import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public static InterfaceStringMetric createSimStringStrategy( final SimStringStrategy strategyType )
{
    switch ( strategyType )
    {
        case DICE_COEFF:
            return new DiceSimilarity();
        case JARO:
            return new Jaro();
        case JAROWINKLER:
            return new JaroWinkler();
        case LEVENSTHEIN:
            return new Levenshtein();
        case JACCARD:
            return new JaccardSimilarity();
        case MONGEELKAN:
            return new MongeElkan();
        case QGRAMS:
            return new QGramsDistance();
        default:
            throw new IllegalArgumentException( "No valid SimStringStrategyType!" );
    }
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public float[] similarities(String s1, String s2) {
    float[] result = new float[15];
    DiceSimilarity ds = new DiceSimilarity();
    result[0] = ds.getSimilarity(s1, s2);
    CosineSimilarity cs = new CosineSimilarity();
    result[1] = ds.getSimilarity(s1, s2);
    JaccardSimilarity js = new JaccardSimilarity();
    result[2] = js.getSimilarity(s1, s2);
    Levenshtein ls = new Levenshtein();
    result[3] = ls.getSimilarity(s1, s2);
    ChapmanOrderedNameCompoundSimilarity concs = new ChapmanOrderedNameCompoundSimilarity();
    result[4] = concs.getSimilarity(s1, s2);
    BlockDistance bd = new BlockDistance();
    result[5] = bd.getSimilarity(s1, s2);
    Jaro j = new Jaro();
    result[6] = j.getSimilarity(s1, s2);
    JaroWinkler jw = new JaroWinkler();
    result[7] = jw.getSimilarity(s1, s2);
    MongeElkan me = new MongeElkan();
    result[8] = me.getSimilarity(s1, s2);
    NeedlemanWunch nw = new NeedlemanWunch();
    result[9] = nw.getSimilarity(s1, s2);
    SmithWaterman sw = new SmithWaterman();
    result[10] = sw.getSimilarity(s1, s2);
    QGramsDistance qgd = new QGramsDistance();
    result[11] = qgd.getSimilarity(s1, s2);
    Soundex s = new Soundex();
    result[12] = s.getSimilarity(s1, s2);
    EuclideanDistance ed = new EuclideanDistance();
    result[13] = ed.getSimilarity(s1, s2);
    TagLink tl = new TagLink();
    result[14] = tl.getSimilarity(s1, s2);
    return result;
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public static List<String> filterByLevensteinSimilarity(List<RankedTitle> tokenTitles) {

        if(tokenTitles.size() == 0){
            throw new IllegalArgumentException("Cannot process an empty list");
        }


        List<String> finalTitles = new ArrayList<String>();
        finalTitles.add(tokenTitles.get(0).getTitle());
        if(tokenTitles.size() < 2){
            return finalTitles;
        }
        
        
        

        AbstractStringMetric metric = new Levenshtein();
        for(int i = 1; i < tokenTitles.size(); i++){
            boolean reject = false;
            for(String title : finalTitles){
                float result = metric.getSimilarity(title, tokenTitles.get(i).getTitle());
                if (result >= 0.7){
                    reject = true;
                    break;
                }
            }
            if(!reject){
                finalTitles.add(tokenTitles.get(i).getTitle());
            }
        }

        return finalTitles;
    }
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
@Override
public Object compute(String[] args) throws IllegalArgumentException {
	if(args.length != NUM_ARGS){
		throw new IllegalArgumentException("Expected number of arguments: " + NUM_ARGS);
	}
	AbstractStringMetric metric = new Levenshtein();
	return metric.getSimilarity(args[0], args[1]);
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public String similaritiesVerbose(String s1, String s2) {
    DiceSimilarity ds = new DiceSimilarity();
    float resultDS = ds.getSimilarity(s1, s2);
    CosineSimilarity cs = new CosineSimilarity();
    float resultCS = ds.getSimilarity(s1, s2);
    JaccardSimilarity js = new JaccardSimilarity();
    float resultJS = js.getSimilarity(s1, s2);
    Levenshtein ls = new Levenshtein();
    float resultLS = ls.getSimilarity(s1, s2);
    ChapmanOrderedNameCompoundSimilarity concs = new ChapmanOrderedNameCompoundSimilarity();
    float resultCONCS = concs.getSimilarity(s1, s2);
    BlockDistance bd = new BlockDistance();
    float resultBD = bd.getSimilarity(s1, s2);
    Jaro j = new Jaro();
    float resultJ = j.getSimilarity(s1, s2);
    JaroWinkler jw = new JaroWinkler();
    float resultJW = jw.getSimilarity(s1, s2);
    MongeElkan me = new MongeElkan();
    float resultME = me.getSimilarity(s1, s2);
    NeedlemanWunch nw = new NeedlemanWunch();
    float resultNW = nw.getSimilarity(s1, s2);
    SmithWaterman sw = new SmithWaterman();
    float resultSW = sw.getSimilarity(s1, s2);
    QGramsDistance qgd = new QGramsDistance();
    float resultQGD = qgd.getSimilarity(s1, s2);
    Soundex s = new Soundex();
    float resultS = s.getSimilarity(s1, s2);
    EuclideanDistance ed = new EuclideanDistance();
    float resultED = ed.getSimilarity(s1, s2);
    TagLink tl = new TagLink();
    float resultTL = tl.getSimilarity(s1, s2);

