Reputation: 4578
Suppose I have a list die_faces = [1, 2, 3, 4, 5, 6]
. I want to generate all 36 possible results for rolling two dice: (1, 1)
, (1, 2)
, (2, 1)
etc. If I try using permutations
from the itertools
standard library:
>>> import itertools
>>> die_faces = [1, 2, 3, 4, 5, 6]
>>> list(itertools.permutations(die_faces, 2))
[(1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (2, 1), (2, 3), (2, 4), (2, 5), (2, 6), (3, 1), (3, 2), (3, 4), (3, 5), (3, 6), (4, 1), (4, 2), (4, 3), (4, 5), (4, 6), (5, 1), (5, 2), (5, 3), (5, 4), (5, 6), (6, 1), (6, 2), (6, 3), (6, 4), (6, 5)]
there are only 30 results, missing the ones where the same number comes up on both dice. It seems that it only generates permutations without repetitions. How can I fix this?
Upvotes: 142
Views: 130969
Reputation: 44525
In this case, a list comprehension is not particularly needed.
Given
import itertools as it
seq = range(1, 7)
r = 2
Code
list(it.product(seq, repeat=r))
Details
Unobviously, Cartesian product can generate subsets of permutations. However, it follows that:
product
Permutations with replacement, nr
[x for x in it.product(seq, repeat=r)]
Permutations without replacement, n!
[x for x in it.product(seq, repeat=r) if len(set(x)) == r]
# Equivalent
list(it.permutations(seq, r))
Consequently, all combinatoric functions could be implemented from product
:
combinations_with_replacement
implemented from product
combinations
implemented from permutations
, which can be implemented with product
(see above)Upvotes: 4
Reputation: 3561
I think I found a solution using only lambdas
, map
and reduce
.
product_function = lambda n: reduce(lambda x, y: x+y, map(lambda i: list(map(lambda j: (i, j), np.arange(n))), np.arange(n)), [])
Essentially I'm mapping a first lambda function that given a row, iterates the columnns
list(map(lambda j: (i, j), np.arange(n)))
then this is used as the output of a new lambda function
lambda i:list(map(lambda j: (i, j), np.arange(n)))
which is mapped across all the possible rows
map(lambda i: list(map(lambda j: (i, j), np.arange(n))), np.arange(m))
and then we reduce all the resulting lists into one.
Can also use two different numbers.
prod= lambda n, m: reduce(lambda x, y: x+y, map(lambda i: list(map(lambda j: (i, j), np.arange(m))), np.arange(n)), [])
Upvotes: 0
Reputation: 188064
You are looking for the Cartesian Product.
In mathematics, a Cartesian product (or product set) is the direct product of two sets.
In your case, this would be {1, 2, 3, 4, 5, 6}
x {1, 2, 3, 4, 5, 6}
.
itertools
can help you there:
import itertools
x = [1, 2, 3, 4, 5, 6]
[p for p in itertools.product(x, repeat=2)]
[(1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (2, 1), (2, 2), (2, 3),
(2, 4), (2, 5), (2, 6), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6),
(4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6), (5, 1), (5, 2), (5, 3),
(5, 4), (5, 5), (5, 6), (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6)]
To get a random dice roll (in a totally inefficient way):
import random
random.choice([p for p in itertools.product(x, repeat=2)])
(6, 3)
Upvotes: 223
Reputation: 5
First, you'll want to turn the generator returned by itertools.permutations(list) into a list first. Then secondly, you can use set() to remove duplicates Something like below:
def permutate(a_list):
import itertools
return set(list(itertools.permutations(a_list)))
Upvotes: -3
Reputation: 838376
You're not looking for permutations - you want the Cartesian Product. For this use product from itertools:
from itertools import product
for roll in product([1, 2, 3, 4, 5, 6], repeat = 2):
print(roll)
Upvotes: 43
Reputation: 319651
In python 2.7 and 3.1 there is a itertools.combinations_with_replacement
function:
>>> list(itertools.combinations_with_replacement([1, 2, 3, 4, 5, 6], 2))
[(1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (2, 2), (2, 3), (2, 4),
(2, 5), (2, 6), (3, 3), (3, 4), (3, 5), (3, 6), (4, 4), (4, 5), (4, 6),
(5, 5), (5, 6), (6, 6)]
Upvotes: 14