def dist_abs(self, src, tar, mode='lev', cost=(1, 1, 1, 1)):
        """Return the Levenshtein distance between two strings.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        mode : str
            Specifies a mode for computing the Levenshtein distance:

                - ``lev`` (default) computes the ordinary Levenshtein distance,
                  in which edits may include inserts, deletes, and
                  substitutions
                - ``osa`` computes the Optimal String Alignment distance, in
                  which edits may include inserts, deletes, substitutions, and
                  transpositions but substrings may only be edited once

        cost : tuple
            A 4-tuple representing the cost of the four possible edits:
            inserts, deletes, substitutions, and transpositions, respectively
            (by default: (1, 1, 1, 1))

        Returns
        -------
        int (may return a float if cost has float values)
            The Levenshtein distance between src & tar

        Examples
        --------
        >>> cmp = Levenshtein()
        >>> cmp.dist_abs('cat', 'hat')
        1
        >>> cmp.dist_abs('Niall', 'Neil')
        3
        >>> cmp.dist_abs('aluminum', 'Catalan')
        7
        >>> cmp.dist_abs('ATCG', 'TAGC')
        3

        >>> cmp.dist_abs('ATCG', 'TAGC', mode='osa')
        2
        >>> cmp.dist_abs('ACTG', 'TAGC', mode='osa')
        4

        """
        ins_cost, del_cost, sub_cost, trans_cost = cost

        if src == tar:
            return 0
        if not src:
            return len(tar) * ins_cost
        if not tar:
            return len(src) * del_cost

        d_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_int)
        for i in range(len(src) + 1):
            d_mat[i, 0] = i * del_cost
        for j in range(len(tar) + 1):
            d_mat[0, j] = j * ins_cost

        for i in range(len(src)):
            for j in range(len(tar)):
                d_mat[i + 1, j + 1] = min(
                    d_mat[i + 1, j] + ins_cost,  # ins
                    d_mat[i, j + 1] + del_cost,  # del
                    d_mat[i, j]
                    + (sub_cost if src[i] != tar[j] else 0),  # sub/==
                )

                if mode == 'osa':
                    if (
                        i + 1 > 1
                        and j + 1 > 1
                        and src[i] == tar[j - 1]
                        and src[i - 1] == tar[j]
                    ):
                        # transposition
                        d_mat[i + 1, j + 1] = min(
                            d_mat[i + 1, j + 1],
                            d_mat[i - 1, j - 1] + trans_cost,
                        )

        return d_mat[len(src), len(tar)]
def dist_abs(self, src, tar, mode='lev', cost=(1, 1, 1, 1)):
        """Return the Levenshtein distance between two strings.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        mode : str
            Specifies a mode for computing the Levenshtein distance:

                - ``lev`` (default) computes the ordinary Levenshtein distance,
                  in which edits may include inserts, deletes, and
                  substitutions
                - ``osa`` computes the Optimal String Alignment distance, in
                  which edits may include inserts, deletes, substitutions, and
                  transpositions but substrings may only be edited once

        cost : tuple
            A 4-tuple representing the cost of the four possible edits:
            inserts, deletes, substitutions, and transpositions, respectively
            (by default: (1, 1, 1, 1))

        Returns
        -------
        int (may return a float if cost has float values)
            The Levenshtein distance between src & tar

        Examples
        --------
        >>> cmp = Levenshtein()
        >>> cmp.dist_abs('cat', 'hat')
        1
        >>> cmp.dist_abs('Niall', 'Neil')
        3
        >>> cmp.dist_abs('aluminum', 'Catalan')
        7
        >>> cmp.dist_abs('ATCG', 'TAGC')
        3

        >>> cmp.dist_abs('ATCG', 'TAGC', mode='osa')
        2
        >>> cmp.dist_abs('ACTG', 'TAGC', mode='osa')
        4

        """
        ins_cost, del_cost, sub_cost, trans_cost = cost

        if src == tar:
            return 0
        if not src:
            return len(tar) * ins_cost
        if not tar:
            return len(src) * del_cost

        d_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_int)
        for i in range(len(src) + 1):
            d_mat[i, 0] = i * del_cost
        for j in range(len(tar) + 1):
            d_mat[0, j] = j * ins_cost

        for i in range(len(src)):
            for j in range(len(tar)):
                d_mat[i + 1, j + 1] = min(
                    d_mat[i + 1, j] + ins_cost,  # ins
                    d_mat[i, j + 1] + del_cost,  # del
                    d_mat[i, j]
                    + (sub_cost if src[i] != tar[j] else 0),  # sub/==
                )

                if mode == 'osa':
                    if (
                        i + 1 > 1
                        and j + 1 > 1
                        and src[i] == tar[j - 1]
                        and src[i - 1] == tar[j]
                    ):
                        # transposition
                        d_mat[i + 1, j + 1] = min(
                            d_mat[i + 1, j + 1],
                            d_mat[i - 1, j - 1] + trans_cost,
                        )

        return d_mat[len(src), len(tar)]
def dist_abs ( self , src , tar , mode = 'lev' , cost = ( 1 , 1 , 1 , 1 ) ) : ins_cost , del_cost , sub_cost , trans_cost = cost if src == tar : return 0 if not src : return len ( tar ) * ins_cost if not tar : return len ( src ) * del_cost d_mat = np_zeros ( ( len ( src ) + 1 , len ( tar ) + 1 ) , dtype = np_int ) for i in range ( len ( src ) + 1 ) : d_mat [ i , 0 ] = i * del_cost for j in range ( len ( tar ) + 1 ) : d_mat [ 0 , j ] = j * ins_cost for i in range ( len ( src ) ) : for j in range ( len ( tar ) ) : d_mat [ i + 1 , j + 1 ] = min ( d_mat [ i + 1 , j ] + ins_cost , # ins d_mat [ i , j + 1 ] + del_cost , # del d_mat [ i , j ] + ( sub_cost if src [ i ] != tar [ j ] else 0 ) , # sub/== ) if mode == 'osa' : if ( i + 1 > 1 and j + 1 > 1 and src [ i ] == tar [ j - 1 ] and src [ i - 1 ] == tar [ j ] ) : # transposition d_mat [ i + 1 , j + 1 ] = min ( d_mat [ i + 1 , j + 1 ] , d_mat [ i - 1 , j - 1 ] + trans_cost , ) return d_mat [ len ( src ) , len ( tar ) ]
Return the Levenshtein distance between two strings.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        mode : str
            Specifies a mode for computing the Levenshtein distance:

                - ``lev`` (default) computes the ordinary Levenshtein distance,
                  in which edits may include inserts, deletes, and
                  substitutions
                - ``osa`` computes the Optimal String Alignment distance, in
                  which edits may include inserts, deletes, substitutions, and
                  transpositions but substrings may only be edited once

        cost : tuple
            A 4-tuple representing the cost of the four possible edits:
            inserts, deletes, substitutions, and transpositions, respectively
            (by default: (1, 1, 1, 1))

        Returns
        -------
        int (may return a float if cost has float values)
            The Levenshtein distance between src & tar

        Examples
        --------
        >>> cmp = Levenshtein()
        >>> cmp.dist_abs('cat', 'hat')
        1
        >>> cmp.dist_abs('Niall', 'Neil')
        3
        >>> cmp.dist_abs('aluminum', 'Catalan')
        7
        >>> cmp.dist_abs('ATCG', 'TAGC')
        3

        >>> cmp.dist_abs('ATCG', 'TAGC', mode='osa')
        2
        >>> cmp.dist_abs('ACTG', 'TAGC', mode='osa')
        4
Return the Levenshtein distance between two strings .