class SenseHubSiteSwarmEnvironmentModel(Model):
""" A environemnt to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(SenseHubSiteSwarmEnvironmentModel, self).__init__(seed=None)
else:
super(SenseHubSiteSwarmEnvironmentModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.target = Sites(id=1, location=(10, 10), radius=11, q_value=0.5)
self.grid.add_object_to_grid(self.target.location, self.target)
self.hub = Hub(id=1, location=(-45, -45), radius=11)
self.grid.add_object_to_grid(self.hub.location, self.hub)
for i in range(self.num_agents):
a = SwarmAgentSenseHubSite(i, self)
self.schedule.add(a)
x = 45
y = 45
a.location = (x, y)
a.direction = -2.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agent = a
def step(self):
self.schedule.step()
class GEEnvironmentModel(Model):
""" A environemnt to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(GEEnvironmentModel, self).__init__(seed=None)
else:
super(GEEnvironmentModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.agents = []
for i in range(self.num_agents):
a = GEBTAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = self.random.randint(-self.grid.width / 2, self.grid.width / 2)
y = self.random.randint(-self.grid.height / 2, self.grid.height / 2)
a.location = (x, y)
self.grid.add_object_to_grid((x, y), a)
# a.operation_threshold = self.num_agents // 10
self.agents.append(a)
# exit()
def step(self):
self.schedule.step()
class GEEnvironmentModel(Model):
""" A environemnt to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(GEEnvironmentModel, self).__init__(seed=None)
else:
super(GEEnvironmentModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.agents = []
# Create agents
for i in range(self.num_agents):
a = GEBTAgent(i, self)
self.schedule.add(a)
x = 0
y = 0
a.location = (x, y)
self.grid.add_object_to_grid((x, y), a)
a.operation_threshold = 2 # self.num_agents // 10
self.agents.append(a)
def step(self):
self.schedule.step()
class GoToTowardsSwarmEnvironmentModel(Model):
""" A environemnt to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(GoToTowardsSwarmEnvironmentModel, self).__init__(seed=None)
else:
super(GoToTowardsSwarmEnvironmentModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.target = Sites(id=1, location=(45, 45), radius=5, q_value=0.5)
for i in range(self.num_agents):
a = SwarmAgentGoToTowards(i, self)
self.schedule.add(a)
x = 1
y = 11
a.location = (x, y)
a.direction = -2.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agent = a
def step(self):
self.schedule.step()
class RandomWalkSwarmEnvironmentModel(Model):
""" A environemnt to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(RandomWalkSwarmEnvironmentModel, self).__init__(seed=None)
else:
super(RandomWalkSwarmEnvironmentModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
for i in range(self.num_agents):
a = SwarmAgentRandomWalk(i, self)
self.schedule.add(a)
x = 0
y = 0
a.location = (x, y)
a.direction = -2.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agent = a
def step(self):
self.schedule.step()
class WealthEnvironmentModel(Model):
""" A environemnt to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(WealthEnvironmentModel, self).__init__(seed=None)
else:
super(WealthEnvironmentModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
for i in range(self.num_agents):
a = SwarmAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = self.random.randint(-self.grid.width / 2, self.grid.width / 2)
y = self.random.randint(-self.grid.height / 2,
self.grid.height / 2)
a.location = (x, y)
self.grid.add_object_to_grid((x, y), a)
def step(self):
self.schedule.step()
class SingleCarryFoodModel(Model):
""" A environment to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(SingleCarryFoodModel, self).__init__(seed=None)
else:
super(SingleCarryFoodModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
for a in range(5):
self.thing = Food(id=a, location=(0, 0), radius=16)
self.grid.add_object_to_grid(self.thing.location, self.thing)
for i in range(self.num_agents):
a = SwarmSingleCarryFood(i, self)
self.schedule.add(a)
x = 0
y = 0
a.location = (x, y)
a.direction = -2.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agent = a
