Crate fast_distances Copy item path Source utils approx_log_gamma Approximate the logarithm of the Gamma function (log(Γ(x))) using Stirling’s approximation.
This approximation is valid for x > 0. bray_curtis Computes the Bray-Curtis dissimilarity between two vectors. bray_curtis_grad Computes the Bray-Curtis dissimilarity and its gradient between two vectors. canberra Computes the Canberra distance between two vectors x and y. canberra_grad Computes the Canberra distance and its gradient with respect to the first vector x. chebyshev Chebyshev or l-infinity distance. chebyshev_grad Chebyshev or l-infinity distance with gradient. correlation Computes the Pearson correlation coefficient between two vectors x and y. cosine Computes the cosine similarity between two vectors x and y. cosine_grad Computes the cosine similarity and its gradient between two vectors x and y. dice Computes the Dice coefficient between two binary vectors. euclidean Computes the Euclidean distance between two vectors. euclidean_grad Computes the Euclidean distance and its gradient between two vectors. hamming Computes the Hamming distance between two vectors x and y. haversine Computes the Haversine distance between two points on the Earth’s surface. haversine_grad Computes the gradient of the Haversine distance between two points on the Earth’s surface. hellinger Computes the Hellinger distance between two vectors x and y. hellinger_grad Computes the Hellinger gradient and the Hellinger distance between two vectors x and y. hyperboloid_grad Computes the hyperboloid distance and gradient between two vectors x and y. jaccard Computes the Jaccard similarity between two binary vectors. kulsinski Computes the Kulsinski similarity between two binary vectors. ll_dirichlet Calculates the symmetric relative log likelihood (log Dirichlet likelihood) of rolling
data2 versus data1 in n2 trials on a die that rolled data1 in n1 trials. log_beta Approximate the logarithm of the Beta function (log(B(x, y))) using two cases: log_single_beta Approximate the log of the single Beta function, as defined in the given Python function. mahalanobis Computes the Mahalanobis distance between two vectors x and y using the inverse covariance matrix vinv. mahalanobis_grad Computes the Mahalanobis distance and its gradient with respect to x
using the inverse covariance matrix vinv. manhattan Computes the Manhattan, taxicab, or L1 distance between two vectors. manhattan_grad Manhattan, taxicab, or l1 distance with gradient. matching Computes the Matching similarity between two binary vectors. minkowski Minkowski distance. minkowski_grad Minkowski distance with gradient. poincare Computes the Poincaré distance between two vectors. rogers_tanimoto Computes the Rogers-Tanimoto similarity between two binary vectors. russell_rao Computes the Russell-Rao similarity between two binary vectors. sokal_michener Computes the Sokal-Michener similarity between two binary vectors. sokal_sneath Computes the Sokal-Sneath similarity between two binary vectors. standardised_euclidean Computes the standardised Euclidean distance between two vectors. standardised_euclidean_grad Euclidean distance standardised against a vector of standard deviations per coordinate with gradient. weighted_minkowski Computes the weighted Minkowski distance between two vectors x and y with optional weights w
and a parameter p (defaulting to 2 for Euclidean distance). weighted_minkowski_grad A weighted version of the Minkowski distance with gradient. yule Computes the Yule’s Q statistic (a measure of association between two binary variables).