我有以下Python字典:
{1: [('4', 0.761),
('6', 0.752),
('5', 0.741),
('15', 0.671),
('8', 0.657),
('1', 0.649),
('10', 0.649),
('11', 0.645),
('2', 0.637),
('7', 0.632),
('17', 0.632),
('19', 0.623),
('13', 0.623),
('20', 0.617),
('9', 0.617),
('3', 0.601),
('16', 0.601),
('14', 0.601),
('18', 0.594),
('12', 0.581)],
2: [('4', 0.773),
('5', 0.755),
('6', 0.751),
('8', 0.666),
('11', 0.651),
('2', 0.642),
('12', 0.637),
('10', 0.637),
('17', 0.623),
('3', 0.617),
('16', 0.617),
('1', 0.612),
('18', 0.606),
('14', 0.606),
('13', 0.606),
('15', 0.599),
('7', 0.599),
('19', 0.599),
('9', 0.593),
('20', 0.562)]}
字典中有2个键(1和2)，我想要做的是获得每个值组合之间的笛卡尔积…元组中的第一个值表示代理的编号(例如:'4')，元组的第二个值表示相应的分数。
期望输出:
4,4,0.767(两值平均值)
4、5 0758
等等…
有解决这个问题的办法吗?
谢谢。
对于这种特殊情况(字典中只有两个键)，以下操作应该足够了:
[(x[0], y[0], (x[1]+y[1])/2) for x in d[1] for y in d[2]]
我想包含完整的输出，但它有400行。这是其中的一部分。
[('4', '4', 0.767),
('4', '5', 0.758),
('4', '6', 0.756),
('4', '8', 0.7135),
('4', '11', 0.706),
('4', '2', 0.7015),
import itertools
from operator import itemgetter
# this a general solution and will compute cartesian product for
# more than 2 lists in the dictionary
d = {1: [('4', 0.761),
('6', 0.752),
............
('9', 0.593),
('20', 0.562)]}
# create product of tuples
l = list(itertools.product(*d.values()))
# use this to compute average
avg = lambda x: (','.join(map(itemgetter(0),x)), float("{0:.3f}".format(sum(map(itemgetter(1),x)) / 2 )))
print(*map(avg,l))
('4,4', 0.767) ('4,5', 0.758) ('4,6', 0.756) ('4,8', 0.714) ('4,11', 0.706) ('4,2', 0.702) ('4,12', 0.699) ('4,10', 0.699) ('4,17', 0.692) ('4,3', 0.689) ('4,16', 0.689) ('4,1', 0.686) ('4,18', 0.683) ('4,14', 0.683) ('4,13', 0.683) ('4,15', 0.68) ('4,7', 0.68) ('4,19', 0.68) ('4,9', 0.677) ('4,20', 0.661) ('6,4', 0.762) ('6,5', 0.754) ('6,6', 0.752) ('6,8', 0.709) ('6,11', 0.702) ('6,2', 0.697) ('6,12', 0.695) ('6,10', 0.695) ('6,17', 0.688) ('6,3', 0.684) ('6,16', 0.684) ('6,1', 0.682) ('6,18', 0.679) ('6,14', 0.679) ('6,13', 0.679) ('6,15', 0.675) ('6,7', 0.675) ('6,19', 0.675) ('6,9', 0.672) ('6,20', 0.657) ('5,4', 0.757) ('5,5', 0.748) ('5,6', 0.746) ('5,8', 0.704) ('5,11', 0.696) ('5,2', 0.692) ('5,12', 0.689) ('5,10', 0.689) ('5,17', 0.682) ('5,3', 0.679) ('5,16', 0.679) ('5,1', 0.676) ('5,18', 0.673) ('5,14', 0.673) ('5,13', 0.673) ('5,15', 0.67) ('5,7', 0.67) ('5,19', 0.67) ('5,9', 0.667) ('5,20', 0.651) ('15,4', 0.722) ('15,5', 0.713) ('15,6', 0.711) ('15,8', 0.669) ('15,11', 0.661) ('15,2', 0.657) ('15,12', 0.654) ('15,10', 0.654) ('15,17', 0.647) ('15,3', 0.644) ('15,16', 0.644) ('15,1', 0.641) ('15,18', 0.639) ('15,14', 0.639) ('15,13', 0.639) ('15,15', 0.635) ('15,7', 0.635) ('15,19', 0.635) ('15,9', 0.632) ('15,20', 0.617) ('8,4', 0.715) ('8,5', 0.706) ('8,6', 0.704) ('8,8', 0.661) ('8,11', 0.654) ('8,2', 0.649) ('8,12', 0.647) ('8,10', 0.647) ('8,17', 0.64) ('8,3', 0.637) ('8,16', 0.637) ('8,1', 0.635) ('8,18', 0.631) ('8,14', 0.631) ('8,13', 0.631) ('8,15', 0.628) ('8,7', 0.628) ('8,19', 0.628) ('8,9', 