Instruction:
Create a Python 3 function for
returning the Synoname similarity type of two words.

Response:
def dist_abs(
        self,
        src,
        tar,
        word_approx_min=0.3,
        char_approx_min=0.73,
        tests=2 ** 12 - 1,
        ret_name=False,
    ):
        """Return the Synoname similarity type of two words.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        word_approx_min : float
            The minimum word approximation value to signal a 'word_approx'
            match
        char_approx_min : float
            The minimum character approximation value to signal a 'char_approx'
            match
        tests : int or Iterable
            Either an integer indicating tests to perform or a list of test
            names to perform (defaults to performing all tests)
        ret_name : bool
            If True, returns the match name rather than its integer equivalent

        Returns
        -------
        int (or str if ret_name is True)
            Synoname value

        Examples
        --------
        >>> cmp = Synoname()
        >>> cmp.dist_abs(('Breghel', 'Pieter', ''), ('Brueghel', 'Pieter', ''))
        2
        >>> cmp.dist_abs(('Breghel', 'Pieter', ''), ('Brueghel', 'Pieter', ''),
        ... ret_name=True)
        'omission'
        >>> cmp.dist_abs(('Dore', 'Gustave', ''),
        ... ('Dore', 'Paul Gustave Louis Christophe', ''), ret_name=True)
        'inclusion'
        >>> cmp.dist_abs(('Pereira', 'I. R.', ''), ('Pereira', 'I. Smith', ''),
        ... ret_name=True)
        'word_approx'

        """
        if isinstance(tests, Iterable):
            new_tests = 0
            for term in tests:
                if term in self._test_dict:
                    new_tests += self._test_dict[term]
            tests = new_tests

        if isinstance(src, tuple):
            src_ln, src_fn, src_qual = src
        elif '#' in src:
            src_ln, src_fn, src_qual = src.split('#')[-3:]
        else:
            src_ln, src_fn, src_qual = src, '', ''

        if isinstance(tar, tuple):
            tar_ln, tar_fn, tar_qual = tar
        elif '#' in tar:
            tar_ln, tar_fn, tar_qual = tar.split('#')[-3:]
        else:
            tar_ln, tar_fn, tar_qual = tar, '', ''

        def _split_special(spec):
            spec_list = []
            while spec:
                spec_list.append((int(spec[:3]), spec[3:4]))
                spec = spec[4:]
            return spec_list

        def _fmt_retval(val):
            if ret_name:
                return self._match_name[val]
            return val

        # 1. Preprocessing

        # Lowercasing
        src_fn = src_fn.strip().lower()
        src_ln = src_ln.strip().lower()
        src_qual = src_qual.strip().lower()

        tar_fn = tar_fn.strip().lower()
        tar_ln = tar_ln.strip().lower()
        tar_qual = tar_qual.strip().lower()

        # Create toolcodes
        src_ln, src_fn, src_tc = self._stc.fingerprint(
            src_ln, src_fn, src_qual
        )
        tar_ln, tar_fn, tar_tc = self._stc.fingerprint(
            tar_ln, tar_fn, tar_qual
        )

        src_generation = int(src_tc[2])
        src_romancode = int(src_tc[3:6])
        src_len_fn = int(src_tc[6:8])
        src_tc = src_tc.split('$')
        src_specials = _split_special(src_tc[1])

        tar_generation = int(tar_tc[2])
        tar_romancode = int(tar_tc[3:6])
        tar_len_fn = int(tar_tc[6:8])
        tar_tc = tar_tc.split('$')
        tar_specials = _split_special(tar_tc[1])

        gen_conflict = (src_generation != tar_generation) and bool(
            src_generation or tar_generation
        )
        roman_conflict = (src_romancode != tar_romancode) and bool(
            src_romancode or tar_romancode
        )

        ln_equal = src_ln == tar_ln
        fn_equal = src_fn == tar_fn

        # approx_c
        def _approx_c():
            if gen_conflict or roman_conflict:
                return False, 0

            full_src = ' '.join((src_ln, src_fn))
            if full_src.startswith('master '):
                full_src = full_src[len('master ') :]
                for intro in [
                    'of the ',
                    'of ',
                    'known as the ',
                    'with the ',
                    'with ',
                ]:
                    if full_src.startswith(intro):
                        full_src = full_src[len(intro) :]

            full_tar = ' '.join((tar_ln, tar_fn))
            if full_tar.startswith('master '):
                full_tar = full_tar[len('master ') :]
                for intro in [
                    'of the ',
                    'of ',
                    'known as the ',
                    'with the ',
                    'with ',
                ]:
                    if full_tar.startswith(intro):
                        full_tar = full_tar[len(intro) :]

            loc_ratio = sim_ratcliff_obershelp(full_src, full_tar)
            return loc_ratio >= char_approx_min, loc_ratio

        approx_c_result, ca_ratio = _approx_c()

