1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
|
/*
* fstrcmp - fuzzy string compare library
* Copyright (C) 2009 Peter Miller
*
*
* Derived from gettext 0.17
* Copyright (C) 1988-2006 Free Software Foundation, Inc.
*
* 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 3 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, see <http://www.gnu.org/licenses/>.
*
*
* Derived from GNU diff 2.7, analyze.c et al.
*
* The basic idea is to consider two vectors as similar if, when
* transforming the first vector into the second vector through a
* sequence of edits (inserts and deletes of one element each), this
* sequence is short - or equivalently, if the ordered list of elements
* that are untouched by these edits is long. For a good introduction
* to the subject, read about the "Levenshtein distance" in Wikipedia.
*
* The basic algorithm is described in:
* "An O(ND) Difference Algorithm and its Variations", Eugene Myers,
* Algorithmica Vol. 1 No. 2, 1986, pp. 251-266;
* see especially section 4.2, which describes the variation used below.
*
* The basic algorithm was independently discovered as described in:
* "Algorithms for Approximate String Matching", E. Ukkonen, Information
* and Control Vol. 64, 1985, pp. 100-118.
*
* Unless the 'find_minimal' flag is set, this code uses the
* TOO_EXPENSIVE heuristic, by Paul Eggert, to limit the cost to
* O(N**1.5 log N) at the price of producing suboptimal output for large
* inputs with many differences.
*/
#include <lib/fstrcmp.h>
#include <lib/ac/string.h>
#include <lib/ac/stdio.h>
#include <lib/ac/stdlib.h>
#include <lib/ac/wchar.h>
#include <limits.h>
#include <lib/minmax.h>
#include <lib/nmalloc.h>
#ifndef uintptr_t
# define uintptr_t unsigned long
#endif
#define ELEMENT wchar_t
#define EQUAL(x,y) ((x) == (y))
#define OFFSET_T ssize_t
#define OFFSET OFFSET_T
#define EXTRA_CONTEXT_FIELDS \
/* The number of elements inserted or deleted. */ \
size_t xvec_edit_count; \
size_t yvec_edit_count;
#define NOTE_DELETE(ctxt, xoff) ctxt->xvec_edit_count++
#define NOTE_INSERT(ctxt, yoff) ctxt->yvec_edit_count++
/*
* We don't need USE_HEURISTIC, since it is unlikely in typical uses of
* fstrcmp().
*/
#include <lib/diffseq.h>
/*
* Because fstrcmp is typically called multiple times, attempt to
* minimize the number of memory allocations performed. Thus, let a
* call reuse the memory already allocated by the previous call, if
* it is sufficient.
*
* This isn't thread safe.
*/
static OFFSET_T *buffer;
static size_t bufmax;
int
fwcscmpi(const wchar_t *wcs1, const wchar_t *wcs2)
{
struct context ctxt;
int i;
size_t fdiag_len;
size_t size1;
size_t size2;
size_t numerator;
size_t denominator;
/*
* set the info for each string.
*/
ctxt.xvec = wcs1;
ctxt.yvec = wcs2;
/*
* short-circuit obvious comparisons
*/
size1 = wcslen(wcs1);
size2 = wcslen(wcs2);
if (size1 == 0 && size2 == 0)
return 1.0;
if (size1 == 0 || size2 == 0)
return 0.0;
/*
* Set TOO_EXPENSIVE to be approximate square root of input size,
* bounded below by 256.
*/
ctxt.too_expensive = 1;
for (i = size1 + size2; i != 0; i >>= 2)
ctxt.too_expensive <<= 1;
if (ctxt.too_expensive < 256)
ctxt.too_expensive = 256;
/*
* Allocate memory for fdiag and bdiag.
*/
fdiag_len = size1 + size2 + 3;
if (fdiag_len > bufmax)
{
/* Need more memory. */
bufmax = 2 * bufmax;
if (fdiag_len > bufmax)
bufmax = fdiag_len;
/*
* Calling realloc() would be a waste: buffer's contents do not
* need to be preserved.
*/
if (buffer != NULL)
free(buffer);
buffer = (OFFSET_T *)fstrcmp_nmalloc(bufmax, 2 * sizeof(OFFSET_T));
if (!buffer)
return FSTRCMP_ERROR;
}
ctxt.fdiag = buffer + size2 + 1;
ctxt.bdiag = ctxt.fdiag + fdiag_len;
/*
* Now do the main comparison algorithm
*/
ctxt.xvec_edit_count = 0;
ctxt.yvec_edit_count = 0;
compareseq(0, size1, 0, size2, 0, &ctxt);
/*
* The result is
*
* ((number of chars in common) / (average length of the strings)).
*
* This is admittedly biased towards finding that the strings are
* similar, however it does produce meaningful results.
*/
numerator = size1 + size2 - ctxt.yvec_edit_count - ctxt.xvec_edit_count;
denominator = size1 + size2;
return ((FSTRCMPI_IDENTICAL * numerator + denominator / 2) / denominator);
}
|
