def steels_uniwersal_basic_experiment(num_iter, agents, interaction, classifier=SteelsClassifier, topology=None, inc_category_treshold=None, dump_freq=50, stimuli=None, chooser=None): topology = topology or generate_simple_network(agents) # if stimuli == None: # stimuli = def_value(None, default_stimuli()) if inc_category_treshold is not None: interaction.__class__.def_inc_category_treshold = inc_category_treshold chooser = chooser or RandomStimuliChooser(use_distance=True, distance=50.) env = Environment(stimuli, chooser) for agent in agents: agent.env = env s = Simulation(topology, interaction, agents) res = s.run(num_iter, dump_freq) # import pprint # print pprint.pprint(error_counter) try: # s = sum(error_counter.values()) # for k,v in error_counter.iteritems(): # print "%s: %s" % (k, float(v)/s) for a in agents: print "[%s]:%s" % (len(a.state.lexicon.known_words()), a.state.lexicon.known_words()) print "OK" except: pass return res
def steels_uniwersal_basic_experiment(num_iter, agents, interaction, classifier=SteelsClassifier, topology=None, inc_category_treshold=None, dump_freq=50, stimuli=None, chooser=None): topology = topology or generate_simple_network(agents) # if stimuli == None: # stimuli = def_value(None, default_stimuli()) if inc_category_treshold is not None: interaction.__class__.def_inc_category_treshold = inc_category_treshold chooser = chooser or RandomStimuliChooser(use_distance=True, distance=50.) env = Environment(stimuli, chooser) for agent in agents: agent.env = env s = Simulation(topology, interaction, agents) res = s.run(num_iter, dump_freq) # import pprint # print pprint.pprint(error_counter) try: # s = sum(error_counter.values()) # for k,v in error_counter.iteritems(): # print "%s: %s" % (k, float(v)/s) for a in agents: print "[%s]:%s" % (len( a.state.lexicon.known_words()), a.state.lexicon.known_words()) print "OK" except: pass return res
def flu_experiment(num_agents, num_doctors, num_ill, iters): agents = prepare_agents(num_agents, num_doctors, num_ill) topology = generate_simple_network(agents) s = Simulation(topology, FluInteraction(), agents) return s.run(iters, DUMP_FREQ)