        if tests & self._test_dict['exact'] and fn_equal and ln_equal:
            return _fmt_retval(self._match_type_dict['exact'])
        if tests & self._test_dict['omission']:
            if (
                fn_equal
                and levenshtein(src_ln, tar_ln, cost=(1, 1, 99, 99)) == 1
            ):
                if not roman_conflict:
                    return _fmt_retval(self._match_type_dict['omission'])
            elif (
                ln_equal
                and levenshtein(src_fn, tar_fn, cost=(1, 1, 99, 99)) == 1
            ):
                return _fmt_retval(self._match_type_dict['omission'])
        if tests & self._test_dict['substitution']:
            if (
                fn_equal
                and levenshtein(src_ln, tar_ln, cost=(99, 99, 1, 99)) == 1
            ):
                return _fmt_retval(self._match_type_dict['substitution'])
            elif (
                ln_equal
                and levenshtein(src_fn, tar_fn, cost=(99, 99, 1, 99)) == 1
            ):
                return _fmt_retval(self._match_type_dict['substitution'])
        if tests & self._test_dict['transposition']:
            if fn_equal and (
                levenshtein(src_ln, tar_ln, mode='osa', cost=(99, 99, 99, 1))
                == 1
            ):
                return _fmt_retval(self._match_type_dict['transposition'])
            elif ln_equal and (
                levenshtein(src_fn, tar_fn, mode='osa', cost=(99, 99, 99, 1))
                == 1
            ):
                return _fmt_retval(self._match_type_dict['transposition'])
        if tests & self._test_dict['punctuation']:
            np_src_fn = self._synoname_strip_punct(src_fn)
            np_tar_fn = self._synoname_strip_punct(tar_fn)
            np_src_ln = self._synoname_strip_punct(src_ln)
            np_tar_ln = self._synoname_strip_punct(tar_ln)

            if (np_src_fn == np_tar_fn) and (np_src_ln == np_tar_ln):
                return _fmt_retval(self._match_type_dict['punctuation'])

            np_src_fn = self._synoname_strip_punct(src_fn.replace('-', ' '))
            np_tar_fn = self._synoname_strip_punct(tar_fn.replace('-', ' '))
            np_src_ln = self._synoname_strip_punct(src_ln.replace('-', ' '))
            np_tar_ln = self._synoname_strip_punct(tar_ln.replace('-', ' '))

            if (np_src_fn == np_tar_fn) and (np_src_ln == np_tar_ln):
                return _fmt_retval(self._match_type_dict['punctuation'])

        if tests & self._test_dict['initials'] and ln_equal:
            if src_fn and tar_fn:
                src_initials = self._synoname_strip_punct(src_fn).split()
                tar_initials = self._synoname_strip_punct(tar_fn).split()
                initials = bool(
                    (len(src_initials) == len(''.join(src_initials)))
                    or (len(tar_initials) == len(''.join(tar_initials)))
                )
                if initials:
                    src_initials = ''.join(_[0] for _ in src_initials)
                    tar_initials = ''.join(_[0] for _ in tar_initials)
                    if src_initials == tar_initials:
                        return _fmt_retval(self._match_type_dict['initials'])
                    initial_diff = abs(len(src_initials) - len(tar_initials))
                    if initial_diff and (
                        (
                            initial_diff
                            == levenshtein(
                                src_initials,
                                tar_initials,
                                cost=(1, 99, 99, 99),
                            )
                        )
                        or (
                            initial_diff
                            == levenshtein(
                                tar_initials,
                                src_initials,
                                cost=(1, 99, 99, 99),
                            )
                        )
                    ):
                        return _fmt_retval(self._match_type_dict['initials'])
        if tests & self._test_dict['extension']:
            if src_ln[1] == tar_ln[1] and (
                src_ln.startswith(tar_ln) or tar_ln.startswith(src_ln)
            ):
                if (
                    (not src_len_fn and not tar_len_fn)
                    or (tar_fn and src_fn.startswith(tar_fn))
                    or (src_fn and tar_fn.startswith(src_fn))
                ) and not roman_conflict:
                    return _fmt_retval(self._match_type_dict['extension'])
        if tests & self._test_dict['inclusion'] and ln_equal:
            if (src_fn and src_fn in tar_fn) or (tar_fn and tar_fn in src_ln):
                return _fmt_retval(self._match_type_dict['inclusion'])
        if tests & self._test_dict['no_first'] and ln_equal:
            if src_fn == '' or tar_fn == '':
                return _fmt_retval(self._match_type_dict['no_first'])
        if tests & self._test_dict['word_approx']:
            ratio = self._synoname_word_approximation(
                src_ln,
                tar_ln,
                src_fn,
                tar_fn,
                {
                    'gen_conflict': gen_conflict,
                    'roman_conflict': roman_conflict,
                    'src_specials': src_specials,
                    'tar_specials': tar_specials,
                },
            )
            if ratio == 1 and tests & self._test_dict['confusions']:
                if (
                    ' '.join((src_fn, src_ln)).strip()
                    == ' '.join((tar_fn, tar_ln)).strip()
                ):
                    return _fmt_retval(self._match_type_dict['confusions'])
            if ratio >= word_approx_min:
                return _fmt_retval(self._match_type_dict['word_approx'])
        if tests & self._test_dict['char_approx']:
            if ca_ratio >= char_approx_min:
                return _fmt_retval(self._match_type_dict['char_approx'])
        return _fmt_retval(self._match_type_dict['no_match'])


Instruction:
Write a Python 3 script to
return the normalized Synoname distance between two words.

