def get_model1_forward(theta, obs_id, fc):
global source, target, trellis
obs = trellis[obs_id]
max_bt = [-1] * len(obs)
p_st = 0.0
for t_idx in obs:
t_tok = target[obs_id][t_idx]
sum_e = float('-inf')
max_e = float('-inf')
max_s_idx = None
sum_sj = float('-inf')
for _, s_idx in obs[t_idx]:
s_tok = source[obs_id][s_idx] if s_idx is not NULL else NULL
e = get_decision_given_context(theta, E_TYPE, decision=t_tok, context=s_tok)
sum_e = utils.logadd(sum_e, e)
q = log(1.0 / len(obs[t_idx]))
sum_sj = utils.logadd(sum_sj, e + q)
if e > max_e:
max_e = e
max_s_idx = s_idx
max_bt[t_idx] = (t_idx, max_s_idx)
p_st += sum_sj
# update fractional counts
if fc is not None:
for _, s_idx in obs[t_idx]:
s_tok = source[obs_id][s_idx] if s_idx is not NULL else NULL
e = get_decision_given_context(theta, E_TYPE, decision=t_tok, context=s_tok)
delta = e - sum_e
event = (E_TYPE, t_tok, s_tok)
fc[event] = utils.logadd(delta, fc.get(event, float('-inf')))
return max_bt[:-1], p_st, fc
def accumulate_fc(self, type, alpha, beta, d, S, c=None, q=None, e=None, fc=None):
if type == T_TYPE:
update = alpha + q + e + beta - S
if fc is None:
self.fractional_counts[T_TYPE, d, c] = utils.logadd(update,
self.fractional_counts.get(
(T_TYPE, d, c,),
float('-inf')))
else:
fc[T_TYPE, d, c] = utils.logadd(update,
fc.get((T_TYPE, d, c,), float('-inf')))
elif type == E_TYPE:
update = alpha + beta - S # the emission should be included in alpha
if fc is None:
self.fractional_counts[E_TYPE, d, c] = utils.logadd(update,
self.fractional_counts.get(
(E_TYPE, d, c,),
float('-inf')))
else:
fc[E_TYPE, d, c] = utils.logadd(update,
fc.get((E_TYPE, d, c,), float('-inf')))
else:
raise "Wrong type"
if fc is not None:
return fc
def accumulate_fc(type, alpha, beta, d, S, c=None, k=None, q=None, e=None, fc=None):
if type == T_TYPE:
update = alpha + q + e + beta - S
fc[T_TYPE, d, c] = utils.logadd(update, fc.get((T_TYPE, d, c,), float('-inf')))
elif type == E_TYPE:
update = alpha + beta - S # the emission should be included in alpha
fc[E_TYPE, d, c] = utils.logadd(update, fc.get((E_TYPE, d, c,), float('-inf')))
else:
raise "Wrong type"
return fc
def get_backwards(self, theta, obs_seq, alpha_pi, fc=None):
n = len(obs_seq) - 1 # index of last word
beta_pi = {(n, END_SYM): 0.0}
S = alpha_pi[(n, END_SYM)] # from line 13 in pseudo code
fc = self.accumulate_fc(type=E_TYPE, alpha=0.0, beta=S, e=0.0, S=S, d=START_SYM, c=START_SYM, fc=fc)
for k in range(n, 0, -1):
for v in self.get_possible_states(obs_seq[k]):
e = self.get_decision_given_context(theta=theta, type=E_TYPE, decision=obs_seq[k],
context=v) # p(obs[k]|v)
pb = beta_pi[(k, v)]
fc = self.accumulate_fc(type=E_TYPE, alpha=alpha_pi[(k, v)], beta=beta_pi[k, v], e=e,
d=obs_seq[k], c=v, S=S, fc=fc)
for u in self.get_possible_states(obs_seq[k - 1]):
q = self.get_decision_given_context(type=T_TYPE, decision=v, context=u, theta=theta) # p(v|u)
fc = self.accumulate_fc(type=T_TYPE, alpha=alpha_pi[k - 1, u], beta=beta_pi[k, v],
q=q, e=e, d=v, c=u, S=S, fc=fc)
p = q + e
beta_p = pb + p # The beta includes the emission probability
new_pi_key = (k - 1, u)
if new_pi_key not in beta_pi: # implements lines 16
beta_pi[new_pi_key] = beta_p
else:
beta_pi[new_pi_key] = utils.logadd(beta_pi[new_pi_key], beta_p)
alpha_pi[(k - 1, u)] + p + beta_pi[(k, v)] - S
if fc is None:
return S, beta_pi
else:
return S, beta_pi, fc
def batch_accumilate_likelihood(result):
global data_likelihood, fractional_counts, emp_feat
data_likelihood += result[0]
fc = result[1]
emp_feat += result[2]
for k in fc:
fractional_counts[k] = utils.logadd(fc[k], fractional_counts.get(k, float('-inf')))
def get_best_seq(theta, obs_id):
global source, target, trellis
obs = trellis[obs_id]
max_bt = [-1] * len(obs)
p_st = 0.0
for t_idx in obs:
t_tok = target[obs_id][t_idx]
sum_e = float('-inf')
max_e = float('-inf')
