Numpy向量化2D阵列运算错误 [英] Numpy vectorized 2d array operation error

问题描述

我正在尝试将矢量化函数应用于numpy行的2-d数组，并且遇到了ValueError: setting an array element with a sequence.

I'm trying to apply a vectorized function over a 2-d array in numpy row-wise, and I'm encountering ValueError: setting an array element with a sequence.

import numpy as np

X = np.array([[0, 1], [2, 2], [3, 0]], dtype=float)
coeffs = np.array([1, 1], dtype=float)

np.apply_along_axis(
    np.vectorize(lambda row: 1.0 / (1.0 + np.exp(-coeffs.dot(row)))),
    0, X
)

我不完全知道如何解释此错误.如何设置具有序列的数组元素?

I don't totally know how to interpret this error. How am I setting an array element with a sequence?

当我在单行上测试lambda函数时，它可以工作并返回单个浮点数.某种程度上，它在此矢量化函数的范围内失败了，这使我相信该矢量化函数是错误的，或者我没有正确使用apply_along_axis.

When I test the lambda function on a single row, it works and returns a single float. Somehow it's failing within the scope of this vectorized function, which leads me to believe that either the vectorized function is wrong or I'm not using apply_along_axis correctly.

在这种情况下可以使用向量化函数吗?如果是这样，怎么办?向量化函数可以接受数组吗?还是我误解了文档?

Is it possible to use a vectorized function in this context? If so, how? Can a vectorized function take an array or am I misunderstanding the documentation?

推荐答案

您正在将X的第二个轴与coeffs的唯一轴相减.因此，您只需将np.dot(X,coeffs)用作sum-reductions.

You are sum-reducing the second axis of X against the only axis of coeffs. So, you could simply use np.dot(X,coeffs) for sum-reductions.

因此，矢量化解决方案应为-

Thus, a vectorized solution would be -

1.0 / (1.0 + np.exp(-X.dot(coeffs)))

样品运行-

In [227]: X = np.array([[0, 1], [2, 2], [3, 0]], dtype=float)
     ...: coeffs = np.array([1, 1], dtype=float)
     ...: 

# Using list comprehension    
In [228]: [1.0 / (1.0 + np.exp(-coeffs.dot(x))) for x in X]
Out[228]: [0.7310585786300049, 0.98201379003790845, 0.95257412682243336]

# Using proposed method
In [229]: 1.0 / (1.0 + np.exp(-X.dot(coeffs)))
Out[229]: array([ 0.73105858,  0.98201379,  0.95257413])

使用np.apply_along_axis的正确方法是放下np.vectorize并沿X的第二个轴应用它，即X-

The correct way to use np.apply_along_axis would be to drop np.vectorize and apply it along the second axis of X, i.e. every row of X -

np.apply_along_axis(lambda row: 1.0 / (1.0 + np.exp(-coeffs.dot(row))), 1,X)

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