I presume that the Simhash "String". Now, when I try to compute distance between "aaaa" and "aaas" using the width 2, it gives out the distance as 0. Then Simhash calculates these list as unweighted features which means the default weight of each feature is 1 and output like:.
The calculation process can be divied into 4 steps: 1 hash each splitted word featureto transform string into binary numbers; 2 weight them; 3 assumble weighted bits together; 4 change the assumbled number into binary and output as the value.
Learn more. Hamming distance Simhash python giving out unexpected value Ask Question. Asked 4 years, 2 months ago. Active 3 years, 6 months ago. Viewed 2k times. Ishan Sharma Ishan Sharma 9 9 bronze badges. Active Oldest Votes. The reason is from simhash algorithm itself. Now, in your case, the step 3 will output like: [-3, 3, -3, -3, 3, -3, -3, -3, 3, -3, -3, 3, -3, -3, 3, -3, -3, 3, -3, 3, 3, 3, 3, -3, -3, -3, -3, -3, -3, 3, -3, -3, -3, 3, -3, 3, 3, 3, -3, 3, -3, -3, 3, -3, -3, 3, -3, -3, 3, 3, 3, 3, -3, 3, 3, -3, -3, -3, -3, 3, -3, -3, -3, -3] [-1, 3, -3, -1, 3, -3, -3, -1, 3, -3, -3, 1, -1, -1, 1, -3, -3, 3, -1, 3, 1, 3, 1, -3, -1, -3, -3, -1, -1, 3, -1, -1, -1, 3, -1, 1, 3, 1, -1, 1, -3, -3, 1, -1, -3, 3, -3, -1, 1, 3, 3, 3, -3, 3, 3, -3, -1, -1, -1, 1, -3, -3, -3, -1] And after step 4, the 2 output the same value.
Your case shows the limitation of simhash with short length text. Matthew Bao Matthew Bao 41 4 4 bronze badges. Sign up or log in Sign up using Google.
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Now, I have replied to your question on the Github issue that you raised here. For reference though, here is some sample code you can use to print the final near duplicate documents after hashing them.
Simhash only allows quite small hamming distances to be detected, up to around 6 or 7 bits difference, at a stretch, depending on the size of your corpus. You should instead look into minhash.
If you're interested, here's an extensive explanation of simhash. Learn more. How to compare the similarity of documents with Simhash algorithm? Ask Question. Asked 2 years, 6 months ago. Active 1 year, 2 months ago.
Viewed 4k times. Can someone help? Dany M. Dany M Dany M 5 5 silver badges 17 17 bronze badges. Active Oldest Votes. Before I answer your question, it is important to keep in mind: Simhash is useful as it detects near duplicates.
This means that near duplicates will end up with the same hash.
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Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. SimHash uses cosine similarity over real-valued data. MinHash calculates resemblance similarity over binary vectors. But I can't decide which one would be better to use. I am creating a backend system for a website to find near duplicates of semi-structured text data.
Specific language implementation aside, which algorithm would be best for a greenfield production system? Simhash is faster very fast and typically requires less storage, but imposes a strict limitation on how dissimilar two documents can be and still be detected as duplicates. If you are using a bit simhash a common choiceand depending on how many permuted tables you are capable of storing, you might be limited to hamming distances of as low as 3 or possibly as high as 6 or 7.
Those are small hamming distances! You'll be limited to detecting documents that are mostly identical, and even then you may need to do some careful tuning of what features you choose to go into the simhash and what weightings you give to them.
The generation of simhashes is patented by google, though in practice they seem to allow at least non-commercial use.
Minhash uses more memory, since you'd be typically storing hashes per document, and it's not as CPU-efficient as simhash, but it allows you to find quite distant similarities, e. It's also a bit easier to understand than simhash, particularly in terms of how the tables work.
It's quite straightforward to implement, typically using shingling, and doesn't need a lot of tuning to get good results. It's not to my knowledge patented. If you're dealing with big data, the most CPU-intensive part of the minhash approach will likely be after you've generated the minhashes for your document, when you're hunting through your table to find other documents that share some of its hashes.
There may be tens or hundreds of thousands of documents that share at least one hash with it, and you've got to weed through all of these to find those few that share e. Simhash is a lot quicker here.
As Otmar points out in his comment below, there are optimizations of minhash that allow you to achieve the same precision on your similarity estimates with fewer hashes per document.