max_s_idx = None
sum_sj = float('-inf')
for _, s_idx in obs[t_idx]:
s_tok = source[obs_id][s_idx] if s_idx is not NULL else NULL
e = get_decision_given_context(theta, E_TYPE, decision=t_tok, context=s_tok)
sum_e = utils.logadd(sum_e, e)
q = log(1.0 / len(obs[t_idx]))
sum_sj = utils.logadd(sum_sj, e + q)
if e > max_e:
max_e = e
max_s_idx = s_idx
max_bt[t_idx] = (t_idx, max_s_idx)
p_st += sum_sj
return max_bt[:-1], p_st
def get_backwards(theta, obs_id, alpha_pi, fc=None):
global max_jump_width, trellis, source, target
obs = trellis[obs_id]
src = source[obs_id]
tar = target[obs_id]
n = len(obs) - 1 # index of last word
end_state = obs[n][0]
beta_pi = {(n, end_state): 0.0}
S = alpha_pi[(n, end_state)] # from line 13 in pseudo code
fc = accumulate_fc(type=E_TYPE, alpha=0.0, beta=S, e=0.0, S=S, d=BOUNDARY_START, c=BOUNDARY_START, fc=fc)
for k in range(n, 0, -1):
for v in obs[k]:
tk, aj = v
t_tok = tar[tk]
s_tok = src[aj] if aj is not NULL else NULL
e = get_decision_given_context(theta, E_TYPE, decision=t_tok, context=s_tok)
pb = beta_pi[(k, v)]
fc = accumulate_fc(type=E_TYPE, alpha=alpha_pi[(k, v)], beta=beta_pi[k, v], e=e, S=S, d=t_tok, c=s_tok,
fc=fc)
for u in obs[k - 1]:
tk_1, aj_1 = u
t_tok_1 = tar[tk_1]
s_tok_1 = src[aj_1] if aj_1 is not NULL else NULL
context = aj_1
if model_type == HMM_MODEL:
q = get_decision_given_context(theta, T_TYPE, decision=aj, context=context)
fc = accumulate_fc(type=T_TYPE, alpha=alpha_pi[k - 1, u], beta=beta_pi[k, v], q=q, e=e, d=aj,
c=context,
S=S, fc=fc)
else:
q = log(1.0 / len(obs[k]))
# q = 0.0
p = q + e
beta_p = pb + p # The beta includes the emission probability
new_pi_key = (k - 1, u)
if new_pi_key not in beta_pi: # implements lines 16
beta_pi[new_pi_key] = beta_p
else:
beta_pi[new_pi_key] = utils.logadd(beta_pi[new_pi_key], beta_p)
alpha_pi[(k - 1, u)] + p + beta_pi[(k, v)] - S
if fc is None:
return S, beta_pi
else:
return S, beta_pi, fc
def get_model1_forward(theta, obs_id, fc=None, ef=None):
global source, target, trellis, diagonal_tension
obs = trellis[obs_id]
m = len(obs)
max_bt = [-1] * len(obs)
p_st = 0.0
for t_idx in obs:
t_tok = target[obs_id][t_idx]
sum_e = float('-inf')
sum_pei = float('-inf')
max_e = float('-inf')
max_s_idx = None
sum_sj = float('-inf')
sum_q = float('-inf')
for _, s_idx in obs[t_idx]:
n = len(obs[t_idx])
s_tok = source[obs_id][s_idx] if s_idx is not NULL else NULL
e = get_decision_given_context(theta, E_TYPE, decision=t_tok, context=s_tok)
sum_e = utils.logadd(sum_e, e)
if t_tok == BOUNDARY_START or t_tok == BOUNDARY_END:
q = 0.0
elif s_tok == NULL:
q = np.log(Po)
else:
# q = get_fast_align_transition(theta, t_idx, s_idx, m - 2, n - 1)
az = compute_z(t_idx, m - 2, n - 1, diagonal_tension) / (1 - Po)
q = np.log(unnormalized_prob(t_idx, s_idx, m - 2, n - 1, diagonal_tension) / az)
sum_pei = utils.logadd(sum_pei, q + e)
sum_sj = utils.logadd(sum_sj, e + q)
sum_q = utils.logadd(sum_q, q)
if e > max_e:
max_e = e
max_s_idx = s_idx
max_bt[t_idx] = (t_idx, max_s_idx)
p_st += sum_sj
if p_st == float('inf'):
pdb.set_trace()
# update fractional counts
if fc is not None:
for _, s_idx in obs[t_idx]:
n = len(obs[t_idx])
s_tok = source[obs_id][s_idx] if s_idx is not NULL else NULL
e = get_decision_given_context(theta, E_TYPE, decision=t_tok, context=s_tok)
if t_tok == BOUNDARY_START or t_tok == BOUNDARY_END:
q = 0.0
hijmn = 0.0
elif s_tok == NULL:
q = np.log(Po)
hijmn = 0.0
else:
az = compute_z(t_idx, m - 2, n - 1, diagonal_tension) / (1 - Po)
q = np.log(unnormalized_prob(t_idx, s_idx, m - 2, n - 1, diagonal_tension) / az)
hijmn = h(t_idx, s_idx, m - 2, n - 1)
p_ai = e + q - sum_pei # TODO: times h(j',i,m,n)
# p_q = q - sum_q
event = (E_TYPE, t_tok, s_tok)
fc[event] = utils.logadd(p_ai, fc.get(event, float('-inf')))
ef += (exp(p_ai) * hijmn)
return max_bt[:-1], p_st, fc, ef
