A common approach (at least common to me) is to divide your hash bit string in several chunks and query on these chunks for an exact match. This is a “pre-filter” step. You then can perform a bitwise hamming distance computation on the returned results which should be only a smaller subset of your overall … Read more
Use pairwise_distances to calculate the distance and subtract that distance from 1 to find the similarity score: from sklearn.metrics.pairwise import pairwise_distances 1 – pairwise_distances(df.T.to_numpy(), metric=”jaccard”) Explanation: In newer versions of scikit learn, the definition of jaccard_score is similar to the Jaccard similarity coefficient definition in Wikipedia: where M11 represents the total number of attributes where … Read more
You might want to check out the Levenshtein Distance algorithm as a starting point. It will rate the “distance” between two words. This SO thread on implementing a Google-style “Do you mean…?” system may provide some ideas as well.
As Julia points out Sujit Pal’s example is very useful but the Lucene 4 API has substantial changes. Here is a version rewritten for Lucene 4. import java.io.IOException; import java.util.*; import org.apache.commons.math3.linear.*; import org.apache.lucene.analysis.Analyzer; import org.apache.lucene.analysis.core.SimpleAnalyzer; import org.apache.lucene.document.*; import org.apache.lucene.document.Field.Store; import org.apache.lucene.index.*; import org.apache.lucene.store.*; import org.apache.lucene.util.*; public class CosineDocumentSimilarity { public static final String CONTENT … Read more
Approach #1: With NumPy broadcasting, we can look for absolute element-wise subtractions between the input arrays and use an appropriate threshold to filter out unwanted elements from A. It seems for the given sample inputs, a threshold of 90 works. Thus, we would have an implementation, like so – thresh = 90 Aout = A[(np.abs(A[:,None] … Read more
I figured it out on my own. I have stored my own implementation of DefaultSimilarity under /dist/ folder in solr. Then i add <lib dir=”../../../dist/org/apache/lucene/search/similarities/” regex=”.*\.jar”/> to my solrconfig.xml and everything works fine. package org.apache.lucene.search.similarities; import org.apache.lucene.index.FieldInvertState; import org.apache.lucene.search.similarities.DefaultSimilarity; public class MyNewSimilarityClass extends DefaultSimilarity { @Override public float coord(int overlap, int maxOverlap) { return 1.0f; … Read more
Here is a ready-to-use example test.py: import sqlite3 db = sqlite3.connect(‘:memory:’) db.enable_load_extension(True) db.load_extension(‘./spellfix’) # for Linux #db.load_extension(‘./spellfix.dll’) # <– UNCOMMENT HERE FOR WINDOWS db.enable_load_extension(False) c = db.cursor() c.execute(‘CREATE TABLE mytable (id integer, description text)’) c.execute(‘INSERT INTO mytable VALUES (1, “hello world, guys”)’) c.execute(‘INSERT INTO mytable VALUES (2, “hello there everybody”)’) c.execute(‘SELECT * FROM mytable WHERE … Read more
Edit distance isn’t a likely candidate, as it would be spelling/word-order dependent, and much more computationally expensive than Will is leading you to believe, considering the size and number of the documents you’d actually be interested in searching. Something like Lucene is the way to go. You index all your documents, and then when you … Read more
These sort of questions come up all the time (for me–and as evidenced by the r-tagged SO question list–others as well): is there a function, either in R core or in any R Package, that does x? and if so, where can i find it among the +2000 R Packages in CRAN? short answer: give … Read more
You could take a look at two answers on SO itself: this one is about image comparison itself, offering links to stuff in C++ (if I read correctly) while this one offers links to broader approaches, one being in C. I would suggest starting with the second link since there’s links on that discussion that’ll … Read more