Merge remote-tracking branch 'origin/feature/v4_docs_review' into fea…

…ture/v4_docs_review

# Conflicts:
#	docs/source/utilities/distances/euclidean_distances.rst
#	src/UQpy/utilities/distances/euclidean_distances/__init__.py

src/UQpy/utilities/distances/baseclass/Distance.py

@@ -6,7 +6,9 @@
 
 class Distance(ABC):
     """
-    This class serves as a baseclass under which the distance implementations for all spaces exist.
+    This is the baseclass for all distances in :py:mod:`UQpy`.
+
+    This serves as a blueprint to show the methods for distances implemented in the :py:mod:`.distances` module .
     """
     def __init__(self):
         self.distance_matrix: np.ndarray = None
@@ -24,7 +26,7 @@ def calculate_distance_matrix(self, points):
     @abstractmethod
     def compute_distance(self, xi, xj) -> float:
         """
-        Given a two points, this method calculates their distance. Each concrete distance implementation
+        Given two points, this method calculates their distance. Each concrete distance implementation
         must override this method and provide its own implementation.
 
         :param xi: First point.

src/UQpy/utilities/distances/baseclass/EuclideanDistance.py

@@ -13,7 +13,7 @@ class EuclideanDistance(Distance, ABC):
     @beartype
     def calculate_distance_matrix(self, points: list[NumpyFloatArray]):
         """
-        Given a list of cartesian points calculates a matrix that contains the distances between them.
+        Given a list of cartesian points, calculates a matrix that contains the distances between them.
 
         :param points: A list of cartesian points.
         :return: :class:`.ndarray`

src/UQpy/utilities/distances/euclidean_distances/BrayCurtisDistance.py

@@ -7,4 +7,12 @@
 
 class BrayCurtisDistance(EuclideanDistance):
     def compute_distance(self, xi: NumpyFloatArray, xj: NumpyFloatArray) -> float:
+        """
+        Given two points, this method calculates the Bray-Curtis distance.
+
+        :param xi: First point.
+        :param xj: Second point.
+        :return: A float representing the distance between the points.
+        """
+
         return pdist([xi, xj], "braycurtis")[0]

src/UQpy/utilities/distances/euclidean_distances/CanberraDistance.py

@@ -8,4 +8,12 @@
 class CanberraDistance(EuclideanDistance):
 
     def compute_distance(self, xi: NumpyFloatArray, xj: NumpyFloatArray) -> float:
+        """
+        Given two points, this method calculates the Canberra distance.
+
+        :param xi: First point.
+        :param xj: Second point.
+        :return: A float representing the distance between the points.
+        """
+
         return pdist([xi, xj], "canberra")[0]

src/UQpy/utilities/distances/euclidean_distances/ChebyshevDistance.py

@@ -8,4 +8,11 @@
 class ChebyshevDistance(EuclideanDistance):
 
     def compute_distance(self, xi: NumpyFloatArray, xj: NumpyFloatArray) -> float:
+        """
+        Given two points, this method calculates the Chebyshev distance.
+
+        :param xi: First point.
+        :param xj: Second point.
+        :return: A float representing the distance between the points.
+        """
         return pdist([xi, xj], "chebyshev")[0]

src/UQpy/utilities/distances/euclidean_distances/CityBlockDistance.py

@@ -8,4 +8,12 @@
 class CityBlockDistance(EuclideanDistance):
 
     def compute_distance(self, xi: NumpyFloatArray, xj: NumpyFloatArray) -> float:
+        """
+        Given two points, this method calculates the City Block (Manhattan) distance.
+
+        :param xi: First point.
+        :param xj: Second point.
+        :return: A float representing the distance between the points.
+        """
+
         return pdist([xi, xj], "cityblock")[0]

src/UQpy/utilities/distances/euclidean_distances/CorrelationDistance.py

@@ -8,4 +8,11 @@
 class CorrelationDistance(EuclideanDistance):
 
     def compute_distance(self, xi: NumpyFloatArray, xj: NumpyFloatArray) -> float:
+        """
+        Given two points, this method calculates the Correlation distance.
+
+        :param xi: First point.
+        :param xj: Second point.
+        :return: A float representing the distance between the points.
+        """
         return pdist([xi, xj], "correlation")[0]

