similarity

问题 The main goal of this algorithm is to find similar titles of news articles from different sources of web and group them, let's say above 55.55% similarity. My current approach of the algorithm consist of following steps: Feed data from MYSQL database into a two-dimensional array ex. $arrayOne . Make another copy of that array into ex. $arrayTwo . Create a clean array which will only contain similar titles and other content ex. $array_smlr . Loop, foreach $arrayOne article_title check for

问题 I have two dataframes and each have a column called Song. However sometimes the songs are spelled differently. How can I used difflib (or something similar) to get the Song spelling of one dataframe in a new column of the other dataframe? ex: Dataframe1 Song Artist like a virgi madonna Dataframe2 Song Rank like a virgin 2 Result Song Artist SongAlt like a virgin Madonna like a virgi 回答1: Step 1: Merge whatever can be merged In [67]: df1 Out[67]: Song Artist 0 mysong myartist 1 like a virgi

问题 I have two dataframes and each have a column called Song. However sometimes the songs are spelled differently. How can I used difflib (or something similar) to get the Song spelling of one dataframe in a new column of the other dataframe? ex: Dataframe1 Song Artist like a virgi madonna Dataframe2 Song Rank like a virgin 2 Result Song Artist SongAlt like a virgin Madonna like a virgi 回答1: Step 1: Merge whatever can be merged In [67]: df1 Out[67]: Song Artist 0 mysong myartist 1 like a virgi

问题 Simple code like follows gives out similarity score of 0.75 for both cases. As you can see both the words are the exact same. To avoid any confusion I also compared a word with itself. The score refuses to bulge from 0.75. What is going on here? from nltk.corpus import wordnet as wn actual=wn.synsets('orange')[0] predicted=wn.synsets('orange')[0] similarity=actual.wup_similarity(predicted) print similarity similarity=actual.wup_similarity(actual) print similarity 回答1: This is an interesting

问题 Simple code like follows gives out similarity score of 0.75 for both cases. As you can see both the words are the exact same. To avoid any confusion I also compared a word with itself. The score refuses to bulge from 0.75. What is going on here? from nltk.corpus import wordnet as wn actual=wn.synsets('orange')[0] predicted=wn.synsets('orange')[0] similarity=actual.wup_similarity(predicted) print similarity similarity=actual.wup_similarity(actual) print similarity 回答1: This is an interesting

问题 I am working with Python, scikit-learn and keras. I have 3000 thousands images of front-faced watches like the following ones: Watch_1, Watch_2, Watch_3. I like to write program which receives as an input a photo of a real watch which maybe taken under less ideal conditions than the photos above (different background colour, darker lightning etc) and find the most similar watches among the 3000 ones to it. By similarity I mean that if I give as an input a photo of a round, brown watch with

问题 I am interested in calculating similarity between vectors, however this similarity has to be a number between 0 and 1. There are many questions concerning tf-idf and cosine similarity, all indicating that the value lies between 0 and 1. From Wikipedia: In the case of information retrieval, the cosine similarity of two documents will range from 0 to 1, since the term frequencies (using tf–idf weights) cannot be negative. The angle between two term frequency vectors cannot be greater than 90°.

问题 I want to find visually identical characters for a specific character in Unicode. I know how to find canonical or compatibility decompositions of a character; but they do not give me what I want. I want to find characters that are visually identical (not similar), and their only difference can be their sizes. for example I want : (s,S), or (S,S) (whose code points are different). I do not want (ß, β), or (e, é). Any suggestions? Thanks. 回答1: For a particular character, you could start from

问题 I have trained my word2vec model from gensim and I am getting the nearest neighbors for some words in the corpus. Here are the similarity scores: top neighbors for الاحتلال: الاحتلال: 1.0000001192092896 الاختلال: 0.9541053175926208 الاهتلال: 0.872565507888794 الاحثلال: 0.8386293649673462 الاكتلال: 0.8209128379821777 It is odd to get a similarity greater than 1. I cannot apply any stemming to my text because the text includes many OCR spelling mistakes (I got the text from ORC-ed documents).

问题 I have a SQL table with FirstName, LastName, Add1 and other fields. I am working to get this data cleaned up. There are a few instances of likely dupes - All 3 columns are the exact same for more than 1 record The First and Last are the same, only 1 has an address, the other is blank The First and Last are similar (John | Doe vs John C. | Doe) and the address is the same or one is blank I'm wanting to generate a query I can provide to the users, so they can check these records out, compare