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Levenshtein.c
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/*
* Levenshtein.c
* @(#) $Id: Levenshtein.c,v 1.41 2005/01/13 20:05:36 yeti Exp $
* Python extension computing Levenshtein distances, string similarities,
* median strings and other goodies.
*
* Copyright (C) 2002-2003 David Necas (Yeti) <[email protected]>.
*
* The Taus113 random generator:
* Copyright (C) 2002 Atakan Gurkan
* Copyright (C) 1996, 1997, 1998, 1999, 2000 James Theiler, Brian Gough
* (see below for more)
*
* This program is free software; you can redistribute it and/or modify it
* under the terms of the GNU General Public License as published by the Free
* Software Foundation; either version 2 of the License, or (at your option)
* any later version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
* more details.
*
* You should have received a copy of the GNU General Public License along
* with this program; if not, write to the Free Software Foundation, Inc.,
* 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA.
**/
/**
* TODO:
*
* - Implement weighted string averaging, see:
* H. Bunke et. al.: On the Weighted Mean of a Pair of Strings,
* Pattern Analysis and Applications 2002, 5(1): 23-30.
* X. Jiang et. al.: Dynamic Computations of Generalized Median Strings,
* Pattern Analysis and Applications 2002, ???.
* The latter also contains an interesting median-search algorithm.
*
* - Deal with stray symbols in greedy median() and median_improve().
* There are two possibilities:
* (i) Remember which strings contain which symbols. This allows certain
* small optimizations when processing them.
* (ii) Use some overall heuristics to find symbols which don't worth
* trying. This is very appealing, but hard to do properly
* (requires some inequality strong enough to allow practical exclusion
* of certain symbols -- at certain positions)
*
* - Editops should be an object that only *looks* like a list (which means
* it is a list in duck typing) to avoid never-ending conversions from
* Python lists to LevEditOp arrays and back
*
* - Optimize munkers_blackman(), it's pretty dumb (no memory of visited
* columns/rows)
*
* - Make it really usable as a C library (needs some wrappers, headers, ...,
* and maybe even documentation ;-)
*
* - Add interface to various interesting auxiliary results, namely
* set and sequence distance (only ratio is exported), the map from
* munkers_blackman() itself, ...
*
* - Generalizations:
* - character weight matrix/function
* - arbitrary edit operation costs, decomposable edit operations
*
* - Create a test suite
*
* - Add more interesting algorithms ;-)
*
* Postponed TODO (investigated, and a big `but' was found):
*
* - A linear approximate set median algorithm:
* P. Indyk: Sublinear time algorithms for metric space problems,
* STOC 1999, http://citeseer.nj.nec.com/indyk00sublinear.html.
* BUT: The algorithm seems to be advantageous only in the case of very
* large sets -- if my estimates are correct (the article itself is quite
* `asymptotic'), say 10^5 at least. On smaller sets either one would get
* only an extermely rough median estimate, or the number of distance
* computations would be in fact higher than in the dumb O(n^2) algorithm.
*
* - Improve setmedian() speed with triangular inequality, see:
* Juan, A., E. Vidal: An Algorithm for Fast Median Search,
* 1997, http://citeseer.nj.nec.com/article/juan97algorithm.html
* BUT: It doesn't seem to help much in spaces of high dimension (see the
* discussion and graphs in the article itself), a few percents at most,
* and strings behave like a space with a very high dimension (locally), so
* who knows, it probably wouldn't help much.
*
**/
#ifdef NO_PYTHON
#define _GNU_SOURCE
#include <string.h>
#include <math.h>
/* for debugging */
#include <stdio.h>
#else /* NO_PYTHON */
#define _LEV_STATIC_PY static
#define lev_wchar Py_UNICODE
#include <Python.h>
#endif /* NO_PYTHON */
#include <assert.h>
#include "Levenshtein.h"
/* FIXME: inline avaliability should be solved in setup.py, somehow, or
* even better in Python.h, like const is...
* this should inline at least with gcc and msvc */
#ifndef __GNUC__
# ifdef _MSC_VER
# define inline __inline
# else
# define inline /* */
# endif
# define __attribute__(x) /* */
#endif
#define LEV_EPSILON 1e-14
#define LEV_INFINITY 1e100
/* Me thinks the second argument of PyArg_UnpackTuple() should be const.
* Anyway I habitually pass a constant string.
