maverick.holding.Holding

class maverick.holding.Holding(*, cards: list[Card])

A number of cards held by a player.

Fields

cardslist[Card]

The list of cards in the holding.

Examples

>>> from maverick import Holding, Card
... pair_of_aces = Holding(cards=[
...     Card(suit='S', rank=14),  # Ace of Spades
...     Card(suit='H', rank=14)   # Ace of Hearts
... ])
>>> pair_of_aces.estimate_strength(n_simulations=1000, n_players=8)
0.85  # Example output, actual value may vary

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
all_possible_holdings(cards[, n])	Generate all possible holdings of n cards from the given deck.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
estimate_strength(**kwargs)	Estimate the strength of the holding via Monte Carlo simulation.
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
model_json_schema(by_alias, ref_template, ...)	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
random(*[, n, deck])	Generate a random holding of 2 cards.
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
score()	Classifies and scores the hand.
update_forward_refs(**localns)
validate(value)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
cards

classmethod random(*, n: int = 2, deck: Deck | None = None) → Holding

Generate a random holding of 2 cards.

Parameters:

• n (int, optional) – The number of cards in the holding (default is 2).

• deck (Deck, optional) – An optional deck to draw cards from. If not provided, random cards will be generated.

classmethod all_possible_holdings(cards: list[Card], n: int = 2) → iter

Generate all possible holdings of n cards from the given deck.

Parameters:

• cards (list[Card]) – The list of cards to choose from.

• n (int, optional) – The number of cards in each holding (default is 2).

score() → Tuple[HandType, float]

Classifies and scores the hand.

Returns (HandType, float_score) where higher scores = stronger hands.

estimate_strength(**kwargs) → float

Estimate the strength of the holding via Monte Carlo simulation.

Returns a number between 0 and 1, representing the probability of the current hand being the strongest.

The keyword arguments are passed to estimate_holding_strength.

model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].