numpy ValueError：shape mismatch：

y53ybaqx  于 2023-04-21  发布在  其他
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我正在尝试使用二进制集创建上三角矩阵：
我创建了所有可能的n x n二进制矩阵，只有上三角形值（不包括对角线）。我可以为n = 3的情况做一个for循环。这是3 x 3矩阵，有2^3 = 8个置换。所以8个矩阵。当我做n = 4，5，6，...时，我得到了一个形状不匹配错误。不知道为什么for循环不会迭代。
请参见下面的代码示例：

n = 3
# Now we need to make permutation of binary upper_count tuples
# This will give 2^n sets of n binary values.  
from itertools import *
t1 = list(product([0,1],repeat= n)) 
print(t1)
print('\n')

# Now we create 2^n matrices of size nxn:
for i in t1:
  m1 = np.zeros((n,n))

  indices = np.triu_indices_from(m1,1) 
  upper = m1[indices]

  m1[indices] = i
  print(m1)
  print('\n')

给出以下所需输出：

[(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)]

[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

[[0. 0. 0.]
 [0. 0. 1.]
 [0. 0. 0.]]

[[0. 0. 1.]
 [0. 0. 0.]
 [0. 0. 0.]]

[[0. 0. 1.]
 [0. 0. 1.]
 [0. 0. 0.]]

[[0. 1. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

[[0. 1. 0.]
 [0. 0. 1.]
 [0. 0. 0.]]

[[0. 1. 1.]
 [0. 0. 0.]
 [0. 0. 0.]]

[[0. 1. 1.]
 [0. 0. 1.]
 [0. 0. 0.]]

对于n = 4：

  • 我得到以下错误*我错过了什么？
[(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 1, 0), (0, 0, 1, 1), (0, 1, 0, 0), (0, 1, 0, 1), (0, 1, 1, 0), (0, 1, 1, 1), (1, 0, 0, 0), (1, 0, 0, 1), (1, 0, 1, 0), (1, 0, 1, 1), (1, 1, 0, 0), (1, 1, 0, 1), (1, 1, 1, 0), (1, 1, 1, 1)]

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-68-75f42bce2989> in <cell line: 8>()
     12   upper = m1[indices]
     13 
---> 14   m1[indices] = i
     15   print(m1)
     16   print('\n')

ValueError: shape mismatch: value array of shape (4,) could not be broadcast to indexing result of shape (6,)
numpy

1条答案

按热度按时间
nwsw7zdq

nwsw7zdq1#

我的t1是错的。我没有数对。

from itertools import *
size_n = (n*(n-1))/2 
size_n = int(size_n)
t1 = list(product([0,1],repeat= size_n))

更新代码：

n = 4
# Now we need to make permutation of binary upper_count tuples
from itertools import *
size_n = (n*(n-1))/2 
size_n = int(size_n)
t1 = list(product([0,1],repeat= size_n))
print(t1)
print('\n')

for i in t1:
  print(i)
  m1 = np.zeros((n,n))

  indices = np.triu_indices_from(m1,1)
  print('\n') 
  # print(m1)
  upper = m1[indices] # error here.  
  # print(upper)

  m1[indices] = i
  print(m1)
  print('\n')

预期结果

[(0, 0, 0, 0, 0, 0), (0, 0, 0, 0, 0, 1), (0, 0, 0, 0, 1, 0), (0, 0, 0, 0, 1, 1), (0, 0, 0, 1, 0, 0), (0, 0, 0, 1, 0, 1), (0, 0, 0, 1, 1, 0), (0, 0, 0, 1, 1, 1), (0, 0, 1, 0, 0, 0), (0, 0, 1, 0, 0, 1), (0, 0, 1, 0, 1, 0), (0, 0, 1, 0, 1, 1), (0, 0, 1, 1, 0, 0), (0, 0, 1, 1, 0, 1), (0, 0, 1, 1, 1, 0), (0, 0, 1, 1, 1, 1), (0, 1, 0, 0, 0, 0), (0, 1, 0, 0, 0, 1), (0, 1, 0, 0, 1, 0), (0, 1, 0, 0, 1, 1), (0, 1, 0, 1, 0, 0), (0, 1, 0, 1, 0, 1), (0, 1, 0, 1, 1, 0), (0, 1, 0, 1, 1, 1), (0, 1, 1, 0, 0, 0), (0, 1, 1, 0, 0, 1), (0, 1, 1, 0, 1, 0), (0, 1, 1, 0, 1, 1), (0, 1, 1, 1, 0, 0), (0, 1, 1, 1, 0, 1), (0, 1, 1, 1, 1, 0), (0, 1, 1, 1, 1, 1), (1, 0, 0, 0, 0, 0), (1, 0, 0, 0, 0, 1), (1, 0, 0, 0, 1, 0), (1, 0, 0, 0, 1, 1), (1, 0, 0, 1, 0, 0), (1, 0, 0, 1, 0, 1), (1, 0, 0, 1, 1, 0), (1, 0, 0, 1, 1, 1), (1, 0, 1, 0, 0, 0), (1, 0, 1, 0, 0, 1), (1, 0, 1, 0, 1, 0), (1, 0, 1, 0, 1, 1), (1, 0, 1, 1, 0, 0), (1, 0, 1, 1, 0, 1), (1, 0, 1, 1, 1, 0), (1, 0, 1, 1, 1, 1), (1, 1, 0, 0, 0, 0), (1, 1, 0, 0, 0, 1), (1, 1, 0, 0, 1, 0), (1, 1, 0, 0, 1, 1), (1, 1, 0, 1, 0, 0), (1, 1, 0, 1, 0, 1), (1, 1, 0, 1, 1, 0), (1, 1, 0, 1, 1, 1), (1, 1, 1, 0, 0, 0), (1, 1, 1, 0, 0, 1), (1, 1, 1, 0, 1, 0), (1, 1, 1, 0, 1, 1), (1, 1, 1, 1, 0, 0), (1, 1, 1, 1, 0, 1), (1, 1, 1, 1, 1, 0), (1, 1, 1, 1, 1, 1)]