* A cast to placate the compiler. */
#define PYARGCFIX(x) (char*)(x)
/* local functions */
static size_t*
munkers_blackman(size_t n1,
size_t n2,
double *dists);
#define TAUS113_LCG(n) ((69069UL * n) & 0xffffffffUL)
#define TAUS113_MASK 0xffffffffUL
typedef struct {
unsigned long int z1, z2, z3, z4;
} taus113_state_t;
static inline unsigned long int
taus113_get(taus113_state_t *state);
static void
taus113_set(taus113_state_t *state,
unsigned long int s);
#ifndef NO_PYTHON
/* python interface and wrappers */
/* declarations and docstrings {{{ */
static PyObject* distance_py(PyObject *self, PyObject *args);
static PyObject* ratio_py(PyObject *self, PyObject *args);
static PyObject* hamming_py(PyObject *self, PyObject *args);
static PyObject* jaro_py(PyObject *self, PyObject *args);
static PyObject* jaro_winkler_py(PyObject *self, PyObject *args);
static PyObject* median_py(PyObject *self, PyObject *args);
static PyObject* median_improve_py(PyObject *self, PyObject *args);
static PyObject* quickmedian_py(PyObject *self, PyObject *args);
static PyObject* setmedian_py(PyObject *self, PyObject *args);
static PyObject* seqratio_py(PyObject *self, PyObject *args);
static PyObject* setratio_py(PyObject *self, PyObject *args);
static PyObject* editops_py(PyObject *self, PyObject *args);
static PyObject* opcodes_py(PyObject *self, PyObject *args);
static PyObject* inverse_py(PyObject *self, PyObject *args);
static PyObject* apply_edit_py(PyObject *self, PyObject *args);
static PyObject* matching_blocks_py(PyObject *self, PyObject *args);
static PyObject* subtract_edit_py(PyObject *self, PyObject *args);
#define Levenshtein_DESC \
"A C extension module for fast computation of:\n" \
"- Levenshtein (edit) distance and edit sequence manipluation\n" \
"- string similarity\n" \
"- approximate median strings, and generally string averaging\n" \
"- string sequence and set similarity\n" \
"\n" \
"Levenshtein has a some overlap with difflib (SequenceMatcher). It\n" \
"supports only strings, not arbitrary sequence types, but on the\n" \
"other hand it's much faster.\n" \
"\n" \
"It supports both normal and Unicode strings, but can't mix them, all\n" \
"arguments to a function (method) have to be of the same type (or its\n" \
"subclasses).\n"
#define distance_DESC \
"Compute absolute Levenshtein distance of two strings.\n" \
"\n" \
"distance(string1, string2)\n" \
"\n" \
"Examples (it's hard to spell Levenshtein correctly):\n" \
">>> distance('Levenshtein', 'Lenvinsten')\n" \
"4\n" \
">>> distance('Levenshtein', 'Levensthein')\n" \
"2\n" \
">>> distance('Levenshtein', 'Levenshten')\n" \
"1\n" \
">>> distance('Levenshtein', 'Levenshtein')\n" \
"0\n" \
"\n" \
"Yeah, we've managed it at last.\n"
#define ratio_DESC \
"Compute similarity of two strings.\n" \
"\n" \
"ratio(string1, string2)\n" \
"\n" \
"The similarity is a number between 0 and 1, it's usually equal or\n" \
"somewhat higher than difflib.SequenceMatcher.ratio(), becuase it's\n" \
"based on real minimal edit distance.\n" \
"\n" \
"Examples:\n" \
">>> ratio('Hello world!', 'Holly grail!')\n" \
"0.58333333333333337\n" \
">>> ratio('Brian', 'Jesus')\n" \
"0.0\n" \
"\n" \
"Really? I thought there was some similarity.\n"
#define hamming_DESC \
"Compute Hamming distance of two strings.\n" \
"\n" \
"hamming(string1, string2)\n" \
"\n" \
"The Hamming distance is simply the number of differing characters.\n" \
"That means the length of the strings must be the same.\n" \
"\n" \
"Examples:\n" \
">>> hamming('Hello world!', 'Holly grail!')\n" \
"7\n" \
">>> hamming('Brian', 'Jesus')\n" \
"5\n"
#define jaro_DESC \
"Compute Jaro string similarity metric of two strings.\n" \
