我有一个由单个矩阵的下对角线元素组成的数组。我可以按照如何在 NumPy 中将三角形矩阵转换为正方形中的方法将其转换为完整矩阵？. 对于单个矩阵，示例如下所示：

# Create the lower diagonal elements of a 6x6 matrix.
ld = np.arange(21)

# Create full 6x6 matrix
x = np.zeros((6,6))

# Stuff lower triangular values into it
x[np.tril_indices(6)] = ld

# Populate upper triangular elements
x = x + x.T

# Fix diagonals (they got doubled)
diag_idx = [0, 2, 5, 9, 14, 20]
np.fill_diagonal(x, ld[diag_idx])

print(x)

我们得到了预期的完整矩阵

[[ 0.  1.  3.  6. 10. 15.]
 [ 1.  2.  4.  7. 11. 16.]
 [ 3.  4.  5.  8. 12. 17.]
 [ 6.  7.  8.  9. 13. 18.]
 [10. 11. 12. 13. 14. 19.]
 [15. 16. 17. 18. 19. 20.]]

现在我想将其扩展到具有 N 组下对角线元素的数组，并想要返回 N 个完整矩阵的数组。前者有形状（N, 21），后者有形状（N, 6, 6）。我将单个矩阵示例扩展为包含 2 个矩阵的示例

# Two sets of lower diagonal elements
ld = np.arange(2*21).reshape(2, 21)

# Two sets of full 6x6 matrices
x = np.zeros((ld.shape[0], 6, 6))

# Find the lower triangular indices of each row and stuff them with the 
# values from the corresponding row in the lower diagonal array
x[:, np.tril_indices(6)] = ld[:]

# Populate upper triangular elements
x[:] = x[:] + x[:].T

# Fix diagonals (they got doubled)
diag_idx = [0, 2, 5, 9, 14, 20]
np.fill_diagonal(x[:], ld[:][diag_idx])

但我在线上出现形状不匹配x[:, np.tril_indices(6)] = ld[:]

ValueError: shape mismatch: value array of shape (2,21) could not be broadcast to indexing result of shape (2,2,21,6)

我可以对 N 组较低的对角线值进行正常的 Python 循环，但我试图通过 Numpy 来完成这一切。关于我的索引出错的地方有什么建议吗？

X 中的期望值为：

[[[ 0.  1.  3.  6. 10. 15.]
  [ 1.  2.  4.  7. 11. 16.]
  [ 3.  4.  5.  8. 12. 17.]
  [ 6.  7.  8.  9. 13. 18.]
  [10. 11. 12. 13. 14. 19.]
  [15. 16. 17. 18. 19. 20.]],
 [[21., 22., 24., 27., 31., 36.],
  [22., 23., 25., 28., 32., 37.],
  [24., 25., 26., 29., 33., 38.],
  [27., 28., 29., 30., 34., 39.],
  [31., 32., 33., 34., 35., 40.],
  [36., 37., 38., 39., 40., 41.]]]