could not broadcast input array from shape (87,1) into shape (264,1)

这个错误通常是由于两个数组在进行某种操作时，它们的形状不兼容导致的。具体地说，你正在尝试将一个形状为 (87,1) 的数组广播成一个形状为 (264,1) 的数组，但这是不可能的。

要解决这个问题，你需要检查代码中涉及到的两个数组，确保它们的形状是兼容的。你可以使用 NumPy 的广播机制来处理形状不兼容的数组。如果你不熟悉广播，可以先了解一下 NumPy 的广播机制，它可以帮助你理解为什么有些数组可以进行某些操作，而有些数组不能。

如果你需要进一步帮助，请提供更多的上下文和代码。

相关问题

could not broadcast input array from shape (2) into shape (1,35)

This error message indicates that there is a mismatch between the dimensions of two arrays that you are trying to merge or broadcast together. Specifically, you are trying to broadcast an array with shape (2) onto an array with shape (1, 35), which is not possible.

To resolve this error, you will need to reshape or resize one or both of the arrays so that they have compatible shapes. One approach might be to reshape the (2) array to have shape (1, 2) using the np.reshape() function, and then broadcast it onto the (1, 35) array.

For example:

import numpy as np

a = np.array([1, 2])
b = np.zeros((1, 35))

a_reshaped = np.reshape(a, (1, 2))
b[:a_reshaped.shape[0], :a_reshaped.shape[1]] += a_reshaped

print(b)

This code reshapes the (2) array to have shape (1, 2), and then adds it to the (1, 35) array using array indexing. The resulting output should be a (1, 35) array with the first two elements set to the values of the (2) array.

could not broadcast input array from shape (12) into shape (16)

This error message indicates that you are trying to perform a broadcasting operation on two NumPy arrays that have incompatible shapes. Specifically, you are trying to broadcast an array with 12 elements into an array with 16 elements, which is not possible.

To fix this error, you need to ensure that the shapes of the arrays are compatible for broadcasting. One way to do this is to reshape the array with 12 elements to have the same shape as the array with 16 elements, or vice versa.

For example, if you have an array with shape (12,) and you want to broadcast it with an array with shape (4, 4), you can reshape the first array to have shape (4, 3) using the `reshape` method:

import numpy as np

a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
b = np.zeros((4, 4))

a_reshaped = a.reshape((4, 3))
result = a_reshaped + b

Alternatively, you could reshape the second array to have shape (12,) using the `flatten` method:

import numpy as np

a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
b = np.zeros((4, 4))

b_flat = b.flatten()
result = a + b_flat

Both of these approaches will ensure that the arrays have compatible shapes for broadcasting.