63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
import array
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import numpy as np
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import random
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import itertools
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import tools
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import deap.tools
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from deap import base
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from deap import creator
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from genetic import GeneticOperators
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from genetic import eaMuPlusLambda
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def exhaustive(statement, context, TemplateType, build_template, parameter_names, all_parameters, compute_perf, perf_metric, out):
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device = context.devices[0]
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nvalid = 0
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current = 0
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minT = float('inf')
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for individual in itertools.product(*all_parameters):
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template = build_template(TemplateType.Parameters(*individual))
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if not tools.skip(template, statement, device):
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nvalid = nvalid + 1
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for individual in itertools.product(*all_parameters):
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template = build_template(TemplateType.Parameters(*individual))
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try:
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T = tools.benchmark(template,statement,device)
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current = current + 1
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if T < minT:
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minT = T
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best = individual
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print '%d / %d , Best is %d %s for %s\r'%(current, nvalid, compute_perf(minT), perf_metric, best)
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except:
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pass
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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.register("evaluate", gen.evaluate)
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toolbox.register("mate", tools.cxTwoPoint)
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toolbox.decorate("mate", gen.repair)
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toolbox.register("mutate", gen.mutate)
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toolbox.decorate("mutate", gen.repair)
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toolbox.register("select", tools.selBest)
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pop = toolbox.population(n=30)
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hof = deap.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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stats = deap.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 = eaMuPlusLambda(pop, toolbox, 30, 50, cxpb=0.2, mutpb=0.3, maxtime='3m0s', maxgen=200, halloffame=hof, compute_perf=compute_perf, perf_metric=perf_metric)
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