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' )