Using numpy to build an array of all combinations of two arrays
Cartesian product function by Stackoverflow user, pv.
See Answer here.
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A numpy implementation ~5× faster than using itertools.
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| import numpy as np | |
| def cartesian(arrays, out=None): | |
| """ | |
| Generate a cartesian product of input arrays. | |
| Parameters | |
| ---------- | |
| arrays : list of array-like | |
| 1-D arrays to form the cartesian product of. | |
| out : ndarray | |
| Array to place the cartesian product in. | |
| Returns | |
| ------- | |
| out : ndarray | |
| 2-D array of shape (M, len(arrays)) containing cartesian products | |
| formed of input arrays. | |
| Examples | |
| -------- | |
| >>> cartesian(([1, 2, 3], [4, 5], [6, 7])) | |
| array([[1, 4, 6], | |
| [1, 4, 7], | |
| [1, 5, 6], | |
| [1, 5, 7], | |
| [2, 4, 6], | |
| [2, 4, 7], | |
| [2, 5, 6], | |
| [2, 5, 7], | |
| [3, 4, 6], | |
| [3, 4, 7], | |
| [3, 5, 6], | |
| [3, 5, 7]]) | |
| """ | |
| arrays = [np.asarray(x) for x in arrays] | |
| dtype = arrays[0].dtype | |
| n = np.prod([x.size for x in arrays]) | |
| if out is None: | |
| out = np.zeros([n, len(arrays)], dtype=dtype) | |
| m = n / arrays[0].size | |
| out[:,0] = np.repeat(arrays[0], m) | |
| if arrays[1:]: | |
| cartesian(arrays[1:], out=out[0:m,1:]) | |
| for j in xrange(1, arrays[0].size): | |
| out[j*m:(j+1)*m,1:] = out[0:m,1:] | |
| return out |
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