public static bool FuzzyMatch(string pattern, string str_origin, ref long outScore, List <int> matches)
{
int str_n;
int pattern_n;
int str_start;
string str;
using (new ScopedProfiler("[FM] Init"))
{
outScore = -100000;
matches.Clear();
if (string.IsNullOrEmpty(str_origin))
{
return(false);
}
if (string.IsNullOrEmpty(pattern))
{
return(true);
}
str = str_origin.ToLowerInvariant();
pattern_n = pattern.Length;
var allowEmptyQuery = false; //filter.HasFilters();
if (allowEmptyQuery && string.IsNullOrEmpty(pattern))
{
outScore = 10;
return(true);
}
// find [str_start..str_end) that contains pattern's first and last letter
str_start = 0;
var str_end = str.Length - 1;
var pattern_first_lower = pattern[0];
var pattern_end_lower = pattern[pattern.Length - 1];
for (; str_start < str.Length; ++str_start)
{
if (pattern_first_lower == str[str_start])
{
break;
}
}
for (; str_end >= 0; --str_end)
{
if (pattern_end_lower == str[str_end])
{
break;
}
}
++str_end;
str_n = str_end - str_start;
// str subset is shorter than pattern
if (str_n < pattern_n)
{
return(false);
}
// do check that pattern is fully inside [str_start..str_end)
var pattern_i = 0;
var str_i = str_start;
while (pattern_i < pattern_n && str_i < str_end)
{
if (pattern[pattern_i] == str[str_i])
{
++pattern_i;
}
++str_i;
}
if (pattern_i < pattern_n)
{
return(false);
}
}
using (new ScopedProfiler("[FM] Body"))
using (var d = FuzzyMatchData.Request(str_n, pattern_n))
{
var str_n_minus_pattern_n_plus_1 = str_n - pattern_n + 1;
var prev_min_i = 0;
using (var _3 = new ScopedProfiler("[FM] Match loop"))
for (var j = 0; j < pattern_n; ++j)
{
var min_i = str_n + 1;
var first_match = true;
for (int i = Math.Max(j, prev_min_i), end_i = str_n_minus_pattern_n_plus_1 + j; i < end_i; ++i)
{
// Skip existing <> tags
if (str[i] == '<')
{
for (; i < end_i; ++i)
{
if (str[i] == '>')
{
break;
}
}
}
var si = i + str_start;
var inQuoteExpectEquality = false; //i > 0 && j > 0 && filter.indexesAreInSameQuotes(j, j - 1);
var match = false;
var cancel = false;
if (pattern[j] == str[si])
{
if (inQuoteExpectEquality)
{
if (d.matchData[i - 1, j - 1])
{
match = true;
}
else
{
cancel = true;
}
}
else
{
match = true;
}
}
else
{
if (inQuoteExpectEquality && d.matchData[i - 1, j - 1])
{
cancel = true;
}
}
if (cancel)
{
// cancel quote
var j_start = 0; //filter.indexOfQuoteStart(j);
var n = j - j_start;
for (var k = 1; k <= n; k++)
{
//Assert.IsTrue(matchData[i - k, j - k]);
if (i < k || j < k)
{
break;
}
d.matchData[i - k, j - k] = false;
}
}
if (i >= d.matchData.GetLength(0) || j >= d.matchData.GetLength(1))
{
return(false);
}
d.matchData[i, j] = match;
if (match)
{
if (first_match)
{
min_i = i;
first_match = false;
}
d.matches_indx[j].Add(new ScoreIndx {
i = i, score = 1, prev_mi = -1
});
}
}
if (first_match)
{
return(false); // no match for pattern[j]
}
prev_min_i = min_i;
}
const int sequential_bonus = 75; // bonus for adjacent matches
const int separator_bonus = 30; // bonus if match occurs after a separator
const int camel_bonus = 30; // bonus if match is uppercase and prev is lower or symbol
const int first_letter_bonus = 35; // bonus if the first letter is matched
const int leading_letter_penalty = -5; // penalty applied for every letter in str before the first match
const int max_leading_letter_penalty = -15; // maximum penalty for leading letters
const int unmatched_letter_penalty = -1; // penalty for every letter that doesn't matter
int unmatched = str_n - matches.Count;
// find best score
using (new ScopedProfiler("[FM] Best score 0"))
for (var mi = 0; mi < d.matches_indx[0].Count; ++mi)
{
var i = d.matches_indx[0][mi].i;
var si = str_start + i;
var s = 100 + unmatched_letter_penalty * unmatched;
var penalty = leading_letter_penalty * si;
if (penalty < max_leading_letter_penalty)
{
penalty = max_leading_letter_penalty;
}
s += penalty;
if (si == 0)
{
s += first_letter_bonus;
}
else
{
var currOrigI = str_origin[si];
var prevOrigI = str_origin[si - 1];
