This is documentation for an old release of SciPy (version 1.8.0). Read this page in the documentation of the latest stable release (version 1.14.1).
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 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.
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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 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. |
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 Kulczynski 1 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.