"\n" \
"jaro(string1, string2)\n" \
"\n" \
"The Jaro string similarity metric is intended for short strings like\n" \
"personal last names. It is 0 for completely different strings and\n" \
"1 for identical strings.\n" \
"\n" \
"Examples:\n" \
">>> jaro('Brian', 'Jesus')\n" \
"0.0\n" \
">>> jaro('Thorkel', 'Thorgier')\n" \
"0.77976190476190477\n" \
">>> jaro('Dinsdale', 'D')\n" \
"0.70833333333333337\n"
#define jaro_winkler_DESC \
"Compute Jaro string similarity metric of two strings.\n" \
"\n" \
"jaro_winkler(string1, string2[, prefix_weight])\n" \
"\n" \
"The Jaro-Winkler string similarity metric is a modification of Jaro\n" \
"metric giving more weight to common prefix, as spelling mistakes are\n" \
"more likely to occur near ends of words.\n" \
"\n" \
"The prefix weight is inverse value of common prefix length sufficient\n" \
"to consider the strings `identical'. If no prefix weight is\n" \
"specified, 1/10 is used.\n" \
"\n" \
"Examples:\n" \
">>> jaro_winkler('Brian', 'Jesus')\n" \
"0.0\n" \
">>> jaro_winkler('Thorkel', 'Thorgier')\n" \
"0.86785714285714288\n" \
">>> jaro_winkler('Dinsdale', 'D')\n" \
"0.73750000000000004\n" \
">>> jaro_winkler('Thorkel', 'Thorgier', 0.25)\n" \
"1.0\n"
#define median_DESC \
"Find an approximate generalized median string using greedy algorithm.\n" \
"\n" \
"median(string_sequence[, weight_sequence])\n" \
"\n" \
"You can optionally pass a weight for each string as the second\n" \
"argument. The weights are interpreted as item multiplicities,\n" \
"although any non-negative real numbers are accepted. Use them to\n" \
"improve computation speed when strings often appear multiple times\n" \
"in the sequence.\n" \
"\n" \
"Examples:\n" \
">>> median(['SpSm', 'mpamm', 'Spam', 'Spa', 'Sua', 'hSam'])\n" \
"'Spam'\n" \
">>> fixme = ['Levnhtein', 'Leveshein', 'Leenshten',\n" \
"... 'Leveshtei', 'Lenshtein', 'Lvenstein',\n" \
"... 'Levenhtin', 'evenshtei']\n" \
">>> median(fixme)\n" \
"'Levenshtein'\n" \
"\n" \
"Hm. Even a computer program can spell Levenshtein better than me.\n"
#define median_improve_DESC \
"Improve an approximate generalized median string by perturbations.\n" \
"\n" \
"median_improve(string, string_sequence[, weight_sequence])\n" \
"\n" \
"The first argument is the estimated generalized median string you\n" \
"want to improve, the others are the same as in median(). It returns\n" \
"a string with total distance less or equal to that of the given string.\n" \
"\n" \
"Note this is much slower than median(). Also note it performs only\n" \
"one improvement step, calling median_improve() again on the result\n" \
"may improve it further, though this is unlikely to happen unless the\n" \
"given string was not very similar to the actual generalized median.\n" \
"\n" \
"Examples:\n" \
">>> fixme = ['Levnhtein', 'Leveshein', 'Leenshten',\n" \
"... 'Leveshtei', 'Lenshtein', 'Lvenstein',\n" \
"... 'Levenhtin', 'evenshtei']\n" \
">>> median_improve('spam', fixme)\n" \
"'enhtein'\n" \
">>> median_improve(median_improve('spam', fixme), fixme)\n" \
"'Levenshtein'\n" \
"\n" \
"It takes some work to change spam to Levenshtein.\n"
#define quickmedian_DESC \
"Find a very approximate generalized median string, but fast.\n" \
"\n" \
"quickmedian(string[, weight_sequence])\n" \
"\n" \
"See median() for argument description.\n" \
"\n" \
"This method is somewhere between setmedian() and picking a random\n" \
"string from the set; both speedwise and quality-wise.\n" \
"\n" \
"Examples:\n" \
">>> fixme = ['Levnhtein', 'Leveshein', 'Leenshten',\n" \
"... 'Leveshtei', 'Lenshtein', 'Lvenstein',\n" \
"... 'Levenhtin', 'evenshtei']\n" \
">>> quickmedian(fixme)\n" \
