def fixed_lag_smoothing(e_t, HMM, d, ev, t):
"""[Figure 15.6]
Smoothing algorithm with a fixed time lag of 'd' steps.
Online algorithm that outputs the new smoothed estimate if observation
for new time step is given."""
ev.insert(0, None)
T_model = HMM.transition_model
f = HMM.prior
B = [[1, 0], [0, 1]]
evidence = []
evidence.append(e_t)
O_t = vector_to_diagonal(HMM.sensor_dist(e_t))
if t > d:
f = forward(HMM, f, e_t)
O_tmd = vector_to_diagonal(HMM.sensor_dist(ev[t- d]))
B = matrix_multiplication(inverse_matrix(O_tmd), inverse_matrix(T_model), B, T_model, O_t)
else:
B = matrix_multiplication(B, T_model, O_t)
t = t + 1
if t > d:
# always returns a 1x2 matrix
return([normalize(i) for i in matrix_multiplication([f], B)][0])
else:
return None
def fixed_lag_smoothing(e_t, HMM, d, ev, t):
"""
[Figure 15.6]
Smoothing algorithm with a fixed time lag of 'd' steps.
Online algorithm that outputs the new smoothed estimate if observation
for new time step is given."""
ev.insert(0, None)
T_model = HMM.transition_model
f = HMM.prior
B = [[1, 0], [0, 1]]
evidence = []
evidence.append(e_t)
O_t = vector_to_diagonal(HMM.sensor_dist(e_t))
if t > d:
f = forward(HMM, f, e_t)
O_tmd = vector_to_diagonal(HMM.sensor_dist(ev[t - d]))
B = matrix_multiplication(inverse_matrix(O_tmd), inverse_matrix(T_model), B, T_model, O_t)
else:
B = matrix_multiplication(B, T_model, O_t)
t += 1
if t > d:
# always returns a 1x2 matrix
return [normalize(i) for i in matrix_multiplication([f], B)][0]
else:
return None
def fixed_lag_smoothing(e_t, HMM, d, ev, t):
"""Algoritmo de suavização com um intervalo de tempo fixo de passos 'd'.
Algoritmo online que produz a nova estimativa suavizada se a observação
Para novo passo de tempo é dado."""
ev.insert(0, None)
T_model = HMM.transition_model
f = HMM.prior
B = [[1, 0], [0, 1]]
evidence = []
evidence.append(e_t)
O_t = vector_to_diagonal(HMM.sensor_dist(e_t))
if t > d:
f = forward(HMM, f, e_t)
O_tmd = vector_to_diagonal(HMM.sensor_dist(ev[t - d]))
B = matrix_multiplication(inverse_matrix(O_tmd),
inverse_matrix(T_model), B, T_model, O_t)
else:
B = matrix_multiplication(B, T_model, O_t)
t = t + 1
if t > d:
return [normalize(i) for i in matrix_multiplication([f], B)][0]
else:
return None
