def test_json_phenotype(json):
# jname = '/home/aadeshnpn/Documents/BYU/hcmi/swarm/results/1543189989736115-62000EvoSForge/' + json # noqa : E501
jname = '/tmp/1543367322976111-8000EvoSForge/' + json
phenotype = JsonPhenotypeData.load_json_file(jname)['phenotypes']
# print (phenotype)
# phenotype = ' '
if validation_loop(phenotype, 2000):
print('foraging success')
def evolve(iteration):
"""Learning Algorithm block."""
# iteration = 10000
env = EvolModel(100,
100,
100,
10,
iter=iteration,
expname='NMComm',
agent='EvolAgent',
parm='swarm_comm.txt')
env.build_environment_from_json()
# for all agents store the information about hub
for agent in env.agents:
agent.shared_content['Hub'] = {env.hub}
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
env.experiment.update_experiment()
# Collecting phenotypes on the basis of debris collected
# Find if debris has been cleaned from the hub
debris_objects = env.debris_cleaned()
env.phenotypes = []
for debris in debris_objects:
print(debris.phenotype)
env.phenotypes += list(debris.phenotype.values())
jfilename = env.pname + '/' + env.runid + '.json'
JsonPhenotypeData.to_json(env.phenotypes, jfilename)
# Not using this method right now
# env.phenotypes = extract_phenotype(env.agents, jfilename)
# Plot the fitness in the graph
graph = Graph(env.pname, 'best.csv', ['explore', 'foraging'])
graph.gen_best_plots()
# Test the evolved behavior
return env
def evolve(iteration):
"""Learning Algorithm block."""
# iteration = 10000
env = EvolModelComm(100, 100, 100, 10, iter=iteration)
env.build_environment_from_json()
# for all agents store the information about hub
for agent in env.agents:
agent.shared_content['Hub'] = {env.hub}
# Hub and site object
# print(env.hub, env.site)
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
env.experiment.update_experiment()
# Collecting phenotypes on the basis of food collected
# Find if food has been deposited in the hub
food_objects = env.food_in_loc(env.hub.location)
# print('Total food in the hub evolution:', len(food_objects))
env.phenotypes = []
for food in food_objects:
print(food.phenotype)
env.phenotypes += list(food.phenotype.values())
jfilename = env.pname + '/' + env.runid + '.json'
JsonPhenotypeData.to_json(env.phenotypes, jfilename)
# Not using this method right now
# env.phenotypes = extract_phenotype(env.agents, jfilename)
# Plot the fitness in the graph
graph = Graph(env.pname, 'best.csv', ['explore', 'foraging'])
graph.gen_best_plots()
# Test the evolved behavior
return env
def main():
"""Parse args and call bt visualize module."""
# dname = '/home/aadeshnpn/Documents/BYU/hcmi/hri/supporting_materials/nest_maintenance' # noqa: E501
# jfname = dname + '/1539014820252.json'
# dname = '/home/aadeshnpn/Documents/BYU/hcmi/hri/supporting_materials/cooperative_transport' # noqa: E501
# jfname = dname + '/1538447335350.json'
dname = '/home/aadeshnpn/Documents/BYU/hcmi/hri/supporting_materials/foraging' # noqa: E501
jfname = dname + '/1538473090382007.json'
jdata = JsonPhenotypeData.load_json_file(jfname)
phenotypes = jdata['phenotypes']
for i in range(len(phenotypes)):
# if i >= 5:
# break
m = Model()
a = Agent('1', m)
bt = BTConstruct(None, a, phenotypes[i])
bt.construct()
bt.visualize(name=dname + '/' + str(i))
def phenotype_to_json(pname, runid, phenotypes):
"""Store the phenotype to a json file."""
jfilename = pname + '/' + runid + '.json'
JsonPhenotypeData.to_json(phenotypes, jfilename)
def simulate(env, iteration):
"""Test the performane of evolved behavior."""
# phenotype = agent.individual[0].phenotype
# phenotypes = extract_phenotype(agents)
phenotypes = JsonPhenotypeData.load_json_file(env[0])
# print (phenotypes)
if len(phenotypes['phenotypes']) < 1:
return
else:
phenotypes = phenotypes['phenotypes']
threshold = 1.0
sim = SimModel(
100, 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()
simresults = SimulationResults(
sim.pname, sim.connect, sim.sn, sim.stepcnt, sim.food_in_hub(),
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))
value = sim.food_in_hub()
foraging_percent = (
value * 100.0) / (sim.num_agents * 2.0)
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()