python-string-similarity/strsimpy/weighted_levenshtein.py

# Copyright (c) 2018 luozhouyang
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# SOFTWARE.

from .string_distance import StringDistance


def default_insertion_cost(char):
    return 1.0


def default_deletion_cost(char):
    return 1.0


def default_substitution_cost(char_a, char_b):
    return 1.0


class WeightedLevenshtein(StringDistance):

    def __init__(self,
                 substitution_cost_fn=default_substitution_cost,
                 insertion_cost_fn=default_insertion_cost,
                 deletion_cost_fn=default_deletion_cost,
                 ):
        self.substitution_cost_fn = substitution_cost_fn
        self.insertion_cost_fn = insertion_cost_fn
        self.deletion_cost_fn = deletion_cost_fn

    def distance(self, s0, s1):
        if s0 is None:
            raise TypeError("Argument s0 is NoneType.")
        if s1 is None:
            raise TypeError("Argument s1 is NoneType.")
        if s0 == s1:
            return 0.0
        if len(s0) == 0:
            return len(s1)
        if len(s1) == 0:
            return len(s0)

        v0, v1 = [0.0] * (len(s1) + 1), [0.0] * (len(s1) + 1)

        v0[0] = 0
        for i in range(1, len(v0)):
            v0[i] = v0[i - 1] + self.insertion_cost_fn(s1[i - 1])

        for i in range(len(s0)):
            s0i = s0[i]
            deletion_cost = self.deletion_cost_fn(s0i)
            v1[0] = v0[0] + deletion_cost

            for j in range(len(s1)):
                s1j = s1[j]
                cost = 0
                if s0i != s1j:
                    cost = self.substitution_cost_fn(s0i, s1j)
                insertion_cost = self.insertion_cost_fn(s1j)
                v1[j + 1] = min(v1[j] + insertion_cost, v0[j + 1] + deletion_cost, v0[j] + cost)
            v0, v1 = v1, v0

        return v0[len(s1)]