Response:
def dist(
        self,
        src,
        tar,
        word_approx_min=0.3,
        char_approx_min=0.73,
        tests=2 ** 12 - 1,
    ):
        """Return the normalized Synoname distance between two words.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        word_approx_min : float
            The minimum word approximation value to signal a 'word_approx'
            match
        char_approx_min : float
            The minimum character approximation value to signal a 'char_approx'
            match
        tests : int or Iterable
            Either an integer indicating tests to perform or a list of test
            names to perform (defaults to performing all tests)

        Returns
        -------
        float
            Normalized Synoname distance

        """
        return (
            synoname(src, tar, word_approx_min, char_approx_min, tests, False)
            / 14
        )


Instruction:
Given the following Python 3 function, write the documentation
def sim(self, src, tar, qval=2):
        return super(self.__class__, self).sim(src, tar, qval, 1, 1)

Response: Return the Jaccard similarity of two strings.


Instruction:
How would you implement a function in Python 3 that
returns the Tanimoto distance between two strings.

Response:
def tanimoto_coeff(self, src, tar, qval=2):
        """Return the Tanimoto distance between two strings.

        Tanimoto distance :cite:`Tanimoto:1958` is
        :math:`-log_{2} sim_{Tanimoto}(X, Y)`.

        Parameters
        ----------
        src : str
            Source string (or QGrams/Counter objects) for comparison
        tar : str
            Target string (or QGrams/Counter objects) for comparison
        qval : int
            The length of each q-gram; 0 for non-q-gram version

        Returns
        -------
        float
            Tanimoto distance

        Examples
        --------
        >>> cmp = Jaccard()
        >>> cmp.tanimoto_coeff('cat', 'hat')
        -1.5849625007211563
        >>> cmp.tanimoto_coeff('Niall', 'Neil')
        -2.1699250014423126
        >>> cmp.tanimoto_coeff('aluminum', 'Catalan')
        -4.0
        >>> cmp.tanimoto_coeff('ATCG', 'TAGC')
        -inf

        """
        coeff = self.sim(src, tar, qval)
        if coeff != 0:
            return log(coeff, 2)

        return float('-inf')


Instruction:
Implement a Python 3 function for
returning the common Sift4 distance between two terms.

Response:
def sift4_common(src, tar, max_offset=5, max_distance=0):
    """Return the "common" Sift4 distance between two terms.

    This is a wrapper for :py:meth:`Sift4.dist_abs`.

    Parameters
    ----------
    src : str
        Source string for comparison
    tar : str
        Target string for comparison
    max_offset : int
        The number of characters to search for matching letters
    max_distance : int
        The distance at which to stop and exit

    Returns
    -------
    int
        The Sift4 distance according to the common formula

    Examples
    --------
    >>> sift4_common('cat', 'hat')
    1
    >>> sift4_common('Niall', 'Neil')
    2
    >>> sift4_common('Colin', 'Cuilen')
    3
    >>> sift4_common('ATCG', 'TAGC')
    2

    """
    return Sift4().dist_abs(src, tar, max_offset, max_distance)


Instruction:
Given the following Python 3 function, write the documentation
def dist_sift4(src, tar, max_offset=5, max_distance=0):
    return Sift4().dist(src, tar, max_offset, max_distance)

Response: Return the normalized common Sift4 distance between two terms.


Instruction:
Write a Python 3 function for
returning the normalized common Sift4 similarity of two terms.

Response:
def sim_sift4(src, tar, max_offset=5, max_distance=0):
    """Return the normalized "common" Sift4 similarity of two terms.

    This is a wrapper for :py:meth:`Sift4.sim`.

    Parameters
    ----------
    src : str
        Source string for comparison
    tar : str
        Target string for comparison
    max_offset : int
        The number of characters to search for matching letters
    max_distance : int
        The distance at which to stop and exit

    Returns
    -------
    float
        The normalized Sift4 similarity

    Examples
    --------
    >>> round(sim_sift4('cat', 'hat'), 12)
    0.666666666667
    >>> sim_sift4('Niall', 'Neil')
    0.6
    >>> sim_sift4('Colin', 'Cuilen')
    0.5
    >>> sim_sift4('ATCG', 'TAGC')
    0.5

    """
    return Sift4().sim(src, tar, max_offset, max_distance)


Instruction:
Write a Python 3 function that can
return the common Sift4 distance between two strings.

Response:
def dist_abs(self, src, tar, max_offset=5, max_distance=0):
        """Return the "common" Sift4 distance between two terms.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        max_offset : int
            The number of characters to search for matching letters
        max_distance : int
            The distance at which to stop and exit

        Returns
        -------
        int
            The Sift4 distance according to the common formula

        Examples
        --------
        >>> cmp = Sift4()
        >>> cmp.dist_abs('cat', 'hat')
        1
        >>> cmp.dist_abs('Niall', 'Neil')
        2
        >>> cmp.dist_abs('Colin', 'Cuilen')
        3
        >>> cmp.dist_abs('ATCG', 'TAGC')
        2

