refactoring + is_stochastic function for continous time models

stormvogel/model.py

@@ -79,8 +79,8 @@ def __init__(
                 "There is already a state with this id. Make sure the id is unique."
             )
 
-        names = [state.name for state in self.model.states.values()]
-        if name in names:
+        used_names = [state.name for state in self.model.states.values()]
+        if name in used_names:
             raise RuntimeError(
                 "There is already a state with this name. Make sure the name is unique."
             )
@@ -162,16 +162,16 @@ def __str__(self):
     def __eq__(self, other):
         if isinstance(other, State):
             if self.id == other.id:
-                self.labels.sort()
-                other.labels.sort()
                 if self.model.supports_observations():
                     if self.observation is not None and other.observation is not None:
                         observations_equal = self.observation == other.observation
                     else:
                         observations_equal = True
                 else:
                     observations_equal = True
-                return self.labels == other.labels and observations_equal
+                return (
+                    sorted(self.labels) == sorted(other.labels) and observations_equal
+                )
             return False
         return False
 
@@ -222,9 +222,7 @@ def __str__(self):
 
     def __eq__(self, other):
         if isinstance(other, Branch):
-            self.branch.sort()
-            other.branch.sort()
-            return self.branch == other.branch
+            return sorted(self.branch) == sorted(other.branch)
         return False
 
     def __add__(self, other):
@@ -261,11 +259,9 @@ def __str__(self):
 
     def __eq__(self, other):
         if isinstance(other, Transition):
-            self_values = list(self.transition.values())
-            other_values = list(other.transition.values())
-            self_values.sort()
-            other_values.sort()
-            return self_values == other_values
+            return sorted(list(self.transition.values())) == sorted(
+                list(other.transition.values())
+            )
         return False
 
     def has_empty_action(self) -> bool:
@@ -309,7 +305,6 @@ def transition_from_shorthand(shorthand: TransitionShorthand) -> Transition:
 @dataclass(order=True)
 class RewardModel:
     """Represents a state-exit reward model.
-    dtmc.delete_state(dtmc.get_state_by_id(1), True, True)
     Args:
         name: Name of the reward model.
         rewards: The rewards, the keys are the state's ids (or state action pair ids).
@@ -340,10 +335,10 @@ class Model:
         name: An optional name for this model.
         type: The model type.
         states: The states of the model. The keys are the state's ids.
+        transitions: The transitions of this model.
         actions: The actions of the model, if this is a model that supports actions.
         rewards: The rewardsmodels of this model.
         exit_rates: The exit rates of the model, optional if this model supports rates.
-        transitions: The transitions of this model.
         markovian_states: list of markovian states in the case of a ma.
     """
 
@@ -392,7 +387,7 @@ def __init__(
         else:
             self.markovian_states = None
 
-        # Add the initial state
+        # Add the initial state if specified to do so
         if create_initial_state:
             self.new_state(["init"])
 
@@ -408,10 +403,12 @@ def supports_observations(self):
         """Returns whether this model supports observations."""
         return self.type == ModelType.POMDP
 
-    def is_well_defined(self) -> bool:
-        """Checks if all sums of outgoing transition probabilities for all states equal 1"""
+    def is_stochastic(self) -> bool:
+        """For discrete models: Checks if all sums of outgoing transition probabilities for all states equal 1
+        For continuous models: Checks if all sums of outgoing rates sum to 0
+        """
 
-        if self.get_type() in (ModelType.DTMC, ModelType.MDP, ModelType.POMDP):
+        if not self.supports_rates():
             for state in self.states.values():
                 for action in state.available_actions():
                     sum_prob = 0
@@ -424,12 +421,18 @@ def is_well_defined(self) -> bool:
                             sum_prob += transition[0]
                     if sum_prob != 1:
                         return False
-        elif self.get_type() in (
-            ModelType.CTMC,
-            ModelType.MA,
-        ):
-            # TODO make it work for these models
-            raise RuntimeError("Not implemented")
+        else:
+            for state in self.states.values():
+                sum_rates = 0
+                for transition in state.get_outgoing_transitions():
+                    if (
+                        isinstance(transition[0], float)
+                        or isinstance(transition[0], Fraction)
+                        or isinstance(transition[0], int)
+                    ):
+                        sum_rates += transition[0]
+                if sum_rates != 0:
+                    return False
 
         return True
 

tests/test_model_methods.py

@@ -79,26 +79,26 @@ def test_transition_from_shorthand():
     )
 
 
-def test_is_well_defined():
-    # we check for an instance where it is not well defined
+def test_is_stochastic():
+    # we check for an instance where it is not stochastic
     dtmc = stormvogel.model.new_dtmc()
     state = dtmc.new_state()
     dtmc.set_transitions(
         dtmc.get_initial_state(),
         [(1 / 2, state)],
     )
 
-    assert not dtmc.is_well_defined()
+    assert not dtmc.is_stochastic()
 
-    # we check for an instance where it is well defined
+    # we check for an instance where it is stochastic
     dtmc.set_transitions(
         dtmc.get_initial_state(),
         [(1 / 2, state), (1 / 2, state)],
     )
 
     dtmc.add_self_loops()
 
-    assert dtmc.is_well_defined()
+    assert dtmc.is_stochastic()
 
 
 def test_normalize():