add a parameter to the sampling module to allow user defining the thr…

…eshold for slice sampling

grainlearning/sampling.py

@@ -85,6 +85,8 @@ class GaussianMixtureModel:
         This can speed up convergence when fit is called several times on similar problems. See the Glossary.
     :param expand_factor: factor used when converting the ensemble from weighted to unweighted, defaults to 10, optional
     :param slice_sampling: flag to use slice sampling, defaults to False, optional
+    :param deviation_factor: factor used to determine the threshold,
+        scores.mean() - deviation_factor * scores.std(), for slice sampling, defaults to 0.0, optional
     :param gmm: The class of the Gaussian Mixture Model
     :param max_params: Current maximum values of the parameters
     """
@@ -102,6 +104,7 @@ def __init__(
         warm_start: bool = False,
         expand_factor: int = 10,
         slice_sampling: bool = False,
+        deviation_factor: float = 0.0,
     ):
         """ Initialize the Gaussian Mixture Model class"""
         self.max_num_components = max_num_components
@@ -129,6 +132,8 @@ def __init__(
 
         self.slice_sampling = slice_sampling
 
+        self.deviation_factor = deviation_factor
+
         self.max_params = None
 
         self.expanded_normalized_params = None
@@ -154,6 +159,7 @@ def from_dict(cls: Type["GaussianMixtureModel"], obj: dict):
             warm_start=obj.get("warm_start", False),
             expand_factor=obj.get("expand_factor", 10),
             slice_sampling=obj.get("slice_sampling", False),
+            deviation_factor=obj.get("deviation_factor", 0.0),
         )
 
     def expand_and_normalize_weighted_samples(self, weights: np.ndarray, system: Type["DynamicSystem"]):
@@ -245,7 +251,7 @@ def draw_samples_within_bounds(self, system: Type["DynamicSystem"], num: int = 1
 
             scores = self.gmm.score_samples(self.expanded_normalized_params)
             new_params = new_params[np.where(
-                self.gmm.score_samples(new_params) > scores.mean())]
+                self.gmm.score_samples(new_params) > scores.mean() - self.deviation_factor * scores.std())]
 
         new_params *= self.max_params