        """
        if not src:
            return len(tar)

        if not tar:
            return len(src)

        src_len = len(src)
        tar_len = len(tar)

        src_cur = 0
        tar_cur = 0
        lcss = 0
        local_cs = 0
        trans = 0
        offset_arr = []

        while (src_cur < src_len) and (tar_cur < tar_len):
            if src[src_cur] == tar[tar_cur]:
                local_cs += 1
                is_trans = False
                i = 0
                while i < len(offset_arr):
                    ofs = offset_arr[i]
                    if src_cur <= ofs['src_cur'] or tar_cur <= ofs['tar_cur']:
                        is_trans = abs(tar_cur - src_cur) >= abs(
                            ofs['tar_cur'] - ofs['src_cur']
                        )
                        if is_trans:
                            trans += 1
                        elif not ofs['trans']:
                            ofs['trans'] = True
                            trans += 1
                        break
                    elif src_cur > ofs['tar_cur'] and tar_cur > ofs['src_cur']:
                        del offset_arr[i]
                    else:
                        i += 1

                offset_arr.append(
                    {'src_cur': src_cur, 'tar_cur': tar_cur, 'trans': is_trans}
                )
            else:
                lcss += local_cs
                local_cs = 0
                if src_cur != tar_cur:
                    src_cur = tar_cur = min(src_cur, tar_cur)
                for i in range(max_offset):
                    if not (
                        (src_cur + i < src_len) or (tar_cur + i < tar_len)
                    ):
                        break
                    if (src_cur + i < src_len) and (
                        src[src_cur + i] == tar[tar_cur]
                    ):
                        src_cur += i - 1
                        tar_cur -= 1
                        break
                    if (tar_cur + i < tar_len) and (
                        src[src_cur] == tar[tar_cur + i]
                    ):
                        src_cur -= 1
                        tar_cur += i - 1
                        break

            src_cur += 1
            tar_cur += 1

            if max_distance:
                temporary_distance = max(src_cur, tar_cur) - lcss + trans
                if temporary_distance >= max_distance:
                    return round(temporary_distance)

            if (src_cur >= src_len) or (tar_cur >= tar_len):
                lcss += local_cs
                local_cs = 0
                src_cur = tar_cur = min(src_cur, tar_cur)

        lcss += local_cs
        return round(max(src_len, tar_len) - lcss + trans)


Instruction:
Create a Python 3 function for
returning the normalized common Sift4 distance between two terms.

Response:
def dist(self, src, tar, max_offset=5, max_distance=0):
        """Return the normalized "common" Sift4 distance between two terms.

        This is Sift4 distance, normalized to [0, 1].

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        max_offset : int
            The number of characters to search for matching letters
        max_distance : int
            The distance at which to stop and exit

        Returns
        -------
        float
            The normalized Sift4 distance

        Examples
        --------
        >>> cmp = Sift4()
        >>> round(cmp.dist('cat', 'hat'), 12)
        0.333333333333
        >>> cmp.dist('Niall', 'Neil')
        0.4
        >>> cmp.dist('Colin', 'Cuilen')
        0.5
        >>> cmp.dist('ATCG', 'TAGC')
        0.5

        """
        return self.dist_abs(src, tar, max_offset, max_distance) / (
            max(len(src), len(tar), 1)
        )


Instruction:
Write a Python 3 function for
returning the NCD between two strings using bzip2 compression.

Response:
def dist(self, src, tar):
        """Return the NCD between two strings using bzip2 compression.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison

        Returns
        -------
        float
            Compression distance

        Examples
        --------
        >>> cmp = NCDbz2()
        >>> cmp.dist('cat', 'hat')
        0.06666666666666667
        >>> cmp.dist('Niall', 'Neil')
        0.03125
        >>> cmp.dist('aluminum', 'Catalan')
        0.17647058823529413
        >>> cmp.dist('ATCG', 'TAGC')
        0.03125

        """
        if src == tar:
            return 0.0

        src = src.encode('utf-8')
        tar = tar.encode('utf-8')

        src_comp = bz2.compress(src, self._level)[10:]
        tar_comp = bz2.compress(tar, self._level)[10:]
        concat_comp = bz2.compress(src + tar, self._level)[10:]
        concat_comp2 = bz2.compress(tar + src, self._level)[10:]

        return (
            min(len(concat_comp), len(concat_comp2))
            - min(len(src_comp), len(tar_comp))
        ) / max(len(src_comp), len(tar_comp))


Instruction:
Write a Python 3 script to
return the MetaSoundex code for a word.

Response:
def encode(self, word, lang='en'):
        """Return the MetaSoundex code for a word.

