plot_n_cumregrets(cum_regs, alg_names, noises, ds_label=ds_label)
plot_n_cumregrets(cum_regs, alg_names, noises, axis='log', ds_label=ds_label)
plot_n_cumregrets(cum_regs, alg_names, noises, axis='loglog', ds_label=ds_label)
if __name__ == "__main__":
# Test number of players
alg_list = [
lambda x,y: Swiss(x),\
lambda x,y: Naive_RankEL(x,y, horizon=float('inf')),\
lambda x,y: Modified_RankEL(x,y, horizon=float('inf')),\
lambda x,y: RUCB(x),\
lambda x,y: Pagerank(x),\
lambda x,y: EvenOddSorter(x),\
lambda x,y: MaxLikelihood(x,True),\
lambda x,y: MaxLikelihood(x),
]
noises = [0.01, 0.5, 1, 1.5, 2, 2.5]
n_noise_plots(alg_list, noises, ds=NormalGenDataset)
noises = [0.0, 0.1, 0.2, 0.3, 0.4, 0.49]
n_noise_plots(alg_list, noises, ds=GenDataset)
analysis = Analysis(alg_list, soccer=True, name='soccer_SE', ranking_procedure=SE, n_trials = 10, horizon=15)
analysis.plot_cumulative_regret()
analysis = Analysis(alg_list, dataset=BradleyTerryDataset, name='BT_Copeland', ranking_procedure=Copeland, n_trials = 10, horizon=100, n_players=10)
analysis.plot_cumulative_regret()
