Trying mixing=1.0 for KPCovC/PCovC match

Author: rvasav26

src/skmatter/decomposition/_kernel_pcovc.py

@@ -23,7 +23,7 @@
 
 
 class KernelPCovC(LinearClassifierMixin, _BaseKPCov):
-    r"""Kernel Principal Covariates Classification
+    r"""Kernel Principal Covariates Classification, as described in [Jorgensen2025]_,
     determines a latent-space projection :math:`\mathbf{T}` which minimizes a combined
     loss in supervised and unsupervised tasks in the reproducing kernel Hilbert space
     (RKHS).
@@ -68,7 +68,7 @@ class KernelPCovC(LinearClassifierMixin, _BaseKPCov):
             0 < n_components < min(X.shape)
         If randomized :
             run randomized SVD by the method of Halko et al.
-    
+
     classifier: {`LogisticRegression`, `LogisticRegressionCV`, `LinearSVC`, `LinearDiscriminantAnalysis`,
         `RidgeClassifier`, `RidgeClassifierCV`, `SGDClassifier`, `Perceptron`, `precomputed`}, default=None
         The classifier to use for computing
@@ -253,7 +253,7 @@ def fit(self, X, Y, W=None):
         self.classes_ = np.unique(Y)
 
         K = super().fit(X)
-       
+
         compatible_classifiers = (
             LogisticRegression,
             LogisticRegressionCV,

src/skmatter/decomposition/_kernel_pcovr.py

@@ -9,7 +9,7 @@
 
 
 class KernelPCovR(_BaseKPCov):
-    r"""Kernel Principal Covariates Regression, as described in [Helfrecht2020]_
+    r"""Kernel Principal Covariates Regression, as described in [Helfrecht2020]_,
     determines a latent-space projection :math:`\mathbf{T}` which minimizes a combined
     loss in supervised and unsupervised tasks in the reproducing kernel Hilbert space
     (RKHS).

tests/test_kernel_pcovc.py

@@ -79,6 +79,7 @@ def test_reconstruction_errors(self):
         prev_x_error = 10.0
 
         for mixing in np.linspace(0, 1, 6):
+            print(mixing)
             kpcovc = KernelPCovC(
                 mixing=mixing,
                 n_components=4,
@@ -369,7 +370,7 @@ def test_linear_matches_pcovc(self):
             n_components=2,
         )
 
-        kpcovc = KernelPCovC(kernel="linear", center=True, **hypers)
+        kpcovc = KernelPCovC(kernel="linear", **hypers)
         kpcovc.fit(self.X, self.Y)
         K = kpcovc._get_kernel(self.X)
         print(K[:5, 0])
@@ -403,10 +404,10 @@ def test_linear_matches_pcovc(self):
         lk = np.linalg.norm(K - k) ** 2.0 / np.linalg.norm(K) ** 2.0
 
         rounding = 3
-        # self.assertEqual(
-        #     round(ly, rounding),
-        #     round(ly_ref, rounding),
-        # )
+        self.assertEqual(
+            round(ly, rounding),
+            round(ly_ref, rounding),
+        )
 
         self.assertEqual(
             round(lk, rounding),