I have a 2D numpy array which has only 0s in (N,N) size. I randomly want to insert twelve 1s to this array while keeping the diagonal locations' value equal to 0. What I have tried until now is :
import numpy as np
def func(N=20):
x= np.zeros((N,N))
for m in range(N):
for n in range(N):
if m == n:
x[m][n] == 0
else:
if np.count_nonzero(x) <= 12:
x.fill(1)
return (np.count_nonzero)
print (x)
The output I am getting is an N,N array full of 1s. I cannot stop inserting the 1s after their quantity reaches to 12. How can I fix it?
Since you are using NumPy and if you are okay with an alternate vectorized solution, here's one with masking and choosing those places with np.random.choice -
def random_off_diag_fill(N, num_rand = 12, fillval=1):
# Initialize array
x= np.zeros((N,N),dtype=type(fillval))
# Generate flat nondiagonal indices using masking
idx = np.flatnonzero(~np.eye(N,dtype=bool))
# Select num_rand random indices from those and set those
# in a flattened view of the array to be as fillval
x.ravel()[np.random.choice(idx, num_rand, replace=0)] = fillval
return x
Sample runs -
In [57]: random_off_diag_fill(N=8, num_rand=12, fillval=1)
Out[57]:
array([[0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0, 0]])
In [63]: random_off_diag_fill(N=5, num_rand=12, fillval=2.45)
Out[63]:
array([[ 0. , 0. , 0. , 0. , 2.45],
[ 2.45, 0. , 2.45, 0. , 2.45],
[ 0. , 2.45, 0. , 2.45, 2.45],
[ 2.45, 2.45, 0. , 0. , 0. ],
[ 2.45, 2.45, 0. , 2.45, 0. ]])