Remove private function _accept_greedy

src/tranquilo/acceptance_decision.py

@@ -60,15 +60,52 @@ def accept_greedy(
         AcceptanceResult
 
     """
-    out = _accept_greedy(
-        subproblem_solution=subproblem_solution,
-        state=state,
-        history=history,
-        wrapped_criterion=wrapped_criterion,
-        min_improvement=min_improvement,
-        n_evals=1,
+    candidate_x = subproblem_solution.x
+    candidate_index = history.add_xs(candidate_x)
+    wrapped_criterion({candidate_index: 1})
+
+    candidate_fval = np.mean(history.get_fvals(candidate_index))
+    actual_improvement = -(candidate_fval - state.fval)
+
+    rho = calculate_rho(
+        actual_improvement=actual_improvement,
+        expected_improvement=subproblem_solution.expected_improvement,
     )
-    return out
+
+    best_x, best_fval, best_index = history.get_best()
+
+    if best_fval < candidate_fval:
+        candidate_x = best_x
+        candidate_fval = best_fval
+        candidate_index = best_index
+        overall_improvement = -(candidate_fval - state.fval)
+    else:
+        overall_improvement = actual_improvement
+
+    is_accepted = overall_improvement >= min_improvement
+
+    if np.isfinite(candidate_fval):
+        res = _get_acceptance_result(
+            candidate_x=candidate_x,
+            candidate_fval=candidate_fval,
+            candidate_index=candidate_index,
+            rho=rho,
+            is_accepted=is_accepted,
+            old_state=state,
+            n_evals=1,
+        )
+    else:
+        res = _get_acceptance_result(
+            candidate_x=state.x,
+            candidate_fval=state.fval,
+            candidate_index=state.index,
+            rho=-np.inf,
+            is_accepted=False,
+            old_state=state,
+            n_evals=1,
+        )
+
+    return res
 
 
 def _accept_classic(
@@ -272,76 +309,6 @@ def accept_classic_line_search(
     return res
 
 
-def _accept_greedy(
-    subproblem_solution,
-    state,
-    history,
-    *,
-    wrapped_criterion,
-    min_improvement,
-    n_evals,
-):
-    """Do a simple greedy acceptance step for a trustregion algorithm.
-
-    Args:
-        subproblem_solution (SubproblemResult): Result of the subproblem solution.
-        state (State): Namedtuple containing the trustregion, criterion value of
-            previously accepted point, indices of model points, etc.
-        wrapped_criterion (callable): The criterion function.
-        min_improvement (float): Minimum improvement required to accept a point.
-
-    Returns:
-        AcceptanceResult
-
-    """
-    candidate_x = subproblem_solution.x
-    candidate_index = history.add_xs(candidate_x)
-    wrapped_criterion({candidate_index: n_evals})
-
-    candidate_fval = np.mean(history.get_fvals(candidate_index))
-    actual_improvement = -(candidate_fval - state.fval)
-
-    rho = calculate_rho(
-        actual_improvement=actual_improvement,
-        expected_improvement=subproblem_solution.expected_improvement,
-    )
-
-    best_x, best_fval, best_index = history.get_best()
-
-    if best_fval < candidate_fval:
-        candidate_x = best_x
-        candidate_fval = best_fval
-        candidate_index = best_index
-        overall_improvement = -(candidate_fval - state.fval)
-    else:
-        overall_improvement = actual_improvement
-
-    is_accepted = overall_improvement >= min_improvement
-
-    if np.isfinite(candidate_fval):
-        res = _get_acceptance_result(
-            candidate_x=candidate_x,
-            candidate_fval=candidate_fval,
-            candidate_index=candidate_index,
-            rho=rho,
-            is_accepted=is_accepted,
-            old_state=state,
-            n_evals=n_evals,
-        )
-    else:
-        res = _get_acceptance_result(
-            candidate_x=state.x,
-            candidate_fval=state.fval,
-            candidate_index=state.index,
-            rho=-np.inf,
-            is_accepted=False,
-            old_state=state,
-            n_evals=n_evals,
-        )
-
-    return res
-
-
 def _accept_simple(
     subproblem_solution,
     state,