Copy upper triangle to lower triangle in a python matrix
Solution 1:
To do this in NumPy, without using a double loop, you can use tril_indices
. Note that depending on your matrix size, this may be slower that adding the transpose and subtracting the diagonal though perhaps this method is more readable.
>>> i_lower = np.tril_indices(n, -1)
>>> matrix[i_lower] = matrix.T[i_lower] # make the matrix symmetric
Be careful that you do not try to mix tril_indices
and triu_indices
as they both use row major indexing, i.e., this does not work:
>>> i_upper = np.triu_indices(n, 1)
>>> i_lower = np.tril_indices(n, -1)
>>> matrix[i_lower] = matrix[i_upper] # make the matrix symmetric
>>> np.allclose(matrix.T, matrix)
False
Solution 2:
The easiest AND FASTEST (no loop) way to do this for NumPy arrays is the following:
The following is ~3x faster for 100x100 matrices compared to the accepted answer and roughly the same speed for 10x10 matrices.
import numpy as np
X= np.array([[0., 2., 3.],
[0., 0., 6.],
[0., 0., 0.]])
X = X + X.T - np.diag(np.diag(X))
print(X)
#array([[0., 2., 3.],
# [2., 0., 6.],
# [3., 6., 0.]])
Note that the matrix must either be upper triangular to begin with or it should be made upper triangular as follows.
rng = np.random.RandomState(123)
X = rng.randomint(10, size=(3, 3))
print(X)
#array([[2, 2, 6],
# [1, 3, 9],
# [6, 1, 0]])
X = np.triu(X)
X = X + X.T - np.diag(np.diag(X))
print(X)
#array([[2, 2, 6],
# [2, 3, 9],
# [6, 9, 0]])
Solution 3:
If I understand the question correctly, I believe this will work
for i in range(num_rows):
for j in range(i, num_cols):
matrix[j][i] = matrix[i][j]
Solution 4:
Heres a better one i guess :
>>> a = np.arange(16).reshape(4, 4)
>>> print(a)
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> iu = np.triu_indices(4,1)
>>> il = (iu[1],iu[0])
>>> a[il]=a[iu]
>>> a
array([[ 0, 1, 2, 3],
[ 1, 5, 6, 7],
[ 2, 6, 10, 11],
[ 3, 7, 11, 15]])