plt.legend()
plt.xscale('log')
plt.show()
# Linear plot
plt.plot(c_1, label='eps = .1')
plt.plot(c_05, label='eps = .05')
plt.plot(c_01, label='eps = .01')
plt.legend()
plt.show()
if __name__ == '__main__':
m1, m2, m3 = 1.0, 2.0, 3.0
upper_limit = 10
eps_1 = run_experiment_eps(m1, m2, m3, 0.1, 100000)
eps_01 = run_experiment_eps(m1, m2, m3, 0.01, 100000)
opt = run_experiment(m1, m2, m3, 100000, upper_limit)
# Log scale plot
plt.plot(eps_1, label='eps = .1')
plt.plot(eps_01, label='eps = .01')
plt.plot(opt, label='optimistic')
plt.legend()
plt.xscale('log')
plt.show()
# Linear plot
plt.plot(eps_1, label='eps = .1')
plt.plot(eps_01, label='eps = .01')
plt.plot(opt, label='optimistic')
plt.plot(np.ones(N) * m2)
plt.plot(np.ones(N) * m3)
plt.xscale('log')
plt.show()
# Print our esimate of each bandits mean and their actual mean
print('Estimate of mean Actual mean')
for bandit in bandits:
print('{:<20}{}'.format(bandit.mean, bandit.m))
return cumulative_avg
if __name__ == '__main__':
m1, m2, m3 = 1.0, 2.0, 3.0
eps_1 = run_experiment_eps(m1, m2, m3, .1, 100000)
ucb = run_experiment(m1, m2, m3, 100000)
# Log scale plot
plt.plot(eps_1, label='eps = .1')
plt.plot(ucb, label='UCB')
plt.legend()
plt.xscale('log')
plt.show()
# Linear plot
plt.plot(eps_1, label='eps = .1')
plt.plot(ucb, label='UCB')
plt.legend()
plt.show()
data[i] = x
cumulative_average = np.cumsum(data) / (np.arange(N) + 1)
plt.plot(cumulative_average)
plt.plot(np.ones(N)*m1)
plt.plot(np.ones(N)*m2)
plt.plot(np.ones(N)*m3)
plt.xscale('log')
plt.show()
for b in bandits:
print(b.mean)
return cumulative_average
if __name__ == '__main__':
c_1 = run_experiment_eps(1.0, 2.0, 3.0, 0.1, 100000)
ucb1 = run_experiment(1.0, 2.0, 3.0, 100000)
plt.plot(c_1, label='eps = 0.1')
plt.plot(ucb1, label='ucb1')
plt.legend()
plt.xscale('log')
plt.show()
plt.plot(c_1, label='eps = 0.1')
plt.plot(ucb1, label='ucb1')
plt.legend()
plt.show()
# plot moving average ctr
plt.plot(cumulative_average)
plt.plot(np.ones(N)*m1)
plt.plot(np.ones(N)*m2)
plt.plot(np.ones(N)*m3)
plt.xscale('log')
plt.show()
for b in bandits:
print(b.mean)
return cumulative_average
if __name__ == '__main__':
c_1 = run_experiment_eps(1.0, 2.0, 3.0, 0.1, 100000)
oiv = run_experiment(1.0, 2.0, 3.0, 100000)
# log scale plot
plt.plot(c_1, label='eps = 0.1')
plt.plot(oiv, label='ucb1')
plt.legend()
plt.xscale('log')
plt.show()
# linear plot
plt.plot(c_1, label='eps = 0.1')
plt.plot(oiv, label='ucb1')
plt.legend()
plt.show()
