egttools.numerical.PairwiseComparisonEstimator¶
- class PairwiseComparisonEstimator(game, pop_size, cache_size=100000, strategy_names=None, mutation_weights=None)[source]¶
Bases:
objectWraps
egttools.numerical.PairwiseComparisonNumericaland returns rich result objects with standard errors and optional progress bars.- Parameters:
game (AbstractGame) – The game to associate with the estimator.
nb_strategiesis read from the game object.pop_size (int) – Population size.
cache_size (int, optional) – LRU cache size for fitness computations (default 100 000).
strategy_names (list[str], optional) – Human-readable strategy names used in result reprs.
mutation_weights (array_like, optional) – Biases the mutation kernel away from uniform. Either a 1D array of length
nb_strategies(target-strategy bias shared by all source strategies) or a 2D array of shape(nb_strategies, nb_strategies)(rowiis the bias over target strategies when mutating away from strategyi). SeePairwiseComparisonNumerical.set_mutation_weightsfor details. Defaults to uniform mutation when omitted.
Methods
- estimate_absorption_probabilities(beta, init_state, nb_runs, verbose=0, n_chunks=10)[source]¶
- Return type:
- estimate_fixation_probability(invader, resident, nb_runs, nb_generations, beta, verbose=0, n_chunks=10)[source]¶
- Return type:
- estimate_mean_absorption_time(beta, init_state, nb_runs, verbose=0, n_chunks=10)[source]¶
- Return type:
- estimate_stationary_distribution(nb_runs, nb_generations, transitory, beta, mu, verbose=0, n_chunks=10, **kwargs)[source]¶
- Return type:
- estimate_strategy_distribution(nb_runs, nb_generations, transitory, beta, mu, verbose=0, n_chunks=10, **kwargs)[source]¶
- Return type:
- __annotations__ = {}¶