feat(Matching): Use Dice Coefficient Algorithm to compare strings#2
Open
emahuni wants to merge 9 commits intomediabounds:masterfrom
Open
feat(Matching): Use Dice Coefficient Algorithm to compare strings#2emahuni wants to merge 9 commits intomediabounds:masterfrom
emahuni wants to merge 9 commits intomediabounds:masterfrom
Conversation
Add new levenshtein distance matching
Add levenshtein distance to determine matches. This enables truly fuzzy matches even if the strings have a little difference
changed the matching package from leven to string similarity
String Similarity Returns a fraction between 0 and 1, which indicates the degree of similarity between the two strings. 0 indicates completely different strings, 1 indicates identical strings and that is easier to determine the fuzziness of the match
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Add the ability to specify a similarity threshold to consider matches. This allows for a truly fuzzy match. eg: "David" can match "Dabid was here" making it useful on matching data that has typos or different in syntax and order of words.