        Parameters
        ----------
        word : str
            The word to transform
        lang : str
            Either ``en`` for English or ``es`` for Spanish

        Returns
        -------
        str
            The MetaSoundex code

        Examples
        --------
        >>> pe = MetaSoundex()
        >>> pe.encode('Smith')
        '4500'
        >>> pe.encode('Waters')
        '7362'
        >>> pe.encode('James')
        '1520'
        >>> pe.encode('Schmidt')
        '4530'
        >>> pe.encode('Ashcroft')
        '0261'
        >>> pe.encode('Perez', lang='es')
        '094'
        >>> pe.encode('Martinez', lang='es')
        '69364'
        >>> pe.encode('Gutierrez', lang='es')
        '83994'
        >>> pe.encode('Santiago', lang='es')
        '4638'
        >>> pe.encode('Nicolás', lang='es')
        '6754'

        """
        if lang == 'es':
            return self._phonetic_spanish.encode(
                self._spanish_metaphone.encode(word)
            )

        word = self._soundex.encode(self._metaphone.encode(word))
        word = word[0].translate(self._trans) + word[1:]
        return word


Instruction:
Explain what the following Python 3 code does
def stem(self, word):
        lowered = word.lower()
        if lowered[-3:] == 'ies' and lowered[-4:-3] not in {'e', 'a'}:
            return word[:-3] + ('Y' if word[-1:].isupper() else 'y')
        if lowered[-2:] == 'es' and lowered[-3:-2] not in {'a', 'e', 'o'}:
            return word[:-1]
        if lowered[-1:] == 's' and lowered[-2:-1] not in {'u', 's'}:
            return word[:-1]
        return word

Response: Returns the S - stemmed form of a word.


Instruction:
Create a Python 3 function for
returning the similarity of two strings src and tar.

Response:
def sim(self, src, tar):
        """Return the Ratcliff-Obershelp similarity of two strings.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison

        Returns
        -------
        float
            Ratcliff-Obershelp similarity

        Examples
        --------
        >>> cmp = RatcliffObershelp()
        >>> round(cmp.sim('cat', 'hat'), 12)
        0.666666666667
        >>> round(cmp.sim('Niall', 'Neil'), 12)
        0.666666666667
        >>> round(cmp.sim('aluminum', 'Catalan'), 12)
        0.4
        >>> cmp.sim('ATCG', 'TAGC')
        0.5

        """

        def _lcsstr_stl(src, tar):
            """Return start positions & length for Ratcliff-Obershelp.

            Parameters
            ----------
            src : str
                Source string for comparison
            tar : str
            Target string for comparison

            Returns
            -------
            tuple
                The start position in the source string, start position in the
                target string, and length of the longest common substring of
                strings src and tar.

            """
            lengths = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_int)
            longest, src_longest, tar_longest = 0, 0, 0
            for i in range(1, len(src) + 1):
                for j in range(1, len(tar) + 1):
                    if src[i - 1] == tar[j - 1]:
                        lengths[i, j] = lengths[i - 1, j - 1] + 1
                        if lengths[i, j] > longest:
                            longest = lengths[i, j]
                            src_longest = i
                            tar_longest = j
                    else:
                        lengths[i, j] = 0
            return src_longest - longest, tar_longest - longest, longest

        def _sstr_matches(src, tar):
            """Return the sum of substring match lengths.

            This follows the Ratcliff-Obershelp algorithm
            :cite:`Ratcliff:1988`:
                 1. Find the length of the longest common substring in src &
                     tar.
                 2. Recurse on the strings to the left & right of each this
                     substring in src & tar.
                 3. Base case is a 0 length common substring, in which case,
                     return 0.
                 4. Return the sum.

            Parameters
            ----------
            src : str
                Source string for comparison
            tar : str
                Target string for comparison

            Returns
            -------
            int
                Sum of substring match lengths

            """
            src_start, tar_start, length = _lcsstr_stl(src, tar)
            if length == 0:
                return 0
            return (
                _sstr_matches(src[:src_start], tar[:tar_start])
                + length
                + _sstr_matches(
                    src[src_start + length :], tar[tar_start + length :]
                )
            )

        if src == tar:
            return 1.0
        elif not src or not tar:
            return 0.0
        return 2 * _sstr_matches(src, tar) / (len(src) + len(tar))


Instruction:
How would you implement a function in Python 3 that
returns the Refined Soundex code for a word.

Response:
def refined_soundex(word, max_length=-1, zero_pad=False, retain_vowels=False):
    """Return the Refined Soundex code for a word.

    This is a wrapper for :py:meth:`RefinedSoundex.encode`.

    Parameters
    ----------
    word : str
        The word to transform
    max_length : int
        The length of the code returned (defaults to unlimited)
    zero_pad : bool
        Pad the end of the return value with 0s to achieve a max_length string
    retain_vowels : bool
        Retain vowels (as 0) in the resulting code

    Returns
    -------
    str
        The Refined Soundex value

    Examples
    --------
    >>> refined_soundex('Christopher')
    'C393619'
    >>> refined_soundex('Niall')
    'N87'
    >>> refined_soundex('Smith')
    'S386'
    >>> refined_soundex('Schmidt')
    'S386'

    """
    return RefinedSoundex().encode(word, max_length, zero_pad, retain_vowels)


Instruction:
How would you implement a function in Python 3 that
returns the Refined Soundex code for a word.

Response:
def encode(self, word, max_length=-1, zero_pad=False, retain_vowels=False):
        """Return the Refined Soundex code for a word.

