From ef7646f556814db6567690ad9c96cdbd905df9c7 Mon Sep 17 00:00:00 2001 From: AlexBocken Date: Tue, 27 Jun 2023 22:24:40 +0200 Subject: [PATCH] add viviane's bfs implementation --- main.py | 53 ++++++++++++++++++++++++++++++++++++++++++++++++++++ model.py | 57 +++++++++++++++++++++++++++++--------------------------- 2 files changed, 83 insertions(+), 27 deletions(-) diff --git a/main.py b/main.py index 86a1875..db82f3c 100755 --- a/main.py +++ b/main.py @@ -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__": diff --git a/model.py b/model.py index c858b1c..4510db8 100644 --- a/model.py +++ b/model.py @@ -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={} )