Distance computations (scipy.spatial.distance)¶
Function Reference¶
Distance matrix computation from a collection of raw observation vectors stored in a rectangular array.
pdist(X[, metric, p, w, V, VI]) | Pairwise distances between observations in n-dimensional space. |
cdist(XA, XB[, metric, p, V, VI, w]) | Computes distance between each pair of the two collections of inputs. |
squareform(X[, force, checks]) | Converts a vector-form distance vector to a square-form distance matrix, and vice-versa. |
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]) | Returns True if input array is a valid distance matrix. |
is_valid_y(y[, warning, throw, name]) | Returns True if the input array is a valid condensed distance matrix. |
num_obs_dm(d) | Returns the number of original observations that correspond to a square, redundant distance matrix. |
num_obs_y(Y) | Returns the number of original observations that correspond to a condensed distance matrix. |
Distance functions between two 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) | Computes the Bray-Curtis distance between two 1-D arrays. |
canberra(u, v) | Computes the Canberra distance between two 1-D arrays. |
chebyshev(u, v) | Computes the Chebyshev distance. |
cityblock(u, v) | Computes the City Block (Manhattan) distance. |
correlation(u, v) | Computes the correlation distance between two 1-D arrays. |
cosine(u, v) | Computes the Cosine distance between 1-D arrays. |
dice(u, v) | Computes the Dice dissimilarity between two boolean 1-D arrays. |
euclidean(u, v) | Computes the Euclidean distance between two 1-D arrays. |
hamming(u, v) | Computes the Hamming distance between two 1-D arrays. |
jaccard(u, v) | Computes the Jaccard-Needham dissimilarity between two boolean 1-D arrays. |
kulsinski(u, v) | Computes the Kulsinski dissimilarity between two boolean 1-D arrays. |
mahalanobis(u, v, VI) | Computes the Mahalanobis distance between two 1-D arrays. |
matching(u, v) | Computes the Matching dissimilarity between two boolean 1-D arrays. |
minkowski(u, v, p) | Computes the Minkowski distance between two 1-D arrays. |
rogerstanimoto(u, v) | Computes the Rogers-Tanimoto dissimilarity between two boolean 1-D arrays. |
russellrao(u, v) | Computes the Russell-Rao dissimilarity between two boolean 1-D arrays. |
seuclidean(u, v, V) | Returns the standardized Euclidean distance between two 1-D arrays. |
sokalmichener(u, v) | Computes the Sokal-Michener dissimilarity between two boolean 1-D arrays. |
sokalsneath(u, v) | Computes the Sokal-Sneath dissimilarity between two boolean 1-D arrays. |
sqeuclidean(u, v) | Computes the squared Euclidean distance between two 1-D arrays. |
wminkowski(u, v, p, w) | Computes the weighted Minkowski distance between two 1-D arrays. |
yule(u, v) | Computes the Yule dissimilarity between two boolean 1-D arrays. |