def two_agents_LG(self):
""" language game with two agents (typically teacher and learner)
"""
all_results = []
for i in range(pm.n_replicas):
agent1 = agent.Agent("agent1", learning=False)
agent1.load_knowledge() # as agent1 is the teacher, it needs some predefined knowledge
agent2= agent.Agent("agent2")
for x, j in enumerate(self.td):
self.guessing_game(agent1, agent2, j, random.randint(0, pm.context_size-1))
if x == 500:
pass
print "end replica " + str(i)
all_results.append(agent2.gg_running_av)
av_results = hp.calc_average(all_results)
output.plot_DG(av_results)
agent2.matrix.to_csv("output.csv")
import csv
w = csv.writer(open("output2.csv", "w"))
for key, val in agent2.cs.percepts.items():
w.writerow([key, val])
def run_discrimination_game(self):
""" run a series of discrimination games
"""
print "starting DG"
all_results, all_n_percepts = [], []
for i in range(pm.n_replicas):
agent1 = agent.Agent("agent1")
for j in self.td:
self.discrimination_game(agent1, j, random.randint(0, pm.context_size-1))
agent1.dg_n_percepts.append(len(agent1.cs.percepts))
print "percepts: ", len(agent1.cs.percepts)
print "success at end of replica " + str(i) + ": " + str(agent1.dg_running_av[-1])
all_results.append(agent1.dg_running_av)
all_n_percepts.append(agent1.dg_n_percepts)
av_results = hp.calc_average(all_results)
av_percepts = hp.calc_average(all_n_percepts)
output.plot_DG(av_results, av_percepts)
