binary_plot_winning_frequencies

poisson_approval.binary_plot_winning_frequencies(xyy_to_profile, xscale, yscale, 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>, winning_frequency_update_ratio=<function one_over_log_t_plus_one>, title='Winning frequencies', legend_title='Winners', meth='fictitious_play', reverse_right=False, **kwargs)[source]

Shortcut: binary plot for the winning frequencies in fictitious play / iterated voting.

Parameters:
  • xyy_to_profile (XyyToProfile) – This is responsible to generate the profiles.
  • xscale (Number) – Scale of the plot (resolution) on the x-axis.
  • yscale (Number) – Scale of the plot (resolution) on the y-axis.
  • n_max_episodes (int) – Maximum number of episodes for the fictitious play / iterated voting.
  • init (Strategy or TauVector or str) – Cf. Profile.fictitious_play() or Profile.iterated_voting().
  • samples_per_point (int) – How many trials are made for each point drawn. Useful only when initialization is random.
  • ballot_update_ratio, winning_frequency_update_ratio (perception_update_ratio,) – Cf. Profile.fictitious_play() or Profile.iterated_voting().
  • title (str) – Title of the plot.
  • legend_title (str) – Title of the legend of the plot.
  • meth (str) – The name of the method ('fictitious_play' or 'iterated_voting').
  • reverse_right (bool) – If True, then the y-axis on the right goes decreasing from 1 to 0 (whereas the y-axis on the left goes increasing from 0 to 1).
  • kwargs – Other keyword arguments are passed to the function heatmap_candidates.