def get_all_outputs(self):
transducer = self.get_transducer()
transducer_symbol_table = SegmentTable().transducer_symbol_table
outputs = list()
for path in transducer.paths():
output = ""
for i in path:
symbol = transducer_symbol_table.find(i.olabel)
if symbol != u"\u03b5" and symbol != MORPHEME_BOUNDARY and symbol != WORD_BOUNDARY:
output += symbol
outputs.append(output)
return outputs
def get_all_outputs(self, with_noise=True):
transducer = self.get_transducer(with_noise=with_noise)
if configurations["MINIMIZE_TRANSDUCER"]:
transducer = self.minimize_transducer(transducer)
transducer_symbol_table = SegmentTable().transducer_symbol_table
outputs = list()
for path in transducer.paths():
output = ""
for i in path:
symbol = transducer_symbol_table.find(i.olabel)
if symbol != u"\u03b5" and symbol != MORPHEME_BOUNDARY and symbol != WORD_BOUNDARY:
output += symbol
outputs.append(output)
return outputs
def get_transducer_outputs(transducer, limit=float("inf")):
transducer_symbol_table = SegmentTable().transducer_symbol_table
outputs = list()
counter = 0
for path in transducer.paths():
output = ""
for arc in path:
symbol = transducer_symbol_table.find(arc.olabel)
if symbol != u"\u03b5":
output += symbol
outputs.append(output)
counter += 1
if counter > limit:
break
return outputs
def get_from_pyfst_transducer(cls, transducer):
transducer_symbol_table = SegmentTable().transducer_symbol_table
nfa = ParsingNFA()
nfa.final_states = list()
arcs_dict = dict()
probabilities = dict()
num_states = len(list(transducer.states))
transition_matrix = np.ones(
(num_states, num_states)) * NO_TRANSITION_IDX
for state in transducer:
m = re.match(
r".*#(\w*).*", str(state)
) # get sate number from the string: "<StdState #x with y arcs>"
nfa_state1 = m.group(1)
if state.initial:
nfa.initial_state = nfa_state1
if state.final:
nfa.final_states.append(nfa_state1)
for arc in state:
nfa_state2 = str(arc.nextstate)
output_symbol = transducer_symbol_table.find(arc.olabel)
if output_symbol == u"\u03b5":
output_symbol = NULL_SEGMENT
if nfa_state1 not in arcs_dict:
arcs_dict[nfa_state1] = {}
probabilities[nfa_state1] = []
if output_symbol not in arcs_dict[nfa_state1]:
arcs_dict[nfa_state1][output_symbol] = []
arcs_dict[nfa_state1][output_symbol].append(nfa_state2)
probabilities[nfa_state1].append((output_symbol, nfa_state2))
segment_idx = NULL_SEGMENT_IDX if output_symbol == NULL_SEGMENT else arc.olabel
transition_matrix[int(nfa_state1),
int(nfa_state2)] = segment_idx
nfa.arcs_dict = arcs_dict
nfa.probabilities = probabilities
nfa.transition_matrix = transition_matrix
return nfa
def pyfst_to_dfa(transducer, alphabet):
transducer_symbol_table = SegmentTable().transducer_symbol_table
nfa = NFA()
nfa.Sigma = alphabet
delta = dict()
States = list()
nfa.Initial = set()
for state in transducer:
m = re.match(r".*#(\w*).*", str(state)) # get sate number from the string: "<StdState #x with y arcs>"
nfa_state1_name = m.group(1)
States.append(nfa_state1_name)
nfa_state1 = States.index(nfa_state1_name)
if state.initial:
nfa.Initial.add(nfa_state1)
if state.final:
nfa.Final.add(nfa_state1)
for state in transducer:
m = re.match(r".*#(\w*).*", str(state))
nfa_state1_name = m.group(1)
nfa_state1 = States.index(nfa_state1_name)
for arc in state:
nfa_state2 = States.index(str(arc.nextstate))
output_symbol = transducer_symbol_table.find(arc.olabel)
if output_symbol == u"\u03b5":
output_symbol = FAdo.common.Epsilon
if nfa_state1 not in delta:
delta[nfa_state1] = dict()
if output_symbol not in delta[nfa_state1]:
delta[nfa_state1][output_symbol] = set()
delta[nfa_state1][output_symbol].add(nfa_state2)
nfa.delta = delta
nfa.States = States
dfa = nfa.toDFA()
return dfa