def run(config_file, output_loc, is_slave, identity):
random.seed(10)
## Read input configuration
f = open(config_file)
config = sj.loads(f.read())
f.close()
## Set output configuration
output(output_loc, is_slave, identity, sj.dumps(config) + '\n')
num_steps = config['num_steps']
num_trials = config['num_trials']
trust_used = config['trust_used']
inbox_trust_sorted = config['inbox_trust_sorted']
if 'trust_filter_on' in config.keys():
trust_filter_on = config['trust_filter_on']
else:
trust_filter_on = True
i = 1
corroboration_threshold = 4
for num_fpro in config['num_fpro']:
for num_fcon in config['num_fcon']:
for num_npro in config['num_npro']:
for num_ncon in config['num_ncon']:
for num_groups in config['num_groups']:
for num_agents in config['num_agents']:
for agent_per_fact in config['agent_per_fact']:
for graph in config['graph_description']:
graph_type = graph['type']
radius = graph['radius']
for agent_setup in config['agent_setup']:
for w in config['willingness']:
for c in config['competence']:
for spam in config['spamminess']:
for selfish in config['selfishness']:
for e in config['engagement']:
for d in config ['decisiveness']:
for cap in config ['capacity']:
for corroboration_threshold in config['corroboration_threshold']:
print "Case", i, "being executed"
print "running for %d/%d facts per group %d groups %d agents"\
%(num_fpro+num_fcon, num_npro+num_ncon, num_groups, num_agents)
print "\t%d agents per fact "\
%agent_per_fact
print "\t%s/%.1f graph for %s steps" \
%(graph_type, radius, num_steps)
print "\tw:%.1f/c:%.1f/e:%.1f/d:%.1f for %d trials"\
%(w,c,e,d,num_trials)
print "\tagent setup", agent_setup
i += 1
results = sim.run_simulation(num_fpro, \
num_fcon, \
num_npro,\
num_ncon, \
num_groups, \
num_agents, \
agent_per_fact,\
radius, \
num_steps, \
w, c, e, d, \
corroboration_threshold, \
cap, \
num_trials, \
graph_type,\
agent_setup,\
spam, selfish,\
trust_used,\
inbox_trust_sorted, \
trust_filter_on)
output(output_loc, is_slave, identity, sj.dumps(results) + '\n' )
for value in params[param]:
for layer in range(5):
change_list = [value]
change_list.append(range(pow(2,layer)-1,pow(2,layer+1)-1))
agent_setup = [{param : change_list}]
for (num_npro, num_ncon) in [ (150,0), (125,25),(100,50), (75,75), (50,100), (25,125), (0,150) ]:
results = sim.run_simulation(num_fpro, \
num_fcon, \
num_npro,\
num_ncon, \
num_groups, \
num_agents, \
agent_per_fact,\
radius, \
num_steps, \
w, c, e, decisiveness, cm, \
corroboration_threshold, \
disp, \
cap, \
num_trials, \
graph_type,\
agent_setup,\
spam, selfish,\
trust_used,\
inbox_trust_sorted, \
trust_filter_on)
f.write(sj.dumps(results) + "\n")
infostr = "comp: %.2f, e: %.2f, good: %d/%d, bad: %d/%d, "\
def run(config_file, output_loc, is_slave, identity):
random.seed(10)
## Read input configuration
f = open(config_file)
config = sj.loads(f.read())
f.close()
## Set output configuration
output(output_loc, is_slave, identity, sj.dumps(config) + '\n')
num_steps = config['num_steps']
num_trials = config['num_trials']
trust_used = config['trust_used']
inbox_trust_sorted = config['inbox_trust_sorted']
if 'trust_filter_on' in config.keys():
trust_filter_on = config['trust_filter_on']
else:
trust_filter_on = True
i = 1
corroboration_threshold = 4
for num_fpro in config['num_fpro']:
for num_fcon in config['num_fcon']:
for num_npro in config['num_npro']:
for num_ncon in config['num_ncon']:
for num_groups in config['num_groups']:
for num_agents in config['num_agents']:
for agent_per_fact in config['agent_per_fact']:
for graph in config['graph_description']:
graph_type = graph['type']
radius = graph['radius']
for agent_setup in config['agent_setup']:
for w in config['willingness']:
for c in config['competence']:
for spam in config[
'spamminess']:
for selfish in config[
'selfishness']:
for e in config[
'engagement']:
for d in config[
'decisiveness']:
for cap in config[
'capacity']:
for corroboration_threshold in config[
'corroboration_threshold']:
print "Case", i, "being executed"
print "running for %d/%d facts per group %d groups %d agents"\
%(num_fpro+num_fcon, num_npro+num_ncon, num_groups, num_agents)
print "\t%d agents per fact "\
%agent_per_fact
print "\t%s/%.1f graph for %s steps" \
%(graph_type, radius, num_steps)
print "\tw:%.1f/c:%.1f/e:%.1f/d:%.1f for %d trials"\
%(w,c,e,d,num_trials)
print "\tagent setup", agent_setup
i += 1
results = sim.run_simulation(num_fpro, \
num_fcon, \
num_npro,\
num_ncon, \
num_groups, \
num_agents, \
agent_per_fact,\
radius, \
num_steps, \
w, c, e, d, \
corroboration_threshold, \
cap, \
num_trials, \
graph_type,\
agent_setup,\
spam, selfish,\
trust_used,\
inbox_trust_sorted, \
trust_filter_on)
output(
output_loc,
is_slave,
identity,
sj.
dumps(
results
) +
'\n'
)
