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fix: formatting

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This commit is contained in:
Volodymyr Orlov
2020-08-29 20:20:36 -07:00
parent fa0918cee3
commit f7c229f167
2 changed files with 7 additions and 5 deletions
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src/math/distance/mod.rs
+4 -4
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@@ -1,5 +1,5 @@
//! # Collection of Distance Functions
//!
//! # 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:
//! 1. \\( d(x, y) = 0 \\) if and only \\( x = y \\), positive definiteness
@@ -7,9 +7,9 @@
//! 1. \\( 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.
//!
//!
//! <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_CHTML"></script>
/// Euclidean Distance is the straight-line distance between two points in Euclidean spacere that presents the shortest distance between these points.