modify bfs connectivity
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c4ee305256
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30
model.py
30
model.py
@ -156,15 +156,14 @@ class ActiveWalkerModel(Model):
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def bfs(self):
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threshold = 0.0000001 #the value of A
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connectivity = 0 #initial value of connectivity
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connected_food_sources = list() #empty list of connected food sources
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visited = list() #empty list of visited (by the algorithm) nodes
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connected_food_sources = [] #empty list of connected food sources
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visited = [] #empty list of visited (by the algorithm) nodes
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nest = np.argwhere(self.grid.fields["nests"] == 1) #get nest location
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nest = nest[0].tolist() #transforming not to have type errors
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nest = tuple(nest) #transforming not to have type errors
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nest = (nest[0][0], nest[0][1])
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start_node = nest #rename
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neighbours_to_check = list([start_node]) #start node gets checked first
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neighbours_to_check = [start_node] #start node gets checked first
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neighbours_to_check = neighbours_to_check + self.grid.get_neighborhood(start_node) #start node neighbours get added to the to check list
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while neighbours_to_check: #as long as there is something on the to check list
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@ -172,19 +171,19 @@ class ActiveWalkerModel(Model):
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del neighbours_to_check[0] #and deleted on the to check list
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if current_node not in visited: #if it has not previously been checked
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if self.grid.fields["A"][current_node] >= threshold: #and its A value is above our threshold
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if np.max([self.grid.fields["B"][current_node], self.grid.fields["A"][current_node]]) >= threshold: #and its A value is above our threshold
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new_neighbors = self.grid.get_neighborhood(current_node) #then we get its neighbours
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if new_neighbors not in visited: #if they have not yet been visited
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neighbours_to_check = neighbours_to_check + new_neighbors #then they are also added to our to check list
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visited = visited + list([current_node]) #and the current node has now been checked
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for new_neighbor in new_neighbors:
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if new_neighbor not in visited and new_neighbor not in neighbours_to_check:
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neighbours_to_check.append(new_neighbor) #then they are also added to our to check list
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visited.append(current_node) #and the current node has now been checked
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neighbours_to_check = list(dict.fromkeys(neighbours_to_check)) #only check nodes once (unique values)
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if self.grid.fields["food"][current_node] > 0: #in case the node we check is food
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connectivity += 1 #then we have found a connected path to a food source
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connected_food_sources = connected_food_sources + list([current_node]) #and it is added to the list of connected food sources
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if self.grid.fields["food"][current_node] > 0: #in case the node we check is food
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connectivity += 1 #then we have found a connected path to a food source
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connected_food_sources = connected_food_sources + list([current_node]) #and it is added to the list of connected food sources
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# why not normalize to 0-1 ?
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print(f"{connectivity=}")
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return connectivity #we want the connectivity (0-5)
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def agent_density(self):
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@ -197,7 +196,8 @@ class ActiveWalkerModel(Model):
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def step(self):
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self.schedule.step() # step() and advance() all agents
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self.connectivity = self.bfs(self)
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if self.schedule.steps % 100 == 0:
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self.connectivity = self.bfs()
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# apply decay rate on pheromone levels
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for key in ("A", "B"):
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