if (char.IsUpper(currOrigI) && char.IsUpper(prevOrigI) == false)
{
s += camel_bonus;
}
else if (prevOrigI == '_' || prevOrigI == ' ')
{
s += separator_bonus;
}
}
d.matches_indx[0][mi] = new ScoreIndx
{
i = i,
score = s,
prev_mi = -1
};
}
using (new ScopedProfiler("[FM] Best score 1..pattern_n"))
for (var j = 1; j < pattern_n; ++j)
{
for (var mi = 0; mi < d.matches_indx[j].Count; ++mi)
{
var match = d.matches_indx[j][mi];
var si = str_start + d.matches_indx[j][mi].i;
var currOrigI = str_origin[si];
var prevOrigI = str_origin[si - 1];
if (char.IsUpper(currOrigI) && char.IsUpper(prevOrigI) == false)
{
match.score += camel_bonus;
}
else if (prevOrigI == '_' || prevOrigI == ' ')
{
match.score += separator_bonus;
}
// select from prev
var best_pmi = 0;
var best_score = -1;
for (var pmi = 0; pmi < d.matches_indx[j - 1].Count; ++pmi)
{
var prev_i = d.matches_indx[j - 1][pmi].i;
if (prev_i >= match.i)
{
break;
}
var pmi_score = d.matches_indx[j - 1][pmi].score;
if (prev_i == match.i - 1)
{
pmi_score += sequential_bonus;
}
if (best_score < pmi_score)
{
best_score = pmi_score;
best_pmi = pmi;
}
}
match.score += best_score;
match.prev_mi = best_pmi;
d.matches_indx[j][mi] = match;
}
}
var best_mi = 0;
var max_j = pattern_n - 1;
for (var mi = 1; mi < d.matches_indx[max_j].Count; ++mi)
{
if (d.matches_indx[max_j][best_mi].score < d.matches_indx[max_j][mi].score)
{
best_mi = mi;
}
}
var bestScore = d.matches_indx[max_j][best_mi];
// DebugPrint(pattern, str_origin, str_start, str_n, (i, j) =>
// {
// var arr = d.matches_indx[j];
// for (int k = 0; k < arr.Count; ++k)
// {
// if (arr[k].i == i)
// return $"{arr[k].i + str_start} {arr[k].score} {arr[k].prev_mi}";
// }
//
// return ".";
// });
outScore = bestScore.score;
using (new ScopedProfiler("[FM] Matches calc"))
{
matches.Capacity = pattern_n;
matches.Add(bestScore.i + str_start);
{
var mi = bestScore.prev_mi;
for (var j = pattern_n - 2; j >= 0; --j)
{
matches.Add(d.matches_indx[j][mi].i + str_start);
mi = d.matches_indx[j][mi].prev_mi;
}
}
matches.Reverse();
}
/*var prev_si = -1;
* foreach (var si in matches)
* {
* Assert.IsTrue(si > prev_si, $"find <{pattern}> in <{str_origin}>. {si} > {prev_si}");
*
* Assert.IsTrue(si >= 0);
* Assert.IsTrue(si < str_origin.Length);
*
* prev_si = si;
* }*/
return(true);
}
}
/// <summary>
/// Performs a fuzzy search on a string to see if it matches a pattern.
/// </summary>
/// <param name="pattern">Pattern that we try to match the source string</param>
/// <param name="origin">String we are looking into for a match</param>
/// <param name="outScore">Score of the match. A higher score means the pattern is a better match for the string.</param>
/// <param name="matches">List of indices in the source string where a match was found.</param>
/// <returns>Returns true if a match was found</returns>
public static bool FuzzyMatch(string pattern, string origin, ref long outScore, List <int> matches = null)
{
int str_n;
int pattern_n;
int str_start;
string str;
using (new ScopedProfiler("[FM] Init"))
{
outScore = -100000;
matches?.Clear();
if (string.IsNullOrEmpty(origin))
{
return(false);
}
if (string.IsNullOrEmpty(pattern))
{
return(true);
}
str = origin.ToLowerInvariant();
pattern_n = pattern.Length;
// find [str_start..str_end) that contains pattern's first and last letter
str_start = 0;
var str_end = str.Length - 1;
var pattern_first_lower = pattern[0];
var pattern_end_lower = pattern[pattern.Length - 1];
for (; str_start < str.Length; ++str_start)
{
if (pattern_first_lower == str[str_start])
{
break;
}
}
for (; str_end >= 0; --str_end)
{
if (pattern_end_lower == str[str_end])
{
break;
}
}
++str_end;
str_n = str_end - str_start;
// str subset is shorter than pattern
if (str_n < pattern_n)
{
return(false);
}
// do check that pattern is fully inside [str_start..str_end)
var pattern_i = 0;
var str_i = str_start;
while (pattern_i < pattern_n && str_i < str_end)
{
if (pattern[pattern_i] == str[str_i])
