本文整理汇总了Python中population.Population.size方法的典型用法代码示例。如果您正苦于以下问题：Python Population.size方法的具体用法？Python Population.size怎么用？Python Population.size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.size方法的3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: evolve_population
# 需要导入模块: from population import Population [as 别名]
# 或者: from population.Population import size [as 别名]
def evolve_population(population):
new_population = Population(population.size(), False, population.tour_manager)
elitism_offset = 0
# save the fittest tour
if elitism:
new_population.save_tour(0, population.get_fittest())
elitism_offset = 1
# crossover
for i in range(elitism_offset, new_population.size()):
parent_1 = tournament_selection(population)
parent_2 = tournament_selection(population)
child = crossover(parent_1, parent_2)
new_population.save_tour(i, child)
# mutate population
for i in range(elitism_offset, new_population.size()):
mutate(new_population.get_tour(i))
return new_population示例2: nextGeneration
# 需要导入模块: from population import Population [as 别名]
# 或者: from population.Population import size [as 别名]
def nextGeneration(self,population,data,dataEvaluation,heuristicStrategy,attributeCount):
nextGen = Population(1+(population.size()*2),data,dataEvaluation,False,heuristicStrategy)
nextGen.saveIndividuals(0,population.getFittest())
count = 1
for index in range(population.size()):
challenger = self.tournament(population,data,dataEvaluation,heuristicStrategy,attributeCount)
champion = self.tournament(population,data,dataEvaluation,heuristicStrategy,attributeCount)
nextGenChallenger, nextGenChamp = self.crossover(challenger,champion,int(np.floor(challenger.size()/2)),heuristicStrategy)
nextGen.saveIndividuals(count, nextGenChamp)
nextGen.saveIndividuals(count+1, nextGenChallenger)
count += 2
for index in range(nextGen.size()):
neo = self.xmen(nextGen.getIndividual(index))
nextGen.saveIndividuals(index,neo)
return nextGen示例3: Environment
# 需要导入模块: from population import Population [as 别名]
# 或者: from population.Population import size [as 别名]
# Create an Environment
R, P, A, B, O = 10, 0.5, 1, 0.5, 0
test_env = Environment(R,P,A,B,O,name="Test environment")
# Evaluate the state of the environment at a given time
t = 10
E,C = test_env.evaluate(t)
# E and C must be of type list of float to allow for multiple environments
# Consider three equal environments:
E,C = np.array([E,E,E]), np.array([C,C,C])
# Let the animals react to the environment
random_duck.react(E,C)
# Access some information about the animal
print(random_duck.mismatch)
print(random_duck.lifetime_payoff(np.array([1000])))
# Create some more animals and combine them to a Population
more_ducks = [Animal(),Animal(),Animal()]
duck_population = Population(len(more_ducks),more_ducks)
# Make them react to the environment and breed with variable population size
duck_population.react(E,C)
duck_population.breed_variable()
# Display the new size of the population
print(duck_population.size())