Scipy 最小化 fmin - 语法问题 [英] Scipy minimize fmin - problems with syntax

问题描述

我有一个函数，它接受多个参数(一个数组和两个浮点数)并返回一个标量(浮点数).现在我想通过改变两个参数来最小化这个函数:两个浮点数.数组在函数内部解包"，然后使用其内容(数组和浮点数).

I have a function which takes several arguments (one array and two floats) and returns a scalar (float). Now I want to minimize this function by varying two of the arguments: the two floats. The array is "unpacked" inside the function at its contents (arrays and floats) are then used.

如何使用 SciPy 的 fmin 函数完成此操作?我很难为此找出正确的语法..

How can this be done using SciPy's fmin function? I am having a very hard time figuring out the right syntax for this..

函数类似于:

def func(x, y, data)
    data1=data[0]
    data2=data[...]
    ...
    ...
    result = ...x...y...data1...data2... #result is a scalar (float)
    return result

在这种情况下 scipy.optimize.fmin 应该是什么样子?

What should scipy.optimize.fmin look like in this case?

optimize.fmin(func, ???)

非常感谢！

一切顺利，p.p.

推荐答案

scipy 假定参数在一个数组中.你可以定义一个辅助函数:

scipy assumes that the arguments are in an array. You can define a helper function:

def helper(xy):
    return func(xy[0], xy[1], data)

并使用 optimize.fmin 将其最小化:

and minimize it with optimize.fmin:

optimize.fmin(helper, np.array([x0, y0]), ...)

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