def predict(self, alpha):
m = Message()
# add change component
m.add_potential(Potential(self.prior.alpha, self.prior.a, self.prior.b, self.log_p1 + alpha.log_likelihood()))
# add no-change components
for p in alpha.potentials:
m.add_potential(Potential(p.alpha, p.a, p.b, p.log_c + self.log_p0))
return m
def forward(self, obs):
alpha = []
alpha_predict = []
for i in range(obs.shape[0]):
if i == 0:
m = Message()
m.add_potential(Potential(self.prior.alpha, self.prior.a, self.prior.b, self.log_p1))
m.add_potential(Potential(self.prior.alpha, self.prior.a, self.prior.b, self.log_p0))
alpha_predict.append(m)
else:
alpha_predict.append(self.predict(alpha[-1]))
alpha.append(self.update(alpha_predict[-1], obs[i, :]))
return [alpha_predict, alpha]
def backward(self, obs, start=0, length=0):
if length == 0:
length = obs.shape[0]
start = length-1
beta = []
for i in range(start, start - length, -1):
message = Message()
# change
p_obs = Potential.from_observation(obs[i, :], self.m, self.n)
pot_change = p_obs.copy()
if len(beta) > 0:
temp = Message()
for p in beta[-1].potentials:
temp.add_potential(p * self.prior)
pot_change.log_c += self.log_p1 + temp.log_likelihood()
message.add_potential(pot_change)
# no change
if len(beta) > 0:
for p in beta[-1].potentials:
p2 = p * p_obs
p2.log_c += self.log_p0
message.add_potential(p2)
beta.append(message)
beta.reverse()
return beta
def update(self, predict, obs):
m = Message()
p_obs = Potential.from_observation(obs, self.m, self.n)
for p in predict.potentials:
m.add_potential(p * p_obs)
return m
