improved svg, fixed docstring

flatironinstitute · Dec 14, 2024 · 15f547e · 15f547e
1 parent 9629f7f
commit 15f547e
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diff --git a/docs/assets/pipeline.svg b/docs/assets/pipeline.svg
diff --git a/src/nemos/basis/_basis_mixin.py b/src/nemos/basis/_basis_mixin.py
@@ -215,7 +215,7 @@ def setup_basis(self, *xi: NDArray) -> Basis:
         Set all basis states.
 
         This method corresponds sklearn transformer ``fit``. As fit, it must receive the input and
-        it must set all basis states, i.e. kernel_ and all the states relative to the input shape.
+        it must set all basis states, i.e. ``kernel_`` and all the states relative to the input shape.
         The difference between this method and the transformer ``fit`` is in the expected input structure,
         where the transformer ``fit`` method requires the inputs to be concatenated in a 2D array, while here
         each input is provided as a separate time series for each basis element.
@@ -323,7 +323,7 @@ def setup_basis(self, *xi: NDArray) -> Basis:
         Set all basis states.
 
         This method corresponds sklearn transformer ``fit``. As fit, it must receive the input and
-        it must set all basis states, i.e. kernel_ and all the states relative to the input shape.
+        it must set all basis states, i.e. ``kernel_`` and all the states relative to the input shape.
         The difference between this method and the transformer ``fit`` is in the expected input structure,
         where the transformer ``fit`` method requires the inputs to be concatenated in a 2D array, while here
         each input is provided as a separate time series for each basis element.
@@ -539,7 +539,7 @@ def setup_basis(self, *xi: NDArray) -> Basis:
         Set all basis states.
 
         This method corresponds sklearn transformer ``fit``. As fit, it must receive the input and
-        it must set all basis states, i.e. kernel_ and all the states relative to the input shape.
+        it must set all basis states, i.e. ``kernel_`` and all the states relative to the input shape.
         The difference between this method and the transformer ``fit`` is in the expected input structure,
         where the transformer ``fit`` method requires the inputs to be concatenated in a 2D array, while here
         each input is provided as a separate time series for each basis element.