DiscreteOutcomeSpace

class negmas.outcomes.DiscreteOutcomeSpace(*args, **kwargs)[source]

Bases: OutcomeSpace, Collection, Protocol

The base protocol for all outcome spaces with a finite number of items.

This type of outcome-space acts as a standard python Collection which means that its length can be found using len() and it can be iterated over to return outcomes.

Attributes Summary

cardinality

The space cardinality = the number of outcomes

Methods Summary

`are_types_ok`(outcome)	Checks if the type of each value in the outcome is correct for the given issue
`cardinality_if_discretized`(levels[, ...])	Returns the cardinality if discretized the given way.
`ensure_correct_types`(outcome)	Returns an outcome that is guaratneed to have correct types or raises an exception
`enumerate`()	Enumerates the outcome space returning all its outcomes (or up to max_cardinality for infinite ones)
`enumerate_or_sample`([levels, max_cardinality])	Enumerates all outcomes if possible (i.e. discrete space) or returns `max_cardinality` different outcomes otherwise.
`is_discrete`()	Checks whether there are no continua components of the space
`is_finite`()	Checks whether the space is finite
`is_float`()	Checks whether all values in all outcomes are real
`is_integer`()	Checks whether all values in all outcomes are integers
`is_numeric`()	Checks whether all values in all outcomes are numeric
`is_valid`(outcome)	Checks if the given outcome is valid for that outcome space
`limit_cardinality`([max_cardinality, levels])	Limits the cardinality of the outcome space to the given maximum (or the number of levels for each issue to `Logging Levels`)
`random_outcome`()	Returns a single random outcome.
`sample`(n_outcomes[, with_replacement, ...])	Samples up to n_outcomes with or without replacement
`to_discrete`(args, *kwargs)	Returns a stable finite outcome space.
`to_largest_discrete`(levels[, max_cardinality])
`to_single_issue`([numeric, stringify])

Attributes Documentation

cardinality: The space cardinality = the number of outcomes

Methods Documentation

are_types_ok(outcome: Outcome) → bool: Checks if the type of each value in the outcome is correct for the given issue

cardinality_if_discretized(levels: int, max_cardinality: int | float = inf) → int: Returns the cardinality if discretized the given way.

ensure_correct_types(outcome: Outcome) → Outcome: Returns an outcome that is guaratneed to have correct types or raises an exception

enumerate() → Iterable[Outcome][source]: Enumerates the outcome space returning all its outcomes (or up to max_cardinality for infinite ones)

enumerate_or_sample(levels: int | float = inf, max_cardinality: int | float = inf) → Iterable[Outcome]: Enumerates all outcomes if possible (i.e. discrete space) or returns max_cardinality different outcomes otherwise

is_discrete() → bool[source]: Checks whether there are no continua components of the space

is_finite() → bool: Checks whether the space is finite

is_float() → bool: Checks whether all values in all outcomes are real

is_integer() → bool: Checks whether all values in all outcomes are integers

is_numeric() → bool: Checks whether all values in all outcomes are numeric

is_valid(outcome: Outcome) → bool: Checks if the given outcome is valid for that outcome space

limit_cardinality(max_cardinality: int | float = inf, levels: int | float = inf) → DiscreteOutcomeSpace[source]

Limits the cardinality of the outcome space to the given maximum (or the number of levels for each issue to Logging Levels)

Parameters:

max_cardinality – The maximum number of outcomes in the resulting space
levels – The maximum levels allowed per issue (if issues are defined for this outcome space)

random_outcome() → Outcome: Returns a single random outcome.

sample(n_outcomes: int, with_replacement: bool = False, fail_if_not_enough=True) → Iterable[Outcome][source]: Samples up to n_outcomes with or without replacement

to_discrete(*args, **kwargs) → DiscreteOutcomeSpace[source]

Returns a stable finite outcome space. If the outcome-space is already finite. It shoud return itself.

Parameters:

levels – The levels of discretization of any continuous dimension (or subdimension)
max_cardintlity – The maximum cardinality allowed for the resulting outcomespace (if the original OS was infinite). This limitation is NOT applied for outcome spaces that are alredy discretized. See limit_cardinality() for a method to limit the cardinality of an already discrete space

If called again, it should return the same discrete outcome space every time.

to_largest_discrete(levels: int, max_cardinality: int | float = inf, **kwargs) → DiscreteOutcomeSpace

to_single_issue(numeric: bool = False, stringify: bool = True) → CartesianOutcomeSpace[source]