"'Levnshein'\n"
#define setmedian_DESC \
"Find set median of a string set (passed as a sequence).\n" \
"\n" \
"setmedian(string[, weight_sequence])\n" \
"\n" \
"See median() for argument description.\n" \
"\n" \
"The returned string is always one of the strings in the sequence.\n" \
"\n" \
"Examples:\n" \
">>> setmedian(['ehee', 'cceaes', 'chees', 'chreesc',\n" \
"... 'chees', 'cheesee', 'cseese', 'chetese'])\n" \
"'chees'\n" \
"\n" \
"You haven't asked me about Limburger, sir.\n"
#define seqratio_DESC \
"Compute similarity ratio of two sequences of strings.\n" \
"\n" \
"seqratio(string_sequence1, string_sequence2)\n" \
"\n" \
"This is like ratio(), but for string sequences. A kind of ratio()\n" \
"is used to to measure the cost of item change operation for the\n" \
"strings.\n" \
"\n" \
"Examples:\n" \
">>> seqratio(['newspaper', 'litter bin', 'tinny', 'antelope'],\n" \
"... ['caribou', 'sausage', 'gorn', 'woody'])\n" \
"0.21517857142857144\n"
#define setratio_DESC \
"Compute similarity ratio of two strings sets (passed as sequences).\n" \
"\n" \
"setratio(string_sequence1, string_sequence2)\n" \
"\n" \
"The best match between any strings in the first set and the second\n" \
"set (passed as sequences) is attempted. I.e., the order doesn't\n" \
"matter here.\n" \
"\n" \
"Examples:\n" \
">>> setratio(['newspaper', 'litter bin', 'tinny', 'antelope'],\n" \
"... ['caribou', 'sausage', 'gorn', 'woody'])\n" \
"0.28184523809523809\n" \
"\n" \
"No, even reordering doesn't help the tinny words to match the\n" \
"woody ones.\n"
#define editops_DESC \
"Find sequence of edit operations transforming one string to another.\n" \
"\n" \
"editops(source_string, destination_string)\n" \
"editops(edit_operations, source_length, destination_length)\n" \
"\n" \
"The result is a list of triples (operation, spos, dpos), where\n" \
"operation is one of `equal', `replace', `insert', or `delete'; spos\n" \
"and dpos are position of characters in the first (source) and the\n" \
"second (destination) strings. These are operations on signle\n" \
"characters. In fact the returned list doesn't contain the `equal',\n" \
"but all the related functions accept both lists with and without\n" \
"`equal's.\n" \
"\n" \
"Examples:\n" \
">>> editops('spam', 'park')\n" \
"[('delete', 0, 0), ('insert', 3, 2), ('replace', 3, 3)]\n" \
"\n" \
"The alternate form editops(opcodes, source_string, destination_string)\n" \
"can be used for conversion from opcodes (5-tuples) to editops (you can\n" \
"pass strings or their lengths, it doesn't matter).\n"
#define opcodes_DESC \
"Find sequence of edit operations transforming one string to another.\n" \
"\n" \
"opcodes(source_string, destination_string)\n" \
"opcodes(edit_operations, source_length, destination_length)\n" \
"\n" \
"The result is a list of 5-tuples with the same meaning as in\n" \
"SequenceMatcher's get_opcodes() output. But since the algorithms\n" \
"differ, the actual sequences from Levenshtein and SequenceMatcher\n" \
"may differ too.\n" \
"\n" \
"Examples:\n" \
">>> for x in opcodes('spam', 'park'):\n" \
"... print x\n" \
"...\n" \
"('delete', 0, 1, 0, 0)\n" \
"('equal', 1, 3, 0, 2)\n" \
"('insert', 3, 3, 2, 3)\n" \
"('replace', 3, 4, 3, 4)\n" \
"\n" \
"The alternate form opcodes(editops, source_string, destination_string)\n" \
"can be used for conversion from editops (triples) to opcodes (you can\n" \
"pass strings or their lengths, it doesn't matter).\n"
#define inverse_DESC \
"Invert the sense of an edit operation sequence.\n" \
"\n" \
"inverse(edit_operations)\n" \
"\n" \
"In other words, it returns a list of edit operations transforming the\n" \
