Search Results for "levenshtein"

...Delta aims to make this both efficient and enjoyable: it allows you to make extensive changes to the layout and styling of diffs, as well as allowing you to stay arbitrarily close to the default git/diff output. Language syntax highlighting with color themes. Within-line highlights based on a Levenshtein edit inference algorithm. Git style strings (foreground color, background color, font attributes) are supported for >20 stylable elements. Delta provides Stylable box/line decorations to draw attention to commit, file and hunk header sections. Support for Git's color-moved feature. Code can be copied directly from the diff. n and N keybindings to move between files in large diffs, and between diffs in log -p views. ...

The goal of this project is to provide a generator for lexical analyzers of maximum computational efficiency and maximum range of applications. This includes the support for Unicode (UTF8, UTF16, ...) and a large variety of other encodings directly and via nested converters such as ICU(tm) and IConv. Sophisticated buffer handling allows to operate on plain file streams, on sockets, or manually fed buffer content. 'Ready-to-build' examples explain related concepts and facilitate practical...

Implementation of various string similarity and distance algorithms: Levenshtein, Jaro-winkler, n-Gram, Q-Gram, Jaccard index, Longest Common Subsequence edit distance, cosine similarity. A library implementing different string similarity and distance measures. A dozen of algorithms (including Levenshtein edit distance and sibblings, Jaro-Winkler, Longest Common Subsequence, cosine similarity etc.) are currently implemented.

We’ve made it our mission to pull in event tickets from every corner of the internet, showing you them all on the same screen so you can compare them and get to your game/concert/show as quickly as possible. Of course, a big problem with most corners of the internet is labeling. One of our most consistently frustrating issues is trying to figure out whether two ticket listings are for the same real-life event (that is, without enlisting the help of our army of interns). To pick an example...

This is a tool for concern mining which uses a KDM model as input and the output is the same model with annotated concerns. It uses a Concern Library and a modified String Clustering K-means algorithm with Levenshtein metric to cluster the strings.

The fuzzy-matching library provides an OmniMark pattern function that attempts to approximately match the input prefix against any of the given target strings. The allowed Damerau–Levenshtein distance from each target string is user-specified. This distance equals the minimum number of character deletions, insertions, replacements, and transpositions required to transform the target string into the input. Each of the four transformations can be individually weighed or completely disallowed.

Implementation of Damerau-Levenshtein fuzzy string matching in Java. Returns the distance adding one point per character insert, deletion, mismatche and swap.. This is an extension of apache/commons/lang/StringUtils.getLevenshteinDistance()

SimMetrics is a Similarity Metric Library, e.g. from edit distance's (Levenshtein, Gotoh, Jaro etc) to other metrics, (e.g Soundex, Chapman). Work provided by UK Sheffield University funded by (AKT) an IRC sponsored by EPSRC, grant number GR/N15764/01.