Generation of numpy arrays for permutations with constraints

Question

To make a long story short, I'm trying to generate all the possible permutations of a set of numpy arrays. I have three numbers [j,k,m] and I would like to specify a maximum value for each one [J,K,M]. How would I then get all the combinations of arrays under these values? How could I force the k values to always be even as well? For instance:

So with the max values set to [1,2,2], the permutations would be: [0,0,0], [0,0,1], [0,0,2], [0,2,0], [0,2,1], [0,2,2], [1,0,0], [1,0,1] ...

I realise I don't have any example to code to show but I'm afraid I have literally no idea where to start with this.

From other answers it seems like sympy would be of some use?

mathfux · Accepted Answer · 2020-02-14 22:31:15Z

1

I found answer that might be interested for you here and generalised it. So you can construct list of possible values for each item like so:

X = [[0, 1], [0, 1, 2], [0, 1, 2]]

And then use:

np.array(np.meshgrid(*X)).T.reshape(-1, len(X))

Output contains 18 items that you wanted. Actually, if you have only maximum values [J, K, L], you can construct X using X = [range(J+1), range(K+1), range(L+1)]

answered Feb 14, 2020 at 22:31

mathfux

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jolene Over a year ago

Thanks! This is exactly the expertise that makes this site so great! I think my question was a little unclear, I needed the middle value of each array to be even but using a different solution on a different site I got what I need ed:

def permute(j, k, m):     a =  np.stack([[i, 2*l, n] for i in range(j + 1) for l in range(k + 1) for n in range(m + 1)])     return a

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