    StringBuilder stringBuilder = new StringBuilder();
    stringBuilder.append("Comparison of strings: \"");
    stringBuilder.append(s1);
    stringBuilder.append("\" and \"");
    stringBuilder.append(s2);
    stringBuilder.append("\"\n\nDice Similarity: ");
    stringBuilder.append(resultDS);
    stringBuilder.append("\nCosine Similarity: ");
    stringBuilder.append(resultCS);
    stringBuilder.append("\nJaccard Similarity: ");
    stringBuilder.append(resultJS);
    stringBuilder.append("\nLevenshtein Distance: ");
    stringBuilder.append(resultLS);
    stringBuilder.append("\nChapman Ordered Name Compound Similarity: ");
    stringBuilder.append(resultCONCS);
    stringBuilder.append("\nBlock Distance: ");
    stringBuilder.append(resultBD);
    stringBuilder.append("\nJaro: ");
    stringBuilder.append(resultJ);
    stringBuilder.append("\nJaro-Winkler: ");
    stringBuilder.append(resultJW);
    stringBuilder.append("\nMonge-Elkan: ");
    stringBuilder.append(resultME);
    stringBuilder.append("\nNeedleman-Wunch: ");
    stringBuilder.append(resultNW);
    stringBuilder.append("\nSmith-Waterman: ");
    stringBuilder.append(resultSW);
    stringBuilder.append("\nQGrams Distance: ");
    stringBuilder.append(resultQGD);
    stringBuilder.append("\nSoundex: ");
    stringBuilder.append(resultS);
    stringBuilder.append("\nEuclidean Distance: ");
    stringBuilder.append(resultED);
    stringBuilder.append("\nTag Link: ");
    stringBuilder.append(resultTL);
    return stringBuilder.toString();
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
SimilarityWordConfiguration() {

    super();
    
    setAssociateScore(true);
    
    THRESHOLD = 0.5f;
    
    STRING_METRICS = new DualHashBidiMap();
    STRING_METRICS.put("Levenshtein", new Levenshtein());
    STRING_METRICS.put("Jaro", new Jaro());
    STRING_METRICS.put("Jaro Winkler", new JaroWinkler());
    
    STRING_METRIC = new Levenshtein();
    
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public HighestOccurrenceSimilarity() {
    stringMetric = new Levenshtein();
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public HighestSISimilarity() {
    stringMetric = new Levenshtein();
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public BasenameSimilarity() {
    stringMetric = new Levenshtein();
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public SLSimilarity() {
    stringMetric = new Levenshtein();
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public HighestVariantNameSimilarity() {
    stringMetric = new Levenshtein();
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public AssociationStringSimilarity() {
    stringMetric = new Levenshtein();
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public static boolean LEVMatch(String str1, String str2, double threshold){
	Levenshtein lev = new Levenshtein();
	float thresh = lev.getSimilarity(str1, str2);
	return thresh > threshold;
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public static float getLEVMatchSimilarity(String str1, String str2){
	Levenshtein lev = new Levenshtein();
	float thresh = lev.getSimilarity(str1, str2);
	return thresh;
}
 

import uk.ac.shef.wit.simmetrics.similaritymetrics.Levenshtein; //导入依赖的package包/类
public static float LEVMatch(String str1, String str2){
	Levenshtein lev = new Levenshtein();
	float thresh = lev.getSimilarity(str1, str2);
	return thresh;
}