[−][src]Module smartcore::math::distance
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
| euclidian | Euclidean Distance is the straight-line distance between two points in Euclidean spacere that presents the shortest distance between these points. |
| hamming | Hamming Distance between two strings is the number of positions at which the corresponding symbols are different. |
| mahalanobis | The Mahalanobis distance is the distance between two points in multivariate space. |
| manhattan | Also known as rectilinear distance, city block distance, taxicab metric. |
| minkowski | A generalization of both the Euclidean distance and the Manhattan distance. |
Structs
| Distances | Multitude of distance metric functions |
Traits
| Distance | Distance metric, a function that calculates distance between two points |