错误:使用序列设置数组元素.巨蟒/Numpy [英] Error: Setting an array element with a sequence. Python / Numpy

问题描述

尝试将一个数组分配给另一个特定于数组的位置时，我收到此错误. 在创建简单列表和进行此类分配之前，我一直在这样做.但是Numpy比简单列表要快，我现在正尝试使用它.

I'm receiving this error when trying to assign an array to another array specific position. I was doing this before creating simple lists and doing such assignment. But Numpy is faster than simple lists and I was trying to use it now.

问题是因为我有一个存储一些数据的2D数组，并且在我的代码中，例如，我要计算每个位置值的梯度，所以我创建了另一个2D数组，其中每个位置为其存储梯度值.

The problem is cause I have a 2D array that stores some data and, in my code, I have, e.g., to calculate the gradient for each position value, so I create another 2D array where each position stores the gradient for its value.

import numpy as np

cols = 2
rows = 3

# This works
matrix_a = []

for i in range(rows):
    matrix_a.append([0.0] * cols)

print matrix_a    
matrix_a[0][0] = np.matrix([[0], [0]])    
print matrix_a

# This doesn't work
matrix_b = np.zeros((rows, cols)) 
print matrix_b   

matrix_b[0, 0] = np.matrix([[0], [0]])

发生的事情是因为我有一个定义 np.zeros((rows，cols))对象的类，该对象存储有关某些数据的信息，从而简化了例如图像数据.

What happens is 'cause I have a class defining a np.zeros((rows, cols)) object, that stores information about some data, simplifying, e.g., images data.

class Data2D(object):
    def __init__(self, rows=200, cols=300):
        self.cols = cols
        self.rows = rows
        # The 2D data structure
        self.data = np.zeros((rows, cols))

在一种特定的方法中，我必须为此数据计算梯度，该梯度是2 x 2的矩阵(因此，我想使用 ndarray 而不是简单的数组)，为此，我创建了该对象的另一个实例来存储此新数据，其中每个点(像素)都应存储其梯度.我当时使用的是简单列表，但可以，但是尽管我可以通过numpy获得一些性能.

In a specific method, I have to calculate the gradient for this data, which is a 2 x 2 matrix (cause of this I would like to use ndarray, and not a simple array), and, to do this, I create another instance of this object to store this new data, in which each point (pixel) should store its gradient. I was using simple lists, which works, but I though I could gain some performance with numpy.

有办法解决这个问题吗?还是做这种事情的更好方法? 我知道我可以将数组类型定义为 object ，但是我不知道这样做是否会降低性能.

There is a way to work around this? Or a better way to do such thing? I know that I can define the array type to object, but I don't know if I lose performance doing such thing.

谢谢.

推荐答案

麻烦的是，matrix_b默认为float dtype.在我的机器上，检查

The trouble is that matrix_b is defaulting to a float dtype. On my machine, checking

matrix_b.dtype

返回dtype('float64').要创建一个可以容纳任何内容的numpy数组，您可以手动将dtype设置为object，这将允许您在其中放置一个矩阵:

returns dtype('float64'). To create a numpy array that can hold anything, you can manually set dtype to object, which will allow you to place a matrix inside of it:

matrix_b = np.zeros((rows, cols), dtype=object)
matrix_b[0, 0] = np.matrix([[0], [0], [1]])

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