Generate the Cartesian product of input iterables.
Parameters:
-----------
*args : list of iterable
Input iterables for which the Cartesian product will be computed.
outputdtype (optional) :
dtype of output array
Returns:
--------
numpy.ndarray
2D array containing the Cartesian product of the input iterables.
Notes:
------
This function efficiently computes the Cartesian product of the input iterables
using Cython implementation. It outperforms the equivalent functionality provided
by itertools.product, and returns a NumPy array (not a list of tuples like itertools.product).
Examples:
---------
from cythoncartesian import cartesian_product
# Mem usage 2 GB
# Out[4]:
# array([[0, 0, 0, ..., 0, 0, 0],
# [1, 0, 0, ..., 0, 0, 0],
# [2, 0, 0, ..., 0, 0, 0],
# ...,
# [5, 7, 7, ..., 7, 7, 7],
# [6, 7, 7, ..., 7, 7, 7],
# [7, 7, 7, ..., 7, 7, 7]], dtype=uint8)
# %timeit dataresults=cartesian_product(*args2)
# 2.65 s ± 163 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# dataresults.shape
# Out[6]: (134217728, 9)
# itertools.product
# Mem usage 16 GB
# import itertools
# %timeit (list(itertools.product(*args2)))
# 11.5 s ± 203 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# --------------------------------------------------------------------------
# Mem usage 1.2 GB
# args = [[411, 231.33, 4342, 12341, 1.142, 1.33, 13],
# [34, 231.33, 4132, 1231],
# [14, 44, 23454.1, .1, 23, 1],
# [9, 12, 1, 3, 32, 23, 21, 31],
# [1114, 44, 23454.1, .1, 23, 1],
# ]+[list(range(6)),list(range(3)),list(range(3)),list(range(3))
# ,list(range(3))]+[list(range(6))]
# %timeit dataresults=cartesian_product(*args)
# 621 ms ± 46.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# Mem usage 4 GB
# import itertools
# %timeit (list(itertools.product(*args)))
# 2.13 s ± 26.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)