cosine-similarity

问题 In my script Cosine similarity need first, to convert an Arabic string into a vector before perform Cosine similarity on terminal under Linux --> problem while convert Arabic string to vector producing Arabic as: [u'\u0627\u0644\u0634\u0645\u0633 \u0645\u0634\u0631\u0642\u0647 \u0646\u0647\u0627\u0631\u0627', u'\u0627\u0644\u0633\u0645\u0627\u0621 \u0632\u0631\u0642\u0627\u0621'] My script: train_set = ["السماء زرقاء", "الشمس مشرقه نهارا"] #Documents test_set = ["الشمس التى فى السماء مشرقه",

问题 I want to perform a large matrix multiplication C = A * B.T and then filter C by applying a stringent threshold, collecting a list of the form (row index, column index, value). A and B are sparse, with mostly zero entries. They are initially represented as sparse scipy csr matrices. Sizes of the matrices (when they are in dense format): A: 9G (900,000 x 1200) B: 6.75G (700,000 x 1200) C, before thresholding: 5000G C, after thresholding: 0.5G Using pyspark, what strategy would you expect to be

问题 I have a table id_vectors that contains id and their corresponding coordinates . Each of the coordinates is a repeated fields with 512 elements inside it. I am looking for pairwise cosine similarity between all those vectors, e.g. If I have three ids 1,2 and 3 then I am looking for a table where I have cosine similarity between them (based on the calculation using 512 coordinates) like below: id1 id2 similarity 1 2 0.5 1 3 0.1 2 3 0.99 Now in my table I have 424,970 unique ID and their

问题 Basically given some vector v, I want to get another random vector w with some cosine similarity between v and w. Is there any way we can get this in python? Example: for simplicity I will have 2D vector of v [3,-4]. I want to get random vector w with cosine similarity of 60% or plus 0.6. This should generate vector w with values [0.875, 3] or any other vector with same cosine similarity. So I hope this is clear enough. 回答1: Given the vector v and cosine similarity costheta (a scalar between

问题 I'm conducting a research using elasticsearch. I was planning to use cosine similarity but I noted that it is unavailable and instead we have BM25 as default scoring function. Is there a reason for that? Is cosine similarity improper for querying documents? Why was BM25 chosen as default? Thanks 回答1: Longtime elasticsearch use TF/IDF algorithm to find similarity in queries. But number versions ago is changed to BM25 as more efficient. You can read the information in the documentation. And

问题 As the title says, I'm trying to apply a function over each pair of columns of a dataframe under some conditions. I'm going to try to illustrate this. My df is of the form: Code | 14 | 17 | 19 | ... w1 | 0 | 5 | 3 | ... w2 | 2 | 5 | 4 | ... w3 | 0 | 0 | 5 | ... The Code corresponds to a determined location in a rectangular grid and the ws are different words. I would like to apply cosine similarity measure between each pair of columns only (EDITED!) if the sum of items in one of the columns

问题 This question was migrated from Data Science Stack Exchange because it can be answered on Stack Overflow. Migrated last year . I am using Spark Scala to calculate cosine similarity between the Dataframe rows. Dataframe schema is below: root |-- itemId: string (nullable = true) |-- features: vector (nullable = true) Sample of the dataframe below +-------+--------------------+ | itemId| features| +-------+--------------------+ | ab |[4.7143,0.0,5.785...| | cd |[5.5,0.0,6.4286,4...| | ef |[4

问题 I have two files: A and B A has 400,000 lines each having 50 float values B has 40,000 lines having 50 float values. For every line in B, I need to find corresponding lines in A which have >90% similarity (cosine). For linear search and computation, the code takes ginormous computing time. (40-50 hours) Reaching out to the community for suggestions on how to fasten the process (link of blogs/resources such as AWS/Cloud to be used to achieve it). Have been stuck with this for quite a while!

问题 I want to calculate cosine similarity between articles. And I am running into the problem that my implementation approach would take a long time for the size of the data that I am going to run. from scipy import spatial import numpy as np from numpy import array import sklearn from sklearn.metrics.pairwise import cosine_similarity I = [[3, 45, 7, 2],[2, 54, 13, 15], [2, 54, 1, 13]] II = [2, 54, 13, 15] print cosine_similarity(II, I) With the example above, to calculate I and II already took 1

问题 I gather Text documents (in Node.js) where one document i is represented as a list of words. What is an efficient way to compute the similarity between these documents, taking into account that new documents are coming as a sort of stream of documents? I currently use cos-similarity on the Normalized Frequency of the words within each document. I don't use the TF-IDF (Term frequency, Inverse document frequency) because of the scalability issue since I get more and more documents. Initially My