Expand description
Multitude of distance metrics are defined here
Collection of Distance Functions
Many algorithms in machine learning require a measure of distance between data points. Distance metric (or metric) is a function that defines a distance between a pair of point elements of a set. Formally, the distance can be any metric measure that is defined as \( d(x, y) \geq 0\) and follows three conditions:
- \( d(x, y) = 0 \) if and only \( x = y \), positive definiteness
- \( d(x, y) = d(y, x) \), symmetry
- \( d(x, y) \leq d(x, z) + d(z, y) \), subadditivity or triangle inequality
for all \(x, y, z \in Z \)
A good distance metric helps to improve the performance of classification, clustering and information retrieval algorithms significantly.
Modules
Euclidean Distance is the straight-line distance between two points in Euclidean spacere that presents the shortest distance between these points.
Hamming Distance between two strings is the number of positions at which the corresponding symbols are different.
The Mahalanobis distance is the distance between two points in multivariate space.
Also known as rectilinear distance, city block distance, taxicab metric.
A generalization of both the Euclidean distance and the Manhattan distance.
Structs
Multitude of distance metric functions
Traits
Distance metric, a function that calculates distance between two points