How can I measure the similarity between 2 strings

2019-01-08 06:42发布

Given two strings text1 and text2

public SOMEUSABLERETURNTYPE Compare(string text1, string text2)
{
     // DO SOMETHING HERE TO COMPARE
}

Examples:

  1. First String: StackOverflow

    Second String: StaqOverflow

    Return: Similarity is 91%

    The return can be in % or something like that.

  2. First String: The simple text test

    Second String: The complex text test

    Return: The values can be considered equal

Any ideas? What is the best way to do this?

12条回答
仙女界的扛把子
2楼-- · 2019-01-08 07:02

Here is some code I have written for a project I am working on. I need to know the Similarity Ratio of the strings and the Similarity Ratio based on words of the strings. This last one, I want to know both the Words Similarity Ratio of the smallest string(so if all words exist and match in the larger string the result will be 100%) and the Words Similarity Ratio of the larger string(which I call RealWordsRatio). I use the Levenshtein algorithm to find the distance. The code is unoptimised, so far, but it works as expected. I hope you find it useful.

public static int Compute(string s, string t)
    {
        int n = s.Length;
        int m = t.Length;
        int[,] d = new int[n + 1, m + 1];

        // Step 1
        if (n == 0)
        {
            return m;
        }

        if (m == 0)
        {
            return n;
        }

        // Step 2
        for (int i = 0; i <= n; d[i, 0] = i++)
        {
        }

        for (int j = 0; j <= m; d[0, j] = j++)
        {
        }

        // Step 3
        for (int i = 1; i <= n; i++)
        {
            //Step 4
            for (int j = 1; j <= m; j++)
            {
                // Step 5
                int cost = (t[j - 1] == s[i - 1]) ? 0 : 1;

                // Step 6
                d[i, j] = Math.Min(
                    Math.Min(d[i - 1, j] + 1, d[i, j - 1] + 1),
                    d[i - 1, j - 1] + cost);
            }
        }
        // Step 7
        return d[n, m];
    }

double GetSimilarityRatio(String FullString1, String FullString2, out double WordsRatio, out double RealWordsRatio)
    {
        double theResult = 0;
        String[] Splitted1 = FullString1.Split(new char[]{' '}, StringSplitOptions.RemoveEmptyEntries);
        String[] Splitted2 = FullString2.Split(new char[]{' '}, StringSplitOptions.RemoveEmptyEntries);
        if (Splitted1.Length < Splitted2.Length)
        {
            String[] Temp = Splitted2;
            Splitted2 = Splitted1;
            Splitted1 = Temp;
        }
        int[,] theScores = new int[Splitted1.Length, Splitted2.Length];//Keep the best scores for each word.0 is the best, 1000 is the starting.
        int[] BestWord = new int[Splitted1.Length];//Index to the best word of Splitted2 for the Splitted1.

        for (int loop = 0; loop < Splitted1.Length; loop++) 
        {
            for (int loop1 = 0; loop1 < Splitted2.Length; loop1++) theScores[loop, loop1] = 1000;
            BestWord[loop] = -1;
        }
        int WordsMatched = 0;
        for (int loop = 0; loop < Splitted1.Length; loop++)
        {
            String String1 = Splitted1[loop];
            for (int loop1 = 0; loop1 < Splitted2.Length; loop1++)
            {
                String String2 = Splitted2[loop1];
                int LevenshteinDistance = Compute(String1, String2);
                theScores[loop, loop1] = LevenshteinDistance;
                if (BestWord[loop] == -1 || theScores[loop, BestWord[loop]] > LevenshteinDistance) BestWord[loop] = loop1;
            }
        }

        for (int loop = 0; loop < Splitted1.Length; loop++)
        {
            if (theScores[loop, BestWord[loop]] == 1000) continue;
            for (int loop1 = loop + 1; loop1 < Splitted1.Length; loop1++)
            {
                if (theScores[loop1, BestWord[loop1]] == 1000) continue;//the worst score available, so there are no more words left
                if (BestWord[loop] == BestWord[loop1])//2 words have the same best word
                {
                    //The first in order has the advantage of keeping the word in equality
                    if (theScores[loop, BestWord[loop]] <= theScores[loop1, BestWord[loop1]])
                    {
                        theScores[loop1, BestWord[loop1]] = 1000;
                        int CurrentBest = -1;
                        int CurrentScore = 1000;
                        for (int loop2 = 0; loop2 < Splitted2.Length; loop2++)
                        {
                            //Find next bestword
                            if (CurrentBest == -1 || CurrentScore > theScores[loop1, loop2])
                            {
                                CurrentBest = loop2;
                                CurrentScore = theScores[loop1, loop2];
                            }
                        }
                        BestWord[loop1] = CurrentBest;
                    }
                    else//the latter has a better score
                    {
                        theScores[loop, BestWord[loop]] = 1000;
                        int CurrentBest = -1;
                        int CurrentScore = 1000;
                        for (int loop2 = 0; loop2 < Splitted2.Length; loop2++)
                        {
                            //Find next bestword
                            if (CurrentBest == -1 || CurrentScore > theScores[loop, loop2])
                            {
                                CurrentBest = loop2;
                                CurrentScore = theScores[loop, loop2];
                            }
                        }
                        BestWord[loop] = CurrentBest;
                    }

                    loop = -1;
                    break;//recalculate all
                }
            }
        }
        for (int loop = 0; loop < Splitted1.Length; loop++)
        {
            if (theScores[loop, BestWord[loop]] == 1000) theResult += Splitted1[loop].Length;//All words without a score for best word are max failures
            else
            {
                theResult += theScores[loop, BestWord[loop]];
                if (theScores[loop, BestWord[loop]] == 0) WordsMatched++;
            }
        }
        int theLength = (FullString1.Replace(" ", "").Length > FullString2.Replace(" ", "").Length) ? FullString1.Replace(" ", "").Length : FullString2.Replace(" ", "").Length;
        if(theResult > theLength) theResult = theLength;
        theResult = (1 - (theResult / theLength)) * 100;
        WordsRatio = ((double)WordsMatched / (double)Splitted2.Length) * 100;
        RealWordsRatio = ((double)WordsMatched / (double)Splitted1.Length) * 100;
        return theResult;
    }
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不美不萌又怎样
3楼-- · 2019-01-08 07:03

Perl module Text::Phonetic has implementations of various algorithms.

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爱情/是我丢掉的垃圾
4楼-- · 2019-01-08 07:06

There are various different ways of doing this. Have a look at the Wikipedia "String similarity measures" page for links to other pages with algorithms.

I don't think any of those algorithms take sounds into consideration, however - so "staq overflow" would be as similar to "stack overflow" as "staw overflow" despite the first being more similar in terms of pronunciation.

I've just found another page which gives rather more options... in particular, the Soundex algorithm (Wikipedia) may be closer to what you're after.

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Rolldiameter
5楼-- · 2019-01-08 07:07

If you want to compare phonetically, check out the Soundex and Metaphone algorithms: http://www.blackbeltcoder.com/Articles/algorithms/phonetic-string-comparison-with-soundex

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三岁会撩人
6楼-- · 2019-01-08 07:11

Levenshtein distance is probably what you're looking for.

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霸刀☆藐视天下
7楼-- · 2019-01-08 07:12

I wrote a Double Metaphone implementation in C# a while back. You'll find it vastly superior to Soundex and the like.

Levenshtein distance has also been suggested, and it's a great algorithm for a lot of uses, but phonetic matching is not really what it does; it only seems that way sometimes because phonetically similar words are also usually spelled similarly. I did an analysis of various fuzzy matching algorithms which you might also find useful.

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