def run_simulation(hole_cards, num, exact, given_board, deck, verbose):
num_players = len(hole_cards)
# Choose whether we're running a Monte Carlo or exhaustive simulation
board_length = 0 if given_board is None else len(given_board)
# Create data structures to manage multiple processes:
# 1) winner_list: number of times each player wins a hand
# 2) result_histograms: a list for each player that shows the number of
# times each type of poker hand (e.g. flush, straight) was gotten
num_processes = multiprocessing.cpu_count()
num_poker_hands = len(holdem_functions.hand_rankings)
num_histograms = num_processes * num_players * num_poker_hands
winner_list = multiprocessing.Array('i', num_processes * (num_players + 1))
result_histograms = multiprocessing.Array('i', num_histograms)
# When a board is given, exact calculation is much faster than Monte Carlo
# simulation, so default to exact if a board is given
if exact or given_board is not None:
generate_boards = holdem_functions.generate_exhaustive_boards
else:
generate_boards = holdem_functions.generate_random_boards
if (None, None) in hole_cards:
hole_cards_list = list(hole_cards)
unknown_index = hole_cards.index((None, None))
deck_list = list(deck)
pool = multiprocessing.Pool(processes=num_processes,
initializer=unknown_simulation_init,
initargs=(hole_cards_list, unknown_index,
deck_list, generate_boards, num,
board_length, given_board,
winner_list, result_histograms))
pool.map(unknown_simulation,
holdem_functions.generate_hole_cards(deck))
else:
find_winner(generate_boards, deck, hole_cards, num, board_length,
given_board, winner_list, result_histograms)
# Go through each parallel data structure and aggregate results
combined_winner_list, combined_histograms = [0] * (num_players + 1), []
for _ in range(num_players):
combined_histograms.append([0] * len(holdem_functions.hand_rankings))
for index, element in enumerate(winner_list):
combined_winner_list[index % (num_players + 1)] += element
for index, element in enumerate(result_histograms):
combined_histograms[(index / num_poker_hands) %
num_players][(index % num_poker_hands)] += element
if verbose:
holdem_functions.print_results(hole_cards, combined_winner_list,
combined_histograms)
return holdem_functions.find_winning_percentage(combined_winner_list)
def run_simulation(hole_cards, num, exact, given_board, deck, verbose):
num_players = len(hole_cards)
# Choose whether we're running a Monte Carlo or exhaustive simulation
board_length = 0 if given_board is None else len(given_board)
# Create data structures to manage multiple processes:
# 1) winner_list: number of times each player wins a hand
# 2) result_histograms: a list for each player that shows the number of
# times each type of poker hand (e.g. flush, straight) was gotten
num_processes = multiprocessing.cpu_count()
num_poker_hands = len(holdem_functions.hand_rankings)
num_histograms = num_processes * num_players * num_poker_hands
winner_list = multiprocessing.Array('i', num_processes * (num_players + 1))
result_histograms = multiprocessing.Array('i', num_histograms)
# When a board is given, exact calculation is much faster than Monte Carlo
# simulation, so default to exact if a board is given
if exact or given_board is not None:
generate_boards = holdem_functions.generate_exhaustive_boards
else:
generate_boards = holdem_functions.generate_random_boards
if (None, None) in hole_cards:
hole_cards_list = list(hole_cards)
unknown_index = hole_cards.index((None, None))
deck_list = list(deck)
pool = multiprocessing.Pool(processes=num_processes,
initializer=unknown_simulation_init,
initargs=(hole_cards_list, unknown_index,
deck_list, generate_boards,
num, board_length, given_board,
winner_list, result_histograms))
pool.map(unknown_simulation, holdem_functions.generate_hole_cards(deck))
else:
find_winner(generate_boards, deck, hole_cards, num, board_length,
given_board, winner_list, result_histograms)
# Go through each parallel data structure and aggregate results
combined_winner_list, combined_histograms = [0] * (num_players + 1), []
for _ in xrange(num_players):
combined_histograms.append([0] * len(holdem_functions.hand_rankings))
for index, element in enumerate(winner_list):
combined_winner_list[index % (num_players + 1)] += element
for index, element in enumerate(result_histograms):
combined_histograms[(index / num_poker_hands) % num_players][
(index % num_poker_hands)] += element
if verbose:
holdem_functions.print_results(hole_cards, combined_winner_list,
combined_histograms)
return holdem_functions.find_winning_percentage(combined_winner_list)
def run_simulation(hole_cards, num, exact, given_board, deck, verbose, pad_opp=True):
num_players = len(hole_cards)
if pad_opp:
num_players += 1
#import pdb
#pdb.set_trace()
# Create results data structures which track results of comparisons
# 1) result_histograms: a list for each player that shows the number of
# times each type of poker hand (e.g. flush, straight) was gotten
# 2) winner_list: number of times each player wins the given round
# 3) result_list: list of the best possible poker hand for each pair of
# hole cards for a given board
result_histograms, winner_list = [], [0] * (num_players + 1)
