From 0c119cc02494bad4cdd375f7027846371d226eae Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Wed, 1 Nov 2023 12:20:53 +0100 Subject: [PATCH] docs: update performance test --- publication/paper.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/publication/paper.md b/publication/paper.md index ff69fe3..37c8dd4 100644 --- a/publication/paper.md +++ b/publication/paper.md @@ -73,8 +73,9 @@ and shows improved performance in particular for larger datasets (\autoref{fig:computation_times}) due to its usage of randomized Singular Value Decomposition (SVD) [@halko_finding_2011]. -![Comparison of computation times of PCA for varying number of features between `xeofs` and `eofs`.\label{fig:computation_times}](../docs/img/timings_light.png){ width=100% } +![(A) Evaluation of xeofs computation times for processing 3D data sets of varying sizes. (B) Performance comparison between xeofs and eofs across different data set dimensions. Tests conducted [^1] on an Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz, 12 threads (6 cores), with 16GB DDR4 RAM at 2667 MT/s. \label{fig:computation_times}](../docs/perf/timings_light.png){ width=100% } +[^1]: The script used to generate these results is available at https://github.com/nicrie/xeofs/blob/main/docs/perf/ . # Implementation `xeofs` adopts the familiar `scikit-learn` style, delivering an intuitive interface @@ -104,7 +105,7 @@ At the time of publication, `xeofs` provides the following methods: | Canonical Correlation Analysis| CCA | [@hotelling_relations_1936; @vinod_canonical_1976; @bretherton_intercomparison_1992] | Additionally, we are actively developing further enhancements to `xeofs`, with plans to incorporate advanced methods -such as ROCK-PCA and spectral, rotated PCA in upcoming releases. +such as ROCK-PCA [@bueso_nonlinear_2020] and spectral, rotated PCA [@guilloteau_rotated_2020] in upcoming releases. # Acknowledgements