Making PCovC accessible via API reference on docs for now.

Author: rvasav26

docs/src/references/index.rst

@@ -11,7 +11,8 @@ API Reference
   selection
   linear_models
   clustering
-  decomposition
+  pcovc_decomposition
+  pcovr_decomposition
   metrics
   neighbors
   datasets

docs/src/references/pcovc_decomposition.rst

@@ -0,0 +1,22 @@
+Principal Covariates Classification (PCovC)
+================================================================
+
+.. _PCovC-api:
+
+PCovC
+-----
+
+.. autoclass:: skmatter.decomposition.PCovC
+    :show-inheritance:
+    :special-members:
+
+    .. automethod:: fit
+
+        .. automethod:: _fit_feature_space
+        .. automethod:: _fit_sample_space
+
+    .. automethod:: transform
+    .. automethod:: predict
+    .. automethod:: inverse_transform
+    .. automethod:: decision_function
+    .. automethod:: score

docs/src/references/decomposition.rst renamed to docs/src/references/pcovr_decomposition.rst

@@ -1,5 +1,5 @@
 Principal Covariates Regression (PCovR)
-=======================================
+================================================================
 
 .. _PCovR-api:
 
@@ -19,20 +19,6 @@ PCovR
     .. automethod:: predict
     .. automethod:: inverse_transform
     .. automethod:: score
-.. _PCovR-api:
-
-PCovC
------
-
-.. autoclass:: skmatter.decomposition.PCovC
-    :show-inheritance:
-    :special-members:
-
-    .. automethod:: fit
-    .. automethod:: transform
-    .. automethod:: predict
-    .. automethod:: inverse_transform
-    .. automethod:: score
 
 .. _KPCovR-api:
 

src/skmatter/decomposition/_pcovc.py

@@ -158,20 +158,20 @@ class PCovC(LinearClassifierMixin, _BasePCov):
         the projector, or weights, from the input space :math:`\mathbf{X}`
         to the latent-space projection :math:`\mathbf{T}`
 
-    pxz_ : ndarray of size :math: `({n_{features}, })` or `({n_{features}, n_{classes}})`
+    pxz_ : ndarray of size :math:`({n_{features}, })` or :math:`({n_{features}, n_{classes}})`
         the projector, or weights, from the input space :math:`\mathbf{X}`
         to the class confidence scores :math:`\mathbf{Z}`
 
-    ptz_ : ndarray of size :math: `({n_{components}, })` or `({n_{components}, n_{classes}})`
+    ptz_ : ndarray of size :math:`({n_{components}, })` or :math:`({n_{components}, n_{classes}})`
         the projector, or weights, from the latent-space projection
         :math:`\mathbf{T}` to the class confidence scores :math:`\mathbf{Z}`
 
-    explained_variance_ : ndarray of shape (n_components,)
+    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
         of the PCovC-modified covariance matrix of :math:`\mathbf{X}`.
 
-    singular_values_ : ndarray of shape (n_components,)
+    singular_values_ : numpy.ndarray of shape (n_components,)
         The singular values corresponding to each of the selected components.
 
     Examples
@@ -315,7 +315,7 @@ def _fit_feature_space(self, X, Y, Z):
             \mathbf{\tilde{C}} = \alpha \mathbf{X}^T \mathbf{X} +
             (1 - \alpha) \left(\left(\mathbf{X}^T
             \mathbf{X}\right)^{-\frac{1}{2}} \mathbf{X}^T
-            \mathbf{Z}\mathbf{Z}}^T \mathbf{X} \left(\mathbf{X}^T
+            \mathbf{Z}\mathbf{Z}^T \mathbf{X} \left(\mathbf{X}^T
             \mathbf{X}\right)^{-\frac{1}{2}}\right)
 
         where