chore(tests): standardize mixture and parameter tests got dtype

Author: xImoZA

rework_pysatl_mpest/core/mixture.py

@@ -134,6 +134,7 @@ def _validate_weights(self, n_components: int, weights: NDArray[DType]):
     @property
     def dtype(self) -> type[DType]:
         """type[DType]: The numpy data type of the mixture's outputs."""
+
         return self._dtype
 
     @property
@@ -396,7 +397,8 @@ def _get_sorted_pairs(self, for_hashing: bool = False) -> list[tuple["Continuous
         if self._sorted_pairs_cache is None or for_hashing:
             weights_to_use = self.weights
             if for_hashing:
-                weights_to_use = np.round(weights_to_use, 8)
+                decimals = np.finfo(self.dtype).precision
+                weights_to_use = np.round(weights_to_use, decimals)
 
             pairs = sorted(zip(self.components, weights_to_use), key=lambda p: hash(p[0]))
             if not for_hashing:

rework_pysatl_mpest/distributions/uniform.py

@@ -97,7 +97,7 @@ def pdf(self, X):
 
         Returns
         -------
-        NDArray[np.float64]
+        NDArray[DType]
             The PDF values corresponding to each point in :attr:`X`.
         """
         X = np.asarray(X, dtype=self.dtype)
@@ -128,7 +128,7 @@ def ppf(self, P):
 
         Returns
         -------
-        NDArray[np.float64]
+        NDArray[DType]
             The PPF values corresponding to each probability in :attr:`P`.
         """
         P = np.asarray(P, dtype=self.dtype)
@@ -158,7 +158,7 @@ def lpdf(self, X):
 
         Returns
         -------
-        NDArray[np.float64]
+        NDArray[DType]
             The log-PDF values corresponding to each point in :attr:`X`.
         """
         X = np.asarray(X, dtype=self.dtype)
@@ -214,7 +214,7 @@ def log_gradients(self, X):
 
         Returns
         -------
-        NDArray[np.float64]
+        NDArray[DType]
             An array where each row corresponds to a data point in :attr:`X`
             and each column corresponds to the gradient with respect to a
             specific optimizable parameter. The order of columns corresponds
@@ -247,7 +247,7 @@ def generate(self, size: int):
 
         Returns
         -------
-        NDArray[np.float64]
+        NDArray[DType]
             A NumPy array containing the generated samples.
         """