"second (destination) string to the first (source). It can be used\n" \
"with both editops and opcodes.\n" \
"\n" \
"Examples:\n" \
">>> inverse(editops('spam', 'park'))\n" \
"[('insert', 0, 0), ('delete', 2, 3), ('replace', 3, 3)]\n" \
">>> editops('park', 'spam')\n" \
"[('insert', 0, 0), ('delete', 2, 3), ('replace', 3, 3)]\n"
#define apply_edit_DESC \
"Apply a sequence of edit operations to a string.\n" \
"\n" \
"apply_edit(edit_operations, source_string, destination_string)\n" \
"\n" \
"In the case of editops, the sequence can be arbitrary ordered subset\n" \
"of the edit sequence transforming source_string to destination_string.\n" \
"\n" \
"Examples:\n" \
">>> e = editops('man', 'scotsman')\n" \
">>> apply_edit(e, 'man', 'scotsman')\n" \
"'scotsman'\n" \
">>> apply_edit(e[:3], 'man', 'scotsman')\n" \
"'scoman'\n" \
"\n" \
"The other form of edit operations, opcodes, is not very suitable for\n" \
"such a tricks, because it has to always span over complete strings,\n" \
"subsets can be created by carefully replacing blocks with `equal'\n" \
"blocks, or by enlarging `equal' block at the expense of other blocks\n" \
"and adjusting the other blocks accordingly.\n" \
"\n" \
"Examples:\n" \
">>> a, b = 'spam and eggs', 'foo and bar'\n" \
">>> e = opcodes(a, b)\n" \
">>> apply_edit(inverse(e), b, a)\n" \
"'spam and eggs'\n" \
">>> e[4] = ('equal', 10, 13, 8, 11)\n" \
">>> apply_edit(e, a, b)\n" \
"'foo and ggs'\n"
#define matching_blocks_DESC \
"Find identical blocks in two strings.\n" \
"\n" \
"matching_blocks(edit_operations, source_length, destination_length)\n" \
"\n" \
"The result is a list of triples with the same meaning as in\n" \
"SequenceMatcher's get_matching_blocks() output. It can be used with\n" \
"both editops and opcodes. The second and third arguments don't\n" \
"have to be actually strings, their lengths are enough.\n" \
"\n" \
"Examples:\n" \
">>> a, b = 'spam', 'park'\n" \
">>> matching_blocks(editops(a, b), a, b)\n" \
"[(1, 0, 2), (4, 4, 0)]\n" \
">>> matching_blocks(editops(a, b), len(a), len(b))\n" \
"[(1, 0, 2), (4, 4, 0)]\n" \
"\n" \
"The last zero-length block is not an error, but it's there for\n" \
"compatibility with difflib which always emits it.\n" \
"\n" \
"One can join the matching blocks to get two identical strings:\n" \
">>> a, b = 'dog kennels', 'mattresses'\n" \
">>> mb = matching_blocks(editops(a,b), a, b)\n" \
">>> ''.join([a[x[0]:x[0]+x[2]] for x in mb])\n" \
"'ees'\n" \
">>> ''.join([b[x[1]:x[1]+x[2]] for x in mb])\n" \
"'ees'\n"
#define subtract_edit_DESC \
"Subtract an edit subsequence from a sequence.\n" \
"\n" \
"subtract_edit(edit_operations, subsequence)\n" \
"\n" \
"The result is equivalent to\n" \
"editops(apply_edit(subsequence, s1, s2), s2), except that is\n" \
"constructed directly from the edit operations. That is, if you apply\n" \
"it to the result of subsequence application, you get the same final\n" \
"string as from application complete edit_operations. It may be not\n" \
"identical, though (in amibuous cases, like insertion of a character\n" \
"next to the same character).\n" \
"\n" \
"The subtracted subsequence must be an ordered subset of\n" \
"edit_operations.\n" \
"\n" \
"Note this function does not accept difflib-style opcodes as no one in\n" \
"his right mind wants to create subsequences from them.\n" \
"\n" \
"Examples:\n" \
">>> e = editops('man', 'scotsman')\n" \
">>> e1 = e[:3]\n" \
">>> bastard = apply_edit(e1, 'man', 'scotsman')\n" \
">>> bastard\n" \
"'scoman'\n" \
">>> apply_edit(subtract_edit(e, e1), bastard, 'scotsman')\n" \
"'scotsman'\n" \
#define METHODS_ITEM(x) { #x, x##_py, METH_VARARGS, x##_DESC }
static PyMethodDef methods[] = {
