levenshtein-distance

I put "chunk transposition" in quotes because I don't know whether or what the technical term should be. Just knowing if there is a technical term for the process would be very helpful. The Wikipedia article on edit distance gives some good background on the concept. By taking "chunk transposition" into account, I mean that Turing, Alan. should match Alan Turing more closely than it matches Turing Machine I.e. the distance calculation should detect when substrings of the text have simply been moved within the text. This is not the case with the common Levenshtein distance formula. The strings

I wrote Levenshtein algorithm in in C++ If I input: string s: democrat string t: republican I get the matrix D filled-up and the number of operations (the Levenshtein distance) can be read in D[10][8] = 8 Beyond the filled matrix I want to construct the optimal solution. How must look this solution? I don't have an idea. Please only write me HOW MUST LOOK for this example. The question is Given the matrix produced by the Levenshtein algorithm, how can one find " the optimal solution "? i.e. how can we find the precise sequence of string operations: inserts, deletes and substitution [of a

I am trying to run a simulation to test the average Levenshtein distance between random binary strings. My program is in python but I am using this C extension . The function that is relevant and takes most of the time computes the Levenshtein distance between two strings and is this. lev_edit_distance(size_t len1, const lev_byte *string1, size_t len2, const lev_byte *string2, int xcost) { size_t i; size_t *row; /* we only need to keep one row of costs */ size_t *end; size_t half; /* strip common prefix */ while (len1 > 0 && len2 > 0 && *string1 == *string2) { len1--; len2--; string1++;

I'm just wondering if, like for strings where we have the Levenshtein distance (or edit distance) between two strings, is there something similar for graphs? I mean, a scalar measure that identifies the number of atomic operations (node and edges insertion/deletion) to transform a graph G1 to a graph G2 . I think graph edit distance is the measure that you were looking for. Graph edit distance measures the minimum number of graph edit operations to transform one graph to another, and the allowed graph edit operations includes: Insert/delete an isolated vertex Insert/delete an edge Change the

For the problem I'm working on, finding distances between two sequences to determine their similarity, sequence order is very important. However, the sequences that I have are not all the same length, so I pad any deficient strings with empty points such that both sequences are the same length in order to satisfy the Hamming distance requirement. Is there any major problem with me doing this, since all I care about are the number of transpositions (not insertions or deletions like Levenshtein does)? I've found that Hamming distance is much, much faster than Levenshtein as a distance metric for

I have following working Java code for searching for a word against a list of words and it works perfectly and as expected: public class Levenshtein { private int[][] wordMartix; public Set similarExists(String searchWord) { int maxDistance = searchWord.length(); int curDistance; int sumCurMax; String checkWord; // preventing double words on returning list Set<String> fuzzyWordList = new HashSet<>(); for (Object wordList : Searcher.wordList) { checkWord = String.valueOf(wordList); curDistance = calculateDistance(searchWord, checkWord); sumCurMax = maxDistance + curDistance; if (sumCurMax ==

Profiling shows this is the slowest segment of my code for a little word game I wrote: def distance(word1, word2): difference = 0 for i in range(len(word1)): if word1[i] != word2[i]: difference += 1 return difference def getchildren(word, wordlist): return [ w for w in wordlist if distance(word, w) == 1 ] Notes: distance() is called over 5 million times, majority of which is from getchildren, which is supposed to get all words in the wordlist that differ from word by exactly 1 letter. wordlist is pre-filtered to only have words containing the same number of letters as word so it's guaranteed

I have 5000, sometimes more, street address strings in an array. I'd like to compare them all with levenshtein to find similar matches. How can I do this without looping through all 5000 and comparing them directly with every other 4999? Edit: I am also interested in alternate methods if anyone has suggestions. The overall goal is to find similar entries (and eliminate duplicates) based on user-submitted street addresses. I think a better way to group similar addresses would be to: create a database with two tables - one for the address (and a id), one for the soundexes of words or literal