levenshtein-distance

I want to find number of similar words between two texts Example $str1=the cat is on the roof $str2=the mouse is on the roof the,is,on,the,roof words are similar in $str1 and $str2 So output will be in number 5 OR In percentage 86% I am try similar_text() function but this function not work as which i want. Easy, explode them and then use array_diff: $totalWords = count($array_1); $array_1 = explode(" ", $str1); $array_2 = explode(" ", $str2); $differenceCount = count(array_diff($array_1, $array_2)); $differentPercent = $differenceCount / ($totalWords / 100); @Edit: Edited code above to

This question already has an answer here: Potential Duplicates Detection, with 3 Severity Level 1 answer This question based on the answer of my question yesterday. To solve my problem, Jean-François Corbett suggested a Levenshtein distance approach. Then I found this code somewhere to get Levenshtein distance percentage. Public Function GetLevenshteinPercentMatch( _ ByVal string1 As String, ByVal string2 As String, _ Optional Normalised As Boolean = False) As Single Dim iLen As Integer If Normalised = False Then string1 = UCase$(WorksheetFunction.Trim(string1)) string2 = UCase$

I am using the Levenshtein distance algorithm to compare a company name provided as a user input against a database of known company names to find closest match. By itself, the algorithm works okay, but I want to build in a Bias so that the edit distance is considered lower if the initial parts of the strings match. For Example, if the search criteria is "ABCD", then both "ABCD Co." and "XYX ABCD" have identical Edit Distance. However I want to add weight to the fact that the initial parts of the first string matches the search criteria more closely than the second string. One way of doing

I'm trying to come up with the fastest way to make search suggestions. At first I thought a Levenstein UDF function combined with a mysql table would do the job. But using levenshtein, mysql would have to go over every row in the table (tons of words) which would make the query really slow. Now I recently installed and started to use Sphinx (http://sphinxsearch.com/) for fulltext searching mainly because of its performance and tight mysql integration with SphinxSE. So I asked myself if I can implement a "did you mean" algorithm using sphinx to boost performance somehow, and I think I found a

I maintain a music database in mySQL, how do I return results stored under e.g. 'Tiësto' when people search for 'Tiesto'? All the data is stored under full text indexing, if that makes any difference. I'm already employing a combination of Levenshtein in PHP and REGEXP in SQL - not in trying to solve this problem, but just for increased searchability in general. PHP: function Levenshtein($word) { $words = array(); for ($i = 0; $i < strlen($word); $i++) { $words[] = substr($word, 0, $i) . '_' . substr($word, $i); $words[] = substr($word, 0, $i) . substr($word, $i + 1); $words[] = substr($word,

I'm trying to create a damerau-levenshtein distance function in JS. I've found a description off the algorithm on WIkipedia, but they is no implementation off it. It says: To devise a proper algorithm to calculate unrestricted Damerau–Levenshtein distance note that there always exists an optimal sequence of edit operations, where once-transposed letters are never modified afterwards. Thus, we need to consider only two symmetric ways of modifying a substring more than once: (1) transpose letters and insert an arbitrary number of characters between them, or (2) delete a sequence of characters

I am using the Levenshtein distance to find similar strings after OCR. However, for some strings the edit distance is the same, although the visual appearance is obviously different. For example the string Co will return these matches: CY (1) CZ (1) Ca (1) Considering, that Co is the result from an OCR engine, Ca would be the more likely match than the ones. Therefore, after calculating the Levenshtein distance, I'd like to refine query result by ordering by visual similarity. In order to calculate this similarity a I'd like to use standard sans-serif font, like Arial. Is there a library I can