def step(self):
self.schedule.step()
class MultipleCarryModel(Model):
""" A environment to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(MultipleCarryModel, self).__init__(seed=None)
else:
super(MultipleCarryModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.thing = Debris(id=1, location=(0, 0), radius=40)
self.grid.add_object_to_grid(self.thing.location, self.thing)
self.agent = []
for i in range(self.num_agents):
a = SwarmMultipleCarry(i, self)
self.schedule.add(a)
x = 1
y = 1
a.location = (x, y)
a.direction = 1.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agent.append(a)
def step(self):
self.schedule.step()
class NMModel(Model):
""" A environment to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(NMModel, self).__init__(seed=None)
else:
super(NMModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.hub = Hub(id=1, location=(0, 0), radius=11)
self.grid.add_object_to_grid(self.hub.location, self.hub)
# Create a place for the agents to drop the derbis
self.obstacles = []
try:
for i in range(4):
dx, dy = self.random.randint(5, 10, 2)
dx = self.hub.location[0] + 25 + dx
dy = self.hub.location[1] + 25 + dy
o = Obstacles(id=i, location=(dx, dy), radius=10)
self.grid.add_object_to_grid(o.location, o)
self.obstacles.append(o)
except AttributeError:
pass
self.agent = []
for i in range(self.num_agents):
a = NM(i, self)
self.schedule.add(a)
x = 19
y = 10
a.location = (x, y)
a.direction = 2.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agent.append(a)
self.debris = []
try:
for i in range(self.num_agents * 10):
dx, dy = self.random.randint(10, 20, 2)
dx = self.hub.location[0] + dx
dy = self.hub.location[1] + dy
d = Debris(
i, location=(dx, dy),
radius=5)
# print (i, dx, dy)
d.agent_name = None
self.grid.add_object_to_grid(d.location, d)
self.debris.append(d)
except KeyError:
pass
def step(self):
self.schedule.step()
class SingleCarryDropReturnSwarmEnvironmentModel(Model):
"""A environment to model swarms."""
def __init__(self, N, width, height, grid=10, seed=None, viewer=False):
if seed is None:
super(SingleCarryDropReturnSwarmEnvironmentModel,
self).__init__(seed=None)
else:
super(SingleCarryDropReturnSwarmEnvironmentModel,
self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.viewer = viewer
self.schedule = SimultaneousActivation(self)
self.hub = Hub(id=2, location=(0, 0), radius=5)
self.grid.add_object_to_grid(self.hub.location, self.hub)
self.site = Sites(id=3, location=(-35, -5), radius=5)
self.grid.add_object_to_grid(self.site.location, self.site)
self.agents = []
self.foods = []
for i in range(self.num_agents):
f = Food(i, location=(-35, -5), radius=5)
self.grid.add_object_to_grid(f.location, f)
self.foods.append(f)
for i in range(self.num_agents):
# a = SwarmAgentRandomSingleCarryDropReturn(i, self)
a = SwarmAgentHandCodedForaging(i, self)
self.schedule.add(a)
x = -45
y = -45
a.location = (x, y)
a.direction = -2.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agents.append(a)
if self.viewer:
self.ui = UI((width, height), [self.hub],
self.agents, [self.site],
food=self.foods)
def step(self):
self.schedule.step()
if self.viewer:
self.ui.step()
class XMLEnvironmentModel(Model):
""" A environemnt to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(XMLEnvironmentModel, self).__init__(seed=None)
else:
super(XMLEnvironmentModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.site = Sites(id=1, location=(45, 45), radius=5, q_value=0.5)
self.grid.add_object_to_grid(self.site.location, self.site)
self.hub = Hub(id=1, location=(0, 0), radius=11)
self.grid.add_object_to_grid(self.hub.location, self.hub)
self.agents = []
# Create agents
for i in range(self.num_agents):
a = XMLTestAgent(i, self)
self.schedule.add(a)
x = 45
y = 45
a.location = (x, y)
self.grid.add_object_to_grid((x, y), a)
a.operation_threshold = 2 # self.num_agents // 10
self.agents.append(a)
# Add equal number of food source
for i in range(5): # self.num_agents * 2):
f = Food(i, location=(45, 45), radius=3)
f.agent_name = None
self.grid.add_object_to_grid(f.location, f)
def step(self):
self.schedule.step()
class RandomWalkSwarmEnvironmentModel(Model):
"""A environment to model swarms."""
