Adds optimize_parameters method in the Kernel baseclass.

docs/source/utilities/kernels/euclidean_kernels.rst

@@ -24,7 +24,7 @@ Methods
 ~~~~~~~~~
 
 .. autoclass:: UQpy.utilities.kernels.GaussianKernel
-    :members: optimize_epsilon
+    :members: optimize_parameters
 
 Attributes
 ~~~~~~~~~~

src/UQpy/utilities/kernels/GaussianKernel.py

@@ -27,10 +27,10 @@ def _kernel_entry(self, xi, xj):
     def kernel_function(self, distance_pairs):
         return np.exp(-sd.squareform(distance_pairs) / (4 * self.epsilon))
 
-    def optimize_epsilon(self, data: np.ndarray, tolerance: float,
-                         n_nearest_neighbors: int,
-                         n_cutoff_samples: int,
-                         random_state: RandomStateType):
+    def optimize_parameters(self, data: np.ndarray, tolerance: float,
+                            n_nearest_neighbors: int,
+                            n_cutoff_samples: int,
+                            random_state: RandomStateType):
         """
 
         :param data: Cloud of data points.

src/UQpy/utilities/kernels/baseclass/Kernel.py

@@ -18,6 +18,18 @@ def __init__(self):
     def calculate_kernel_matrix(self, points: Union[list, NumpyFloatArray]):
         pass
 
+    def optimize_parameters(self, data, **kwargs_optimization):
+        """
+        This serves as a blueprint function in case a kernel provides the ability to optimize its parameters. In that
+        case, the implemented kernel will provide an implementation of this method, that computes the optimized
+        parameters and stores their values in the kernel's attributes.
+
+        :param data: Set of data points.
+        :param kwargs_optimization: Keyword arguments containing any extra parameters needed to perform the optimization
+            of the kernel parameters.
+        """
+        pass
+
     @abstractmethod
     def _kernel_entry(self, xi, xj):
         """