0.625) ('8,20', 0.61) ('1,4', 0.711) ('1,5', 0.702) ('1,6', 0.7) ('1,8', 0.657) ('1,11', 0.65) ('1,2', 0.645) ('1,12', 0.643) ('1,10', 0.643) ('1,17', 0.636) ('1,3', 0.633) ('1,16', 0.633) ('1,1', 0.631) ('1,18', 0.627) ('1,14', 0.627) ('1,13', 0.627) ('1,15', 0.624) ('1,7', 0.624) ('1,19', 0.624) ('1,9', 0.621) ('1,20', 0.606) ('10,4', 0.711) ('10,5', 0.702) ('10,6', 0.7) ('10,8', 0.657) ('10,11', 0.65) ('10,2', 0.645) ('10,12', 0.643) ('10,10', 0.643) ('10,17', 0.636) ('10,3', 0.633) ('10,16', 0.633) ('10,1', 0.631) ('10,18', 0.627) ('10,14', 0.627) ('10,13', 0.627) ('10,15', 0.624) ('10,7', 0.624) ('10,19', 0.624) ('10,9', 0.621) ('10,20', 0.606) ('11,4', 0.709) ('11,5', 0.7) ('11,6', 0.698) ('11,8', 0.655) ('11,11', 0.648) ('11,2', 0.643) ('11,12', 0.641) ('11,10', 0.641) ('11,17', 0.634) ('11,3', 0.631) ('11,16', 0.631) ('11,1', 0.629) ('11,18', 0.625) ('11,14', 0.625) ('11,13', 0.625) ('11,15', 0.622) ('11,7', 0.622) ('11,19', 0.622) ('11,9', 0.619) ('11,20', 0.604) ('2,4', 0.705) ('2,5', 0.696) ('2,6', 0.694) ('2,8', 0.651) ('2,11', 0.644) ('2,2', 0.639) ('2,12', 0.637) ('2,10', 0.637) ('2,17', 0.63) ('2,3', 0.627) ('2,16', 0.627) ('2,1', 0.625) ('2,18', 0.621) ('2,14', 0.621) ('2,13', 0.621) ('2,15', 0.618) ('2,7', 0.618) ('2,19', 0.618) ('2,9', 0.615) ('2,20', 0.6) ('7,4', 0.703) ('7,5', 0.694) ('7,6', 0.692) ('7,8', 0.649) ('7,11', 0.641) ('7,2', 0.637) ('7,12', 0.635) ('7,10', 0.635) ('7,17', 0.627) ('7,3', 0.625) ('7,16', 0.625) ('7,1', 0.622) ('7,18', 0.619) ('7,14', 0.619) ('7,13', 0.619) ('7,15', 0.615) ('7,7', 0.615) ('7,19', 0.615) ('7,9', 0.613) ('7,20', 0.597) ('17,4', 0.703) ('17,5', 0.694) ('17,6', 0.692) ('17,8', 0.649) ('17,11', 0.641) ('17,2', 0.637) ('17,12', 0.635) ('17,10', 0.635) ('17,17', 0.627) ('17,3', 0.625) ('17,16', 0.625) ('17,1', 0.622) ('17,18', 0.619) ('17,14', 0.619) ('17,13', 0.619) ('17,15', 0.615) ('17,7', 0.615) ('17,19', 0.615) ('17,9', 0.613) ('17,20', 0.597) ('19,4', 0.698) ('19,5', 0.689) ('19,6', 0.687) ('19,8', 0.645) ('19,11', 0.637) ('19,2', 0.633) ('19,12', 0.63) ('19,10', 0.63) ('19,17', 0.623) ('19,3', 0.62) ('19,16', 0.62) ('19,1', 0.617) ('19,18', 0.615) ('19,14', 0.615) ('19,13', 0.615) ('19,15', 0.611) ('19,7', 0.611) ('19,19', 0.611) ('19,9', 0.608) ('19,20', 0.593) ('13,4', 0.698) ('13,5', 0.689) ('13,6', 0.687) ('13,8', 0.645) ('13,11', 0.637) ('13,2', 0.633) ('13,12', 0.63) ('13,10', 0.63) ('13,17', 0.623) ('13,3', 0.62) ('13,16', 0.62) ('13,1', 0.617) ('13,18', 0.615) ('13,14', 0.615) ('13,13', 0.615) ('13,15', 0.611) ('13,7', 0.611) ('13,19', 0.611) ('13,9', 0.608) ('13,20', 0.593) ('20,4', 0.695) ('20,5', 0.686) ('20,6', 0.684) ('20,8', 0.641) ('20,11', 0.634) ('20,2', 0.629) ('20,12', 0.627) ('20,10', 0.627) ('20,17', 0.62) ('20,3', 0.617) ('20,16', 0.617) ('20,1', 0.615) ('20,18', 0.611) ('20,14', 0.611) ('20,13', 0.611) ('20,15', 0.608) ('20,7', 0.608) ('20,19', 0.608) ('20,9', 0.605) ('20,20', 0.59) ('9,4', 0.695) ('9,5', 0.686) ('9,6', 