def viterbi(self, s: list) -> list:
"""
viterbi calculates the most probable state sequence for a set of observed symbols.
PARAMETERS
----------
s : list
A set of observed symbols.
RETURNS
-------
list
The most probable state sequence as an {@link ArrayList}.
"""
result = []
sequenceLength = len(s)
gamma = Matrix(sequenceLength, self.stateCount * self.stateCount)
phi = Matrix(sequenceLength, self.stateCount * self.stateCount)
qs = Vector(sequenceLength, 0)
emission1 = s[0]
emission2 = s[1]
for i in range(self.stateCount):
for j in range(self.stateCount):
observationLikelihood = self.states[i].getEmitProb(
emission1) * self.states[j].getEmitProb(emission2)
gamma.setValue(
1, i * self.stateCount + j,
self.safeLog(self.__pi.getValue(i, j)) +
self.safeLog(observationLikelihood))
for t in range(2, sequenceLength):
emission = s[t]
for j in range(self.stateCount * self.stateCount):
current = self.__logOfColumn(j)
previous = gamma.getRowVector(t - 1).skipVector(
self.stateCount, j // self.stateCount)
current.addVector(previous)
maxIndex = current.maxIndex()
observationLikelihood = self.states[
j % self.stateCount].getEmitProb(emission)
gamma.setValue(
t, j,
current.getValue(maxIndex) +
self.safeLog(observationLikelihood))
phi.setValue(t, j,
maxIndex * self.stateCount + j // self.stateCount)
qs.setValue(sequenceLength - 1,
gamma.getRowVector(sequenceLength - 1).maxIndex())
result.insert(
0, self.states[int(qs.getValue(sequenceLength - 1)) %
self.stateCount].getState())
for i in range(sequenceLength - 2, 0, -1):
qs.setValue(i, phi.getValue(i + 1, int(qs.getValue(i + 1))))
result.insert(
0,
self.states[int(qs.getValue(i)) % self.stateCount].getState())
result.insert(
0, self.states[int(qs.getValue(1)) // self.stateCount].getState())
return result
def viterbi(self, s: list) -> list:
"""
viterbi calculates the most probable state sequence for a set of observed symbols.
PARAMETERS
----------
s : list
A set of observed symbols.
RETURNS
-------
list
The most probable state sequence as an {@link ArrayList}.
"""
result = []
sequenceLength = len(s)
gamma = Matrix(sequenceLength, self.stateCount)
phi = Matrix(sequenceLength, self.stateCount)
qs = Vector(sequenceLength, 0)
emission = s[0]
for i in range(self.stateCount):
observationLikelihood = self.states[i].getEmitProb(emission)
gamma.setValue(0, i, self.safeLog(self.__pi.getValue(i)) + self.safeLog(observationLikelihood))
for t in range(1, sequenceLength):
emission = s[t]
for j in range(self.stateCount):
tempArray = self.__logOfColumn(j)
tempArray.addVector(gamma.getRowVector(t - 1))
maxIndex = tempArray.maxIndex()
observationLikelihood = self.states[j].getEmitProb(emission)
gamma.setValue(t, j, tempArray.getValue(maxIndex) + self.safeLog(observationLikelihood))
phi.setValue(t, j, maxIndex)
qs.setValue(sequenceLength - 1, gamma.getRowVector(sequenceLength - 1).maxIndex())
result.insert(0, self.states[int(qs.getValue(sequenceLength - 1))].getState())
for i in range(sequenceLength - 2, -1, -1):
qs.setValue(i, phi.getValue(i + 1, int(qs.getValue(i + 1))))
result.insert(0, self.states[int(qs.getValue(i))].getState())
return result
