def fscore_match(self, sem1, sem2):
if sem1 is None or sem2 is None:
alignment_score = 0.0
else:
alignment_score = tonal_space_alignment_score(sem1.lf, sem2.lf)
if sem1 is None:
len1 = 0.0
else:
len1 = tonal_space_length(sem1)
if sem2 is None:
len2 = 0.0
else:
len2 = tonal_space_length(sem2)
return alignment_score,len1,len2
def distance(self, sem1, sem2):
# Handle the extra 'dist' case
if self.options['output'] == 'dist':
# If one input is empty, we consider all points to have been deleted
if sem1 is None:
return tonal_space_length(sem2)
elif sem2 is None:
return tonal_space_length(sem1)
# Compute the score using our standard TS distance computation
# This is based on the alignment score of the optimal alignment
# of the two sequences
return tonal_space_distance(sem1.lf, sem2.lf)
else:
# Otherwise the superclass takes care of everything
return super(TonalSpaceEditDistance, self).distance(sem1, sem2)
