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 for generating 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.
fictitious_play()
oriterated_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()
oriterated_voting()
.ballot_update_ratio (callable or Number) – Cf.
fictitious_play()
oriterated_voting()
.winning_frequency_update_ratio (callable or Number) – Cf.
fictitious_play()
oriterated_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
BinaryAxesSubplotPoisson.heatmap_candidates()
.
Examples
>>> xyy_to_profile = XyyToProfile(ProfileNoisyDiscrete, left_ranking='bca', right_ranking='cab', noise=0.01) >>> figure, ax = binary_plot_winning_frequencies(xyy_to_profile, xscale=5, yscale=5, n_max_episodes=10)