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_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()
def test_lexicon_sample_storage_adequacy(self):
game = DiscriminationGame()
the_agent = SteelsClassificationAgent()
iris = datasets.load_iris()
the_environment = environment.Environment(iris.data, iris.target)
the_agent.add_sample(1, the_environment)
self.lexicon_and_sample_storage_adequacy(the_agent)
the_agent.forget()
self.lexicon_and_sample_storage_adequacy(the_agent)
category = the_agent.predict(the_environment.get_sample(1))
self.lexicon_and_sample_storage_adequacy(the_agent)
word = the_agent.find_word_for_category(category)
self.lexicon_and_sample_storage_adequacy(the_agent)
the_agent.forget()
self.lexicon_and_sample_storage_adequacy(the_agent)
game.learning_after_game(the_agent, 2, the_environment, category, True)
self.lexicon_and_sample_storage_adequacy(the_agent)
for _ in range(1000):
the_agent.forget()
self.lexicon_and_sample_storage_adequacy(the_agent)
assert_equal(0, the_agent.sample_storage.get_categories_size())
assert_equal(0, the_agent.lexicon.get_categories_size())
