Alexander/ants
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

add viviane's bfs implementation

Browse Source
This commit is contained in:
Alexander Bocken 2023-06-27 22:24:40 +02:00
parent 9fae37347b
commit ef7646f556
Signed by: Alexander
GPG Key ID: 1D237BE83F9B05E8
2 changed files with 83 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

53
main.py
View File

@ -172,6 +172,59 @@ def check_ants_follow_gradient():
print(20*"#")
model.step()
def viviane_bfs_example_run():
# Breadth-first-search algorithm for connectivity
def bfs(graph, start_node, threshold): #graph=grid, start_node=nest, threshold=TBD?
from collections import deque
visited = set()
queue = deque([(start_node, [])])
paths = {}
connected_food_sources = set()
while queue:
current_node, path = queue.popleft()
#current_node = tuple(current_node)
visited.add(current_node)
if current_node in graph:
for neighbor, m.grid.fields["A"] in graph[current_node].items():
if neighbor not in visited and m.grid.fields["A"] >= threshold:
new_path = path + [neighbor]
queue.append((neighbor, new_path))
# Check if the neighbor is a food source
if neighbor in self.grid_food:
if neighbor not in paths:
paths[neighbor] = new_path
connected_food_sources.add(neighbor)
connectivity = len(connected_food_sources)
return connectivity
# Calculate connectivity through BFS
current_paths = bfs(self.grid, self.grid.fields["nests"], 0.000001)
import numpy as np
N = 121
N_X = int(np.sqrt(N))
N_Y = N // N_X
# fancy way of saying absolutely nothing but 11
xv, yv = np.meshgrid(np.arange(N_X), np.arange(N_Y), sparse=False, indexing='xy')
print(f"{N_X=}")
print(f"{N_Y=}")
print(f"{(xv, yv)=}")
print(f"{xv=}")
from model import kwargs_paper_setup1 as kwargs
if __name__ == "__main__":

57
model.py
View File

@ -15,7 +15,6 @@ from multihex import MultiHexGridScalarFields
from mesa.time import SimultaneousActivation
from mesa.datacollection import DataCollector
from agent import RandomWalkerAnt
from collections import deque
kwargs_paper_setup1 = {
"width": 100,
@ -101,6 +100,7 @@ class ActiveWalkerModel(Model):
self.e_min : float = e_min # energy at which walker dies
self.N_f : int = N_f #num food sources
self.successful_ants = 0 # for viviane's graph
self.connectivity = 0 # for viviane's persistence
fields=["A", "B", "nests", "food", "res"]
self.schedule = SimultaneousActivation(self)
@ -141,39 +141,42 @@ class ActiveWalkerModel(Model):
# Breadth-first-search algorithm for connectivity
#def bfs(graph, start_node, threshold): #graph=grid, start_node=nest, threshold=TBD?
# visited = set()
# queue = deque([(start_node, [])])
# paths = {}
# connected_food_sources = set()
# 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
# while queue:
# current_node, path = queue.popleft()
#current_node = tuple(current_node)
# visited.add(current_node)
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
# if current_node in graph:
# for neighbor, m.grid.fields["A"] in graph[current_node].items():
# if neighbor not in visited and m.grid.fields["A"] >= threshold:
# new_path = path + [neighbor]
# queue.append((neighbor, new_path))
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
# Check if the neighbor is a food source
# if neighbor in self.grid_food:
# if neighbor not in paths:
# paths[neighbor] = new_path
# connected_food_sources.add(neighbor)
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
# connectivity = len(connected_food_sources)
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
# return connectivity
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
# Calculate connectivity through BFS
# current_paths = bfs(self.grid, self.grid.fields["nests"], 0.000001)
return connectivity #we want the connectivity (0-5)
self.connectivity = bfs(self)
self.datacollector = DataCollector(
@ -182,7 +185,7 @@ class ActiveWalkerModel(Model):
"pheromone_b": lambda m: m.grid.fields["B"],
"alive_ants": lambda m: m.schedule.get_agent_count(),
"sucessful_walkers": lambda m: m.successful_ants,
#"connectivity": lambda m: check_food_source_connectivity(self.grid_food,current_paths),
"connectivity": lambda m: m.connectivity,
},
agent_reporters={}
)