def __init__(self, N, width, height, grid=10, seed=None, viewer=False):
"""Initialize the environment methods."""
if seed is None:
super(RandomWalkSwarmEnvironmentModel, self).__init__(seed=None)
else:
super(RandomWalkSwarmEnvironmentModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.viewer = viewer
self.schedule = SimultaneousActivation(self)
self.hub = Hub(id=2, location=(0, 0), radius=5)
self.grid.add_object_to_grid(self.hub.location, self.hub)
self.site = Sites(id=2, location=(25, 25), radius=5)
self.grid.add_object_to_grid(self.site.location, self.site)
self.agents = []
for i in range(self.num_agents):
a = SwarmAgentRandomWalk(i, self)
self.schedule.add(a)
x = 0
y = 0
a.location = (x, y)
a.direction = -2.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agents.append(a)
if self.viewer:
self.ui = UI((width, height), [self.hub], self.agents, [self.site])
def step(self):
"""Execute."""
self.schedule.step()
if self.viewer:
self.ui.step()
class MultipleCarryModel(Model):
""" A environment to model swarms """
def __init__(self, N, width, height, grid=10, seed=None):
if seed is None:
super(MultipleCarryModel, self).__init__(seed=None)
else:
super(MultipleCarryModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.thing = Food(id=1, location=(30, 30), radius=40)
self.grid.add_object_to_grid(self.thing.location, self.thing)
self.thing1 = Food(id=1, location=(30, 30), radius=40)
self.grid.add_object_to_grid(self.thing1.location, self.thing1)
self.site = Sites(id=1, location=(30, 30), radius=5, q_value=0.5)
self.grid.add_object_to_grid(self.site.location, self.site)
self.hub = Hub(id=1, location=(0, 0), radius=11)
self.grid.add_object_to_grid(self.hub.location, self.hub)
self.agent = []
for i in range(self.num_agents):
a = SwarmMultipleCarry(i, self)
self.schedule.add(a)
x = 30
y = 30
a.location = (x, y)
a.direction = 2.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agent.append(a)
def step(self):
self.schedule.step()
class SimModel(Model):
"""A environemnt to model swarms."""
def __init__(
self, N, width, height, grid=10, iter=100000,
xmlstrings=None, seed=None, viewer=False, pname=None,
expname='MSFSimulation', agent='SimAgent'):
"""Initialize the attributes."""
if seed is None:
super(SimModel, self).__init__(seed=None)
else:
super(SimModel, self).__init__(seed)
self.runid = datetime.datetime.now().strftime(
"%s") + str(self.random.randint(1, 1000, 1)[0])
if pname is None:
self.pname = os.getcwd() + '/' + self.runid + expname
else:
self.pname = pname + '/' + self.runid + expname
self.width = width
self.height = height
self.stepcnt = 1
self.iter = iter
self.xmlstrings = xmlstrings
self.viewer = viewer
# Create db connection
connect = Connect('swarm', 'swarm', 'swarm', 'localhost')
self.connect = connect.tns_connect()
# Fill out the experiment table
self.experiment = Experiment(
self.connect, self.runid, N, seed, expname,
iter, width, height, grid, phenotype=xmlstrings[0])
self.experiment.insert_experiment_simulation()
self.sn = self.experiment.sn
# Create a folder to store results
os.mkdir(self.pname)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.agents = []
bound = np.ceil((self.num_agents * 1.0) / len(self.xmlstrings))
j = 0
# Create agents
for i in range(self.num_agents):
# print (i, j, self.xmlstrings[j])
a = eval(agent)(i, self, xmlstring=self.xmlstrings[j])
self.schedule.add(a)
# Add the agent to a random grid cell
# x = self.random.randint(
# -self.grid.width / 2, self.grid.width / 2)
x = 0
# y = self.random.randint(
# -self.grid.height / 2, self.grid.height / 2)
y = 0
a.location = (x, y)
self.grid.add_object_to_grid((x, y), a)
a.operation_threshold = 2 # self.num_agents // 10
self.agents.append(a)
if (i + 1) % bound == 0:
j += 1
def create_environment_object(self, jsondata, obj):
"""Create env from jsondata."""
