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,
environments,
interaction,
classifier=SteelsClassifier,
topology=None,
inc_category_treshold=None,
dump_freq=50,
stimuli=None):
from cog_abm.core import Environment, Simulation
from cog_abm.core.environment import RandomStimuliChooser
from pygraph.algorithms.generators import generate
num_agents = len(agents)
topology = def_value(
topology, generate(num_agents,
num_agents * (num_agents - 1) / 2))
# 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 = RandomStimuliChooser(use_distance=True, distance=50.)
env = Environment(stimuli, chooser)
for key in environments.keys():
Simulation.environments[key] =\
Environment(environments[key].stimuli, chooser)
#TODO: think about this all...
glob = Simulation.environments["global"]
Simulation.global_environment = def_value(glob, 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, environments, interaction, classifier=SteelsClassifier, topology=None, inc_category_treshold=None, dump_freq=50, stimuli=None): from cog_abm.core import Environment, Simulation from cog_abm.core.environment import RandomStimuliChooser from pygraph.algorithms.generators import generate num_agents = len(agents) topology = def_value(topology, generate(num_agents, num_agents*(num_agents-1)/2)) # 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 = RandomStimuliChooser(use_distance=True, distance=50.) env = Environment(stimuli, chooser) for key in environments.keys(): Simulation.environments[key] =\ Environment(environments[key].stimuli, chooser) #TODO: think about this all... glob = Simulation.environments["global"] Simulation.global_environment = def_value(glob , 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)
