ternary_plot_convergence

poisson_approval.ternary_plot_convergence(simplex_to_profile, scale, n_max_episodes, init='sincere', samples_per_point=1, perception_update_ratio=<function one_over_log_t_plus_one>, ballot_update_ratio=<function one_over_log_t_plus_one>, title='Convergence frequency', meth='fictitious_play', **kwargs)[source]

Shortcut: ternary plot for the convergence frequency in fictitious play / iterated voting.

Convergence frequency: out of samples_per_points trials, in which proportion of the cases did fictitious play or iterated voting converge within n_max_episodes iterations?

Parameters

simplex_to_profile (SimplexToProfile) – This is responsible for generating the profiles.
scale (Number) – Scale of the plot (resolution).
n_max_episodes (int) – Maximum number of episodes for the fictitious play / iterated voting.
init (Strategy or TauVector or str) – Cf. fictitious_play() or iterated_voting().
samples_per_point (int) – How many trials are made for each point drawn. Useful only when initialization is random.
perception_update_ratio (callable or Number) – Cf. fictitious_play() or iterated_voting().
ballot_update_ratio (callable or Number) – Cf. fictitious_play() or iterated_voting().
title (str) – Title of the plot.
meth (str) – The name of the method ('fictitious_play' or 'iterated_voting').
kwargs – Other keyword arguments are passed to the function TernaryAxesSubplotPoisson.heatmap_intensity().

Examples

>>> simplex_to_profile = SimplexToProfile(
...     ProfileNoisyDiscrete,
...     left_type=('abc', 0.5, 0.01), right_type=('bac', 0.5, 0.01), top_type=('cab', 0.5, 0.01))
>>> figure, tax = ternary_plot_convergence(simplex_to_profile, scale=10, n_max_episodes=10)