METHODS_ITEM(distance),
METHODS_ITEM(ratio),
METHODS_ITEM(hamming),
METHODS_ITEM(jaro),
METHODS_ITEM(jaro_winkler),
METHODS_ITEM(median),
METHODS_ITEM(median_improve),
METHODS_ITEM(quickmedian),
METHODS_ITEM(setmedian),
METHODS_ITEM(seqratio),
METHODS_ITEM(setratio),
METHODS_ITEM(editops),
METHODS_ITEM(opcodes),
METHODS_ITEM(inverse),
METHODS_ITEM(apply_edit),
METHODS_ITEM(matching_blocks),
METHODS_ITEM(subtract_edit),
{ NULL, NULL, 0, NULL },
};
/* opcode names, these are to be initialized in the init func,
* indexed by LevEditType values */
struct {
PyObject* pystring;
const char *cstring;
size_t len;
}
static opcode_names[] = {
{ NULL, "equal", 0 },
{ NULL, "replace", 0 },
{ NULL, "insert", 0 },
{ NULL, "delete", 0 },
};
#define N_OPCODE_NAMES ((sizeof(opcode_names)/sizeof(opcode_names[0])))
typedef lev_byte *(*MedianFuncString)(size_t n,
const size_t *lengths,
const lev_byte *strings[],
const double *weights,
size_t *medlength);
typedef Py_UNICODE *(*MedianFuncUnicode)(size_t n,
const size_t *lengths,
const Py_UNICODE *strings[],
const double *weights,
size_t *medlength);
typedef struct {
MedianFuncString s;
MedianFuncUnicode u;
} MedianFuncs;
typedef lev_byte *(*MedianImproveFuncString)(size_t len, const lev_byte *s,
size_t n,
const size_t *lengths,
const lev_byte *strings[],
const double *weights,
size_t *medlength);
typedef Py_UNICODE *(*MedianImproveFuncUnicode)(size_t len, const Py_UNICODE *s,
size_t n,
const size_t *lengths,
const Py_UNICODE *strings[],
const double *weights,
size_t *medlength);
typedef struct {
MedianImproveFuncString s;
MedianImproveFuncUnicode u;
} MedianImproveFuncs;
typedef double (*SetSeqFuncString)(size_t n1,
const size_t *lengths1,
const lev_byte *strings1[],
size_t n2,
const size_t *lengths2,
const lev_byte *strings2[]);
typedef double (*SetSeqFuncUnicode)(size_t n1,
const size_t *lengths1,
const Py_UNICODE *strings1[],
size_t n2,
const size_t *lengths2,
const Py_UNICODE *strings2[]);
typedef struct {
SetSeqFuncString s;
SetSeqFuncUnicode u;
} SetSeqFuncs;
static long int
levenshtein_common(PyObject *args,
const char *name,
size_t xcost,
size_t *lensum);
static int
extract_stringlist(PyObject *list,
const char *name,
size_t n,
size_t **sizelist,
void *strlist);
static double*
extract_weightlist(PyObject *wlist,
const char *name,
size_t n);
static PyObject*
median_common(PyObject *args,
const char *name,
MedianFuncs foo);
static PyObject*
median_improve_common(PyObject *args,
const char *name,
MedianImproveFuncs foo);
static double
setseq_common(PyObject *args,
const char *name,
SetSeqFuncs foo,
size_t *lensum);
/* }}} */
/****************************************************************************
*
* Python interface and subroutines
*
****************************************************************************/
/* {{{ */
static long int
levenshtein_common(PyObject *args, const char *name, size_t xcost,
size_t *lensum)
{
PyObject *arg1, *arg2;
size_t len1, len2;
if (!PyArg_UnpackTuple(args, PYARGCFIX(name), 2, 2, &arg1, &arg2))
return -1;
if (PyObject_TypeCheck(arg1, &PyString_Type)
&& PyObject_TypeCheck(arg2, &PyString_Type)) {
lev_byte *string1, *string2;
len1 = PyString_GET_SIZE(arg1);
len2 = PyString_GET_SIZE(arg2);
*lensum = len1 + len2;
string1 = PyString_AS_STRING(arg1);
string2 = PyString_AS_STRING(arg2);
{
size_t d = lev_edit_distance(len1, string1, len2, string2, xcost);
if (d == (size_t)(-1)) {
PyErr_NoMemory();
return -1;
}
return d;
}
}
else if (PyObject_TypeCheck(arg1, &PyUnicode_Type)
&& PyObject_TypeCheck(arg2, &PyUnicode_Type)) {
Py_UNICODE *string1, *string2;
len1 = PyUnicode_GET_SIZE(arg1);
len2 = PyUnicode_GET_SIZE(arg2);
*lensum = len1 + len2;
