docstring updates

Author: cboulay

src/ezmsg/learn/process/linear_regressor.py

@@ -31,7 +31,8 @@ class LinearRegressorTransformer(
     """
     Linear regressor.
 
-    Note: `partial_fit` is not 'partial'. It fully resets the model using the entirety of the SampleMessage provided.
+    Note: `partial_fit` is not 'partial'. It fully resets the model using the entirety of the
+    SampleTriggerMessage in the input message's "training" attrs.
     If you require adaptive fitting, try using the adaptive_linear_regressor module.
     """
 

src/ezmsg/learn/process/ssr.py

@@ -356,7 +356,7 @@ class LRRUnit(
     """ezmsg Unit wrapping :class:`LRRTransformer`.
 
     Follows the :class:`BaseAdaptiveDecompUnit` pattern — accepts
-    :class:`AxisArray` (not :class:`SampleMessage`) for self-supervised
+    :class:`AxisArray` for self-supervised
     training via ``INPUT_SAMPLE``.
     """
 

tests/unit/test_mlp_old.py

@@ -185,7 +185,7 @@ def xy_gen(set: int = 0):
 
         # Train: This is unrealistic in that we would normally do inference on many axisarray messages throughout
         #  the trial, and only do training infrequently at the end of a trial if we can infer the labels.
-        #  But I'm too lazy to split the data into many small axarrs and one large SampleMessage per trial.
+        #  But I'm too lazy to split the data into many small AxisArrays and one large labeled AxisArrays-with-triggers.
         proc.partial_fit(sample_msg)
 
     def eval_test(processor, set: int = 1):