def test_simulation_of_discrimination_game(self):
# Network construction.
agents = {}
for _ in range(10):
agent = SteelsAgent()
agent.set_fitness("DG", StandardFitness())
agents[agent.get_id()] = agent
topology = generate_topology("clique", agents_names=agents.keys())
changes = {1: "clique"}
network = Network(agents, {"clique": topology}, changes)
# Interaction construction.
interactions = BehaviorSwitcher({"DG": DiscriminationGame()}, {1: "DG"})
# Environment construction.
irises = datasets.load_iris()
environment = Environment(irises.data, irises.target)
environments = BehaviorSwitcher({"global": environment}, {1: "global"})
# Results construction.
result = StandardResult(100)
# Condition construction.
condition = IterationCondition(1000)
# Simulation construction.
self.simulation = Simulation(network, interactions, environments, result, condition)
def fit(self, x, y):
"""
Fit the steels classifier according to the given training data.
:param list x: samples.
:param list y: samples' classes.
"""
environment = Environment(x, y)
agents = {}
if self.classifiers is None:
for _ in range(15):
agent = SteelsClassificationAgent(alpha=self.alpha)
agents[agent.id] = agent
else:
for classifier in self.classifiers:
agent = SteelsClassificationAgent(classifier=classifier, alpha=self.alpha)
agents[agent.id] = agent
for agent in agents.values():
agent.set_fitness("DG", CurrentFitness())
agent.set_fitness("GG", CurrentFitness())
network = Network(agents, {1: generate_topology(self.topology, agents_names=agents.keys())})
self.simulation = Simulation(
network, self.interactions, BehaviorSwitcher(environment), SteelsClassifierResults(), self.condition
)
self.result = self.simulation.run()
def test_flu_game(self):
"""
Flu game simulates flu expanding through social network.
"""
# Network construction.
agents = {}
for _ in range(100):
agent = Agent()
agent.set_fitness("infected", InfectedFitness())
agents[agent.get_id()] = agent
for agent in agents:
agents[agent].update_fitness_measure("infected", True)
break
topology = generate_topology("clique", agents_names=agents.keys())
changes = {1: "clique"}
network = Network(agents, {"clique": topology}, changes)
# Interaction construction.
interactions = BehaviorSwitcher({
"infection": InfectInteraction()
}, {1: "infection"})
# Environment construction.
environment = BehaviorSwitcher({}, {})
# Results construction.
result = StandardResult(1)
# Condition construction.
condition = AllInfected()
# Simulation construction.
self.simulation = Simulation(network, interactions, environment,
result, condition)
simulation_result = self.simulation.run()
def test_guessing_game():
agents = {}
for _ in range(10):
the_agent = SteelsClassificationAgent()
the_agent.set_fitness("DG", fitness.StandardFitness())
the_agent.set_fitness("GG", fitness.StandardFitness())
agents[the_agent.get_id] = the_agent
topology = generate_topology("clique", agents_names=agents.keys())
complete_network = network.Network(agents, {"g": topology})
iris = datasets.load_iris()
the_environment = environment.Environment(iris.data, iris.target)
sim = simulation.Simulation(complete_network, BehaviorSwitcher({"a": GuessingGame()}),
BehaviorSwitcher({"a": the_environment}), result.StandardResult(),
condition.IterationCondition(3200))
sim.run()