string1 = PyUnicode_AS_UNICODE(arg1);
string2 = PyUnicode_AS_UNICODE(arg2);
{
size_t d = lev_u_edit_distance(len1, string1, len2, string2, xcost);
if (d == (size_t)(-1)) {
PyErr_NoMemory();
return -1;
}
return d;
}
}
else {
PyErr_Format(PyExc_TypeError,
"%s expected two Strings or two Unicodes", name);
return -1;
}
}
static PyObject*
distance_py(PyObject *self, PyObject *args)
{
size_t lensum;
long int ldist;
if ((ldist = levenshtein_common(args, "distance", 0, &lensum)) < 0)
return NULL;
return PyInt_FromLong((long)ldist);
}
static PyObject*
ratio_py(PyObject *self, PyObject *args)
{
size_t lensum;
long int ldist;
if ((ldist = levenshtein_common(args, "ratio", 1, &lensum)) < 0)
return NULL;
if (lensum == 0)
return PyFloat_FromDouble(1.0);
return PyFloat_FromDouble((double)(lensum - ldist)/(lensum));
}
static PyObject*
hamming_py(PyObject *self, PyObject *args)
{
PyObject *arg1, *arg2;
const char *name = "hamming";
size_t len1, len2;
long int dist;
if (!PyArg_UnpackTuple(args, PYARGCFIX(name), 2, 2, &arg1, &arg2))
return NULL;
if (PyObject_TypeCheck(arg1, &PyString_Type)
&& PyObject_TypeCheck(arg2, &PyString_Type)) {
lev_byte *string1, *string2;
len1 = PyString_GET_SIZE(arg1);
len2 = PyString_GET_SIZE(arg2);
if (len1 != len2) {
PyErr_Format(PyExc_ValueError,
"%s expected two strings of the same length", name);
return NULL;
}
string1 = PyString_AS_STRING(arg1);
string2 = PyString_AS_STRING(arg2);
dist = lev_hamming_distance(len1, string1, string2);
return PyInt_FromLong(dist);
}
else if (PyObject_TypeCheck(arg1, &PyUnicode_Type)
&& PyObject_TypeCheck(arg2, &PyUnicode_Type)) {
Py_UNICODE *string1, *string2;
len1 = PyUnicode_GET_SIZE(arg1);
len2 = PyUnicode_GET_SIZE(arg2);
if (len1 != len2) {
PyErr_Format(PyExc_ValueError,
"%s expected two unicodes of the same length", name);
return NULL;
}
string1 = PyUnicode_AS_UNICODE(arg1);
string2 = PyUnicode_AS_UNICODE(arg2);
dist = lev_u_hamming_distance(len1, string1, string2);
return PyInt_FromLong(dist);
}
else {
PyErr_Format(PyExc_TypeError,
"%s expected two Strings or two Unicodes", name);
return NULL;
}
}
static PyObject*
jaro_py(PyObject *self, PyObject *args)
{
PyObject *arg1, *arg2;
const char *name = "jaro";
size_t len1, len2;
if (!PyArg_UnpackTuple(args, PYARGCFIX(name), 2, 2, &arg1, &arg2))
return NULL;
if (PyObject_TypeCheck(arg1, &PyString_Type)
&& PyObject_TypeCheck(arg2, &PyString_Type)) {
lev_byte *string1, *string2;
len1 = PyString_GET_SIZE(arg1);
len2 = PyString_GET_SIZE(arg2);
string1 = PyString_AS_STRING(arg1);
string2 = PyString_AS_STRING(arg2);
return PyFloat_FromDouble(lev_jaro_ratio(len1, string1, len2, string2));
}
else if (PyObject_TypeCheck(arg1, &PyUnicode_Type)
&& PyObject_TypeCheck(arg2, &PyUnicode_Type)) {
Py_UNICODE *string1, *string2;
len1 = PyUnicode_GET_SIZE(arg1);
len2 = PyUnicode_GET_SIZE(arg2);
string1 = PyUnicode_AS_UNICODE(arg1);
string2 = PyUnicode_AS_UNICODE(arg2);
return PyFloat_FromDouble(lev_u_jaro_ratio(len1, string1, len2, string2));
}
else {
PyErr_Format(PyExc_TypeError,
"%s expected two Strings or two Unicodes", name);
return NULL;
}
}
static PyObject*
jaro_winkler_py(PyObject *self, PyObject *args)
{
PyObject *arg1, *arg2, *arg3 = NULL;
double pfweight = 0.1;
const char *name = "jaro_winkler";
size_t len1, len2;
if (!PyArg_UnpackTuple(args, PYARGCFIX(name), 2, 3, &arg1, &arg2, &arg3))
return NULL;
if (arg3) {
if (!PyObject_TypeCheck(arg3, &PyFloat_Type)) {
PyErr_Format(PyExc_TypeError, "%s third argument must be a Float", name);
return NULL;
}
pfweight = PyFloat_AS_DOUBLE(arg3);
if (pfweight < 0.0) {
PyErr_Format(PyExc_ValueError, "%s negative prefix weight", name);
return NULL;
}
}
