diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index be8cba34a..9b4740cc9 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -27,11 +27,11 @@ # # FPCA is a dimensionality reduction method for functional data that aims to # reduce the complexity of studying observations by finding a finite number of -# principal components, which are the directions that capture the main modes -# of variation across the function (the most important directions in which the -# curves vary). FPCA can be though of as a basis expansion, but what +# principal components. These components are the directions that capture the +# main modes of variation across the function (the directions in which the +# curves vary the most). FPCA can be though of as a basis expansion, but what # distinguishes FPCA is that among all basis expansions that use K components -# for a fixed K, the FPC expansion explains most of the variation in X. +# for a fixed K, the FPCA expansion explains most of the variation in X. # # For more information abour FPCA and its objectives, see # :footcite:ts:`wang+chiou+muller_2016_fpca`. @@ -122,3 +122,9 @@ fpca = FPCA(n_components=2, components_basis=MonomialBasis(n_basis=4)) fpca.fit(basis_fd) fpca.components_.plot() + +# %% +# References +# ---------- +# +# .. footbibliography:: diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index f5bd57caa..3200d3093 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -32,7 +32,8 @@ class FPCA( # noqa: WPS230 (too many public attributes) when fitting a FDataBasis or FDataGrid, except for ``components_basis``. For more information about the implementation of the computation of the - first principal components see :footcite:ts:`silverman_2005_basisfuncexp`. + first principal components see + :footcite:ts:`ramsay+silverman_2005_basisfuncexp`. Parameters: n_components: Number of principal components to keep from