How can I handle very large numbers in Python for poker hand evaluation?
I’m working on speeding up poker hand evaluation in Python, and I thought one approach could be to represent all the card faces and suits as prime numbers, multiplying them together to uniquely represent each hand. Here’s the concept:
class PokerCard:
faces = '23456789TJQKA'
suits = 'cdhs'
facePrimes = [11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 53, 59, 61]
suitPrimes = [2, 3, 5, 7]
def HashVal(self):
return PokerCard.facePrimes[self.cardFace] * PokerCard.suitPrimes[self.cardSuit]
This approach would give each hand a numeric value, which I could use for quick checks like determining how many kings or hearts are in a hand by applying modulo operations. For example, any hand with five or more clubs would divide evenly by 2^5, and any hand with four kings would divide evenly by 59^4.
The issue is that a seven-card hand like AcAdAhAsKdKhKs produces a hash value of approximately 62.7 quadrillion, which requires more than 32 bits for internal representation. Is there a way to store such large numbers in Python that will allow me to perform arithmetic operations on them?
I am looking for a solution involving Python bigint.
Alright, so here’s a simple approach based on my experience: Python’s built-in int type is a great starting point when you need to handle large numbers. Python handles what’s known as python bigint natively, which means it supports arbitrary precision. This is a lifesaver for poker hand evaluation, as it allows you to work with numbers as large as you need without worrying about overflow or running into data size limitations. You can perform arithmetic directly on those large integers.
class PokerCard:
faces = '23456789TJQKA'
suits = 'cdhs'
facePrimes = [11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 53, 59, 61]
suitPrimes = [2, 3, 5, 7]
def HashVal(self):
return int(PokerCard.facePrimes[self.cardFace]) * int(PokerCard.suitPrimes[self.cardSuit])
hand_hash = 1
for card in hand:
hand_hash *= card.HashVal()
print(hand_hash) # No need for special libraries, Python handles python bigint natively
This is perfect for handling poker hand hashes without the need for extra libraries.
Absolutely, @joe-elmoufak! That’s a solid approach. But, if you’re ever dealing with a scenario where precision in floating-point arithmetic becomes important, you might want to check out Python’s decimal module. While it’s not always needed for integers, it’s useful for managing python bigint in cases that need high precision, especially when you’re working with operations that require fine-tuned control.
from decimal import Decimal
class PokerCard:
faces = '23456789TJQKA'
suits = 'cdhs'
facePrimes = [11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 53, 59, 61]
suitPrimes = [2, 3, 5, 7]
def HashVal(self):
return Decimal(PokerCard.facePrimes[self.cardFace]) * Decimal(PokerCard.suitPrimes[self.cardSuit])
hand_hash = Decimal(1)
for card in hand:
hand_hash *= card.HashVal()
print(hand_hash) # High precision for calculations
Using the Decimal type ensures that you’re getting the precision required for extremely large floating-point numbers, especially when you need to fine-tune things.
Great points, both of you! I’ve worked with large numbers in some intensive applications, and I’ll add this: when you need top-tier performance for handling python bigint efficiently, the gmpy2 library is unbeatable. It’s a high-performance library for arbitrary-precision arithmetic, and it’s particularly optimized for both speed and memory efficiency. So if performance is critical in your poker hand evaluation (think millions of calculations), gmpy2 will definitely be worth the extra setup.
import gmpy2
class PokerCard:
faces = '23456789TJQKA'
suits = 'cdhs'
facePrimes = [11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 53, 59, 61]
suitPrimes = [2, 3, 5, 7]
def HashVal(self):
return gmpy2.mpz(PokerCard.facePrimes[self.cardFace]) * gmpy2.mpz(PokerCard.suitPrimes[self.cardSuit])
hand_hash = gmpy2.mpz(1)
for card in hand:
hand_hash *= card.HashVal()
print(hand_hash) # Efficient handling of large integers
With gmpy2, you’re getting both performance and precision, which is ideal for handling large poker hand evaluations where time and space are both factors. So, depending on your needs, these tools—python bigint, decimal, and gmpy2—give you a range of options for efficient large number handling.