        Parameters
        ----------
        word : str
            The word to transform
        max_length : int
            The length of the code returned (defaults to unlimited)
        zero_pad : bool
            Pad the end of the return value with 0s to achieve a max_length
            string
        retain_vowels : bool
            Retain vowels (as 0) in the resulting code

        Returns
        -------
        str
            The Refined Soundex value

        Examples
        --------
        >>> pe = RefinedSoundex()
        >>> pe.encode('Christopher')
        'C393619'
        >>> pe.encode('Niall')
        'N87'
        >>> pe.encode('Smith')
        'S386'
        >>> pe.encode('Schmidt')
        'S386'

        """
        # uppercase, normalize, decompose, and filter non-A-Z out
        word = unicode_normalize('NFKD', text_type(word.upper()))
        word = word.replace('ß', 'SS')
        word = ''.join(c for c in word if c in self._uc_set)

        # apply the Soundex algorithm
        sdx = word[:1] + word.translate(self._trans)
        sdx = self._delete_consecutive_repeats(sdx)
        if not retain_vowels:
            sdx = sdx.replace('0', '')  # Delete vowels, H, W, Y

        if max_length > 0:
            if zero_pad:
                sdx += '0' * max_length
            sdx = sdx[:max_length]

        return sdx


Instruction:
Can you create a Python 3 function that
returns the MRA comparison rating of two strings.

Response:
def dist_abs(self, src, tar):
        """Return the MRA comparison rating of two strings.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison

        Returns
        -------
        int
            MRA comparison rating

        Examples
        --------
        >>> cmp = MRA()
        >>> cmp.dist_abs('cat', 'hat')
        5
        >>> cmp.dist_abs('Niall', 'Neil')
        6
        >>> cmp.dist_abs('aluminum', 'Catalan')
        0
        >>> cmp.dist_abs('ATCG', 'TAGC')
        5

        """
        if src == tar:
            return 6
        if src == '' or tar == '':
            return 0
        src = list(mra(src))
        tar = list(mra(tar))

        if abs(len(src) - len(tar)) > 2:
            return 0

        length_sum = len(src) + len(tar)
        if length_sum < 5:
            min_rating = 5
        elif length_sum < 8:
            min_rating = 4
        elif length_sum < 12:
            min_rating = 3
        else:
            min_rating = 2

        for _ in range(2):
            new_src = []
            new_tar = []
            minlen = min(len(src), len(tar))
            for i in range(minlen):
                if src[i] != tar[i]:
                    new_src.append(src[i])
                    new_tar.append(tar[i])
            src = new_src + src[minlen:]
            tar = new_tar + tar[minlen:]
            src.reverse()
            tar.reverse()

        similarity = 6 - max(len(src), len(tar))

        if similarity >= min_rating:
            return similarity
        return 0


Instruction:
Write a Python 3 function that can
return the Parmar - Kumbharana encoding of a word.

Response:
def encode(self, word):
        """Return the Parmar-Kumbharana encoding of a word.

        Parameters
        ----------
        word : str
            The word to transform

        Returns
        -------
        str
            The Parmar-Kumbharana encoding

        Examples
        --------
        >>> pe = ParmarKumbharana()
        >>> pe.encode('Gough')
        'GF'
        >>> pe.encode('pneuma')
        'NM'
        >>> pe.encode('knight')
        'NT'
        >>> pe.encode('trice')
        'TRS'
        >>> pe.encode('judge')
        'JJ'

        """
        word = word.upper()  # Rule 3
        word = self._delete_consecutive_repeats(word)  # Rule 4

        # Rule 5
        i = 0
        while i < len(word):
            for match_len in range(4, 1, -1):
                if word[i : i + match_len] in self._rules[match_len]:
                    repl = self._rules[match_len][word[i : i + match_len]]
                    word = word[:i] + repl + word[i + match_len :]
                    i += len(repl)
                    break
            else:
                i += 1

        word = word[:1] + word[1:].translate(self._del_trans)  # Rule 6
        return word


Instruction:
Write a Python 3 script to
calculate the Hamming distance between the Eudex hashes of two terms. This is a wrapper for :py:meth:`Eudex.eudex_hamming`. Parameters ---------- src : str Source string for comparison tar : str Target string for comparison weights : str, iterable, or generator function The weights or weights generator function max_length : int The number of characters to encode as a eudex hash normalized : bool Normalizes to [0, 1] if True Returns ------- int The Eudex Hamming distance Examples -------- >>> eudex_hamming('cat', 'hat') 128 >>> eudex_hamming('Niall', 'Neil') 2 >>> eudex_hamming('Colin', 'Cuilen') 10 >>> eudex_hamming('ATCG', 'TAGC') 403 >>> eudex_hamming('cat', 'hat', weights='fibonacci') 34 >>> eudex_hamming('Niall', 'Neil', weights='fibonacci') 2 >>> eudex_hamming('Colin', 'Cuilen', weights='fibonacci') 7 >>> eudex_hamming('ATCG', 'TAGC', weights='fibonacci') 117 >>> eudex_hamming('cat', 'hat', weights=None) 1 >>> eudex_hamming('Niall', 'Neil', weights=None) 1 >>> eudex_hamming('Colin', 'Cuilen', weights=None) 2 >>> eudex_hamming('ATCG', 'TAGC', weights=None) 9 >>> # Using the OEIS A000142: >>> eudex_hamming('cat', 'hat', [1, 1, 2, 6, 24, 120, 720, 5040]) 1 >>> eudex_hamming('Niall', 'Neil', [1, 1, 2, 6, 24, 120, 720, 5040]) 720 >>> eudex_hamming('Colin', 'Cuilen', [1, 1, 2, 6, 24, 120, 720, 5040]) 744 >>> eudex_hamming('ATCG', 'TAGC', [1, 1, 2, 6, 24, 120, 720, 5040]) 6243

Response:
def eudex_hamming(
    src, tar, weights='exponential', max_length=8, normalized=False
):
    """Calculate the Hamming distance between the Eudex hashes of two terms.

