Better initialization
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@@ -27,7 +27,6 @@ def b_gray_to_bin(A='00000000', endian='big'):
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if endian == 'little': A = A[::-1] # Make sure endianness is big before conversion
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b = A[0]
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for i in range(1, len(A)): b += str( int(b[i-1] != A[i]) )
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assert len(A) == len(b), 'Error in this function! len(A) must equal len(b). Oh dear.'
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if endian == 'little': b = b[::-1] # Convert back to little endian if necessary
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return b
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@@ -42,7 +41,7 @@ class GeneticOperators(object):
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self.ParameterType = TemplateType.Parameters
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self.build_template = build_template
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self.cache = {}
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self.indpb = 0.1
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self.indpb = 0.05
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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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@@ -52,7 +51,7 @@ class GeneticOperators(object):
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self.toolbox.register("evaluate", self.evaluate)
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self.toolbox.register("mate", deap_tools.cxTwoPoint)
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self.toolbox.register("mutate", self.mutate)
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self.toolbox.register("select", deap_tools.selBest)
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self.toolbox.register("select", deap_tools.selNSGA2)
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@staticmethod
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def decode(s):
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@@ -78,12 +77,11 @@ class GeneticOperators(object):
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def init(self, N):
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result = []
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def generate(Afetch, Bfetch, K):
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result = []
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while len(result) < K:
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bincode = [Afetch, Bfetch] + [str(random.randint(0,1)) for i in range(23)]
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fetchcount = [0, 0, 0]
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while len(result) < N:
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while True:
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fetch = random.randint(0,2)
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bincode = [fetch, fetch] + [str(random.randint(0,1)) for i in range(23)]
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parameters = self.decode(bincode)
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template = self.build_template(self.TemplateType.Parameters(*parameters))
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registers_usage = template.registers_usage(vcl.atidlas.StatementsTuple(self.statement))/4
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@@ -91,26 +89,26 @@ class GeneticOperators(object):
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local_size = template.parameters.local_size_0*template.parameters.local_size_1
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occupancy_record = tools.OccupancyRecord(self.device, local_size, lmem_usage, registers_usage)
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if not tools.skip(template, self.statement, self.device):
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fetchcount[fetch] = fetchcount[fetch] + 1
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if max(fetchcount) - min(fetchcount) <= 1:
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result.append(creator.Individual(bincode))
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return result
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result += generate(0,0,N/3)
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result += generate(1,1,N/3)
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result += generate(2,2,N/3)
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break
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else:
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fetchcount[fetch] = fetchcount[fetch] - 1
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return result
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def mutate(self, individual):
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while True:
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new_individual = copy.deepcopy(individual)
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for i in range(len(new_individual)):
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if i < 2 and random.random() < self.indpb:
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if i < 2 and random.random() < 0.1:
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while new_individual[i] == individual[i]:
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new_individual[i] = random.randint(0, 2)
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elif i >= 2 and random.random() < self.indpb:
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new_individual[i] = '1' if new_individual[i]=='0' else '0'
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parameters = self.decode(new_individual)
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template = self.build_template(self.TemplateType.Parameters(*parameters))
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#print tools.skip(template, self.statement, self.device), parameters
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if not tools.skip(template, self.statement, self.device):
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break
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return new_individual,
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@@ -133,10 +131,10 @@ class GeneticOperators(object):
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maxtime = maxtime.tm_min*60 + maxtime.tm_sec
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start_time = time.time()
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mu = 30
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_lambda = 50
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cxpb = 0.4
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mutpb = 0.5
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mu = 70
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_lambda = 100
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cxpb = 0.2
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mutpb = 0.7
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population = self.init(mu)
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invalid_ind = [ind for ind in population if not ind.fitness.valid]
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@@ -174,7 +172,7 @@ class GeneticOperators(object):
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gen = gen + 1
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best_profile = '(%s)'%','.join(map(str,GeneticOperators.decode(hof[0])));
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best_performance = compute_perf(hof[0].fitness.values[0])
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sys.stdout.write('Generation %d | Time %d | Best %d %s [ for %s ]\n'%(gen, time.time() - start_time, best_performance, perf_metric, best_profile))
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sys.stdout.write('Time %d | Best %d %s [ for %s ]\r'%(time.time() - start_time, best_performance, perf_metric, best_profile))
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sys.stdout.flush()
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sys.stdout.write('\n')
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return population
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