Remove sum_to_unity

Author: hyanwong

tsdate/base.py

@@ -134,19 +134,6 @@ def force_probability_space(self, probability_space):
         else:
             logging.warning("Cannot force", *descr)
 
-    def sum_to_unity(self, arr):
-        """
-        Return an array for a node in which the (untransformed) values
-        sum to unity
-        """
-        if self.probability_space == LIN:
-            return arr / np.sum(arr)
-        elif self.probability_space == LOG:
-            with np.errstate(divide="ignore"):
-                return np.log(np.exp(arr) / np.sum(np.exp(arr)))
-        else:
-            raise RuntimeError("Probability space is not", LIN, "or", LOG)
-
     def normalize(self):
         """
         normalize grid and fixed data so the max is one
@@ -229,8 +216,7 @@ def fill_fixed(orig, fixed_data):
             new_obj.fixed_data = fill_fixed(
                 self, grid_data if fixed_data is None else fixed_data
             )
-        if probability_space is None:
-            new_obj.probability_space = self.probability_space
-        else:
-            new_obj.probability_space = probability_space
+        new_obj.probability_space = self.probability_space
+        if probability_space is not None:
+            new_obj.force_probability_space(probability_space)
         return new_obj

tsdate/core.py

@@ -652,9 +652,9 @@ def inside_pass(self, *, normalize=True, cache_inside=False, progress=None):
         nonfixed_samples = np.intersect1d(inside.nonfixed_node_ids(), self.ts.samples())
         for u in nonfixed_samples:
             # this is in the same probability space as the prior, so we should be
-            # OK just to copy the prior values straight in (but we should check they
-            # are normalised so that they sum to unity)
-            inside[u][:] = self.priors.sum_to_unity(self.priors[u])
+            # OK just to copy the prior values straight in. It's unclear to me (Yan)
+            # how/if they should be normalised, however
+            inside[u][:] = self.priors[u]
 
         if cache_inside:
             g_i = np.full(