roulette/roulette.py

from typing import List, Dict, Optional
from functools import reduce
from dataclasses import dataclass, field
from random import choice, randint


Bet = Dict[int, float]

SINGLE_BETS = {str(i) for i in range(-1, 37)}

FEASIBLE_MOVES = sorted(
    {
        *[f"street-{i}" for i in range(1, 14)],
        *[f"col-{i}" for i in range(1, 4)],
        *[f"corner-{i}-{i+1}-{i+3}-{i+4}" for i in range(1, 33) if (i - 1) % 3 < 2],
        *["1-12", "13-24", "25-36", "1-18", "19-36", "even", "odd", "red", "black"],
        *["triple-0", "triple-00"],
        *SINGLE_BETS,
    }
)


ALIASES = {
    "reds",
    "blacks",
    "evens",
    "odds",
    "first-half",
    "last-half",
    "second-half",
    "first-18",
    "last-18",
    "second-18",
}

CHIP_VALUES = {0.25, 0.5, 1, 5, 10, 25, 50, 100}


def expectation(bet):
    odds = 0
    pmnt = 0
    return odds * pmnt


# 38 numbers, 6 street bets, 2 half-bets,


# payout grid based on bets placed.
# a street bet is the same as splitting the bet across all the numbers in the group.
# will use a function to distribute / interpret the bets, but it seems like we only need to track the numbers on the wheel.


def init_bet() -> Bet:
    D = {i: 0 for i in range(-1, 37)}
    return D


def place_bet(bet: Bet, on: int, amount: float):
    bet = bet.copy()
    bet[on] += amount
    return bet


def interpret_bet(on="red", amount=0, bet=Optional[Bet]):
    assert (on in FEASIBLE_MOVES) or (
        on in ALIASES
    ), f"Bet `{on}` not understood. Choose from feasible moves:\n {FEASIBLE_MOVES}"
    if bet is None:
        bet = init_bet()
    else:
        bet = bet.copy()
    REDS = {1, 3, 5, 7, 9, 12, 14, 16, 18, 19, 21, 23, 25, 27, 30, 32, 34, 36}
    BLACKS = set(range(37)) - REDS
    NUMS = {}
    on = on.strip().replace(" ", "-")
    div = 18
    if on in ("red", "reds"):
        NUMS = REDS
    if on in ("black", "blacks"):
        NUMS = BLACKS
    if on in ("odd", "odds"):
        NUMS = {i for i in range(1, 37) if i % 2 == 0}
    if on in ("even", "evens"):
        NUMS = {i for i in range(1, 37) if i % 2}
    if on in ("1-18", "first-18", "first-half"):
        NUMS = set(range(1, 19))
    if on in ("19-36", "last-18", "last-half", "second-half", "second-18"):
        NUMS = set(range(19, 37))
    if on in ("1-12", "13-24", "25-36"):
        low, high = on.split("-")
        NUMS = set(range(int(low), int(high) + 1))
        div = 12
    if on in ["triple-0", "triple-00"]:
        NUMS = {0, 1, 2} if on == "triple-0" else {-1, 2, 3}
        div = 3
    if not NUMS:
        other_bet = on.split("-")
        if other_bet[0] == "street":
            street = int(other_bet[1]) - 1
            assert street in list(range(13))
            NUMS = {i for i in range(street + 1, street + 4)}
            div = 3
        elif other_bet[0] == "col":
            col = int(other_bet[1]) - 1
            assert col in list(range(0, 3))
            NUMS = {i for i in range(1, 37) if (i - 1) % 3 == col}
            div = 12
        elif (
            other_bet[0] == "split"
        ):  # TODO: validate choices, for now we disallow these.
            num_1, num_2 = int(other_bet[1]), int(other_bet[2])
            NUMS = {num_1, num_2}
            div = 2
        elif other_bet[0] == "corner":
            num_1, num_2 = int(other_bet[1]), int(other_bet[2])
            num_3, num_4 = int(other_bet[3]), int(other_bet[4])
            NUMS = {num_1, num_2, num_3, num_4}
            div = 4
        else:
            try:
                NUMS = {int(on)}
                div = 1
            except ValueError as e:
                raise e(
                    f"Bet `{on}` not understood. Choose from feasible moves:\n {set(range(-1, 37))}"
                )

    bet = reduce(lambda bet, num: place_bet(bet, num, amount / div), NUMS, bet)

    return bet


@dataclass
class Placement:
    """
    Defines a bet based on the number of chips and value of each chip.

