Jaro Winkler距离是一种用于衡量字符串相似性的算法,常用于数据匹配、拼写纠错和文本分类等应用中。它可以计算两个字符串之间的相似程度,并返回一个范围在0到1之间的值,值越接近1表示字符串越相似。
该算法主要基于两个字符串之间的匹配项、字符顺序以及前缀匹配项的权重。在Objective-C或Swift中,可以使用以下代码示例来计算Jaro Winkler距离:
Objective-C示例代码:
- (CGFloat)jaroWinklerDistance:(NSString *)str1 withString:(NSString *)str2 {
NSInteger len1 = str1.length;
NSInteger len2 = str2.length;
if (len1 == 0 && len2 == 0) {
return 1.0;
}
NSInteger matchDistance = MAX(len1, len2) / 2 - 1;
NSMutableCharacterSet *commonSet = [NSMutableCharacterSet new];
NSMutableString *commonChars1 = [NSMutableString new];
NSMutableString *commonChars2 = [NSMutableString new];
NSInteger matchingCharacters = 0;
for (NSInteger i = 0; i < len1; i++) {
NSInteger start = MAX(0, i - matchDistance);
NSInteger end = MIN(i + matchDistance + 1, len2);
NSRange range = [str2 rangeOfCharacterFromSet:[NSCharacterSet characterSetWithCharactersInString:[str1 substringWithRange:NSMakeRange(i, 1)]] options:NSLiteralSearch range:NSMakeRange(start, end - start)];
if (range.location != NSNotFound) {
[commonChars1 appendString:[str1 substringWithRange:NSMakeRange(i, 1)]];
[commonChars2 appendString:[str2 substringWithRange:range]];
[commonSet addCharactersInString:[str1 substringWithRange:NSMakeRange(i, 1)]];
matchingCharacters++;
}
}
if (matchingCharacters == 0) {
return 0.0;
}
NSInteger transpositions = 0;
for (NSInteger i = 0; i < commonChars1.length; i++) {
if (![commonChars1 characterAtIndex:i] == [commonChars2 characterAtIndex:i]) {
transpositions++;
}
}
transpositions /= 2;
CGFloat jaroDistance = (CGFloat)matchingCharacters / len1;
CGFloat prefixScale = 0.1;
NSInteger prefixLength = MIN(4, MIN(len1, len2));
CGFloat commonPrefixLength = 0;
for (NSInteger i = 0; i < prefixLength; i++) {
if ([str1 characterAtIndex:i] == [str2 characterAtIndex:i]) {
commonPrefixLength++;
} else {
break;
}
}
return jaroDistance + prefixScale * (CGFloat)commonPrefixLength * (1.0 - jaroDistance);
}
Swift示例代码:
func jaroWinklerDistance(str1: String, str2: String) -> CGFloat {
let len1 = str1.count
let len2 = str2.count
if len1 == 0 && len2 == 0 {
return 1.0
}
let matchDistance = max(len1, len2) / 2 - 1
let commonSet = NSMutableCharacterSet()
var commonChars1 = ""
var commonChars2 = ""
var matchingCharacters = 0
for i in 0..<len1 {
let start = max(0, i - matchDistance)
let end = min(i + matchDistance + 1, len2)
if let range = str2.rangeOfCharacter(from: CharacterSet(charactersIn: String(str1[str1.index(str1.startIndex, offsetBy: i)]))), options: .literal, range: Range(NSRange(location: start, length: end - start), in: str2)) {
commonChars1 += String(str1[str1.index(str1.startIndex, offsetBy: i)])
commonChars2 += String(str2[range])
commonSet.addCharacters(in: String(str1[str1.index(str1.startIndex, offsetBy: i)]))
matchingCharacters += 1
}
}
if matchingCharacters == 0 {
return 0.0
}
var transpositions = 0
for i in 0..<commonChars1.count {
if Array(commonChars1)[i] != Array(commonChars2)[i] {
transpositions += 1
}
}
transpositions /= 2
let jaroDistance = CGFloat(matchingCharacters) / CGFloat(len1)
let prefixScale: CGFloat = 0.1
let prefixLength = min(4, min(len1, len2))
var commonPrefixLength = 0
for i in 0..<prefixLength {
if Array(str1)[i] == Array(str2)[i] {
commonPrefixLength += 1
} else {
break
}
}
return jaroDistance + prefixScale * CGFloat(commonPrefixLength) * (1.0 - jaroDistance)
}
使用该算法计算字符串相似性时,可以根据返回的距离值进行相似度的判断和处理。在具体应用中,可以根据业务需求使用该算法来进行搜索、推荐和智能匹配等场景。
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