    This is a wrapper for :py:meth:`Eudex.eudex_hamming`.

    Parameters
    ----------
    src : str
        Source string for comparison
    tar : str
        Target string for comparison
    weights : str, iterable, or generator function
        The weights or weights generator function
    max_length : int
        The number of characters to encode as a eudex hash
    normalized : bool
        Normalizes to [0, 1] if True

    Returns
    -------
    int
        The Eudex Hamming distance

    Examples
    --------
    >>> eudex_hamming('cat', 'hat')
    128
    >>> eudex_hamming('Niall', 'Neil')
    2
    >>> eudex_hamming('Colin', 'Cuilen')
    10
    >>> eudex_hamming('ATCG', 'TAGC')
    403

    >>> eudex_hamming('cat', 'hat', weights='fibonacci')
    34
    >>> eudex_hamming('Niall', 'Neil', weights='fibonacci')
    2
    >>> eudex_hamming('Colin', 'Cuilen', weights='fibonacci')
    7
    >>> eudex_hamming('ATCG', 'TAGC', weights='fibonacci')
    117

    >>> eudex_hamming('cat', 'hat', weights=None)
    1
    >>> eudex_hamming('Niall', 'Neil', weights=None)
    1
    >>> eudex_hamming('Colin', 'Cuilen', weights=None)
    2
    >>> eudex_hamming('ATCG', 'TAGC', weights=None)
    9

    >>> # Using the OEIS A000142:
    >>> eudex_hamming('cat', 'hat', [1, 1, 2, 6, 24, 120, 720, 5040])
    1
    >>> eudex_hamming('Niall', 'Neil', [1, 1, 2, 6, 24, 120, 720, 5040])
    720
    >>> eudex_hamming('Colin', 'Cuilen', [1, 1, 2, 6, 24, 120, 720, 5040])
    744
    >>> eudex_hamming('ATCG', 'TAGC', [1, 1, 2, 6, 24, 120, 720, 5040])
    6243

    """
    return Eudex().dist_abs(src, tar, weights, max_length, normalized)


Instruction:
How would you implement a function in Python 3 that
returns normalized Hamming distance between two Eudex hashes of two terms.

Response:
def dist_eudex(src, tar, weights='exponential', max_length=8):
    """Return normalized Hamming distance between Eudex hashes of two terms.

    This is a wrapper for :py:meth:`Eudex.dist`.

    Parameters
    ----------
    src : str
        Source string for comparison
    tar : str
        Target string for comparison
    weights : str, iterable, or generator function
        The weights or weights generator function
    max_length : int
        The number of characters to encode as a eudex hash

    Returns
    -------
    int
        The normalized Eudex Hamming distance

    Examples
    --------
    >>> round(dist_eudex('cat', 'hat'), 12)
    0.062745098039
    >>> round(dist_eudex('Niall', 'Neil'), 12)
    0.000980392157
    >>> round(dist_eudex('Colin', 'Cuilen'), 12)
    0.004901960784
    >>> round(dist_eudex('ATCG', 'TAGC'), 12)
    0.197549019608

    """
    return Eudex().dist(src, tar, weights, max_length)


Instruction:
How would you implement a function in Python 3 that
returns normalized Eudex hamming similarity between two terms.

Response:
def sim_eudex(src, tar, weights='exponential', max_length=8):
    """Return normalized Hamming similarity between Eudex hashes of two terms.

    This is a wrapper for :py:meth:`Eudex.sim`.

    Parameters
    ----------
    src : str
        Source string for comparison
    tar : str
        Target string for comparison
    weights : str, iterable, or generator function
        The weights or weights generator function
    max_length : int
        The number of characters to encode as a eudex hash

    Returns
    -------
    int
        The normalized Eudex Hamming similarity

    Examples
    --------
    >>> round(sim_eudex('cat', 'hat'), 12)
    0.937254901961
    >>> round(sim_eudex('Niall', 'Neil'), 12)
    0.999019607843
    >>> round(sim_eudex('Colin', 'Cuilen'), 12)
    0.995098039216
    >>> round(sim_eudex('ATCG', 'TAGC'), 12)
    0.802450980392

    """
    return Eudex().sim(src, tar, weights, max_length)


Instruction:
Write a Python 3 script for
yielding the next Fibonacci number.

Response:
def gen_fibonacci():
        """Yield the next Fibonacci number.

        Based on https://www.python-course.eu/generators.php
        Starts at Fibonacci number 3 (the second 1)

        Yields
        ------
        int
            The next Fibonacci number

        """
        num_a, num_b = 1, 2
        while True:
            yield num_a
            num_a, num_b = num_b, num_a + num_b


Instruction:
Can you create a Python 3 function that
calculates the distance between two strings in an absolute form.

