的Python：在附加功能到一个数组FOR循环 [英] Python: Add function to an array in a FOR loop

问题描述

也许这是一个简单的问题，但我无法到目前为止找到有关它的任何信息。
对于 numpy的优化我需要一个函数数组。我需要的功能的数量取决于其应优化当前对象。
我已经想通了如何动态创建这些功能，但现在我想将它们存储在一个这样的数组：

Maybe this is a simple issue, but I could not find any information about it so far. For an optimization in numpy I need an array of functions. The number of functions I need depends on the current object which shall be optimized. I have already figured out how to create these functions dynamically, but now I would like to store them in an array like this:

myArray = zeros(x)   
for i in range(x):
  myArray[i] = createFunction(i)

如果我运行此我得到一个类型不匹配：
    浮动（）参数必须是一个字符串或数字，而不是功能

If I run this I get a type mismatch: float() argument must be a string or a number, not 'function'

直接创建数组运行良好：

Creating the array directly works well:

  myArray = array([createFunction(0)...])

但是，因为我不知道我需要的功能的数量，这正是我想要prevent。

But because I don't know the number of functions I need, this is exactly what I want to prevent.

推荐答案

嗯，我明白了。你真的意味着的函数数组的。

Ah, I get it. You really do mean an array of functions.

类型不匹配错误的产生是由于在调用零默认创建彩车的数组。所以，你原来会工作相反，如果你没有 myArray的= numpy.empty（X，DTYPE = numpy.object）（注意，空使得比零这里）更有意义。稍微更Python化版本是使用一个列表的COM prehension

The type mismatch error arises because the call to zeros creates an array of floats by default. So your original would work if instead you did myArray = numpy.empty(x, dtype=numpy.object) (note that empty makes more sense than zeros here). The slightly more pythonic version is to use a list comprehension

myArray = numpy.array([createFunction(i) for i in range(x)]).

但是，你可能并不需要在所有创建numpy的阵列，这取决于你想用它做什么：

But you might not need to create a numpy array at all, depending on what you want to do with it:

myArray = [createFunction(i) for i in range(x)]

如果你想避免的名单，它可能是最好使用的 numpy.fromfunction 一起的 numpy.vectorize ：

If you want to avoid the list, it might be better to use numpy.fromfunction along with numpy.vectorize:

myArray = numpy.fromfunction(numpy.vectorize(createFunction), 
                             shape=(x,), dtype=numpy.object)

其中，（X，）是一个元组给数组的形状。到矢量是必须的呼叫，因为 fromfunction 假定该函数可以输入数组上工作并返回数组标量和矢量转换功能做到​​这一点。否则numpy的尝试的 DTYPE =对象需要创建彩车组成的数组。

where (x,) is a tuple giving the shape of the array. The call to vectorize is needed because fromfunction assumes that the function can work on an array of inputs and return an array of scalars, and vectorize converts a function to do exactly that. The dtype=object is needed since otherwise numpy tries to create an array of floats.

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