Update examples/plot_fpca.py

Co-authored-by: Carlos Ramos Carreño <[email protected]>

New commit

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::

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