def test_simpleAgent(agentType, expected_monitor_results):
#start agent network server
agentNetwork = AgentNetwork(dashboard_modules=False)
#init agents by adding into the agent network
simple_agent = agentNetwork.add_agent(agentType=agentType)
monitor_agent_1 = agentNetwork.add_agent(agentType=MonitorAgent)
#shorten n wait loop time
simple_agent.init_agent_loop(0.01)
#connect agents
agentNetwork.bind_agents(simple_agent, monitor_agent_1)
# set all agents states to "Running"
agentNetwork.set_running_state()
time.sleep(test_timeout)
# test to see if monitor agents have received the correct data
assert str(
monitor_agent_1.get_attr('memory')) == str(expected_monitor_results)
# shutdown agent network
agentNetwork.shutdown()
time.sleep(3)
if __name__ == '__main__':
# start agent network server
agentNetwork = AgentNetwork()
# init agents
gen_agent = agentNetwork.add_agent(agentType=DataStreamAgent)
trainer_agent = agentNetwork.add_agent(agentType=Trainer)
predictor_agent = agentNetwork.add_agent(agentType=Predictor)
evaluator_agent = agentNetwork.add_agent(agentType=Evaluator)
monitor_agent_1 = agentNetwork.add_agent(agentType=MonitorAgent)
monitor_agent_2 = agentNetwork.add_agent(agentType=MonitorAgent)
gen_agent.init_parameters(stream=SineGenerator(),
pretrain_size=1000,
batch_size=1)
trainer_agent.init_parameters(ml_model=HoeffdingTree())
# connect agents : We can connect multiple agents to any particular agent
# However the agent needs to implement handling multiple input types
agentNetwork.bind_agents(gen_agent, trainer_agent)
agentNetwork.bind_agents(gen_agent, predictor_agent)
agentNetwork.bind_agents(trainer_agent, predictor_agent)
agentNetwork.bind_agents(predictor_agent, evaluator_agent)
agentNetwork.bind_agents(evaluator_agent, monitor_agent_1)
agentNetwork.bind_agents(predictor_agent, monitor_agent_2)
# set all agents states to "Running"
agentNetwork.set_running_state()