This problem sounds simple at first glance, but turns out to be a lot more complicated than it seems. It\'s got me stumped for the moment.
There are 52c5 = 2,598,960
Your problem sounded interesting, so i simple tried to implements it by just looping over all possible hands in a sorted way. I've not looked at your code in details, but it seems my implementation is quite different from yours. Guess what count of hands my script found: 160537
Are you sure, the number on wikipedia is correct?
count = 0
for a1 in range(13):
c1 = 0
for a2 in range(a1, 13):
for c2 in range(2):
if a1==a2 and c1==c2:
continue
nc3 = 2 if c1==c2 else 3
for a3 in range(a2, 13):
for c3 in range(nc3):
if (a1==a3 and c1>=c3) or (a2==a3 and c2>=c3):
continue
nc4 = nc3+1 if c3==nc3-1 else nc3
for a4 in range(a3, 13):
for c4 in range(nc4):
if (a1==a4 and c1>=c4) or (a2==a4 and c2>=c4) or (a3==a4 and c3>=c4):
continue
nc5 = nc4+1 if (c4==nc4-1 and nc4!=4) else nc4
for a5 in range(a4, 13):
for c5 in range(nc5):
if (a1==a5 and c1>=c5) or (a2>=a5 and c2>=c5) or (a3==a5 and c3>=c5) or (a4==a5 and c4>=c5):
continue
#print([(a1,c1),(a2,c2),(a3,c3),(a4,c4),(a5,c5)])
count += 1
print("result: ",count)
Your overall approach is sound. I'm pretty sure the problem lies with your make_canonical
function. You can try printing out the hands with num_cards set to 3 or 4 and look for equivalencies that you've missed.
I found one, but there may be more:
# The inputs are equivalent and should return the same value
print make_canonical([8, 12 | 1]) # returns [8, 13]
print make_canonical([12, 8 | 1]) # returns [12, 9]
For reference, below is my solution (developed prior to looking at your solution). I used a depth-first search instead of a breadth-first search. Also, instead of writing a function to transform a hand to canonical form, I wrote a function to check if a hand is canonical. If it's not canonical, I skip it. I defined rank = card % 13 and suit = card / 13. None of those differences are important.
import collections
def canonical(cards):
"""
Rules for a canonical hand:
1. The cards are in sorted order
2. The i-th suit must have at least many cards as all later suits. If a
suit isn't present, it counts as having 0 cards.
3. If two suits have the same number of cards, the ranks in the first suit
must be lower or equal lexicographically (e.g., [1, 3] <= [2, 4]).
4. Must be a valid hand (no duplicate cards)
"""
if sorted(cards) != cards:
return False
by_suits = collections.defaultdict(list)
for suit in range(0, 52, 13):
by_suits[suit] = [card%13 for card in cards if suit <= card < suit+13]
if len(set(by_suits[suit])) != len(by_suits[suit]):
return False
for suit in range(13, 52, 13):
suit1 = by_suits[suit-13]
suit2 = by_suits[suit]
if not suit2: continue
if len(suit1) < len(suit2):
return False
if len(suit1) == len(suit2) and suit1 > suit2:
return False
return True
def deal_cards(permutations, n, cards):
if len(cards) == n:
permutations.append(list(cards))
return
start = 0
if cards:
start = max(cards) + 1
for card in range(start, 52):
cards.append(card)
if canonical(cards):
deal_cards(permutations, n, cards)
del cards[-1]
def generate_permutations(n):
permutations = []
deal_cards(permutations, n, [])
return permutations
for cards in generate_permutations(5):
print cards
It generates the correct number of permutations:
Cashew:~/$ python2.6 /tmp/cards.py | wc
134459
Here's a Python solution that makes use of numpy and generates the canonical deals as well as their multiplicity. I use Python's itertools module to create all 24 possible permutations of 4 suits and then to iterate over all 2,598,960 possible 5-card deals. Each deal is permuted and converted to a canonical id in just 5 lines. It's quite fast as the loop only goes through 10 iterations to cover all deals and is only needed to manage the memory requirements. All the heavy lifting is done efficiently in numpy except for the use of itertools.combinations
. It's a shame this is not supportedly directly in numpy.
import numpy as np
import itertools
# all 24 permutations of 4 items
s4 = np.fromiter(itertools.permutations(range(4)), dtype='i,i,i,i').view('i').reshape(-1,4)
c_52_5 = 2598960 # = binomial(52,5) : the number of 5-card deals in ascending card-value order
block_n = c_52_5/10
def all5CardDeals():
'''iterate over all possible 5-card deals in 10 blocks of 259896 deals each'''
combos = itertools.combinations(range(52),5)
for i in range(0, c_52_5, block_n):
yield np.fromiter(combos, dtype='i,i,i,i,i', count=block_n).view('i').reshape(-1,5)
canon_id = np.empty(c_52_5, dtype='i')
# process all possible deals block-wise.
for i, block in enumerate(all5CardDeals()):
rank, suit = block/4, block%4 # extract the rank and suit of each card
d = rank[None,...]*4 + s4[:,suit] # generate all 24 permutations of the suits
d.sort(2) # re-sort into ascending card-value order
# convert each deal into a unique integer id
deal_id = d[...,0]+52*(d[...,1]+52*(d[...,2]+52*(d[...,3]+52*d[...,4])))
# arbitrarily select the smallest such id as the canonical one
canon_id[i*block_n:(i+1)*block_n] = deal_id.min(0)
# find the unique canonical deal ids and the index into this list for each enumerated hand
unique_id, indices = np.unique(canon_id, return_inverse=True)
print len(unique_id) # = 134459
multiplicity = np.bincount(indices)
print multiplicity.sum() # = 2598960 = c_52_5
Take a look here:
http://specialk-coding.blogspot.com/
http://code.google.com/p/specialkpokereval/
These regard a 5-card hand (and a 7-card hand) as an integer, the sum the individual cards, which is independent of the suit. Exactly what you need.
This is part of a scheme for quickly ranking 7- and 5-card hands, written in Objective-C and Java.
Chances are you really want to generate the number of distinct hands, in the sense of non-equivalent. In that case, according to the wikipedia article there are 7462 possible hands. Here is a python snippet that will enumerate them all.
The logic is simple: there is one hand for each 5-set of ranks; in addition, if all the ranks are distinct, another, different kind of hand can be formed by making all the suits match.
count = 0
for i in range(0,13):
for j in range (i,13):
for k in range(j,13):
for l in range(k,13):
for m in range(l,13):
d = len(set([i,j,k,l,m])) # number of distinct ranks
if d == 1: continue # reject nonsensical 5-of-a-kind
count += 1
# if all the ranks are distinct then
# count another hand with all suits equal
if d == 5: count += 1
print count # 7462
Generating equivalence classes for 5 card hands is not an easy task. When I need this I usually use the http://www.vpgenius.com/ webpage. At http://www.vpgenius.com/video-poker/games/ you can choose which variety of poker game you need, and in the "Programming tab" you have an section on "Unique Suit Patterns". So just copying that and loading into program might be easier than trying to generate your own.