cosine-similarity

问题 Cosine similarity between two equally-sized vectors (of reals) is defined as the dot product divided by the product of the norms. To represent vectors, I have a large table of float arrays, e.g. CREATE TABLE foo(vec float[])' . Given a certain float array, I need to quickly (with an index, not a seqscan) find the closest arrays in that table by cosine similarity, e.g. SELECT * FROM foo ORDER BY cos_sim(vec, ARRAY[1.0, 4.5, 2.2]) DESC LIMIT 10; But what do I use? pg_trgm 's cosine similarity

问题 I have to compute a cosine distance between each rows but I have no idea how to do it using Spark API Dataframes elegantly. The idea is to compute similarities for each rows(items) and take top 10 similarities by comparing their similarities between rows. --> This is need for Item-Item Recommender System. All that I've read about it is referred to computing similarity over columns Apache Spark Python Cosine Similarity over DataFrames May someone say is it possible to compute a cosine distance

问题 Cosine Similarity article on Wikipedia Can you show the vectors here (in a list or something) and then do the math, and let us see how it works? I'm a beginner. 回答1: Here are two very short texts to compare: Julie loves me more than Linda loves me Jane likes me more than Julie loves me We want to know how similar these texts are, purely in terms of word counts (and ignoring word order). We begin by making a list of the words from both texts: me Julie loves Linda than more likes Jane Now we

问题 Currently i'm studying about data-mining, text comparison and have found this one: https://en.wikipedia.org/wiki/Cosine_similarity. Since i have successfully implemented this algorithm to compare two strings i have decided to try some more complex task to achieve. I have iterated over my DB which contains about 250k documents and compared one random document from DB to whole documents in that DB. To compare all these items time was taken: 316.35898590088 sec, that's, - > 5 minutes to compare

问题 This is my action_slippers Datalist. Please note that this just the part of it: X_id cd iios ui w 1 56548c6ab65dd425cc3dda13 2015-11-24T16:12:26.572Z 194635691 563734c3b65dd40e340eaa56 0.010 2 56548df4b84c321fe4cdfb91 2015-11-24T16:19:00.798Z 194153563 56548df4b84c321fe4cdfb8f 0.010 3 56548fc7735e782a88591662 2015-11-24T16:26:46.952Z 177382028 563e12657d4c410c5832579c 0.010 4 565494e1b84c321fe4ce2f44 2015-11-24T16:48:33.828Z 177382031 563e12657d4c410c5832579c 0.010 5 5654994a735e782a88595802

问题 I am working on a document search problem where given a set of documents and a search query I want to find the document closest to the query. The model that I am using is based on TfidfVectorizer in scikit. I created 4 different tf_idf vectors for all the documents by using 4 different types of tokenizers. Each tokenizer splits the string into n-grams where n is in the range 1 ... 4 . For example: doc_1 = "Singularity is still a confusing phenomenon in physics" doc_2 = "Quantum theory still

问题 ios d.0 d.1 d.2 d.3 d.4 d.5 1 190371877 HDa 2Pb 2 BxU BuQ Bve 2 190890807 HCK 2Pb 2 09 F G 3 193999742 HDa 2Pb 2 1wL 1ye 4 192348099 HDa 2Pb 2 2WP 5 194907960 HDa 2Pb 2 Y F G 6 194306872 HDa 2Pb 2 2WP 7 190571682 HDa 2Pb 2 i F C 8 195878080 HDa 2Pb 2 Y F G 9 195881580 HDa 2Pb 2 Y F G 10 193746161 HDa 2Pb 2 1wL Here is my codes below.ı just able to done for compare 2 vectors and now ı want to built a function library('lsa') td = tempfile() dir.create(td) write( c("HDa","2Pb","2","BxU","BuQ",

问题 I'm new here and my questions is of mathematical rather than programming nature where I would like to get a second opinion on whether my approach makes sense. I was trying to find associations between words in my corpus using the function findAssocs , from the tm package. Even though it appears to perform reasonably well on the data available through the package, such as New York Times and US Congress, I was disappointed with its performance on my own, less tidy dataset. It appears to be

问题 I have a table of images with sentence captions. Given a new sentence I want to find the images that best match it based on how close the new sentence is to the stored old sentences. I know that I can use the @@ operator with a to_tsquery but tsquery accepts specific words as queries. One problem is I don't know how to convert the given sentence into a meaningful query. The sentence may have punctuation and numbers. However, I also feel that some kind of cosine similarity thing is what I need

问题 I'm looking to use the svm() function of the e1071 package in R. I am new to this package and I was wondering if it is possible to write your own custom kernel callable in svm(). I see that there are several kernels pre-loaded, but I don't see a cosine similarity kernel, which is what I need. Alternatively, is there another package in R allowing you to run SVM with cosine similarity kernel? 回答1: The bad news is it is currently not supported in e1071. There was a discussion many years ago