This is documentation for an old release of SciPy (version 1.5.3). Read this page in the documentation of the latest stable release (version 1.15.1).
Distance computations (scipy.spatial.distance
)¶
Function reference¶
Distance matrix computation from a collection of raw observation vectors stored in a rectangular array.
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Pairwise distances between observations in n-dimensional space. |
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Compute distance between each pair of the two collections of inputs. |
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Convert a vector-form distance vector to a square-form distance matrix, and vice-versa. |
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Compute the directed Hausdorff distance between two N-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.
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Return True if input array is a valid distance matrix. |
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Return True if the input array is a valid condensed distance matrix. |
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Return the number of original observations that correspond to a square, redundant distance matrix. |
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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.
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Compute the Bray-Curtis distance between two 1-D arrays. |
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Compute the Canberra distance between two 1-D arrays. |
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Compute the Chebyshev distance. |
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Compute the City Block (Manhattan) distance. |
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Compute the correlation distance between two 1-D arrays. |
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Compute the Cosine distance between 1-D arrays. |
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Computes the Euclidean distance between two 1-D arrays. |
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Compute the Jensen-Shannon distance (metric) between two 1-D probability arrays. |
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Compute the Mahalanobis distance between two 1-D arrays. |
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Compute the Minkowski distance between two 1-D arrays. |
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Return the standardized Euclidean distance between two 1-D arrays. |
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Compute the squared Euclidean distance between two 1-D arrays. |
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Compute the weighted Minkowski distance between two 1-D arrays. |
Distance functions between two boolean vectors (representing sets) u
and
v
. As in the case of numerical vectors, pdist
is more efficient for
computing the distances between all pairs.
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Compute the Dice dissimilarity between two boolean 1-D arrays. |
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Compute the Hamming distance between two 1-D arrays. |
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Compute the Jaccard-Needham dissimilarity between two boolean 1-D arrays. |
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Compute the Kulsinski dissimilarity between two boolean 1-D arrays. |
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Compute the Rogers-Tanimoto dissimilarity between two boolean 1-D arrays. |
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Compute the Russell-Rao dissimilarity between two boolean 1-D arrays. |
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Compute the Sokal-Michener dissimilarity between two boolean 1-D arrays. |
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Compute the Sokal-Sneath dissimilarity between two boolean 1-D arrays. |
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Compute the Yule dissimilarity between two boolean 1-D arrays. |
hamming
also operates over discrete numerical vectors.