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Input-dependent models now activated for all the operations

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This commit is contained in:
Philippe Tillet
2014-10-04 08:58:11 +02:00
parent 044419f9f0
commit fc8b450a7c
5 changed files with 89 additions and 90 deletions
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15
autotune/python/genetic.py
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@@ -40,13 +40,15 @@ class GeneticOperators(object):
self.ParameterType = TemplateType.Parameters
self.build_template = build_template
self.cache = {}
self.indpb = 0.05
self.out = out
self.genome_info = {
vcl.atidlas.VectorAxpyTemplate: [3,4,4,vcl.atidlas.FetchingPolicy],
vcl.atidlas.MatrixAxpyTemplate: [3,3,3,3,3,vcl.atidlas.FetchingPolicy],
vcl.atidlas.RowWiseReductionTemplate: [3,3,3,4,vcl.atidlas.FetchingPolicy],
vcl.atidlas.MatrixProductTemplate: [3,3,3,3,3,3,3,vcl.atidlas.FetchingPolicy,vcl.atidlas.FetchingPolicy,3]
}[TemplateType]
self.indpb = 1.0/sum([1 if x==vcl.atidlas.FetchingPolicy else x for x in self.genome_info])
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin)
@@ -149,7 +151,7 @@ class GeneticOperators(object):
ind.fitness.values = fit
hof.update(population)
while time.time() - start_time < maxtime:
while time.time() - start_time < maxtime and gen < maxgen:
# Vary the population
offspring = []
for _ in xrange(mu):
@@ -166,9 +168,8 @@ class GeneticOperators(object):
offspring.append(ind)
else: # Apply reproduction
offspring.append(random.choice(population))
#~ for x in offspring:
#~ print self.decode(x)
#for x in offspring:
#print self.decode(x)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = self.toolbox.map(self.evaluate, invalid_ind)
@@ -180,9 +181,9 @@ class GeneticOperators(object):
population[:] = self.toolbox.select(population + offspring, mu)
#Update
gen = gen + 1
best_profile = '(%s)'%','.join(map(str,self.decode(hof[0])));
best_profile = '(%s)'%','.join(map(str,self.decode(hof[0])))
best_performance = compute_perf(hof[0].fitness.values[0])
sys.stdout.write('Time %d | Best %d %s [ for %s ]\r'%(time.time() - start_time, best_performance, perf_metric, best_profile))
sys.stdout.write('Generation %d | Time %d | Best %d %s [ for %s ]\r'%(gen, time.time() - start_time, best_performance, perf_metric, best_profile))
sys.stdout.flush()
sys.stdout.write('\n')
return self.decode(hof[0])