similarity

问题 I have found this example here which uses delicious related tags and create a graph. But I don't know how they implemented it. I don't know how to get a list of related tags from delicious API, because in the documentation it is not mentioned at all, but in delicious website when you search for a tag it shows related tags in the right hand. Does anybody know how to get related tags using API? Thank you 回答1: You might want to refer to Delicious' API page. There is specific section on getting

问题 This is the search record: A = { field1: value1, field2: value2, ... fieldN: valueN } I have many such records in the database. Other record (B) almost matches record A if even N-M fields in these records are equal. This is the example, M=2: B = { field1: OTHER_value1, field2: OTHER_value2, field3: value3, ... fieldN: valueN } If can be any fields, not only the first. I can make the very big combinatorial sql query, but may be there is more beautiful solution. P.S.: My database is PostgreSQL.

问题 I have a document, which contains many fields, one of them is country . There are many documents with the same country . When I do match query , or fuzzy search against country , and query for Belgium for example, it returns list of documents, which matched Belgium country, but they all have different score. I believe it's because of tdidf similarity and presence of belgium term in other fields of documents, etc. I'd like it return the same score in this case. What similarity should I use?

问题 Currently I need to implement custom solr similarity. So I found out that I need to override the DefaultSimilarity class in order to do this. Still I can't figure out how exactly it should be done and where to get source code which can be used for this purpose. Any help would be appreciated! 回答1: For anyone who will need an answer: What I needed to do was to create a package project in eclipse, download lucene-core jar and add it to the project. After that I imported the needed library and

问题 I have this dataset: import pandas as pd import itertools A = ['A','B','C'] M = ['1','2','3'] F = ['plus','minus','square'] df = pd.DataFrame(list(itertools.product(A,M,F)), columns=['A','M','F']) print(df) The example output is like this: A M F 0 A 1 plus 1 A 1 minus 2 A 1 square 3 A 2 plus 4 A 2 minus 5 A 2 square I want to pairwise comparison (jaccard similarity) of each row from this data frame, for example, comparing A 1 plus and A 2 square and get the similarity value between those both

问题 Anyone know if there is any Edit Distance similarity implementation, like Levenshtein in Lucene/Solr? Thanks 回答1: Yes, fuzzy queries and fuzzy term enumeration use Levenshtein edit distance. 回答2: Solr has both Levenstein and Jaro-Winkler as query functions, which means you can sort on them, add them to the returned documents or use them to compute the document score http://wiki.apache.org/solr/FunctionQuery#strdist 来源： https://stackoverflow.com/questions/21607413/edit-distance-similarity-in

问题 I have a generic with some filenames (LIST1) and another biggeneric with a full list of names (LIST2). I need to match names from LIST1 to similar ones in LIST2. For example LIST1 - **MAIZE_SLIP_QUANTITY_3_9.1.aif** LIST 2 1- TUTORIAL_FAILURE_CLINCH_4.1.aif 2- **MAIZE_SLIP_QUANTITY_3_5.1.aif** 3- **MAIZE_SLIP_QUANTITY_3_9.2.aif** 4- TUTORIAL_FAILURE_CLINCH_5.1.aif 5- TUTORIAL_FAILURE_CLINCH_6.1.aif 6- TUTORIAL_FAILURE_CLINCH_7.1.aif 7- TUTORIAL_FAILURE_CLINCH_8.1.aif 8- TUTORIAL_FAILURE

问题 I have a df id val1 val2 val3 100 aa bb cc 200 bb cc 0 300 aa cc 0 400 bb aa cc From this I have to generate a df, something like this: 100 200 300 400 100 3 2 2 3 200 2 2 1 2 300 2 1 2 2 400 3 2 2 3 Explaination: id 100 contains aa,bb,cc and 200 contains bb,cc,0 There are 2 similar values. Therefore in my final matrix, the intersection cell for index-100 and column 200 , 2 should be inserted. Similarly for id 200- values are bb,cc,0 and that for id 300 - aa,cc,0 Here the similarity is 1 ,

问题 I have folder with images. Some images have duplicates or similar (images of the same scene from another angle) or modifications(images which differ by size, blur level or noise filters). My task is to define if some of these images have similar images I find this code, but I can't understand how output number describes similarity of two images when of one of them is modified or the same scene from another angle. def compare(file1, file2): im = [None, None] # to hold two arrays for i, f in

问题 I am trying to find similar hashes (hexadecimal hash) using hamming and Levenshtein distance. Lets say two hashes are similar if their hamming distance is less than 10 (number of differing bits). Hash 1= ffffff (base 16) Hash 2= fffff0 (base 16) The hamming distance between two hashes is 4. They are similar. Because, Hash 1= 11111111 11111111 11111111 (base 2) Hash 2= 11111111 11111111 11110000 (base 2) I have 8 million such hashes. I am wondering what will be a suitable data structure for