Revert doctest changes

Author: Christian Jorgensen

src/skmatter/decomposition/_kernel_pcovc.py

@@ -199,10 +199,10 @@ class KernelPCovC(LinearClassifierMixin, _BaseKPCov):
     >>> kpcovc.fit(X, Y)
     KernelPCovC(gamma=1, kernel='rbf', mixing=0.1, n_components=2)
     >>> kpcovc.transform(X)
-    array([[-4.41692911e-01,  6.87831803e-06],
-           [ 4.47719340e-01,  5.47456981e-01],
-           [ 4.47850288e-01, -5.47360522e-01],
-           [-4.41645711e-01, -7.05197801e-05]])
+    array([[-4.45970689e-01,  8.95327566e-06],
+           [ 4.52745933e-01,  5.54810948e-01],
+           [ 4.52881359e-01, -5.54708315e-01],
+           [-4.45921092e-01, -7.32157649e-05]])
     >>> kpcovc.predict(X)
     array([2, 0, 1, 2])
     >>> kpcovc.score(X, Y)

src/skmatter/decomposition/_pcovc.py

@@ -195,6 +195,9 @@ class PCovC(LinearClassifierMixin, _BasePCov):
         the projector, or weights, from the latent-space projection
         :math:`\mathbf{T}` to the class confidence scores :math:`\mathbf{Z}`
 
+    scale_z: bool
+        Whether Z is being scaled prior to eigendecomposition
+
     explained_variance_ : numpy.ndarray of shape (n_components,)
         The amount of variance explained by each of the selected components.
         Equal to n_components largest eigenvalues
@@ -215,10 +218,10 @@ class PCovC(LinearClassifierMixin, _BasePCov):
     >>> pcovc.fit(X, Y)
     PCovC(mixing=0.1, n_components=2)
     >>> pcovc.transform(X)
-    array([[-0.38989065, -0.21368409],
-           [ 1.55313271,  0.20273297],
-           [-0.87105559,  0.68233882],
-           [-0.29218647, -0.6713877 ]])
+    array([[-0.4794854 , -0.46228114],
+           [ 1.9416966 ,  0.2532831 ],
+           [-1.08744947,  0.89117784],
+           [-0.37476173, -0.6821798 ]])
     >>> pcovc.predict(X)
     array([0, 1, 2, 0])
     """  # NoQa: E501