name = obj.__name__.lower()
temp_list = []
i = 0
for json_object in jsondata[name]:
location = (json_object["x"], json_object["y"])
if "q_value" in json_object:
temp_obj = obj(
i, location, json_object["radius"], q_value=json_object[
"q_value"])
else:
temp_obj = obj(i, location, json_object["radius"])
self.grid.add_object_to_grid(location, temp_obj)
temp_list.append(temp_obj)
i += 1
return temp_list
def build_environment_from_json(self):
"""Build env from jsondata."""
jsondata = JsonData.load_json_file(filename)
# Create a instance of JsonData to store object that
# needs to be sent to UI
self.render = JsonData()
self.render.objects = {}
for name in jsondata.keys():
obj = eval(name.capitalize())
self.render.objects[name] = self.create_environment_object(
jsondata, obj)
self.hub = self.render.objects['hub'][0]
try:
self.foods = []
for site in self.render.objects['sites']:
self.site = site # self.render.objects['sites'][0]
for i in range(self.num_agents):
f = Food(
i, location=self.site.location,
radius=self.site.radius)
f.agent_name = None
self.grid.add_object_to_grid(f.location, f)
self.foods.append(f)
except KeyError:
pass
if self.viewer:
self.ui = UI(
(self.width, self.height), [self.hub], self.agents,
[self.site], food=self.foods)
def step(self):
"""Step through the environment."""
# Gather info from all the agents
# self.gather_info()
# Next step
self.schedule.step()
# Increment the step count
self.stepcnt += 1
if self.viewer:
self.ui.step()
def find_higest_performer(self):
"""Find the best agent."""
fitness = self.agents[0].individual[0].fitness
fittest = self.agents[0]
for agent in self.agents:
if agent.individual[0].fitness > fitness:
fittest = agent
return fittest
def find_higest_food_collector(self):
"""Find the best agent to collect food."""
fitness = self.agents[0].food_collected
fittest = self.agents[0]
for agent in self.agents:
if agent.food_collected > fitness:
fittest = agent
return fittest
def detect_food_moved(self):
"""Detect food moved."""
grid = self.grid
food_loc = self.site.location
neighbours = grid.get_neighborhood(food_loc, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
# print (food_objects)
return food_objects
def food_in_hub(self):
"""Find amount of food in hub."""
grid = self.grid
food_loc = self.hub.location
neighbours = grid.get_neighborhood(food_loc, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
return len(food_objects)
def food_in_loc(self, loc):
"""Find amount of food in hub."""
grid = self.grid
neighbours = grid.get_neighborhood(loc, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
return food_objects
class EvolModel(Model):
"""A environemnt to model swarms."""
def __init__(self,
N,
width,
height,
grid=10,
iter=100000,
seed=None,
expname='COT',
agent='EvolAgent',
parm='swarm_mcarry.txt'):
"""Initialize the attributes."""
if seed is None:
super(EvolModel, self).__init__(seed=None)
else:
super(EvolModel, self).__init__(seed)
self.runid = datetime.datetime.now().strftime("%s") + str(
self.random.randint(1, 1000, 1)[0])
self.pname = '/'.join(os.getcwd().split('/')[:-2]) + '/results/' \
+ self.runid + expname
self.stepcnt = 1
self.iter = iter
self.top = None
# Create db connection
connect = Connect('swarm', 'swarm', 'swarm', 'localhost')
self.connect = connect.tns_connect()
# Fill out the experiment table
self.experiment = Experiment(self.connect, self.runid, N, seed,
expname, iter, width, height, grid)
self.experiment.insert_experiment()
self.sn = self.experiment.sn
# Create a folder to store results
os.mkdir(self.pname)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.agents = []
self.parm = parm
# Create agents
for i in range(self.num_agents):
a = eval(agent)(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = self.random.randint(-self.grid.width / 2, self.grid.width / 2)
# x = 0
y = self.random.randint(-self.grid.height / 2,
self.grid.height / 2)
# y = 0
a.location = (x, y)
self.grid.add_object_to_grid((x, y), a)
a.operation_threshold = 2 # self.num_agents // 10
self.agents.append(a)
def create_environment_object(self, jsondata, obj):
"""Create env from jsondata."""
