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fix indents, add bfs to scheduler

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Alexander Bocken 2023-06-27 22:45:40 +02:00
parent ef7646f556
commit 7b5627fafc
Signed by: Alexander
GPG Key ID: 1D237BE83F9B05E8
1 changed files with 38 additions and 45 deletions
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83
model.py
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@ -107,7 +107,6 @@ class ActiveWalkerModel(Model):
self.grid = MultiHexGridScalarFields(width=width, height=height, torus=True, fields=fields)
if resistance_map_type is None:
print("No resistance field")
self.grid.fields["res"] = np.ones((width, height)).astype(float)
elif resistance_map_type == "perlin":
# perlin generates anisotropic noise which may or may not be a good choice
@ -139,46 +138,6 @@ class ActiveWalkerModel(Model):
for _ in range(N_f):
self.grid.add_food(food_size)
# Breadth-first-search algorithm for connectivity
# TODO: Implement pheromone B (take max of the two or sum?)
# alex: what's to say against max?
def bfs(self):
threshold = 0.0000001 #the value of A
connectivity = 0 #initial value of connectivity
connected_food_sources = list() #empty list of connected food sources
visited = list() #empty list of visited (by the algorithm) nodes
nest = np.argwhere(self.grid.fields["nests"] == 1) #get nest location
nest = nest[0].tolist() #transforming not to have type errors
nest = tuple(nest) #transforming not to have type errors
start_node = nest #rename
neighbours_to_check = list([start_node]) #start node gets checked first
neighbours_to_check = neighbours_to_check + self.grid.get_neighborhood(start_node) #start node neighbours get added to the to check list
while neighbours_to_check: #as long as there is something on the to check list
current_node = neighbours_to_check[0] #the first list entry is taken
del neighbours_to_check[0] #and deleted on the to check list
if current_node not in visited: #if it has not previously been checked
if self.grid.fields["A"][current_node] >= threshold: #and its A value is above our threshold
new_neighbors = self.grid.get_neighborhood(current_node) #then we get its neighbours
if new_neighbors not in visited: #if they have not yet been visited
neighbours_to_check = neighbours_to_check + new_neighbors #then they are also added to our to check list
visited = visited + list([current_node]) #and the current node has now been checked
neighbours_to_check = list(dict.fromkeys(neighbours_to_check)) #only check nodes once (unique values)
if self.grid.fields["food"][current_node] > 0: #in case the node we check is food
connectivity += 1 #then we have found a connected path to a food source
connected_food_sources = connected_food_sources + list([current_node]) #and it is added to the list of connected food sources
return connectivity #we want the connectivity (0-5)
self.connectivity = bfs(self)
self.datacollector = DataCollector(
# model_reporters={"agent_dens": lambda m: m.agent_density()},
model_reporters = {"pheromone_a": lambda m: m.grid.fields["A"],
@ -191,10 +150,42 @@ class ActiveWalkerModel(Model):
)
self.datacollector.collect(self) # keep at end of __init___
#def subset_agent_count(self):
# subset_agents = [agent for agent in self.schedule.agents if agent.sensitivity == self.s_0]
# count = float(len(subset_agents))
# return count
# Breadth-first-search algorithm for connectivity
# TODO: Implement pheromone B (take max of the two or sum?)
# alex: what's to say against max?
def bfs(self):
threshold = 0.0000001 #the value of A
connectivity = 0 #initial value of connectivity
connected_food_sources = list() #empty list of connected food sources
visited = list() #empty list of visited (by the algorithm) nodes
nest = np.argwhere(self.grid.fields["nests"] == 1) #get nest location
nest = nest[0].tolist() #transforming not to have type errors
nest = tuple(nest) #transforming not to have type errors
start_node = nest #rename
neighbours_to_check = list([start_node]) #start node gets checked first
neighbours_to_check = neighbours_to_check + self.grid.get_neighborhood(start_node) #start node neighbours get added to the to check list
while neighbours_to_check: #as long as there is something on the to check list
current_node = neighbours_to_check[0] #the first list entry is taken
del neighbours_to_check[0] #and deleted on the to check list
if current_node not in visited: #if it has not previously been checked
if self.grid.fields["A"][current_node] >= threshold: #and its A value is above our threshold
new_neighbors = self.grid.get_neighborhood(current_node) #then we get its neighbours
if new_neighbors not in visited: #if they have not yet been visited
neighbours_to_check = neighbours_to_check + new_neighbors #then they are also added to our to check list
visited = visited + list([current_node]) #and the current node has now been checked
neighbours_to_check = list(dict.fromkeys(neighbours_to_check)) #only check nodes once (unique values)
if self.grid.fields["food"][current_node] > 0: #in case the node we check is food
connectivity += 1 #then we have found a connected path to a food source
connected_food_sources = connected_food_sources + list([current_node]) #and it is added to the list of connected food sources
# why not normalize to 0-1 ?
return connectivity #we want the connectivity (0-5)
def agent_density(self):
a = np.zeros((self.grid.width, self.grid.height))
@ -206,12 +197,14 @@ class ActiveWalkerModel(Model):
def step(self):
self.schedule.step() # step() and advance() all agents
self.connectivity = self.bfs(self)
# apply decay rate on pheromone levels
for key in ("A", "B"):
field = self.grid.fields[key]
self.grid.fields[key] = field - self.gamma*field
self.datacollector.collect(self)
if self.schedule.steps >= self.max_steps: