Automatic pre-commit fixes

aeon/clustering/_r_cluster.py

@@ -19,13 +19,13 @@ class RCluster(BaseClusterer):
     ----------
     num_kernels : int , default = 84
          The number of convolutional kernels used to transform the input time series
-         These kernels are fixed and pre-defined (not random) and are optimized 
+         These kernels are fixed and pre-defined (not random) and are optimized
          for computational speed and
          feature diversity
 
     max_dilations_per_kernel : int , default = 32
          The maximum number of dilation rates applied to each kernel
-         Dilations control the spacing of the kernel's receptive field 
+         Dilations control the spacing of the kernel's receptive field
          over the time series,
          capturing patterns at varying scales
 
@@ -34,9 +34,9 @@ class RCluster(BaseClusterer):
 
     num_cluster : int , default = 8
          The number of clusters used
-         
+
     n_init : int , default = 10
-         The number of times the clustering algorithm (e.g., KMeans) will run with 
+         The number of times the clustering algorithm (e.g., KMeans) will run with
          different centroid seeds
          to avoid poor local optima
 
@@ -88,9 +88,9 @@ def __fit_biases(X, dilations, num_features_per_dilation, quantiles):
 
         # equivalent to:
         # >>> from itertools import combinations
-        # >>> indices = np.array([_ for _ in combinations(np.arange(9), 3)], 
-        #dtype = np.int32)
-        #MODIFICATION
+        # >>> indices = np.array([_ for _ in combinations(np.arange(9), 3)],
+        # dtype = np.int32)
+        # MODIFICATION
         indices = np.array(
             (
                 1,
@@ -471,7 +471,7 @@ def __transform(self, X, parameters):
         # equivalent to:
         # >>> from itertools import combinations
         # >>> indices = np.array([_ for _ in combinations(np.arange(9), 3)]
-        #, dtype = np.int32)
+        # , dtype = np.int32)
         indices = np.array(
             (
                 1,