From 2fe1a4fa62f49eab69d5d1499014cc8d77bce955 Mon Sep 17 00:00:00 2001 From: marineLM Date: Sat, 2 Mar 2019 08:02:17 +0100 Subject: [PATCH] [MRG+1] Fix warning in doc (#13353) * DOC Fix warning in about.rst * DOC Fix warning in datasets/index.rst * DOC Remove backtick causing warning --- doc/about.rst | 2 ++ doc/datasets/index.rst | 24 ------------------------ sklearn/decomposition/pca.py | 2 +- 3 files changed, 3 insertions(+), 25 deletions(-) diff --git a/doc/about.rst b/doc/about.rst index dc6172f23e17b..d5b1430d73e21 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -176,6 +176,8 @@ program. - 2015 - `Raghav RV `_, Wei Xue - 2016 - `Nelson Liu `_, `YenChen Lin `_ +.. _Vlad Niculae: https://vene.ro/ + It also provided funding for sprints and events around scikit-learn. If you would like to participate in the next Google Summer of code program, please see `this page diff --git a/doc/datasets/index.rst b/doc/datasets/index.rst index 5e1a19af189b2..77ecc05c710d0 100644 --- a/doc/datasets/index.rst +++ b/doc/datasets/index.rst @@ -81,18 +81,6 @@ These datasets are useful to quickly illustrate the behavior of the various algorithms implemented in scikit-learn. They are however often too small to be representative of real world machine learning tasks. -.. toctree:: - :maxdepth: 2 - :hidden: - - boston_house_prices - iris - diabetes - digits - linnerud - wine_data - breast_cancer - .. include:: ../../sklearn/datasets/descr/boston_house_prices.rst .. include:: ../../sklearn/datasets/descr/iris.rst @@ -132,18 +120,6 @@ They can be loaded using the following functions: fetch_kddcup99 fetch_california_housing -.. toctree:: - :maxdepth: 2 - :hidden: - - olivetti_faces - twenty_newsgroups - labeled_faces - covtype - rcv1 - kddcup99 - california_housing - .. include:: ../../sklearn/datasets/descr/olivetti_faces.rst .. include:: ../../sklearn/datasets/descr/twenty_newsgroups.rst diff --git a/sklearn/decomposition/pca.py b/sklearn/decomposition/pca.py index 329584e5cc5b2..d1cee0345d5e6 100644 --- a/sklearn/decomposition/pca.py +++ b/sklearn/decomposition/pca.py @@ -251,7 +251,7 @@ class PCA(_BasePCA): "Automatic choice of dimensionality for PCA". In NIPS, pp. 598-604* Implements the probabilistic PCA model from: - `Tipping, M. E., and Bishop, C. M. (1999). "Probabilistic principal + Tipping, M. E., and Bishop, C. M. (1999). "Probabilistic principal component analysis". Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3), 611-622. via the score and score_samples methods.