Adding some method docstrings and return types to the transition solv…

…er base class

src/elexsolver/TransitionSolver.py

@@ -26,7 +26,7 @@ def get_prediction_interval(self, pi: float):
         raise NotImplementedError
 
     @property
-    def transitions(self):
+    def transitions(self) -> np.ndarray:
         return self._transitions
 
     def _check_any_element_nan_or_inf(self, A: np.ndarray):
@@ -55,7 +55,7 @@ def _check_for_zero_units(self, A: np.ndarray):
         if np.any(np.sum(A, axis=1) == 0):
             raise ValueError("Matrix cannot contain any rows (units) where all columns (things) are zero.")
 
-    def _rescale(self, A: np.ndarray):
+    def _rescale(self, A: np.ndarray) -> np.ndarray:
         """
         Rescale rows (units) to ensure they sum to 1 (100%).
         """
@@ -67,7 +67,20 @@ def _rescale(self, A: np.ndarray):
 
         return np.nan_to_num(A, nan=0, posinf=0, neginf=0)
 
-    def _check_and_prepare_weights(self, X: np.ndarray, Y: np.ndarray, weights: np.ndarray | None):
+    def _check_and_prepare_weights(self, X: np.ndarray, Y: np.ndarray, weights: np.ndarray | None) -> np.ndarray:
+        """
+        If `weights` is not None, and `weights` has the same number of rows in both matrices `X` and `Y`,
+        we'll rescale the weights by taking the square root after dividing them by their sum,
+        then return a diagonal matrix containing these now-normalized weights.
+        If `weights` is None, return a diagonal matrix of ones.
+
+        Parameters
+        ----------
+        `X` : np.ndarray matrix of int (same number of rows as `Y`)
+        `Y` : np.ndarray matrix of int (same number of rows as `X`)
+        `weights` : np.ndarray of int of the shape (number of rows in `X` and `Y`, 1), optional
+        """
+
         if weights is not None:
             if len(weights) != X.shape[0] and len(weights) != Y.shape[0]:
                 raise ValueError("weights must be the same length as the number of rows in X and Y.")