Response:
def dist_abs(
        self, src, tar, weights='exponential', max_length=8, normalized=False
    ):
        """Calculate the distance between the Eudex hashes of two terms.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        weights : str, iterable, or generator function
            The weights or weights generator function

                - If set to ``None``, a simple Hamming distance is calculated.
                - If set to ``exponential``, weight decays by powers of 2, as
                  proposed in the eudex specification:
                  https://github.com/ticki/eudex.
                - If set to ``fibonacci``, weight decays through the Fibonacci
                  series, as in the eudex reference implementation.
                - If set to a callable function, this assumes it creates a
                  generator and the generator is used to populate a series of
                  weights.
                - If set to an iterable, the iterable's values should be
                  integers and will be used as the weights.

        max_length : int
            The number of characters to encode as a eudex hash
        normalized : bool
            Normalizes to [0, 1] if True

        Returns
        -------
        int
            The Eudex Hamming distance

        Examples
        --------
        >>> cmp = Eudex()
        >>> cmp.dist_abs('cat', 'hat')
        128
        >>> cmp.dist_abs('Niall', 'Neil')
        2
        >>> cmp.dist_abs('Colin', 'Cuilen')
        10
        >>> cmp.dist_abs('ATCG', 'TAGC')
        403

        >>> cmp.dist_abs('cat', 'hat', weights='fibonacci')
        34
        >>> cmp.dist_abs('Niall', 'Neil', weights='fibonacci')
        2
        >>> cmp.dist_abs('Colin', 'Cuilen', weights='fibonacci')
        7
        >>> cmp.dist_abs('ATCG', 'TAGC', weights='fibonacci')
        117

        >>> cmp.dist_abs('cat', 'hat', weights=None)
        1
        >>> cmp.dist_abs('Niall', 'Neil', weights=None)
        1
        >>> cmp.dist_abs('Colin', 'Cuilen', weights=None)
        2
        >>> cmp.dist_abs('ATCG', 'TAGC', weights=None)
        9

        >>> # Using the OEIS A000142:
        >>> cmp.dist_abs('cat', 'hat', [1, 1, 2, 6, 24, 120, 720, 5040])
        1
        >>> cmp.dist_abs('Niall', 'Neil', [1, 1, 2, 6, 24, 120, 720, 5040])
        720
        >>> cmp.dist_abs('Colin', 'Cuilen',
        ... [1, 1, 2, 6, 24, 120, 720, 5040])
        744
        >>> cmp.dist_abs('ATCG', 'TAGC', [1, 1, 2, 6, 24, 120, 720, 5040])
        6243

        """
        # Calculate the eudex hashes and XOR them
        xored = eudex(src, max_length=max_length) ^ eudex(
            tar, max_length=max_length
        )

        # Simple hamming distance (all bits are equal)
        if not weights:
            binary = bin(xored)
            distance = binary.count('1')
            if normalized:
                return distance / (len(binary) - 2)
            return distance

        # If weights is a function, it should create a generator,
        # which we now use to populate a list
        if callable(weights):
            weights = weights()
        elif weights == 'exponential':
            weights = Eudex.gen_exponential()
        elif weights == 'fibonacci':
            weights = Eudex.gen_fibonacci()
        if isinstance(weights, GeneratorType):
            weights = [next(weights) for _ in range(max_length)][::-1]

        # Sum the weighted hamming distance
        distance = 0
        max_distance = 0
        while (xored or normalized) and weights:
            max_distance += 8 * weights[-1]
            distance += bin(xored & 0xFF).count('1') * weights.pop()
            xored >>= 8

        if normalized:
            distance /= max_distance

        return distance


Instruction:
How would you code a function in Python 3 to
return normalized distance between the Eudex hashes of two terms.

Response:
def dist(self, src, tar, weights='exponential', max_length=8):
        """Return normalized distance between the Eudex hashes of two terms.

        This is Eudex distance normalized to [0, 1].

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        weights : str, iterable, or generator function
            The weights or weights generator function
        max_length : int
            The number of characters to encode as a eudex hash

        Returns
        -------
        int
            The normalized Eudex Hamming distance

        Examples
        --------
        >>> cmp = Eudex()
        >>> round(cmp.dist('cat', 'hat'), 12)
        0.062745098039
        >>> round(cmp.dist('Niall', 'Neil'), 12)
        0.000980392157
        >>> round(cmp.dist('Colin', 'Cuilen'), 12)
        0.004901960784
        >>> round(cmp.dist('ATCG', 'TAGC'), 12)
        0.197549019608

        """
        return self.dist_abs(src, tar, weights, max_length, True)


Instruction:
Make a summary of the following Python 3 code
def euclidean(src, tar, qval=2, normalized=False, alphabet=None):
    return Euclidean().dist_abs(src, tar, qval, normalized, alphabet)

Response: Return the Euclidean distance between two strings.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def dist_euclidean(src, tar, qval=2, alphabet=None):
    return Euclidean().dist(src, tar, qval, alphabet)

Response: Return the normalized Euclidean distance between two strings.


Instruction:
Given the following Python 3 function, write the documentation
def sim_euclidean(src, tar, qval=2, alphabet=None):
    return Euclidean().sim(src, tar, qval, alphabet)

Response: Return the normalized Euclidean similarity of two strings.