cosine-similarity | 易学教程

How to Calculate cosine similarity with tf-idf using Lucene and Java

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问题 I have a query and a set of documents. I need to rank these documents based on the cosine similarity with tf-idf. Can someone please tell me what support I can get from Lucene to compute this ? What parameters I can directly calculate from Lucene (can I get tf, idf directly through some method in lucene?) and how to compute cosine similarity with Lucene (is there any function which directly returns cosine similarity if I pass two vectors of the query and the document ?) Thanx in advance 回答1:

cosine similarity on large sparse matrix with numpy

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问题 The code below causes my system to run out of memory before it completes. Can you suggest a more efficient means of computing the cosine similarity on a large matrix, such as the one below? I would like to have the cosine similarity computed for each of the 65000 rows in my original matrix ( mat ) relative to all of the others so that the result is a 65000 x 65000 matrix where each element is the cosine similarity between two rows in the original matrix. import numpy as np from scipy import

How to get item id from cosine similarity matrix?

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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 |[4.7143,1.4286,6....| ........ +-------+--------------------+ Code to compute the cosine similarities: val

Calculating cosine similarity by featurizing the text into vector using tf-idf

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问题 I'm new to Apache Spark, want to find the similar text from a bunch of text, have tried myself as follows - I have 2 RDD- 1st RDD contain incomplete text as follows - [0,541 Suite 204, Redwood City, CA 94063] [1,6649 N Blue Gum St, New Orleans,LA, 70116] [2,#69, Los Angeles, Los Angeles, CA, 90034] [3,98 Connecticut Ave Nw, Chagrin Falls] [4,56 E Morehead Webb, TX, 78045] 2nd RDD contain correct address as follows - [0,541 Jefferson Avenue, Suite 204, Redwood City, CA 94063] [1,6649 N Blue

How can I calculate Cosine similarity between two strings vectors

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问题 I have 2 vectors of dimensions 6 and I would like to have a number between 0 and 1. a=c("HDa","2Pb","2","BxU","BuQ","Bve") b=c("HCK","2Pb","2","09","F","G") Can anyone explain what I should do? 回答1: using the lsa package and the manual for this package # create some files library('lsa') td = tempfile() dir.create(td) write( c("HDa","2Pb","2","BxU","BuQ","Bve"), file=paste(td, "D1", sep="/")) write( c("HCK","2Pb","2","09","F","G"), file=paste(td, "D2", sep="/")) # read files into a document

How to Calculate cosine similarity with tf-idf using Lucene and Java

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I have a query and a set of documents. I need to rank these documents based on the cosine similarity with tf-idf. Can someone please tell me what support I can get from Lucene to compute this ? What parameters I can directly calculate from Lucene (can I get tf, idf directly through some method in lucene?) and how to compute cosine similarity with Lucene (is there any function which directly returns cosine similarity if I pass two vectors of the query and the document ?) Thanx in advance Lucene already uses a pimped version of cosine similarity, so if you need the raw CS itself, it's probably

TfIdfVectorizer: How does the vectorizer with fixed vocab deal with new words?

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问题 I'm working on a corpus of ~100k research papers. I'm considering three fields: plaintext title abstract I used the TfIdfVectorizer to get a TfIdf representation of the plaintext field and feed the thereby originated vocab back into the Vectorizers of title and abstract to assure that all three representations are working on the same vocab. My idea was that since the the plaintext field is much bigger than the other two, it's vocab will most probably cover all the words in the other fields.

SQL Computation of Cosine Similarity

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问题 Suppose you have a table in a database constructed as follows: create table data (v int, base int, w_td float); insert into data values (99,1,4); insert into data values (99,2,3); insert into data values (99,3,4); insert into data values (1234,2,5); insert into data values (1234,3,2); insert into data values (1234,4,3); To be clear select * from data should output: v |base|w_td -------------- 99 |1 |4.0 99 |2 |3.0 99 |3 |4.0 1234|2 |5.0 1234|3 |2.0 1234|4 |3.0 Note that since the vectors are

Parallel Cosine similarity of two large files with each other

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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! [There were mentions of rpud/rpudplus to do it, but can't seem to perform them on cloud resources] N.B.

how can I implement the tf-idf and cosine similarity in Lucene?

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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.IndexWriter; import java.sql.DriverManager; import java.sql.Connection; import java.sql.ResultSet; import