[DOC,MNT] Documentation configuration clean-up

aeon/classification/deep_learning/base.py

@@ -49,10 +49,9 @@ class BaseDeepClassifier(BaseClassifier):
         The name of the file of the last model, used
         only if save_last_model_to_file is used
 
-    Arguments
-    ---------
+    Attributes
+    ----------
     self.model = None
-
     """
 
     _tags = {

aeon/networks/_ae_abgru.py

@@ -64,8 +64,8 @@ def __init__(
     def build_network(self, input_shape, **kwargs):
         """Construct a network and return its input and output layers.
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         input_shape : tuple of shape = (n_timepoints (m), n_channels (d))
             The shape of the data fed into the input layer.
         kwargs : dict

aeon/networks/_ae_dcnn.py

@@ -85,8 +85,8 @@ def __init__(
     def build_network(self, input_shape):
         """Construct a network and return its input and output layers.
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         input_shape : tuple of shape = (n_timepoints (m), n_channels (d))
             The shape of the data fed into the input layer.
 

docs/about.md

@@ -124,6 +124,14 @@ organisations:
   - GitHub ID
   - Organization
   - Year
+* - Balgopal Moharana
+  - {user}`lucifer4073`
+  - [Google Summer of Code](https://summerofcode.withgoogle.com)
+  - 2025
+* - Jiarong Jin
+  - {user}`TinaJin0228`
+  - [Google Summer of Code](https://summerofcode.withgoogle.com)
+  - 2025
 * - Divya Tiwari
   - {user}`itsdivya1309`
   - [Google Summer of Code](https://summerofcode.withgoogle.com)

docs/changelogs/v1.2.md

@@ -4,17 +4,18 @@ June 2025
 
 ## Highlights
 
-- `aeon` v1.2.0 drops support for Python 3.9, Python 3.10-3.13 are the current supported versions.
+- `aeon` v1.2.0 drops support for Python 3.9, Python 3.10-3.13 are the currently supported versions.
 - Framework for whole-series anomaly detection has been implemented, and the module has been refactored
 into `anomaly_detection.collection` and `anomaly_detection.series` submodules.
-- `ProximityForect` and `ProximityTree` classifiers have been improved to support
+- `ProximityForest` and `ProximityTree` classifiers have been improved to support
 unequal length and multivariate series. The classifiers are also significantly faster.
+- A new implementation for the `TD-MVDC` classifier
 - The forecasting module has been enhanced with additional methods for direct and recursive forecasting,
 as well as new algorithms such as ETS. This module is still in early development, and we expect
 framework changes in future releases.
 - The similarity search module has been significantly reworked to fit the `aeon` style
 framework seen in other modules. See the module examples and documentation for more details.
-- The start of a sub-package for self suprervised learning has been added in
+- The start of an experimental  sub-package for self supervised learning has been added in
 `aeon.transformations.collection.self_supervised`, starting with the `TRILITE` algorithm.
 
 ## Anomaly Detection