cosine-similarity

问题 I am having trouble with calculating cosine similarity between large list of 100-dimensional vectors. When I use from sklearn.metrics.pairwise import cosine_similarity , I get MemoryError on my 16 GB machine. Each array fits perfectly in my memory but I get MemoryError during np.dot() internal call Here's my use-case and how I am currently tackling it. Here's my parent vector of 100-dimension which I need to compare with other 500,000 different vectors of same dimension (i.e. 100) parent

问题 I'm developing a simple NLP project, where we have given a set of words and to find the similar/phonetically similar word from a text. I've found a lot of algorithms but not a sample application. Also it should give the similarity score by comparing the keyword and the word that are found. Can anyone help me out? def word2vec(word): from collections import Counter from math import sqrt cw = Counter(word) sw = set(cw) lw = sqrt(sum(c*c for c in cw.values())) return cw, sw, lw def cosdis(v1, v2

问题 I am trying to learn data science and found this great article online. https://bergvca.github.io/2017/10/14/super-fast-string-matching.html I have this database full of company names, but am finding that the results where the similarity is equal to 1, they are in fact literally the same exact row. I obviously want to catch duplicates, but I do not want the same row to match itself. On a side note, this has opened my eyes to pandas and NLP. Super fascinating field - Hopefully, somebody can

问题 How can I implement the tf-idf and cosine similarity in Lucene? I'm using Lucene 4.2. The program that I've created does not use tf-idf and Cosine similaryty, it only uses TopScoreDocCollector. import com.mysql.jdbc.Statement; import java.io.BufferedReader; import java.io.File; import java.io.InputStreamReader; import org.apache.lucene.analysis.standard.StandardAnalyzer; import org.apache.lucene.util.Version; import org.apache.lucene.index.IndexWriterConfig; import org.apache.lucene.index

问题 It might be similar question asked in this forum but I feel my requirement peculiar. I have a data frame df1 where it consists of variable "WrittenTerms" with 40,000 observations and I have another data-fame df2 with variable "SuggestedTerms" with 17,000 observations I need to calculate the similarity between "written Term" and "suggestedterms" df1$WrittenTerms head pain lung cancer abdminal pain df2$suggestedterms cardio attack breast cancer abdomen pain head ache lung cancer I need to get

问题 I was wondering whether it would be possible for Spark Cosine Similarity to work with Sparse input data? I have seen examples wherein the input consists of lines of space-separated features of the form: id feat1 feat2 feat3 ... but I have an inherently sparse, implicit feedback setting and would like to have input in the form: id1 feat1:1 feat5:1 feat10:1 id2 feat3:1 feat5:1 .. ... I would like to make use of the sparsity to improve the calculation. Also ultimately I wish to use the DIMSUM

问题 I want to calculate the similarity between lists of words, for example : import math,re from collections import Counter test = ['address','ip'] list_a = ['identifiant', 'ip', 'address', 'fixe', 'horadatee', 'cookie', 'mac', 'machine', 'network', 'cable'] list_b = ['address','city'] def counter_cosine_similarity(c1, c2): terms = set(c1).union(c2) print(c2.get('ip',0)**2) dotprod = sum(c1.get(k, 0) * c2.get(k, 0) for k in terms) magA = math.sqrt(sum(c1.get(k, 0)**2 for k in terms)) magB = math

问题 I want to calculate the similarity between lists of words, for example : import math,re from collections import Counter test = ['address','ip'] list_a = ['identifiant', 'ip', 'address', 'fixe', 'horadatee', 'cookie', 'mac', 'machine', 'network', 'cable'] list_b = ['address','city'] def counter_cosine_similarity(c1, c2): terms = set(c1).union(c2) print(c2.get('ip',0)**2) dotprod = sum(c1.get(k, 0) * c2.get(k, 0) for k in terms) magA = math.sqrt(sum(c1.get(k, 0)**2 for k in terms)) magB = math

问题 I was calculating a Cosine Similarity Matrix for sparse vectors, and the elements expected to be float numbers appeared to be 'nan'. 'visits' is a sparse matrix showing how many times each user has visited each website. This matrix used to have a shape 1 500 000 x 1500, but I converted it into sparse matrix, using coo_matrix().tocsc(). The task is to find out, how similar the websites are, so I decided to calculate the cosine metric between each two sites. Here is my code: cosine_distance

问题 I have two matrices, A (dimensions M x N) and B (N x P). In fact, they are collections of vectors - row vectors in A, column vectors in B. I want to get cosine similarity scores for every pair a and b , where a is a vector (row) from matrix A and b is a vector (column) from matrix B. I have started by multiplying the matrices, which results in matrix C (dimensions M x P). C = A*B However, to obtain cosine similarity scores, I need to divide each value C(i,j) by the norm of the two