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)
