random but consistent permutation of two numpy arrays

Question

currently im facing a problem regarding the permutation of 2 numpy arrays of different row sizes, i know how to to utilize the np.random.shuffle function but i cannot seem to find a solution to my specific problem, the examples from the numpy documentation only refers to nd arrays with the same row sizes, e.g x.shape=[10][784] y.shape=[10][784]

I want to permute/random shuffle the column values in a consistent order for both arrays with those shapes:x.shape=[60000][784], y.shape=[10000][784].

e.g.
x[59000] = [0,1,2,3,4,5,6,7,8,9] y[9999] = [0,1,2,3,4,5,6,7,8,9]

After the permutation, both of them should be shuffled in the same consistent way e.g.

x[59000] = [3,0,1,6,7,2,9,8,4,5] y[9999] = [3,0,1,6,7,2,9,8,4,5]

The shuffle order needs to be consistent over the two arrays which have different row sizes. I seem to get a ValueError: Found input variables with inconsistent numbers of samples: [60000, 10000]" Any ideas on how to fix this issue? Really appreciate any help!

Possible duplicate of Numpy shuffle multidimensional array by row only, keep column order unchanged — Arya McCarthy
– Arya McCarthy, Commented Apr 7, 2017 at 16:17

user2357112 · Accepted Answer · 2017-04-07 16:22:28Z

1

Stick the arrays together and permute the combined array:

merged = numpy.concatenate([x, y])
numpy.shuffle(merged.T)
x, y = numpy.split(merged, [x.shape[0]])

answered Apr 7, 2017 at 16:22

user2357112

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Community · Accepted Answer · 2017-05-23 11:46:39Z

0

Check also old threads Better way to shuffle two numpy arrays in unison

Or compute a permutation ahead

your_permutation = np.shuffle(np.array([0, 1, 2, 3, 4, 5]))
i = np.argsort(your_permutation)
x = x[i]
y = y[i]

edited May 23, 2017 at 11:46

CommunityBot

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answered Apr 7, 2017 at 16:49

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