Merge branch 'release/0.8.1'

Fix packaging.

docs/conf.py

@@ -56,7 +56,6 @@
               'sphinx.ext.viewcode',
               'sphinx.ext.napoleon',
               'sphinx.ext.mathjax',
-              'sphinx_rtd_theme',
               'sphinx_gallery.gen_gallery',
               'sphinx.ext.intersphinx',
               'sphinx.ext.doctest',

pyproject.toml

@@ -29,7 +29,6 @@ classifiers = [
 dynamic = ["version"]
 
 dependencies = [
-	"cython",
 	"dcor",
 	"fdasrsf>=2.2.0",
 	"findiff",
@@ -58,5 +57,11 @@ documentation = "https://fda.readthedocs.io"
 repository = "https://github.com/GAA-UAM/scikit-fda"
 
 [build-system]
-# Minimum requirements for the build system to execute.
-requires = ["setuptools", "wheel"]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+include = ["skfda*"]
+
+[tool.setuptools.dynamic]
+version = {attr = "skfda.__version__"}

readthedocs.yml

@@ -18,7 +18,7 @@ sphinx:
 
 # Optionally set the version of Python and requirements required to build your docs
 python:
-  version: 3.8
+  version: "3.8"
   install:
     - requirements: readthedocs-requirements.txt
     - method: pip

requirements.txt

@@ -2,7 +2,6 @@ matplotlib
 numpy
 scipy
 setuptools
-Cython
 scikit-learn
 multimethod>=1.2
 findiff

skfda/__init__.py

@@ -30,18 +30,4 @@
         concatenate as concatenate,
     )
 
-try:
-    with open(
-        _os.path.join(
-            _os.path.dirname(__file__),
-            '..',
-            'VERSION',
-        ),
-        'r',
-    ) as version_file:
-        __version__ = version_file.read().strip()
-except IOError as e:
-    if e.errno != _errno.ENOENT:
-        raise
-
-    __version__ = "0.0"
+__version__ = "0.8.1"

skfda/misc/metrics/_mahalanobis.py

@@ -104,11 +104,11 @@ def fit(
 
         if self.eigenvalues is None or self.eigenvectors is None:
             fpca = FPCA(
-                self.n_components,
-                self.centering,
-                self.regularization,
-                self.weights,
-                self.components_basis,
+                n_components=self.n_components,
+                centering=self.centering,
+                regularization=self.regularization,
+                components_basis=self.components_basis,
+                _weights=self.weights,
             )
             fpca.fit(X)
             self.eigenvalues_ = fpca.explained_variance_

skfda/preprocessing/dim_reduction/_fpca.py

@@ -87,6 +87,7 @@ class FPCA(  # noqa: WPS230 (too many public attributes)
     def __init__(
         self,
         n_components: int = 3,
+        *,
         centering: bool = True,
         regularization: Optional[L2Regularization[FData]] = None,
         components_basis: Optional[Basis] = None,
@@ -354,27 +355,25 @@ def _fit_grid(
             regularization=self.regularization,
         )
 
-        basis_matrix = basis.data_matrix[..., 0]
-        if regularization_matrix is not None:
-            basis_matrix += regularization_matrix
+        # See issue #497 for more information about this approach
+        factorization_matrix = weights_matrix.astype(float)
+        if self.regularization is not None:
+            factorization_matrix += regularization_matrix
 
-        fd_data = np.linalg.solve(
-            basis_matrix.T,
-            fd_data.T,
-        ).T
+        # Tranpose of the Cholesky decomposition
+        Lt = np.linalg.cholesky(factorization_matrix).T
 
-        # see docstring for more information
-        final_matrix = fd_data @ np.sqrt(weights_matrix)
+        new_data_matrix = fd_data @ weights_matrix
+        new_data_matrix = np.linalg.solve(Lt.T, new_data_matrix.T).T
 
         pca = PCA(n_components=self.n_components)
-        pca.fit(final_matrix)
+        pca.fit(new_data_matrix)
+
+        components = pca.components_
+        components = np.linalg.solve(Lt, pca.components_.T).T
+
         self.components_ = X.copy(
-            data_matrix=np.transpose(
-                np.linalg.solve(
-                    np.sqrt(weights_matrix),
-                    np.transpose(pca.components_),
-                ),
-            ),
+            data_matrix=components,
             sample_names=(None,) * self.n_components,
         )