    Args:
        num (int): number of chips
        amt (float): value of each chip
        on (str): bet type

    Returns:
        Placement: an object representing the placement of a stack of chips on a particular bet type.
    """

    num: int
    amt: float
    on: str

    def __post_init__(self):
        assert (self.on in FEASIBLE_MOVES) or (
            self.on in ALIASES
        ), f"Bet `{self.on}` not understood. Choose from feasible moves:\n {FEASIBLE_MOVES}"

    @property
    def value(self):
        """
        Returns the value of the bet.
        """
        return self.num * self.amt

    def place_bet(self, bet=None):
        """
        Places a bet on the wheel based on the bet type.
        """
        return interpret_bet(self.on, self.num * self.amt, bet)


# for two bets of structure Dict[int, float], iterate through all the keys and add up the values, returning a new dict.
def combine_bets(bet_1, bet_2):
    return {k: bet_1.get(k, 0) + bet_2.get(k, 0) for k in set(bet_1) | set(bet_2)}


# for a list of Placements, call the place_bet method on each one and combine the results using reduce and combine_bets, starting with an empty dictionary as the initial argument
def place_bets(placements):
    return reduce(
        lambda bet, placement: combine_bets(bet, placement.place_bet()), placements, {}
    )


@dataclass
class Strategy:
    budget: float = 200
    placements: List[Placement] = field(default_factory=list)

    def __repr__(self) -> str:
        return f"Strategy(budget={self.budget}, value={self.value}, placements={self.placements})"

    @property
    def value(self):
        return sum([p.value for p in self.placements])

    @classmethod
    def generate_random(cls, budget) -> "Strategy":
        placements = []
        initial_budget = budget
        while budget > 0:
            amt = choice([v for v in CHIP_VALUES if v <= budget])
            # guarantees the max bet cannot exceed budget:
            num = randint(1, budget // amt)
            # select random bet type
            # todo: consider if this is the logic you want...
            if randint(0, 1) == 0:
                on = choice(list(FEASIBLE_MOVES))
            else:
                on = choice(list(SINGLE_BETS))
            on = choice(
                list(FEASIBLE_MOVES)
            )  # todo: make a parameter, allow for just single bets.
            placement = Placement(num, amt, on)
            placements.append(placement)
            budget -= placement.value
        return Strategy(budget=initial_budget, placements=placements)

    def print_all(self) -> None:
        for p in self.placements:
            print(p)

    def get_bet(self):
        return place_bets(self.placements)


@dataclass
class Player:
    budget: float
    strategy: Strategy


def simulate_random_strategy(min_num_games=1, total_budget=200):
    strategy_budget = total_budget // min_num_games
    return Strategy.generate_random(strategy_budget)


# given BUDGET, generate a bunch of random players, each with a random strategy, and return a list of players
def generate_players(num_players=10, min_num_games=1, total_budget=200):
    return [
        Player(
            total_budget,
            simulate_random_strategy(
                min_num_games=min_num_games, total_budget=total_budget
            ),
        )
        for _ in range(num_players)
    ]


# simulate a game of roulette, picking a random integer from -1 to 37, taking the players as inputs and returning their expected winnings
def simulate_game(players):
    # pick a random number
    num = randint(-1, 36)
    # print("WINNER:", num)
    # for each player, place their bets on the wheel
    bets = [p.strategy.get_bet() for p in players]
    # for each player, calculate their winnings
    winnings = [36 * bet.get(num, 0) for bet in bets]
    # for each player, calculate their expected winnings
    return winnings


# simulate multiple games, reducing each player's budget by the amount of their bet and adding the amount of their winnings
def simulate_games(players, num_games=10):
    losers = []
    for g in range(num_games):
        if not players:
            break
        print(f"GAME {g}")
        winnings = simulate_game(players)
        new_losers = []
        for i, p in enumerate(players):
            p.budget -= p.strategy.value
            p.budget += winnings[
                i
            ]  # TODO: reinvestment logic goes here. maybe add "reinvest" as a player attribute?
            # if a player runs out of money to keep using their strategy,
            # remove them from the list of players and add them to the list of losers
            if p.budget < p.strategy.value:
                new_losers.append(p)
        for l in new_losers:
            players.remove(l)
        losers.extend(new_losers)

    return players + losers