Esempio n. 1
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def evaluator(candidate, args):
    type_info_price = args.get("type_info_price")
    type_info_ecu = args.get("type_info_ecu")
    task_base_time = args.get("task_base_time")
    task_preds = args.get("task_preds")
    comm_speeds = args.get("comm_speeds")
    comm_sizes = args.get("comm_sizes")
    n_tasks = args.get("n_tasks")
    n_nodes = args.get("n_tasks")
    n_types = args.get("n_types")

    fitness = evaluate(candidate, type_info_price, type_info_ecu,
                       task_base_time, task_preds, comm_speeds, comm_sizes,
                       n_tasks, n_nodes, n_types)
    return inspyred.ec.emo.Pareto(fitness)
Esempio n. 2
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File: emo3.py Progetto: Tefx/Wookie
def evaluator(candidate, args):
	type_info_price = args.get("type_info_price")
	type_info_ecu = args.get("type_info_ecu")
	task_base_time = args.get("task_base_time")
	task_preds = args.get("task_preds")
	comm_speeds = args.get("comm_speeds")
	comm_sizes = args.get("comm_sizes")
	n_tasks = args.get("n_tasks")
	n_nodes = args.get("n_tasks")
	n_types = args.get("n_types")

	fitness = evaluate(candidate, type_info_price,
		               type_info_ecu,
		               task_base_time,
		               task_preds,
		               comm_speeds,
		               comm_sizes,
		               n_tasks,
		               n_nodes,
		               n_types)
	return inspyred.ec.emo.Pareto(fitness)
Esempio n. 3
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if __name__ == '__main__':
    from sys import argv
    from workflow import Workflow
    from pool import AWS
    from emo_tool import get_info, evaluate
    from log import get_details
    from plot import stat, trans, show

    wf = Workflow(argv[1])
    pool = AWS("aws.info")
    info = get_info(wf, pool)

    _, loc, ts = heft2(*info[2:])
    c = get_chromosome(loc, ts)
    makespan, cost = evaluate(c,
                              *info[2:-1],
                              n_tasks=len(loc),
                              n_nodes=len(loc),
                              n_types=len(info[1]))
    scheme = get_details(c,
                         *info[:-1],
                         n_tasks=len(loc),
                         n_nodes=len(loc),
                         n_types=len(info[1]))
    scheme = [(n, ) + v for n, v in scheme.iteritems()]

    print "Makespan: %.0fs\tCost: $%.2f" % (makespan, cost)
    # stat(scheme)
    # show(*trans(scheme))
Esempio n. 4
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File: heft2.py Progetto: Tefx/Wookie
					break
	if len(types) > n_tasks:
		types = types[:n_tasks]

	c = array.array("I", [])
	for l, t in itertools.izip_longest(loc, types, fillvalue=0):
		c.append(l)
		c.append(t)
	return c

if __name__ == '__main__':
	from sys import argv
	from workflow import Workflow
	from pool import AWS
	from emo_tool import get_info, evaluate
	from log import get_details
	from plot import stat, trans, show

	wf = Workflow(argv[1])
	pool = AWS("aws.info")
	info = get_info(wf, pool)

	_, loc, ts = heft2(*info[2:])
	c = get_chromosome(loc, ts)
	makespan, cost = evaluate(c, *info[2:-1], n_tasks=len(loc), n_nodes=len(loc), n_types=len(info[1]))
	scheme = get_details(c, *info[:-1],  n_tasks=len(loc), n_nodes=len(loc), n_types=len(info[1]))
	scheme = [(n,)+v for n,v in scheme.iteritems()]

	print "Makespan: %.0fs\tCost: $%.2f" % (makespan, cost)
	# stat(scheme)
	# show(*trans(scheme))