| directed_hausdorff(u, v[, rng]) | Compute the directed Hausdorff distance between two 2-D arrays. |
Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix.
| is_valid_dm(D[, tol, throw, name, warning]) | Return True if input array is a valid distance matrix. |
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| is_valid_y(y[, warning, throw, name]) | Return True if the input array is a valid condensed distance matrix. |
| num_obs_dm(d) | Return the number of original observations that correspond to a square, redundant distance matrix. |
| num_obs_y(Y) | Return the number of original observations that correspond to a condensed distance matrix. |
Distance functions between two numeric vectors u and v. Computing distances over a large collection of vectors is inefficient for these functions. Use pdist for this purpose.
| braycurtis(u, v[, w]) | Compute the Bray-Curtis distance between two 1-D arrays. |
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| canberra(u, v[, w]) | Compute the Canberra distance between two 1-D arrays. |
| chebyshev(u, v[, w]) | Compute the Chebyshev distance. |
| cityblock(u, v[, w]) | Compute the City Block (Manhattan) distance. |
| correlation(u, v[, w, centered]) | Compute the correlation distance between two 1-D arrays. |
| cosine(u, v[, w]) | Compute the Cosine distance between 1-D arrays. |
| euclidean(u, v[, w]) | Computes the Euclidean distance between two 1-D arrays. |
| jensenshannon(p, q[, base, axis, keepdims]) | Compute the Jensen-Shannon distance (metric) between two probability arrays. |
| mahalanobis(u, v, VI) | Compute the Mahalanobis distance between two 1-D arrays. |
| minkowski(u, v[, p, w]) | Compute the Minkowski distance between two 1-D arrays. |
| seuclidean(u, v, V) | Return the standardized Euclidean distance between two 1-D arrays. |
| sqeuclidean(u, v[, w]) | Compute the squared Euclidean distance between two 1-D arrays. |
Distance functions between two boolean vectors (representing sets) u andv. As in the case of numerical vectors, pdist is more efficient for computing the distances between all pairs.
| dice(u, v[, w]) | Compute the Dice dissimilarity between two boolean 1-D arrays. |
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| hamming(u, v[, w]) | Compute the Hamming distance between two 1-D arrays. |
| jaccard(u, v[, w]) | Compute the Jaccard dissimilarity between two boolean vectors. |
| kulczynski1(u, v, *[, w]) | Compute the Kulczynski 1 dissimilarity between two boolean 1-D arrays. |
| rogerstanimoto(u, v[, w]) | Compute the Rogers-Tanimoto dissimilarity between two boolean 1-D arrays. |
| russellrao(u, v[, w]) | Compute the Russell-Rao dissimilarity between two boolean 1-D arrays. |
| sokalmichener(u, v[, w]) | Compute the Sokal-Michener dissimilarity between two boolean 1-D arrays. |
| sokalsneath(u, v[, w]) | Compute the Sokal-Sneath dissimilarity between two boolean 1-D arrays. |
| yule(u, v[, w]) | Compute the Yule dissimilarity between two boolean 1-D arrays. |
hamming also operates over discrete numerical vectors.