本文整理汇总了Python中population.Population.evaluate方法的典型用法代码示例。如果您正苦于以下问题：Python Population.evaluate方法的具体用法？Python Population.evaluate怎么用？Python Population.evaluate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类population.Population的用法示例。
在下文中一共展示了Population.evaluate方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: Experiment
# 需要导入模块: from population import Population [as 别名]
# 或者: from population.Population import evaluate [as 别名]
class Experiment(object):
POP_SIZE = 500
RANDOM_ACTORS_NUMBER = 50
RANK_PROBABILITY_CONSTANT = 0.2
def __init__(self):
self.world = World(self)
self.population = Population(rank_probability=self.RANK_PROBABILITY_CONSTANT, reverse_sort=False)
self.pop_index = 1
self.current_generation = 1
def start(self):
for _ in xrange(self.POP_SIZE):
self.population.append(self.create_actor())
self.world.start()
@staticmethod
def evaluate_fitness(actor):
if actor.dead:
return 0xFFFFFFF
x = actor.position[0] - float(actor.world.point[0])
y = actor.position[1] - float(actor.world.point[1])
vec_len = float(math.sqrt((x**2) + (y**2)))
color_diff = float(abs(0x00F - actor.body.color))
vertex_handicap = float(len(actor.body.polygon)**3)
return vec_len + color_diff + vertex_handicap
def create_actor(self, genotype=None):
actor = Actor(self.world, self.pop_index, self.evaluate_fitness, genotype=genotype, position=(0, 0))
self.pop_index += 1
return actor
def next_generation(self):
self.world.stop()
self.population.evaluate()
new_pop = Population(rank_probability=self.RANK_PROBABILITY_CONSTANT, reverse_sort=False)
for _ in xrange(self.RANDOM_ACTORS_NUMBER):
new_pop.append(self.create_actor())
for _ in xrange(self.POP_SIZE - self.RANDOM_ACTORS_NUMBER):
new_genotype = Genotype.reproduce(self.population.select_by_rank().genotype,
self.population.select_by_rank().genotype)
new_pop.append(self.create_actor(genotype=new_genotype))
self.population = new_pop
self.current_generation += 1
self.world.start()
def update(self):
for actor in self.population:
actor.update()
def stop(self):
self.world.stop()
self.population.select_best_fitness().brain_graph()示例2: test_evalutaion
# 需要导入模块: from population import Population [as 别名]
# 或者: from population.Population import evaluate [as 别名]
def test_evalutaion(self):
pop1 = Population(4)
result = pop1.evaluate()
self.assertEqual(len(pop1.fitness_table), 4)
self.assertEqual(pop1.fitness_table, result)
self.assertIn(pop1.pop[0].fitness, [0, 1])
self.assertEqual(pop1.pop[0].fitness, result[0])示例3: test_breeding
# 需要导入模块: from population import Population [as 别名]
# 或者: from population.Population import evaluate [as 别名]
def test_breeding(self):
pop1 = Population(4)
result = pop1.evaluate()
pop1.selection()
pop1.breed()
self.assertEqual(len(pop1.pop), 4)
self.assertEqual(pop1.selected, [])
self.assertEqual(pop1.selected_indexes, [])
self.assertEqual(pop1.fitness_table, [])示例4: test_selection
# 需要导入模块: from population import Population [as 别名]
# 或者: from population.Population import evaluate [as 别名]
def test_selection(self):
pop1 = Population(4)
result = pop1.evaluate()
indexes, selected = pop1.selection()
self.assertEqual(pop1.selected_indexes, indexes)
self.assertEqual(pop1.selected, selected)
self.assertEqual(len(selected), 2)
self.assertEqual(len(indexes), 2)
for ind in selected:
self.assertEqual(ind.fitness, 1)
for i in indexes:
self.assertEqual(pop1.pop[i].fitness,1)