levenshtein-distance | 易学教程

Edit distance between two graphs

阅读更多关于 Edit distance between two graphs

问题 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 . 回答1: 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

Fast fuzzy/approximate search in dictionary of strings in Ruby

阅读更多关于 Fast fuzzy/approximate search in dictionary of strings in Ruby

I have a dictionary of 50K to 100K strings (can be up to 50+ characters) and I am trying to find whether a given string is in the dictionary with some "edit" distance tolerance. (Levenshtein for example). I am fine pre-computing any type of data structure before doing the search. My goal to run thousands of strings against that dictionary as fast as possible and returns the closest neighbor. I would be fine just getting a boolean that say whether a given is in the dictionary or not if there was a significantly faster algorithm to do so For this, I first tried to compute all the Levenshtein

Python: String clustering with scikit-learn's dbscan, using Levenshtein distance as metric:

阅读更多关于 Python: String clustering with scikit-learn's dbscan, using Levenshtein distance as metric:

I have been trying to cluster multiple datasets of URLs (around 1 million each), to find the original and the typos of each URL. I decided to use the levenshtein distance as a similarity metric, along with dbscan as the clustering algorithm as k-means algorithms won't work because I do not know the number of clusters. I am facing some problems using Scikit-learn's implementation of dbscan. This snippet below works on small datasets in the format I an using, but since it is precomputing the entire distance matrix, that takes O(n^2) space and time and is way too much for my large datasets. I

Hamming Distance vs. Levenshtein Distance

阅读更多关于 Hamming Distance vs. Levenshtein Distance

问题 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

How can I optimize this Python code to generate all words with word-distance 1?

阅读更多关于 How can I optimize this Python code to generate all words with word-distance 1?

问题 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.

Compare 5000 strings with PHP Levenshtein

阅读更多关于 Compare 5000 strings with PHP Levenshtein

问题 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. 回答1: I think a better way to group similar addresses would be to:

Improving search result using Levenshtein distance in Java

阅读更多关于 Improving search result using Levenshtein distance in Java

问题 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

How do I convert between a measure of similarity and a measure of difference (distance)?

阅读更多关于 How do I convert between a measure of similarity and a measure of difference (distance)?

Is there a general way to convert between a measure of similarity and a measure of distance? Consider a similarity measure like the number of 2-grams that two strings have in common. 2-grams('beta', 'delta') = 1 2-grams('apple', 'dappled') = 4 What if I need to feed this to an optimization algorithm that expects a measure of difference, like Levenshtein distance? This is just an example...I'm looking for a general solution, if one exists. Like how to go from Levenshtein distance to a measure of similarity? I appreciate any guidance you may offer. Let d denotes distance, s denotes similarity.

Fuzzy screenshot comparison with Selenium

阅读更多关于 Fuzzy screenshot comparison with Selenium

I'm using Selenium to automate webpage functional testing. It's important for us to do a pixel-by-pixel comparison when we roll out new code, so we're using Selenium to take screenshots and comparing the base64 encoded strings to see if anything has changed. We're finding that in practice, it's hard to get complete pixel consistency, especially with images. I would like minor blurriness / rendering artifacts to count as a "pass" instead of a "fail", so I'm wondering if there's a way of doing a fuzzy comparison to make our tests a bit less fragile. I was thinking of maybe looking at the

Levenshtein DFA in .NET

阅读更多关于 Levenshtein DFA in .NET

Good afternoon, Does anyone know of an "out-of-the-box" implementation of Levenshtein DFA ( deterministic finite automata ) in .NET (or easily translatable to it)? I have a very big dictionary with more than 160000 different words, and I want to, given an inicial word w , find all known words at Levenshtein distance at most 2 of w in an efficient way. Of course, having a function which computes all possible edits at edit distance one of a given word and applying it again to each of these edits solves the problem (and in a pretty straightforwad way). The problem is effiency --- given a 7 letter