Numpy vectorize() 正在展平整个数组 [英] Numpy vectorize() is flattening the whole array

问题描述

我的输入是一个 numpy 元组数组

My input is a numpy array of tuples

values = np.array([(4, 5, 2, 18), (4, 7, 3, 8)])

我的功能如下:

def outerFunc(values):
    print(values)
    def innerFunc(values):
        print(values)
        mean = np.mean(values)
        result = 0
        for i in range(len(values)):
            result += math.pow(values[i] - mean, 2)
        return result

    if isinstance(values, np.ndarray):
        return np.vectorize(innerFunc)(values)
    else:
        return innerFunc(values)

虽然我想对1维进行向量化，即在innerFunc内部执行一个元组，但我的输出如下:

Although I want to vectorize over 1 dimension, i.e., one tuple is executed inside the innerFunc, but my output is as follows:

[[ 4  5  2 18]
 [ 4  7  3  8]]
4
...

这意味着 vectorize 函数在 2 维上进行矢量化，并且我收到以下错误:

Which means the vectorize function is vectorizing over 2 dimensions, and I am getting the following error:

for i in range(len(values)):
TypeError: object of type 'numpy.int64' has no len()

要进行哪些更改以使输出为:

What changes to make so that the output is:

[[ 4  5  2 18]
 [ 4  7  3  8]]
[4  5  2 18]
...

类似的东西

谢谢.

编辑

当元组长度不同时，它可以正常工作，任何人都可以解释这一点，

It is working as accepted when the tuples are different length, can anyone explain this,

例如，我的输入是

np.array([(4, 5, 2, 18), (4, 7, 3,)])

和函数打印

[(4, 5, 2, 18) (4, 7, 3)]
(4, 5, 2, 18)
(4, 7, 3)

返回值为

[158.75         8.66666667]

因此，只有当所有元组长度相同时，函数才会将它们视为数字.

So, only when all the tuples are the same length, the function treats them as numbers.

谢谢.

推荐答案

In [1]: values = np.array([(4, 5, 2, 18), (4, 7, 3, 8)])                        
In [2]: values                                                                  
Out[2]: 
array([[ 4,  5,  2, 18],
       [ 4,  7,  3,  8]])
In [3]: values.shape                                                            
Out[3]: (2, 4)
In [4]: x=np.array([(4, 5, 2, 18), (4, 7, 3,)])                                 
In [5]: x                                                                       
Out[5]: array([(4, 5, 2, 18), (4, 7, 3)], dtype=object)
In [6]: x.shape                                                                 
Out[6]: (2,)

values 是一个二维数值数组.np.vectorize 将 8 个元素中的每一个传递给您的内部函数，一次一个.它不会按行迭代.

values is a 2d numeric array. np.vectorize passes each of the 8 elements, one at a time, to your inner function. It does not iterate by rows.

x 是一个具有 2 个元素(元组)的一维数组.vectorize 会将这些元组中的每一个传递给您的内部.

x is a 1d array with 2 elements (tuples). vectorize will pass each of those tuples to your inner.

不要在简单迭代可行的情况下使用 vectorize - 它会更慢且更难正确使用.

Don't use vectorize when a simple iteration would work - it's slower and harder to use right.

并在创建数组后查看数组，确保您了解形状和数据类型.不要做假设.

And look at your arrays after you create them, making sure you understand the shape and dtype. Don't make assumptions.

这篇关于Numpy vectorize() 正在展平整个数组的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！