src/UQpy/utilities/distances/euclidean_distances/CosineDistance.py

@@ -8,4 +8,12 @@
 class CosineDistance(EuclideanDistance):
 
     def compute_distance(self, xi: NumpyFloatArray, xj: NumpyFloatArray) -> float:
+        """
+        Given two points, this method calculates the Cosine distance.
+
+        :param xi: First point.
+        :param xj: Second point.
+        :return: A float representing the distance between the points.
+        """
+
         return pdist([xi, xj], "cosine")[0]

src/UQpy/utilities/distances/euclidean_distances/L2Distance.py

@@ -7,4 +7,12 @@
 class L2Distance(EuclideanDistance):
 
     def compute_distance(self, xi: NumpyFloatArray, xj: NumpyFloatArray) -> float:
+        """
+        Given two points, this method calculates the Euclidean distance.
+
+        :param xi: First point.
+        :param xj: Second point.
+        :return: A float representing the distance between the points.
+        """
+
         return pdist([xi, xj], "euclidean")[0]

src/UQpy/utilities/distances/euclidean_distances/MinkowskiDistance.py

@@ -6,8 +6,19 @@
 
 
 class MinkowskiDistance(EuclideanDistance):
-    def __init__(self, p=2):
+    def __init__(self, p: float = 2):
+        """
+        :param p: Order of the norm.
+        """
         self.p = p
 
+
     def compute_distance(self, xi: NumpyFloatArray, xj: NumpyFloatArray) -> float:
+        """
+        Given two points, this method calculates the Minkowski distance.
+
+        :param xi: First point.
+        :param xj: Second point.
+        :return: A float representing the distance between the points.
+        """
         return pdist([xi, xj], "minkowski", p=self.p)[0]

src/UQpy/utilities/distances/euclidean_distances/__init__.py

@@ -4,20 +4,5 @@
 from UQpy.utilities.distances.euclidean_distances.CityBlockDistance import CityBlockDistance
 from UQpy.utilities.distances.euclidean_distances.CorrelationDistance import CorrelationDistance
 from UQpy.utilities.distances.euclidean_distances.CosineDistance import CosineDistance
-from UQpy.utilities.distances.euclidean_distances.DiceDistance import DiceDistance
-from UQpy.utilities.distances.euclidean_distances.L2Distance import EuclideanDistance
-from UQpy.utilities.distances.euclidean_distances.HammingDistance import HammingDistance
-from UQpy.utilities.distances.euclidean_distances.JaccardDistance import JaccardDistance
-from UQpy.utilities.distances.euclidean_distances.JensenShannonDistance import JensenShannonDistance
-from UQpy.utilities.distances.euclidean_distances.KulczynskiDistance import KulczynskiDistance
-from UQpy.utilities.distances.euclidean_distances.KulsinkiDistance import KulsinksiDistance
-from UQpy.utilities.distances.euclidean_distances.MahalanobisDistance import MahalanobisDistance
-from UQpy.utilities.distances.euclidean_distances.MatchingDistance import MatchingDistance
+from UQpy.utilities.distances.euclidean_distances.L2Distance import L2Distance
 from UQpy.utilities.distances.euclidean_distances.MinkowskiDistance import MinkowskiDistance
-from UQpy.utilities.distances.euclidean_distances.RogersTanimotoDistance import RogersTanimotoDistance
-from UQpy.utilities.distances.euclidean_distances.RussellRaoDistance import RussellRaoDistance
-from UQpy.utilities.distances.euclidean_distances.SokalMichenerDistance import SokalMichenerDistance
-from UQpy.utilities.distances.euclidean_distances.SokalSneathDistance import SokalSneathDistance
-from UQpy.utilities.distances.euclidean_distances.SquaredEuclideanDistance import SquaredEuclideanDistance
-from UQpy.utilities.distances.euclidean_distances.StandardizedEuclidean import StandardizedEuclideanDistance
-from UQpy.utilities.distances.euclidean_distances.YuleDistance import YuleDistance

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

@@ -12,7 +12,7 @@ class Kernel(ABC):
     """
     This is the baseclass for all kernels in :py:mod:`UQpy`.
 
-    This serves a blueprint to show the methods for kernels implemented in the :py:mod:`.kernels` module .
+    This serves as a blueprint to show the methods for kernels implemented in the :py:mod:`.kernels` module .
     """
     def __init__(self):
         self.kernel_matrix: np.ndarray = None