93 lines
3.6 KiB
Python
93 lines
3.6 KiB
Python
import array
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import numpy as np
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import random
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import time
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from deap import algorithms
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from deap import base
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from deap import creator
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from deap import tools
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from genetic_operators import GeneticOperators
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def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, maxtime,
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stats=None, halloffame=None, verbose=__debug__):
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logbook = tools.Logbook()
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logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
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# Evaluate the individuals with an invalid fitness
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invalid_ind = [ind for ind in population if not ind.fitness.valid]
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fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
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for ind, fit in zip(invalid_ind, fitnesses):
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ind.fitness.values = fit
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if halloffame is not None:
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halloffame.update(population)
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record = stats.compile(population) if stats is not None else {}
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logbook.record(gen=0, nevals=len(invalid_ind), **record)
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if verbose:
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print logbook.stream
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# Begin the generational process
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gen = 0
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maxtime = time.strptime(maxtime, '%Mm%Ss')
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maxtime = maxtime.tm_min*60 + maxtime.tm_sec
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start_time = time.time()
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while time.time() - start_time < maxtime:
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# Vary the population
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offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
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# Evaluate the individuals with an invalid fitness
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invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
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fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
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for ind, fit in zip(invalid_ind, fitnesses):
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ind.fitness.values = fit
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# Update the hall of fame with the generated individuals
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if halloffame is not None:
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halloffame.update(offspring)
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# Select the next generation population
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population[:] = toolbox.select(population + offspring, mu)
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# Update the statistics with the new population
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gen = gen + 1
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record = stats.compile(population) if stats is not None else {}
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logbook.record(gen=gen, nevals=len(invalid_ind), **record)
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if verbose:
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print logbook.stream
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return population, logbook
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def genetic(statement, context, TemplateType, build_template, parameter_names, all_parameters, compute_perf, perf_metric, out):
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gen = GeneticOperators(context.devices[0], statement, all_parameters, parameter_names, TemplateType, build_template)
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creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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creator.create("Individual", list, fitness=creator.FitnessMin)
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toolbox = base.Toolbox()
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toolbox.register("individual", tools.initIterate, creator.Individual, gen.init)
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toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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toolbox.decorate("population", gen.repair)
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toolbox.register("evaluate", gen.evaluate)
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toolbox.register("mate", tools.cxUniform, indpb=0.3)
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toolbox.decorate("mate", gen.repair)
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toolbox.register("mutate", gen.mutate, indpb=0.2)
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toolbox.decorate("mutate", gen.repair)
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toolbox.register("select", tools.selNSGA2)
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pop = toolbox.population(n=10)
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hof = tools.HallOfFame(1)
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best_performer = lambda x: max([compute_perf(hof[0].fitness.values[0]) for t in x])
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best_profile = lambda x: '(%s)'%','.join(map(str,hof[0]))
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cxpb = 0.5
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mutpb = 0.2
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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stats.register("max (" + perf_metric + ")", lambda x: max([compute_perf(hof[0].fitness.values[0]) for t in x]))
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stats.register("profile ", lambda x: '(%s)'%','.join(map(str,hof[0])))
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pop, log = eaMuPlusLambda(pop, toolbox, 10, 20, cxpb=0.2, mutpb=0.2, maxtime='5m0s', stats=stats, halloffame=hof, verbose=True)
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