I want to generate a random n x n binary matrix using NumPy, where:
0 or 111For example, a valid matrix might be
[[1 0 0]
[0 0 1]
[0 1 0]]
while an invalid one is
[[1 0 0]
[0 0 1]
[0 0 1]]
I tried doing the following, but I couldn't figure out how to shuffle the values in each column using a unique index. How do I generate a matrix that adheres to the above constraints?
N = 10
a = np.zeros((N,N))
a[0,:] = 1
Create an n by n identity matrix, and then shuffle all of the rows. The identity matrix is a binary matrix where each row and column sums to one, and shuffling the rows preserves this property:
n = 5
result = np.identity(n)
np.random.shuffle(result)
print(result)
This will output something like:
[[0. 1. 0. 0. 0.]
[0. 0. 0. 0. 1.]
[0. 0. 0. 1. 0.]
[1. 0. 0. 0. 0.]
[0. 0. 1. 0. 0.]]
Use np.random.permutation to create random column indices and then use advanced indexing fill indices with 1s:
N = 10
a = np.zeros((N,N), dtype=int)
a[np.arange(N), np.random.permutation(N)] = 1
a
array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 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, 0, 0, 0, 0, 1, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]])