name = obj.__name__.lower()
temp_list = []
i = 0
for json_object in jsondata[name]:
location = (json_object["x"], json_object["y"])
if "q_value" in json_object:
temp_obj = obj(i,
location,
json_object["radius"],
q_value=json_object["q_value"])
else:
temp_obj = obj(i, location, json_object["radius"])
self.grid.add_object_to_grid(location, temp_obj)
temp_list.append(temp_obj)
i += 1
return temp_list
def build_environment_from_json(self):
"""Build env from jsondata."""
jsondata = JsonData.load_json_file(filename)
# Create a instance of JsonData to store object that
# needs to be sent to UI
self.render = JsonData()
self.render.objects = {}
for name in jsondata.keys():
obj = eval(name.capitalize())
self.render.objects[name] = self.create_environment_object(
jsondata, obj)
self.hub = self.render.objects['hub'][0]
try:
self.foods = []
self.site = self.render.objects['sites'][0]
food_radius = self.random.randint(20, 30)
for i in range(self.num_agents):
f = Food(i, location=self.site.location, radius=food_radius)
f.agent_name = None
f.phenotype = {}
self.grid.add_object_to_grid(f.location, f)
self.foods.append(f)
except KeyError:
pass
def step(self):
"""Step through the environment."""
# Gather info from all the agents
self.top = self.gather_info()
# Next step
self.schedule.step()
# Increment the step count
self.stepcnt += 1
def gather_info(self):
"""Gather information from all the agents."""
diversity = np.ones(len(self.agents))
exploration = np.ones(len(self.agents))
foraging = np.ones(len(self.agents))
fittest = np.ones(len(self.agents))
for id in range(len(self.agents)):
diversity[id] = self.agents[id].diversity_fitness
exploration[id] = self.agents[id].exploration_fitness()
foraging[id] = self.agents[id].food_collected
fittest[id] = self.agents[id].individual[0].fitness
beta = self.agents[-1].beta
"""
mean = Best(
self.pname, self.connect, self.sn, 1, 'MEAN', self.stepcnt,
beta, np.mean(fittest), np.mean(diversity), np.mean(exploration),
np.mean(foraging), "None"
)
mean.save()
std = Best(
self.pname, self.connect, self.sn, 1, 'STD', self.stepcnt, beta,
np.std(fittest), np.std(diversity), np.std(exploration),
np.std(foraging), "None"
)
std.save()
"""
# Compute best agent for each fitness
self.best_agents(diversity, beta, "DIVERSE")
self.best_agents(exploration, beta, "EXPLORE")
self.best_agents(foraging, beta, "FORGE")
self.best_agents(fittest, beta, "OVERALL")
return np.argmax(foraging)
def best_agents(self, data, beta, header):
"""Find the best agents in each category."""
idx = np.argmax(data)
dfitness = self.agents[idx].diversity_fitness
ofitness = self.agents[idx].individual[0].fitness
ffitness = self.agents[idx].food_collected
efitness = self.agents[idx].exploration_fitness()
phenotype = self.agents[idx].individual[0].phenotype
best_agent = Best(self.pname, self.connect, self.sn, idx, header,
self.stepcnt, beta, ofitness, dfitness, efitness,
ffitness, phenotype)
best_agent.save()
def find_higest_performer(self):
"""Find the best agent."""
fitness = self.agents[0].individual[0].fitness
fittest = self.agents[0]
for agent in self.agents:
if agent.individual[0].fitness > fitness:
fittest = agent
return fittest
def find_higest_food_collector(self):
"""Find the best agent to collect food."""
fitness = self.agents[0].food_collected
fittest = self.agents[0]
for agent in self.agents:
if agent.food_collected > fitness:
fittest = agent
return fittest
def detect_food_moved(self):
"""Detect food moved."""
grid = self.grid
food_loc = self.site.location
neighbours = grid.get_neighborhood(food_loc, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
# print (food_objects)
return food_objects
def food_in_loc(self, loc):
"""Find amount of food in hub."""
grid = self.grid
neighbours = grid.get_neighborhood(loc, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
return food_objects
class RunEnvironmentModel(Model):
"""A environemnt to model swarms."""
