scipy.spatial.distance¶
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]) | Computes the pairwise distances between m original 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 the variable D passed is a valid distance matrix. |
| is_valid_y(y[, warning, throw, name]) | Returns True if the variable y passed is a valid condensed |
| num_obs_dm(d) | Returns the number of original observations that correspond to a |
| num_obs_y(Y) | Returns the number of original observations that correspond to a |
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 n-vectors u and |
| canberra(u, v) | Computes the Canberra distance between two n-vectors u and v, |
| chebyshev(u, v) | Computes the Chebyshev distance between two n-vectors u and v, |
| cityblock(u, v) | Computes the Manhattan distance between two n-vectors u and v, |
| correlation(u, v) | Computes the correlation distance between two n-vectors u and v, which is defined as .. |
| cosine(u, v) | Computes the Cosine distance between two n-vectors u and v, which |
| dice(u, v) | Computes the Dice dissimilarity between two boolean n-vectors |
| euclidean(u, v) | Computes the Euclidean distance between two n-vectors u and v, |
| hamming(u, v) | Computes the Hamming distance between two n-vectors u and |
| jaccard(u, v) | Computes the Jaccard-Needham dissimilarity between two boolean |
| kulsinski(u, v) | Computes the Kulsinski dissimilarity between two boolean n-vectors |
| mahalanobis(u, v, VI) | Computes the Mahalanobis distance between two n-vectors u and v, |
| matching(u, v) | Computes the Matching dissimilarity between two boolean n-vectors |
| minkowski(u, v, p) | Computes the Minkowski distance between two vectors u and v, |
| rogerstanimoto(u, v) | Computes the Rogers-Tanimoto dissimilarity between two boolean |
| russellrao(u, v) | Computes the Russell-Rao dissimilarity between two boolean n-vectors |
| seuclidean(u, v, V) | Returns the standardized Euclidean distance between two n-vectors |
| sokalmichener(u, v) | Computes the Sokal-Michener dissimilarity between two boolean vectors |
| sokalsneath(u, v) | Computes the Sokal-Sneath dissimilarity between two boolean vectors |
| sqeuclidean(u, v) | Computes the squared Euclidean distance between two n-vectors u and v, |
| yule(u, v) | Computes the Yule dissimilarity between two boolean n-vectors u and v, |
Functions
| braycurtis(u, v) | Computes the Bray-Curtis distance between two n-vectors u and |
| canberra(u, v) | Computes the Canberra distance between two n-vectors u and v, |
| cdist(XA, XB[, metric, p, V, VI, w]) | Computes distance between each pair of the two collections of inputs. |
| chebyshev(u, v) | Computes the Chebyshev distance between two n-vectors u and v, |
| cityblock(u, v) | Computes the Manhattan distance between two n-vectors u and v, |
| correlation(u, v) | Computes the correlation distance between two n-vectors u and v, which is defined as .. |
| cosine(u, v) | Computes the Cosine distance between two n-vectors u and v, which |
| dice(u, v) | Computes the Dice dissimilarity between two boolean n-vectors |
| euclidean(u, v) | Computes the Euclidean distance between two n-vectors u and v, |
| hamming(u, v) | Computes the Hamming distance between two n-vectors u and |
| is_valid_dm(D[, tol, throw, name, warning]) | Returns True if the variable D passed is a valid distance matrix. |
| is_valid_y(y[, warning, throw, name]) | Returns True if the variable y passed is a valid condensed |
| jaccard(u, v) | Computes the Jaccard-Needham dissimilarity between two boolean |
| kulsinski(u, v) | Computes the Kulsinski dissimilarity between two boolean n-vectors |
| mahalanobis(u, v, VI) | Computes the Mahalanobis distance between two n-vectors u and v, |
| matching(u, v) | Computes the Matching dissimilarity between two boolean n-vectors |
| minkowski(u, v, p) | Computes the Minkowski distance between two vectors u and v, |
| norm(x[, ord]) | Matrix or vector norm. |
| num_obs_dm(d) | Returns the number of original observations that correspond to a |
| num_obs_y(Y) | Returns the number of original observations that correspond to a |
| pdist(X[, metric, p, w, V, VI]) | Computes the pairwise distances between m original observations in n-dimensional space. |
| rogerstanimoto(u, v) | Computes the Rogers-Tanimoto dissimilarity between two boolean |
| russellrao(u, v) | Computes the Russell-Rao dissimilarity between two boolean n-vectors |
| seuclidean(u, v, V) | Returns the standardized Euclidean distance between two n-vectors |
| sokalmichener(u, v) | Computes the Sokal-Michener dissimilarity between two boolean vectors |
| sokalsneath(u, v) | Computes the Sokal-Sneath dissimilarity between two boolean vectors |
| sqeuclidean(u, v) | Computes the squared Euclidean distance between two n-vectors u and v, |
| squareform(X[, force, checks]) | Converts a vector-form distance vector to a square-form distance matrix, and vice-versa. |
| wminkowski(u, v, p, w) | Computes the weighted Minkowski distance between two vectors u |
| yule(u, v) | Computes the Yule dissimilarity between two boolean n-vectors u and v, |