{
++pattern_i;
}
++str_i;
}
if (pattern_i < pattern_n)
{
return(false);
}
}
using (new ScopedProfiler("[FM] Body"))
using (var d = FuzzyMatchData.Request(str_n, pattern_n))
{
var str_n_minus_pattern_n_plus_1 = str_n - pattern_n + 1;
var prev_min_i = 0;
using (var _3 = new ScopedProfiler("[FM] Match loop"))
for (var j = 0; j < pattern_n; ++j)
{
var min_i = str_n + 1;
var first_match = true;
for (int i = Math.Max(j, prev_min_i), end_i = str_n_minus_pattern_n_plus_1 + j; i < end_i; ++i)
{
// Skip existing <> tags
if (str[i] == '<')
{
for (; i < end_i; ++i)
{
if (str[i] == '>')
{
break;
}
}
}
var si = i + str_start;
var match = false;
if (pattern[j] == str[si])
{
match = true;
}
if (i >= d.matchData.GetLength(0) || j >= d.matchData.GetLength(1))
{
return(false);
}
d.matchData[i, j] = match;
if (match)
{
if (first_match)
{
min_i = i;
first_match = false;
}
d.matches_indx[j].Add(new ScoreIndx {
i = i, score = 1, prev_mi = -1
});
}
}
if (first_match)
{
return(false); // no match for pattern[j]
}
prev_min_i = min_i;
}
const int sequential_bonus = 75; // bonus for adjacent matches
const int separator_bonus = 30; // bonus if match occurs after a separator
const int camel_bonus = 30; // bonus if match is uppercase and prev is lower or symbol
const int first_letter_bonus = 35; // bonus if the first letter is matched
const int leading_letter_penalty = -5; // penalty applied for every letter in str before the first match
const int max_leading_letter_penalty = -15; // maximum penalty for leading letters
const int unmatched_letter_penalty = -1; // penalty for every letter that doesn't matter
int unmatched = str_n - (matches?.Count ?? 0);
// find best score
using (new ScopedProfiler("[FM] Best score 0"))
for (var mi = 0; mi < d.matches_indx[0].Count; ++mi)
{
var i = d.matches_indx[0][mi].i;
var si = str_start + i;
var s = 100 + unmatched_letter_penalty * unmatched;
var penalty = leading_letter_penalty * si;
if (penalty < max_leading_letter_penalty)
{
penalty = max_leading_letter_penalty;
}
s += penalty;
if (si == 0)
{
s += first_letter_bonus;
}
else
{
var currOrigI = origin[si];
var prevOrigI = origin[si - 1];
if (char.IsUpper(currOrigI) && char.IsUpper(prevOrigI) == false)
{
s += camel_bonus;
}
else if (prevOrigI == '_' || prevOrigI == ' ')
{
s += separator_bonus;
}
}
d.matches_indx[0][mi] = new ScoreIndx
{
i = i,
score = s,
prev_mi = -1
};
}
using (new ScopedProfiler("[FM] Best score 1..pattern_n"))
for (var j = 1; j < pattern_n; ++j)
{
for (var mi = 0; mi < d.matches_indx[j].Count; ++mi)
{
var match = d.matches_indx[j][mi];
var si = str_start + d.matches_indx[j][mi].i;
var currOrigI = origin[si];
var prevOrigI = origin[si - 1];
if (char.IsUpper(currOrigI) && char.IsUpper(prevOrigI) == false)
{
match.score += camel_bonus;
}
else if (prevOrigI == '_' || prevOrigI == ' ')
{
match.score += separator_bonus;
}
// select from prev
var best_pmi = 0;
var best_score = -1;
for (var pmi = 0; pmi < d.matches_indx[j - 1].Count; ++pmi)
{
var prev_i = d.matches_indx[j - 1][pmi].i;
if (prev_i >= match.i)
{
break;
}
var pmi_score = d.matches_indx[j - 1][pmi].score;
if (prev_i == match.i - 1)
{
pmi_score += sequential_bonus;
}
if (best_score < pmi_score)
{
best_score = pmi_score;
best_pmi = pmi;
}
}
match.score += best_score;
match.prev_mi = best_pmi;
d.matches_indx[j][mi] = match;
}
}
var best_mi = 0;
var max_j = pattern_n - 1;
for (var mi = 1; mi < d.matches_indx[max_j].Count; ++mi)
{
if (d.matches_indx[max_j][best_mi].score < d.matches_indx[max_j][mi].score)
{
best_mi = mi;
}
}
var bestScore = d.matches_indx[max_j][best_mi];
outScore = bestScore.score;
if (matches != null)
{
using (new ScopedProfiler("[FM] Matches calc"))
{
matches.Capacity = pattern_n;
matches.Add(bestScore.i + str_start);
{
var mi = bestScore.prev_mi;
for (var j = pattern_n - 2; j >= 0; --j)
{
matches.Add(d.matches_indx[j][mi].i + str_start);
mi = d.matches_indx[j][mi].prev_mi;
}
}
matches.Reverse();
}
}
return(true);
}
}