def __init__(self,
N,
width,
height,
grid=10,
iter=100000,
xmlstring=None,
seed=None):
"""Initialize the attributes."""
if seed is None:
super(RunEnvironmentModel, self).__init__(seed=None)
else:
super(RunEnvironmentModel, self).__init__(seed)
self.runid = datetime.datetime.now().strftime("%s") + str(
self.random.randint(1, 1000, 1)[0])
self.pname = os.getcwd() + '/' + self.runid + "SFCommSimulation"
self.stepcnt = 1
self.iter = iter
self.xmlstring = xmlstring
# Create db connection
connect = Connect('swarm', 'swarm', 'swarm', 'localhost')
self.connect = connect.tns_connect()
# Fill out the experiment table
self.experiment = Experiment(self.connect,
self.runid,
N,
seed,
'Simuation SFComm',
iter,
width,
height,
grid,
phenotype=xmlstring)
self.experiment.insert_experiment_simulation()
self.sn = self.experiment.sn
# Create a folder to store results
os.mkdir(self.pname)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
# self.site = Sites(id=1, location=(5, 5), radius=11, q_value=0.5)
# self.grid.add_object_to_grid(self.site.location, self.site)
# self.hub = Hub(id=1, location=(0, 0), radius=11)
# self.grid.add_object_to_grid(self.hub.location, self.hub)
self.agents = []
# Create agents
for i in range(self.num_agents):
a = RunSwarmAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = self.random.randint(-self.grid.width / 2, self.grid.width / 2)
# x = 0
y = self.random.randint(-self.grid.height / 2,
self.grid.height / 2)
# y = 0
a.location = (x, y)
self.grid.add_object_to_grid((x, y), a)
a.operation_threshold = 2 # self.num_agents // 10
self.agents.append(a)
# Add equal number of food source
# for i in range(20):
# f = Food(i, location=(-29, -29), radius=5)
# self.grid.add_object_to_grid(f.location, f)
# print (i,x,y)
def create_environment_object(self, jsondata, obj):
"""Create env from jsondata."""
name = obj.__name__.lower()
temp_list = []
i = 0
for json_object in jsondata[name]:
location = (json_object["x"], json_object["y"])
if "q_value" in json_object:
temp_obj = obj(i,
location,
json_object["radius"],
q_value=json_object["q_value"])
else:
temp_obj = obj(i, location, json_object["radius"])
self.grid.add_object_to_grid(location, temp_obj)
temp_list.append(temp_obj)
i += 1
return temp_list
def build_environment_from_json(self):
"""Build env from jsondata."""
jsondata = JsonData.load_json_file(filename)
# Create a instance of JsonData to store object that
# needs to be sent to UI
self.render = JsonData()
self.render.objects = {}
for name in jsondata.keys():
obj = eval(name.capitalize())
self.render.objects[name] = self.create_environment_object(
jsondata, obj)
self.hub = self.render.objects['hub'][0]
try:
self.site = self.render.objects['sites'][0]
for i in range(self.num_agents * 2):
f = Food(i,
location=self.site.location,
radius=self.site.radius)
f.agent_name = None
self.grid.add_object_to_grid(f.location, f)
except KeyError:
pass
def step(self):
"""Step through the environment."""
# Gather info from all the agents
# self.gather_info()
# Next step
self.schedule.step()
# Increment the step count
self.stepcnt += 1
def find_higest_performer(self):
"""Find the best agent."""
fitness = self.agents[0].individual[0].fitness
fittest = self.agents[0]
for agent in self.agents:
if agent.individual[0].fitness > fitness:
fittest = agent
return fittest
def find_higest_food_collector(self):
"""Find the best agent to collect food."""
fitness = self.agents[0].food_collected
fittest = self.agents[0]
for agent in self.agents:
if agent.food_collected > fitness:
fittest = agent
return fittest
def detect_food_moved(self):
"""Detect food moved."""
grid = self.grid
food_loc = self.site.location
neighbours = grid.get_neighborhood(food_loc, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
# print (food_objects)
return food_objects
def food_in_hub(self):
"""Find amount of food in hub."""
grid = self.grid
food_loc = self.hub.location
neighbours = grid.get_neighborhood(food_loc, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
return len(food_objects)
