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, :])
# parameters p = 0.8 d = [0.2, 0.3, 0.0] sigma = 0.01 T = 12000 # construct experts def expert(i): e = np.random.normal(0, sigma) return p - d[i] + e # construct forecaster N = len(d) 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,) Y = np.zeros(T,) e_pred = np.zeros((T,N)) for t in xrange(T): e_t = np.array([expert(i) for i in xrange(N)]) e_pred[t,:] = e_t p_t = F.predict(e_t)
return 1
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,:])
# plot the losses
for i in xrange(N):
p = 0.8
d = [0.2, 0.3, 0.0]
sigma = 0.01
T = 12000
# construct experts
def expert(i):
e = np.random.normal(0, sigma)
return p - d[i] + e
# construct forecaster
N = len(d)
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, )
Y = np.zeros(T, )
e_pred = np.zeros((T, N))
for t in xrange(T):
e_t = np.array([expert(i) for i in xrange(N)])
e_pred[t, :] = e_t
