def calculateMostLikely(self):
""" Calculates the indices of the most likely trajectory.
The result alternates between point indices and path indices
and correspond to the candidate points and candidate paths.
"""
# a LearningTrajectory is the basic data structure that stores the
# features (scores candidate states candidate paths), and transitions
# (indices to look up those states or paths).
traj = LearningTrajectory(self.features, self.transitions)
# The viterbi is a specific algorithm that calculates the most likely
# states and most likley paths. The key output are the indices noted
# below, which can be used to look up the specific candidate states
# and candidate paths (although those must be stored externally.
# There is one index for each feature.
viterbi = TrajectoryViterbi1(traj, self.THETA)
try:
viterbi.computeAssignments()
except (ValueError):
for f in self.features:
print(f)
for t in self.transitions:
print(t)
print(self.THETA)
raise
# The indexes of the most likely elements of the trajectory
# Point indexes and path indexes are interleaved.
self.most_likely_indices = viterbi.assignments
def test_viterbi_1_7():
""" test_viterbi_1_7
"""
traj = simple_traj7()
theta = np.array([1.0])
viterbi_ref = TrajectoryViterbiRef(traj, theta)
viterbi_ref.computeMostLikely()
viterbi_1 = TrajectoryViterbi1(traj, theta)
viterbi_1.computeMostLikely()
assert len(viterbi_1.most_likely) == traj.L
for l in range(traj.L):
assert viterbi_1.most_likely[l] == viterbi_ref.most_likely[l]
assert traj.num_choices[l] == len(viterbi_ref.most_likely_tree[l])
for i in range(traj.num_choices[l]):
assert viterbi_ref.most_likely_tree[l][i] == \
viterbi_1.most_likely_tree[l][i]
Note: these weights have not be tuned in any way and are here for demonstration only. """ # NOTES (gde): # # theta determines the relative weights of the pathscores versus # the pointscores. What are recommended values? # Also beware of units! theta = np.array([1, 1]) """ Viterbi filter (most likely elements) """ # NOTES (gde): # # The viterbi is a specific algorithm that calculates the most likely # states and most likley paths. The key output are the indices noted # below, which can be used to look up the specific candidate states # and candidate paths (although those must be stored externally. # There is one index for each feature. viterbi = TrajectoryViterbi1(traj, theta) viterbi.computeAssignments() # The indexes of the most likely elements of the trajectory # Point indexes and path indexes are interleaved. most_likely_indexes = viterbi.assignments """ Alpha-beta smoother (distributions) """ # NOTES (gde): # # The smoother is a different algorithm that gives probabilities instead # of the most likley. smoother = TrajectorySmoother1(traj, theta) smoother.computeProbabilities() probabilities = smoother.probabilities