for _ in range(num_players):
result_histograms.append([0] * len(holdem_functions.hand_rankings))
# Choose whether we're running a Monte Carlo or exhaustive simulation
board_length = 0 if given_board is None else len(given_board)
# When a board is given, exact calculation is much faster than Monte Carlo
# simulation, so default to exact if a board is given
if exact or given_board is not None:
generate_boards = holdem_functions.generate_exhaustive_boards
else:
generate_boards = holdem_functions.generate_random_boards
if (None, None) in hole_cards:
hole_cards_list = list(hole_cards)
unknown_index = hole_cards.index((None, None))
for filler_hole_cards in holdem_functions.generate_hole_cards(deck):
hole_cards_list[unknown_index] = filler_hole_cards
deck_list = list(deck)
deck_list.remove(filler_hole_cards[0])
deck_list.remove(filler_hole_cards[1])
holdem_functions.find_winner(generate_boards, tuple(deck_list),
tuple(hole_cards_list), num,
board_length, given_board, winner_list,
result_histograms)
else:
holdem_functions.find_winner(generate_boards, deck, hole_cards, num,
board_length, given_board, winner_list,
result_histograms,pad_opp = pad_opp)
if verbose:
holdem_functions.print_results(hole_cards, winner_list,
result_histograms)
return holdem_functions.return_results(hole_cards, winner_list,
result_histograms,pad_opp = pad_opp, board = given_board )
def run_simulation(hole_cards, num, exact, given_board, deck, verbose):
num_players = len(hole_cards)
# Create results data structures which track results of comparisons
# 1) result_histograms: a list for each player that shows the number of
# times each type of poker hand (e.g. flush, straight) was gotten
# 2) winner_list: number of times each player wins the given round
# 3) result_list: list of the best possible poker hand for each pair of
# hole cards for a given board
result_histograms, winner_list = [], [0] * (num_players + 1)
for _ in range(num_players):
result_histograms.append([0] * len(holdem_functions.hand_rankings))
# Choose whether we're running a Monte Carlo or exhaustive simulation
board_length = 0 if given_board is None else len(given_board)
if exact:
generate_boards = holdem_functions.generate_exhaustive_boards
else:
generate_boards = holdem_functions.generate_random_boards
if (None, None) in hole_cards:
hole_cards_list = list(hole_cards)
unknown_index = hole_cards.index((None, None))
for filler_hole_cards in holdem_functions.generate_hole_cards(deck):
hole_cards_list[unknown_index] = filler_hole_cards
deck_list = list(deck)
deck_list.remove(filler_hole_cards[0])
deck_list.remove(filler_hole_cards[1])
holdem_functions.find_winner(generate_boards, tuple(deck_list),
tuple(hole_cards_list), num,
board_length, given_board,
winner_list, result_histograms)
else:
holdem_functions.find_winner(generate_boards, deck, hole_cards, num,
board_length, given_board, winner_list,
result_histograms)
if verbose:
players_histograms = holdem_functions.calc_histogram(
result_histograms, winner_list)
return [
holdem_functions.find_winning_percentage(winner_list),
players_histograms
]
def run_simulation(hole_cards, num, exact, given_board, deck, verbose):
num_players = len(hole_cards)
# Create results data structures which track results of comparisons
# 1) result_histograms: a list for each player that shows the number of
# times each type of poker hand (e.g. flush, straight) was gotten
# 2) winner_list: number of times each player wins the given round
# 3) result_list: list of the best possible poker hand for each pair of
# hole cards for a given board
result_histograms, winner_list = [], [0] * (num_players + 1)
for _ in xrange(num_players):
result_histograms.append([0] * len(holdem_functions.hand_rankings))
# Choose whether we're running a Monte Carlo or exhaustive simulation
board_length = 0 if given_board is None else len(given_board)
# When a board is given, exact calculation is much faster than Monte Carlo
# simulation, so default to exact if a board is given
if exact or given_board is not None:
generate_boards = holdem_functions.generate_exhaustive_boards
else:
generate_boards = holdem_functions.generate_random_boards
if (None, None) in hole_cards:
hole_cards_list = list(hole_cards)
unknown_index = hole_cards.index((None, None))
for filler_hole_cards in holdem_functions.generate_hole_cards(deck):
hole_cards_list[unknown_index] = filler_hole_cards
deck_list = list(deck)
deck_list.remove(filler_hole_cards[0])
deck_list.remove(filler_hole_cards[1])
holdem_functions.find_winner(generate_boards, tuple(deck_list),
tuple(hole_cards_list), num,
board_length, given_board, winner_list,
result_histograms)
else:
holdem_functions.find_winner(generate_boards, deck, hole_cards, num,
board_length, given_board, winner_list,
result_histograms)
if verbose:
holdem_functions.print_results(hole_cards, winner_list,
result_histograms)
return holdem_functions.find_winning_percentage(winner_list)
