def __init__(self, basename, use_locks=False):
"""
All data will be stored under directory `basename`. If there is a server
there already, it will be loaded (resumed).
The server object is stateless in RAM -- its state is defined entirely by its location.
There is therefore no need to store the server object.
"""
if not os.path.isdir(basename):
raise ValueError("%r must be a writable directory" % basename)
self.basename = basename
self.use_locks = use_locks
self.lock_update = threading.RLock() if use_locks else gensim.utils.nocm
try:
self.fresh_index = SimIndex.load(self.location('index_fresh'))
except:
logger.debug("starting a new fresh index")
self.fresh_index = None
try:
self.opt_index = SimIndex.load(self.location('index_opt'))
except:
logger.debug("starting a new optimized index")
self.opt_index = None
try:
self.model = SimModel.load(self.location('model'))
except:
self.model = None
self.payload = SqliteDict(self.location('payload'), autocommit=True, journal_mode=JOURNAL_MODE)
self.flush(save_index=False, save_model=False, clear_buffer=True)
logger.info("loaded %s" % self)
def simulate(env, iteration, N=150):
"""Test the performane of evolved behavior."""
# phenotype = agent.individual[0].phenotype
# phenotypes = extract_phenotype(agents)
phenotypes = env[0]
threshold = 1.0
sim = SimModel(N,
100,
100,
10,
iter=iteration,
xmlstrings=phenotypes,
pname=env[1])
sim.build_environment_from_json()
# for all agents store the information about hub
for agent in sim.agents:
agent.shared_content['Hub'] = {sim.hub}
# agent.shared_content['Sites'] = {sim.site}
simresults = SimulationResults(sim.pname, sim.connect, sim.sn, sim.stepcnt,
sim.food_in_hub(), phenotypes[0])
simresults.save_phenotype()
simresults.save_to_file()
# Iterate and execute each step in the environment
for i in range(iteration):
# For every iteration we need to store the results
# Save them into db or a file
sim.step()
value = sim.food_in_hub()
foraging_percent = (value * 100.0) / (sim.num_agents * 1.0)
simresults = SimulationResults(sim.pname, sim.connect, sim.sn,
sim.stepcnt, foraging_percent,
phenotypes[0])
simresults.save_to_file()
# print ('food at site', len(sim.food_in_loc(sim.site.location)))
# print ('food at hub', len(sim.food_in_loc(sim.hub.location)))
# print("Total food in the hub", len(food_objects))
# food_objects = sim.food_in_loc(sim.hub.location)
# for food in food_objects:
# print('simulate phenotye:', dir(food))
sucess = False
print('Foraging percent', value)
if foraging_percent >= threshold:
print('Foraging success')
sucess = True
sim.experiment.update_experiment_simulation(value, sucess)
# Plot the fitness in the graph
graph = GraphACC(sim.pname, 'simulation.csv')
graph.gen_plot()
from simmodel import SimModel sim = SimModel() sim.main()
from simmodel import SimModel
import matplotlib.pyplot as plt
elements = 2
timesList = []
elList = []
while elements <= 50:
sim = SimModel()
time = sim.main(elements)
elList.append(elements)
elements += 1
timesList.append(time)
print(timesList)
print(elList)
timesArr = [i['time'] for i in timesList]
plt.plot(timesArr)
plt.ylabel('Execution time')
plt.show()