edit-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'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

I'm back with another longish question. Having experimented with a number of Python-based Damerau-Levenshtein edit distance implementations, I finally found the one listed below as editdistance_reference() . It seems to deliver correct results and appears to have an efficient implementation. So I set down to convert the code to Cython. on my test data, the reference method manages to deliver results for 11,000 comparisons (for pairs of words aound 12 letters long), while the Cythonized method does over 200,000 comparisons per second. Sadly, the results are incorrect: when you look at the

I have a stored procedure that uses Levenshtein distance to determine the result closest to what the user typed. The only thing really affecting the speed is the function that calculates the Levenshtein distance for all the records before selecting the record with the lowest distance (I've verified this by putting a 0 in place of the call to the Levenshtein function). The table has 1.5 million records, so even the slightest adjustment may shave off a few seconds. Right now the entire thing runs over 10 minutes. Here's the method I'm using: ALTER function dbo.Levenshtein ( @Source nvarchar(200)

Right now I am producing a line graph with three observations. Hence, there are three x-axis ticks. I want to manually reduce the distance between the x-axis ticks and basically force the observations to be closer to each other. In other words, I want to reduce the distance between the x-axis ticks. My data: structure(list(Period = c("January 1997 - August 2003", "September 2003 - Jun 2005", "Jul 2005 - Dec 2009", "January 1997 - August 2003", "September 2003 - Jun 2005", "Jul 2005 - Dec 2009"), Time.Period = structure(c(1L, 3L, 2L, 1L, 3L, 2L), .Label = c("Jan 1997 - Aug 2003", "Jul 2005 -