elif i == 1:
return 0
elif i == 2:
return 5
elif 2 * T / 3 < t:
if i == 0:
return 1
elif i == 1:
return 0
elif i == 2:
return -1
# construct forecaster
eta = np.sqrt(2 * np.log(N) / T)
F = EWAForecaster(N, eta)
# we use the L2 loss
def loss(p, y):
return np.sqrt((p - y)**2)
# run experiment
e_losses = np.zeros((T, N))
F_losses = np.zeros(T, )
for t in xrange(T):
e_losses[t, :] = np.array([expert(i, t) for i in xrange(N)])
F_losses[t] = F.predict(e_losses[t, :])
F.observe(e_losses[t, :])