(0, 0, 0, 0, 0, 0)

[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 0, 0, 0, 0, 1)

[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 0, 0, 0, 1, 0)

[[0. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 0, 0, 0, 1, 1)

[[0. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 0, 0, 1, 0, 0)

[[0. 0. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 0, 0, 1, 0, 1)

[[0. 0. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 0, 0, 1, 1, 0)

[[0. 0. 0. 0.]
 [0. 0. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 0, 0, 1, 1, 1)

[[0. 0. 0. 0.]
 [0. 0. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 0, 1, 0, 0, 0)

[[0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 0, 1, 0, 0, 1)

[[0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 0, 1, 0, 1, 0)

[[0. 0. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 0, 1, 0, 1, 1)

[[0. 0. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 0, 1, 1, 0, 0)

[[0. 0. 0. 1.]
 [0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 0, 1, 1, 0, 1)

[[0. 0. 0. 1.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 0, 1, 1, 1, 0)

[[0. 0. 0. 1.]
 [0. 0. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 0, 1, 1, 1, 1)

[[0. 0. 0. 1.]
 [0. 0. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 1, 0, 0, 0, 0)

[[0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 1, 0, 0, 0, 1)

[[0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 1, 0, 0, 1, 0)

[[0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 1, 0, 0, 1, 1)

[[0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 1, 0, 1, 0, 0)

[[0. 0. 1. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 1, 0, 1, 0, 1)

[[0. 0. 1. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 1, 0, 1, 1, 0)

[[0. 0. 1. 0.]
 [0. 0. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 1, 0, 1, 1, 1)

[[0. 0. 1. 0.]
 [0. 0. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 1, 1, 0, 0, 0)

[[0. 0. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 1, 1, 0, 0, 1)

[[0. 0. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 1, 1, 0, 1, 0)

[[0. 0. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 1, 1, 0, 1, 1)

[[0. 0. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 1, 1, 1, 0, 0)

[[0. 0. 1. 1.]
 [0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 1, 1, 1, 0, 1)

[[0. 0. 1. 1.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(0, 1, 1, 1, 1, 0)

[[0. 0. 1. 1.]
 [0. 0. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(0, 1, 1, 1, 1, 1)

[[0. 0. 1. 1.]
 [0. 0. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 0, 0, 0, 0, 0)

[[0. 1. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 0, 0, 0, 0, 1)

[[0. 1. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 0, 0, 0, 1, 0)

[[0. 1. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 0, 0, 0, 1, 1)

[[0. 1. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 0, 0, 1, 0, 0)

[[0. 1. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 0, 0, 1, 0, 1)

[[0. 1. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 0, 0, 1, 1, 0)

[[0. 1. 0. 0.]
 [0. 0. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 0, 0, 1, 1, 1)

[[0. 1. 0. 0.]
 [0. 0. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 0, 1, 0, 0, 0)

[[0. 1. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 0, 1, 0, 0, 1)

[[0. 1. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 0, 1, 0, 1, 0)

[[0. 1. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 0, 1, 0, 1, 1)

[[0. 1. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 0, 1, 1, 0, 0)

[[0. 1. 0. 1.]
 [0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 0, 1, 1, 0, 1)

[[0. 1. 0. 1.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 0, 1, 1, 1, 0)

[[0. 1. 0. 1.]
 [0. 0. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 0, 1, 1, 1, 1)

[[0. 1. 0. 1.]
 [0. 0. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 1, 0, 0, 0, 0)

[[0. 1. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 1, 0, 0, 0, 1)

[[0. 1. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 1, 0, 0, 1, 0)

[[0. 1. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 1, 0, 0, 1, 1)

[[0. 1. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 1, 0, 1, 0, 0)

[[0. 1. 1. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 1, 0, 1, 0, 1)

[[0. 1. 1. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 1, 0, 1, 1, 0)

[[0. 1. 1. 0.]
 [0. 0. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 1, 0, 1, 1, 1)

[[0. 1. 1. 0.]
 [0. 0. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 1, 1, 0, 0, 0)

[[0. 1. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 1, 1, 0, 0, 1)

[[0. 1. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 1, 1, 0, 1, 0)

[[0. 1. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 1, 1, 0, 1, 1)

[[0. 1. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 1, 1, 1, 0, 0)

[[0. 1. 1. 1.]
 [0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 1, 1, 1, 0, 1)

[[0. 1. 1. 1.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]

(1, 1, 1, 1, 1, 0)

[[0. 1. 1. 1.]
 [0. 0. 1. 1.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

(1, 1, 1, 1, 1, 1)

[[0. 1. 1. 1.]
 [0. 0. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 0.]]
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