Permute Rows And Columns Of A Matrix

Assuming that I have the following matrix/array: array([[0, 0, 1, 1, 1], [0, 0, 1, 0, 1], [1, 1, 0, 1, 1], [1, 0, 1, 0, 0], [1, 1, 1, 0, 0]]) and I wan

Solution 1:

You can perform the swap in a one-liner using integer array indexing:

a = np.array([[0, 0, 1, 1, 1],
              [0, 0, 1, 0, 1],
              [1, 1, 0, 1, 1],
              [1, 0, 1, 0, 0],
              [1, 1, 1, 0, 0]])
b = a.copy()

# map 0 -> 4and1 -> 3 (N.B. Python indexing starts at 0 rather than 1)
a[[4, 3, 0, 1]] = a[[0, 1, 4, 3]]print(repr(a))
# array([[1, 1, 1, 0, 0],
#        [1, 0, 1, 0, 0],
#        [1, 1, 0, 1, 1],
#        [0, 0, 1, 0, 1],
#        [0, 0, 1, 1, 1]])

Note that array indexing always returns a copy rather than a view - there's no way to swap arbitrary rows/columns of an array without generating a copy.

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In this particular case you could avoid the copy by using slice indexing, which returns a view rather than a copy:

b = b[::-1] # invert the row orderprint(repr(b))
# array([[1, 1, 1, 0, 0],#        [1, 0, 1, 0, 0],#        [1, 1, 0, 1, 1],#        [0, 0, 1, 0, 1],#        [0, 0, 1, 1, 1]])

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Update:

You can use the same indexing approach to swap columns.

c= np.arange(25).reshape(5,5)
print(repr(c))# array([[ 0,  1,  2,  3,  4],#        [ 5,  6,  7,  8,  9],#        [10, 11, 12, 13, 14],#        [15, 16, 17, 18, 19],#        [20, 21, 22, 23, 24]])c[[0,4],:]=c[[4,0],:]# swap row 0 with row 4...c[:,[0,4]]=c[:,[4,0]]# ...and column 0 with column 4

print(repr(c))# array([[24, 21, 22, 23, 20],#        [ 9,  6,  7,  8,  5],#        [14, 11, 12, 13, 10],#        [19, 16, 17, 18, 15],#        [ 4,  1,  2,  3,  0]])

I've used a different example array in this case - your version will yield an identical output after performing the row/column swaps which makes it difficult to understand what's going on.

Solution 2:

I found a solution to do what I want (though it is expensive):

a2 = deepcopy(a1)
first= randint(0, 5, 10)
second= randint(0, 5, 10)
for i inrange(len(first)):
    a = deepcopy(a2)
    a2[first[i],:] = a[second[i],:]
    a2[second[i],:] = a[first[i],:]
for i inrange(len(first)):
    a = deepcopy(a2)
    a2[:,first[i]] = a[:,second[i]]
    a2[:,second[i]] = a[:,first[i]] 

Basically, I am doing 10 random switches. However, I need to copy the matrix many times. Anyway, a2 now represents a graph which is isomorphic with a1.