This can substantially reduce the amount of weeding you have to do. I have now tried superminhash. It's fairly fast, though my implementation of minhash using a single hash function plus bit-transformations to produce all the other hashes was faster for my purposes. This should mean you need about a third fewer hashes to achieve the same accuracy.
Storing fewer hashes in your table means less "weeding" is needed to identify near duplicates, which delivers a significant speed-up. I'm not aware of any patent on superminhash. Thanks Otmar!Released: Aug 20, View statistics for this project via Libraries. Tags simhash. Aug 20, May 1, Apr 25, Apr 23, Aug 24, Aug 18, Dec 13, Jul 28, Jul 9, Jul 8, Mar 28, Mar 10, Jun 11, Jun 5, May 27, May 7, Feb 18, Feb 13, Feb 10, Feb 7, Jan 6, When it comes to figuring out how similar various pieces of data are from one another and which is the closest matching one in a large group of candidatessimhashing is one of my favourite algorithms.
It's somewhat simple, brilliant in its approach, but still not obvious enough for most people myself included to come up with it on their own. Readers may be familiar with hashing algorithms such as MD5 or SHA, which aim to very quickly create a unique signature hash of the data. These functions are built so that identical files or blobs of data share the same hash, so you can rapidly see whether two blobs are identical or not, or if a blob still has the same signature after transmission to see if it was corrupted or not.
Then different blobs, even if mostly the same, get an entirely different signature. While simhashes still aim to have unique signatures for documents, they also attempt to make sure that documents that look the same get very similar hashes. That way, you can look for similar hashes to figure out if the documents are closely related, without needing to compare them bit by bit.
It's a statistical tool to help us find near-duplicates faster. That's a bit vague, but that's alright. I'll try to explain things in a way that gives a good understanding of things. All letters of the basic English alphabet can be represented that way:. This is nothing new to most programmers out there. I could also represent entire words with sequences of these binary values:.
We can easily see that the first 3 most significant bits on the left of all letters are the same, and we could discard them, getting:. If I were to ask you to group these words in two pairs of most similar to each other, chances are that you would put 1 and 3 together, and 2 and 4 together.
You would be right to do so. The four words, in order, are "banana", "bozo", "cabana", and "ozone".
By looking at the binary patterns, our brains are able to figure out a few things naturally. The position of each bit, the length of the word, the repetition of sequences of bits, and how 'light' or 'dark' a column looks are a few things we may pick up as some kind of signature of each word.
What's interesting is that each of these signatures is unique, yet we're somehow able to figure out how similar they are based on that. Compare this to the following four MD5 hashes of the same strings:. There's no way to know at a glance which is closer to the other. Again, there's nothing surprising there, given MD5 is not meant for hashes to be similar.
While our visual layout was nice to figure out how to match short words together, it's utterly impractical to use such visual signatures in a general manner -- programming software to recognize them the way a human would would take forever, and it would be slower than just comparing letters one by one. What we have, though, is the idea that using the binary representation of a blob of data may be enough to create a decent signature that does respect similarity -- a simhash.
What could be nice would be to be able to compress the information a bit, so it's a bit more obvious what it contains. We could build a histogram of all the binary values: add 1 for each bit set to 1 in a column, the rest is skipped. This could give us an interesting layout, a bit like follows I'm making bars horizontal :.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Work fast with our official CLI.
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This is an efficient implementation of some functions that are useful for implementing near duplicate detection based on Charikar's simhash. It is a python module, written in C with GCC extentions, and includes the following functions:. This code was used from mapreduce jobs against a large dataset of webpages as part of a prototype at Scrapinghub.
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Git stats 5 commits. Failed to load latest commit information. View code. It includes arguments for rotating and grouping hashes.Information about the time in milliseconds consumed in each step of the execution. Example: 1 Default arguments for individual resources or any to apply the argument to all resources.
For more information, see the Configurations below. Example: "This is a description of my new configuration" name optional The name you want to give to the new configuration. Example: "my new configuration" tags optional A list of strings that help classify and index your configuration. This will be 201 upon successful creation of the configuration and 200 afterwards.Hash Tables and Hash Functions
For more information, see the Configurations above. This is the date and time in which the configuration was created with microsecond precision. True when the configuration has been created in the development mode. In a future version, you will be able to share configurations with other co-workers. A description of the status of the configuration. This is the date and time in which the configuration was updated with microsecond precision.
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