追加没有按预期工作 [英] Append not working like expected

问题描述

我有以下问题:对于遗传算法，我正在创建 5 个突变并将它们存储在一个准备好的列表中(参见下面的代码).

i have the following Problem: For a genetic algorithm i'm creating 5 mutations and store them in a prepared list (see code below).

这是我想要附加变异驱动程序的函数:

This is my function where i want to append the mutated drivers:

def startNewRunFromScratch(self):
    self.log.logBlue('Starting new run from scratch', 2, 0)
    parameterSet = []
    parameterSet.append(Parameter('TEST', 0.5, 0, 1))

    defaultGDriver = GDriver(parameterSet)
    gDriverList = []
    gDriverList.append(defaultGDriver)
    m = Mutation(self.mutationRate)
    for i in range(1, self.populationSize, 1):
        g = m.mutate(defaultGDriver)
        self.log.log('After mutaion: '+str(g.parameterSet[0].value), 0, 2)
        gDriverList.append(g)
    self.startSuite(gDriverList)

这里是 startSuite 原型函数:

And here the the startSuite prototype function:

def startSuite(self, gDriverList):
    self.log.logSuccess('Starting suite', 1, 0)
    for g in gDriverList:
        self.log.log('Inside suite: '+str(g.parameterSet[0].value), 0, 2)

问题是，输出与我的逻辑不匹配:

The problem is, that the output does not match my logic:

Starting new run from scratch
        After mutaion: 0.5
        After mutaion: 0.5
        After mutaion: 0.5
        After mutaion: 0.740296236666

Starting suite
        Inside suite: 0.740296236666
        Inside suite: 0.740296236666
        Inside suite: 0.740296236666
        Inside suite: 0.740296236666
        Inside suite: 0.740296236666

预期输出:

Inside suite: 0.5
Inside suite: 0.5
Inside suite: 0.5
Inside suite: 0.5
Inside suite: 0.740296236666

有人知道如何解决这个问题吗?也许我错过了什么.

Does anyone have an good idea how to solve this problem? Maybee im missing something.

推荐答案

您反复追加相同的 Mutation，结果在列表中多次引用它.如果你想要不同的 Mutations，你必须制作新的.(我假设这就是您认为的问题​​"，因为您从未明确说明输出有什么问题.)

You repeatedly append the same Mutation, and end up with multiple references to it in the list. If you want different Mutations, you have to make new ones. (I assume that's what you think is the "problem", as you never explicitly say what is wrong about the output.)

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