From d39b04e549fdf077825ee77205773bc7d044373b Mon Sep 17 00:00:00 2001 From: Volodymyr Orlov Date: Mon, 14 Dec 2020 15:03:10 -0800 Subject: [PATCH] fix: fmt --- src/decomposition/svd.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/decomposition/svd.rs b/src/decomposition/svd.rs index fbaf042..eea1969 100644 --- a/src/decomposition/svd.rs +++ b/src/decomposition/svd.rs @@ -2,7 +2,7 @@ //! //! Similar to [`PCA`](../pca/index.html), SVD is a technique that can be used to reduce the number of input variables _p_ to a smaller number _k_, while preserving //! the most important structure or relationships between the variables observed in the data. -//! +//! //! Contrary to PCA, SVD does not center the data before computing the singular value decomposition. //! //! Example: