Removed list from lite (#2510)

aeon/classification/deep_learning/_lite_time.py

@@ -35,12 +35,10 @@ class LITETimeClassifier(BaseClassifier):
         if set to `True` then LITEMV is used. LITEMV is the
         same architecture as LITE but specifically designed
         to better handle multivariate time series.
-    n_filters : int or list of int32, default = 32
-        The number of filters used in one lite layer, if not a list, the same
-        number of filters is used in all lite layers.
-    kernel_size : int or list of int, default = 40
-        The head kernel size used for each lite layer, if not a list, the same
-        is used in all lite module.
+    n_filters : int, default = 32
+        The number of filters used in one lite layer.
+    kernel_size : int, default = 40
+        The head kernel size used for each lite layer.
     strides : int or list of int, default = 1
         The strides of kernels in convolution layers for each lite layer,
         if not a list, the same is used in all lite layers.
@@ -340,12 +338,10 @@ class IndividualLITEClassifier(BaseDeepClassifier):
         if set to `True` then LITEMV is used. LITEMV is the
         same architecture as LITE but specifically designed
         to better handle multivariate time series.
-    n_filters : int or list of int32, default = 32
-        The number of filters used in one lite layer, if not a list, the same
-        number of filters is used in all lite layers.
-    kernel_size : int or list of int, default = 40
-        The head kernel size used for each lite layer, if not a list, the same
-        is used in all lite layers.
+    n_filters : int, default = 32
+        The number of filters used in one lite layer.
+    kernel_size : int, default = 40
+        The head kernel size used for each lite layer.
     strides : int or list of int, default = 1
         The strides of kernels in convolution layers for each lite layer,
         if not a list, the same is used in all lite layers.

aeon/networks/_lite.py

@@ -22,12 +22,10 @@ class LITENetwork(BaseDeepLearningNetwork):
         if set to `True` then LITEMV is used. LITEMV is the
         same architecture as LITE but specifically designed
         to better handle multivariate time series.
-    n_filters : int or list of int32, default = 32
-        The number of filters used in one lite layer, if not a list, the same
-        number of filters is used in all lite layers.
-    kernel_size : int or list of int, default = 40
-        The head kernel size used for each lite layer, if not a list, the same
-        is used in all lite layers.
+    n_filters : int, default = 32
+        The number of filters used in one lite layer.
+    kernel_size : int , default = 40
+        The head kernel size used for each lite layer,.
     strides : int or list of int, default = 1
         The strides of kernels in convolution layers for each lite layer,
         if not a list, the same is used in all lite layers.

aeon/regression/deep_learning/_lite_time.py

@@ -34,12 +34,10 @@ class LITETimeRegressor(BaseRegressor):
         if set to `True` then LITEMV is used. LITEMV is the
         same architecture as LITE but specifically designed
         to better handle multivariate time series.
-    n_filters : int or list of int32, default = 32
-        The number of filters used in one lite layer, if not a list, the same
-        number of filters is used in all lite layers.
-    kernel_size : int or list of int, default = 40
-        The head kernel size used for each lite layer, if not a list, the same
-        is used in all lite module.
+    n_filters : int, default = 32
+        The number of filters used in one lite layer.
+    kernel_size : int, default = 40
+        The head kernel size used for each lite layer.
     strides : int or list of int, default = 1
         The strides of kernels in convolution layers for each lite layer,
         if not a list, the same is used in all lite layers.
@@ -319,12 +317,10 @@ class IndividualLITERegressor(BaseDeepRegressor):
         if set to `True` then LITEMV is used. LITEMV is the
         same architecture as LITE but specifically designed
         to better handle multivariate time series.
-    n_filters : int or list of int32, default = 32
-        The number of filters used in one lite layer, if not a list, the same
-        number of filters is used in all lite layers.
-    kernel_size : int or list of int, default = 40
-        The head kernel size used for each lite layer, if not a list, the same
-        is used in all lite layers.
+    n_filters : int, default = 32
+        The number of filters used in one lite layer.
+    kernel_size : int, default = 40
+        The head kernel size used for each lite layer.
     strides : int or list of int, default = 1
         The strides of kernels in convolution layers for each lite layer,
         if not a list, the same is used in all lite layers.