ordering

Author: hasnainroopawalla

aco_routing/aco.py

@@ -32,6 +32,31 @@ def __post_init__(self):
         for edge in self.graph.edges:
             self.graph_api.set_edge_pheromones(edge[0], edge[1], 1.0)
 
+    def find_shortest_path(
+        self,
+        source: str,
+        destination: str,
+        num_ants: int,
+    ) -> Tuple[List[str], float]:
+        """Finds the shortest path from the source to the destination in the graph
+
+        Args:
+            source (str): The source node in the graph
+            destination (str): The destination node in the graph
+            num_ants (int): The number of search ants to be deployed
+
+        Returns:
+            List[str]: The shortest path found by the ants
+            float: The cost of the computed shortest path
+        """
+        self._deploy_search_ants(
+            source,
+            destination,
+            num_ants,
+        )
+        solution_ant = self._deploy_solution_ant(source, destination)
+        return solution_ant.path, solution_ant.path_cost
+
     def _deploy_forward_search_ants(self) -> None:
         """Deploy forward search ants in the graph"""
         for ant in self.search_ants:
@@ -101,28 +126,3 @@ def _deploy_solution_ant(self, source: str, destination: str) -> Ant:
         while not ant.reached_destination():
             ant.take_step()
         return ant
-
-    def find_shortest_path(
-        self,
-        source: str,
-        destination: str,
-        num_ants: int,
-    ) -> Tuple[List[str], float]:
-        """Finds the shortest path from the source to the destination in the graph
-
-        Args:
-            source (str): The source node in the graph
-            destination (str): The destination node in the graph
-            num_ants (int): The number of search ants to be deployed
-
-        Returns:
-            List[str]: The shortest path found by the ants
-            float: The cost of the computed shortest path
-        """
-        self._deploy_search_ants(
-            source,
-            destination,
-            num_ants,
-        )
-        solution_ant = self._deploy_solution_ant(source, destination)
-        return solution_ant.path, solution_ant.path_cost

aco_routing/ant.py

@@ -38,6 +38,30 @@ def reached_destination(self) -> bool:
         """
         return self.current_node == self.destination
 
+    def take_step(self) -> None:
+        """Compute and update the ant position"""
+        # Mark the current node as visited
+        self.visited_nodes.add(self.current_node)
+
+        # Pick the next node of the ant
+        next_node = self._choose_next_node()
+
+        # Check if ant is stuck at current node
+        if not next_node:
+            # TODO: optimization: set ant as unfit
+            return
+
+        self.path.append(next_node)
+        self.path_cost += self.graph_api.get_edge_cost(self.current_node, next_node)
+        self.current_node = next_node
+
+    def deposit_pheromones_on_path(self) -> None:
+        """Updates the pheromones along all the edges in the path"""
+        for i in range(len(self.path) - 1):
+            u, v = self.path[i], self.path[i + 1]
+            new_pheromone_value = 1 / self.path_cost
+            self.graph_api.deposit_pheromones(u, v, new_pheromone_value)
+
     def _get_unvisited_neighbors(self) -> List[str]:
         """Returns a subset of the neighbors of the node which are unvisited
 
@@ -134,27 +158,3 @@ def _choose_next_node(self) -> Union[str, None]:
 
         # Pick the next node based on the roulette wheel selection technique
         return utils.roulette_wheel_selection(probabilities)
-
-    def take_step(self) -> None:
-        """Compute and update the ant position"""
-        # Mark the current node as visited
-        self.visited_nodes.add(self.current_node)
-
-        # Pick the next node of the ant
-        next_node = self._choose_next_node()
-
-        # Check if ant is stuck at current node
-        if not next_node:
-            # TODO: optimization: set ant as unfit
-            return
-
-        self.path.append(next_node)
-        self.path_cost += self.graph_api.get_edge_cost(self.current_node, next_node)
-        self.current_node = next_node
-
-    def deposit_pheromones_on_path(self) -> None:
-        """Updates the pheromones along all the edges in the path"""
-        for i in range(len(self.path) - 1):
-            u, v = self.path[i], self.path[i + 1]
-            new_pheromone_value = 1 / self.path_cost
-            self.graph_api.deposit_pheromones(u, v, new_pheromone_value)