Copy upper triangle to lower triangle in a python matrix

Question

       iluropoda_melanoleuca  bos_taurus  callithrix_jacchus  canis_familiaris
ailuropoda_melanoleuca     0        84.6                97.4                44
bos_taurus                 0           0                97.4              84.6
callithrix_jacchus         0           0                   0              97.4
canis_familiaris           0           0                   0                 0

This is a short version of the python matrix I have. I have the information in the upper triangle. Is there an easy function to copy the upper triangle to the down triangle of the matrix?

Answer 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

Answer 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]])

---

Answer 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]

Answer 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]])

Answer 5

If U is an upper triangular matrix, you can use triu and transpose to make it symmetric:

LDU = triu(U,1)+U.T