Comments (2)
I don't believe so. If interested in small edit distances though, you may, depending on your scenario and data, accomplish the same effect by doing some math projecting to a max intended ordinary edit distance to use with this library, then proceeding to performing your nuanced weights/penalty scheme on the cohort that remains after using this library with your chosen first distance value.
I doubt you'd currently find any implementation that does, but this is an interesting project idea.
from liblevenshtein-java.
I don't believe so. If interested in small edit distances though, you may, depending on your scenario and data, accomplish the same effect by doing some math projecting to a max intended ordinary edit distance to use with this library, then proceeding to performing your nuanced weights/penalty scheme on the cohort that remains after using this library with your chosen first distance value.
I doubt you'd currently find any implementation that does, but this is an interesting project idea.
Interesting idea but assuming some substitutions could be made at low or zero cost, it would be difficult to estimate a correct 'max intended ordinary edit distance' in advance.
from liblevenshtein-java.
Related Issues (20)
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