0.684) ('9,8', 0.641) ('9,11', 0.634) ('9,2', 0.629) ('9,12', 0.627) ('9,10', 0.627) ('9,17', 0.62) ('9,3', 0.617) ('9,16', 0.617) ('9,1', 0.615) ('9,18', 0.611) ('9,14', 0.611) ('9,13', 0.611) ('9,15', 0.608) ('9,7', 0.608) ('9,19', 0.608) ('9,9', 0.605) ('9,20', 0.59) ('3,4', 0.687) ('3,5', 0.678) ('3,6', 0.676) ('3,8', 0.633) ('3,11', 0.626) ('3,2', 0.621) ('3,12', 0.619) ('3,10', 0.619) ('3,17', 0.612) ('3,3', 0.609) ('3,16', 0.609) ('3,1', 0.607) ('3,18', 0.603) ('3,14', 0.603) ('3,13', 0.603) ('3,15', 0.6) ('3,7', 0.6) ('3,19', 0.6) ('3,9', 0.597) ('3,20', 0.582) ('16,4', 0.687) ('16,5', 0.678) ('16,6', 0.676) ('16,8', 0.633) ('16,11', 0.626) ('16,2', 0.621) ('16,12', 0.619) ('16,10', 0.619) ('16,17', 0.612) ('16,3', 0.609) ('16,16', 0.609) ('16,1', 0.607) ('16,18', 0.603) ('16,14', 0.603) ('16,13', 0.603) ('16,15', 0.6) ('16,7', 0.6) ('16,19', 0.6) ('16,9', 0.597) ('16,20', 0.582) ('14,4', 0.687) ('14,5', 0.678) ('14,6', 0.676) ('14,8', 0.633) ('14,11', 0.626) ('14,2', 0.621) ('14,12', 0.619) ('14,10', 0.619) ('14,17', 0.612) ('14,3', 0.609) ('14,16', 0.609) ('14,1', 0.607) ('14,18', 0.603) ('14,14', 0.603) ('14,13', 0.603) ('14,15', 0.6) ('14,7', 0.6) ('14,19', 0.6) ('14,9', 0.597) ('14,20', 0.582) ('18,4', 0.683) ('18,5', 0.674) ('18,6', 0.672) ('18,8', 0.63) ('18,11', 0.623) ('18,2', 0.618) ('18,12', 0.615) ('18,10', 0.615) ('18,17', 0.609) ('18,3', 0.605) ('18,16', 0.605) ('18,1', 0.603) ('18,18', 0.6) ('18,14', 0.6) ('18,13', 0.6) ('18,15', 0.597) ('18,7', 0.597) ('18,19', 0.597) ('18,9', 0.593) ('18,20', 0.578) ('12,4', 0.677) ('12,5', 0.668) ('12,6', 0.666) ('12,8', 0.623) ('12,11', 0.616) ('12,2', 0.611) ('12,12', 0.609) ('12,10', 0.609) ('12,17', 0.602) ('12,3', 0.599) ('12,16', 0.599) ('12,1', 0.597) ('12,18', 0.593) ('12,14', 0.593) ('12,13', 0.593) ('12,15', 0.59) ('12,7', 0.59) ('12,19', 0.59) ('12,9', 0.587) ('12,20', 0.572)
类似，但使用reduce支持任意数量的键在开始dict
from itertools import product
# this reduces a list of (agent, score) tuples into a tuple with
# ([list-of-agents], total_score)
reductor = lambda acc, element: (acc[0]+[element[0]], acc[1]+element[1])
# process one entry of the cartesian products
def process_product(product):
(agents, total) = reduce(reductor, product, ([], 0.0))
return "{0},{1:f}".format( ','.join(agents), total/len(agents) )
# product(*d.values()) returns the cartesian products - process+print 'm all
for x in map(process_product, product(*d.values())):
print x
如果您喜欢手动生成列表，您可以遍历列表L中的项的索引并增加索引。
L = [2, 3, 4]
def generate_variant(L):
index = [0] * len(L)
while (index):
print(index)
index = next_index(index, L)
def next_index(index, L):
n = len(index)
i = n - 1
while True:
index[i] += 1
if index[i] < L[i]:
break
index[i] = 0
i -= 1
if (i < 0):
return None
return index
generate_variant(L)
[0, 0, 0]
[0, 0, 1]
[0, 0, 2]
[0, 0, 3]
[0, 1, 0]
[0, 1, 1]
[0, 1, 2]
[0, 1, 3]
[0, 2, 0]
[0, 2, 1]
[0, 2, 2]
[0, 2, 3]
[1, 0, 0]
[1, 0, 1]
[1, 0, 2]
[1, 0, 3]
[1, 1, 0]
[1, 1, 1]
...
[1, 2, 3]