if (PyObject_TypeCheck(arg1, &PyString_Type)
&& PyObject_TypeCheck(arg2, &PyString_Type)) {
lev_byte *string1, *string2;
len1 = PyString_GET_SIZE(arg1);
len2 = PyString_GET_SIZE(arg2);
string1 = PyString_AS_STRING(arg1);
string2 = PyString_AS_STRING(arg2);
return PyFloat_FromDouble(lev_jaro_winkler_ratio(len1, string1,
len2, string2,
pfweight));
}
else if (PyObject_TypeCheck(arg1, &PyUnicode_Type)
&& PyObject_TypeCheck(arg2, &PyUnicode_Type)) {
Py_UNICODE *string1, *string2;
len1 = PyUnicode_GET_SIZE(arg1);
len2 = PyUnicode_GET_SIZE(arg2);
string1 = PyUnicode_AS_UNICODE(arg1);
string2 = PyUnicode_AS_UNICODE(arg2);
return PyFloat_FromDouble(lev_u_jaro_winkler_ratio(len1, string1,
len2, string2,
pfweight));
}
else {
PyErr_Format(PyExc_TypeError,
"%s expected two Strings or two Unicodes", name);
return NULL;
}
}
static PyObject*
median_py(PyObject *self, PyObject *args)
{
MedianFuncs engines = { lev_greedy_median, lev_u_greedy_median };
return median_common(args, "median", engines);
}
static PyObject*
median_improve_py(PyObject *self, PyObject *args)
{
MedianImproveFuncs engines = { lev_median_improve, lev_u_median_improve };
return median_improve_common(args, "median_improve", engines);
}
static PyObject*
quickmedian_py(PyObject *self, PyObject *args)
{
MedianFuncs engines = { lev_quick_median, lev_u_quick_median };
return median_common(args, "quickmedian", engines);
}
static PyObject*
setmedian_py(PyObject *self, PyObject *args)
{
MedianFuncs engines = { lev_set_median, lev_u_set_median };
return median_common(args, "setmedian", engines);
}
static PyObject*
median_common(PyObject *args, const char *name, MedianFuncs foo)
{
size_t n, len;
void *strings = NULL;
size_t *sizes = NULL;
PyObject *strlist = NULL;
PyObject *wlist = NULL;
PyObject *strseq = NULL;
double *weights;
int stringtype;
PyObject *result = NULL;
if (!PyArg_UnpackTuple(args, PYARGCFIX(name), 1, 2, &strlist, &wlist))
return NULL;
if (!PySequence_Check(strlist)) {
PyErr_Format(PyExc_TypeError,
"%s first argument must be a Sequence", name);
return NULL;
}
strseq = PySequence_Fast(strlist, name);
n = PySequence_Fast_GET_SIZE(strseq);
if (n == 0) {
Py_INCREF(Py_None);
Py_DECREF(strseq);
return Py_None;
}
/* get (optional) weights, use 1 if none specified. */
weights = extract_weightlist(wlist, name, n);
if (!weights) {
Py_DECREF(strseq);
return NULL;
}
stringtype = extract_stringlist(strseq, name, n, &sizes, &strings);
Py_DECREF(strseq);
if (stringtype < 0) {
free(weights);
return NULL;
}
if (stringtype == 0) {
lev_byte *medstr = foo.s(n, sizes, strings, weights, &len);
if (!medstr && len)
result = PyErr_NoMemory();
else {
result = PyString_FromStringAndSize(medstr, len);
free(medstr);
}
}
else if (stringtype == 1) {
Py_UNICODE *medstr = foo.u(n, sizes, strings, weights, &len);
if (!medstr && len)
result = PyErr_NoMemory();
else {
result = PyUnicode_FromUnicode(medstr, len);
free(medstr);
}
}
else
PyErr_Format(PyExc_SystemError, "%s internal error", name);
free(strings);
free(weights);
free(sizes);
return result;
}
static PyObject*
median_improve_common(PyObject *args, const char *name, MedianImproveFuncs foo)
{
size_t n, len;
void *strings = NULL;
size_t *sizes = NULL;
PyObject *arg1 = NULL;
PyObject *strlist = NULL;
PyObject *wlist = NULL;
PyObject *strseq = NULL;
double *weights;
int stringtype;
PyObject *result = NULL;
if (!PyArg_UnpackTuple(args, PYARGCFIX(name), 2, 3, &arg1, &strlist, &wlist))
return NULL;
if (PyObject_TypeCheck(arg1, &PyString_Type))
stringtype = 0;
else if (PyObject_TypeCheck(arg1, &PyUnicode_Type))
stringtype = 1;
else {
PyErr_Format(PyExc_TypeError,
"%s first argument must be a String or Unicode", name);
return NULL;