Locality-sensitive-hashing | min-hash and p-stable hash | Hashing library

by guoziqingbupt Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | Locality-sensitive-hashing Summary

Locality-sensitive-hashing is a Python library typically used in Security, Hashing, Example Codes applications. Locality-sensitive-hashing has no bugs, it has no vulnerabilities and it has low support. However Locality-sensitive-hashing build file is not available. You can download it from GitHub.

min-hash and p-stable hash

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Locality-sensitive-hashing has a low active ecosystem.

It has 73 star(s) with 63 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Locality-sensitive-hashing is current.

Quality

Locality-sensitive-hashing has 0 bugs and 0 code smells.

Security

Locality-sensitive-hashing has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Locality-sensitive-hashing code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Locality-sensitive-hashing does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Locality-sensitive-hashing releases are not available. You will need to build from source code and install.

Locality-sensitive-hashing has no build file. You will be need to create the build yourself to build the component from source.

Locality-sensitive-hashing saves you 53 person hours of effort in developing the same functionality from scratch.

It has 139 lines of code, 13 functions and 5 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Locality-sensitive-hashing and discovered the below as its top functions. This is intended to give you an instant insight into Locality-sensitive-hashing implemented functionality, and help decide if they suit your requirements.

Searches the nn
Computes the fingerprint of the data set
Computes the minimum hash value of a matrix
Generate the signature for a matrix
Generate the hash values for a given family
Generate n - grams of a matrix
Generate a para
Generate a list of phonon families
Compute the K2 K2 hash function

Get all kandi verified functions for this library.

Locality-sensitive-hashing Key Features

No Key Features are available at this moment for Locality-sensitive-hashing.

Locality-sensitive-hashing Examples and Code Snippets

No Code Snippets are available at this moment for Locality-sensitive-hashing.

Community Discussions

Trending Discussions on Locality-sensitive-hashing

Locality Sensitive Hashing in Spark for single DataFrame

Matching millions of people: k-d tree or locality-sensitive hashing?

Apache spark text similarity

QUESTION

Locality Sensitive Hashing in Spark for single DataFrame

Asked 2020-Feb-04 at 10:32

I've read the Spark section on Locality Sensitive Hashing and still don't understand some of it:

https://spark.apache.org/docs/latest/ml-features.html#locality-sensitive-hashing

And there's Bucketed Random Projection example for two DataFrames. I have one simple, spatial Dataset of points, like:

(Of course later I will have millions of points) and DataFrame looks like:

...

ANSWER

Answered 2020-Feb-03 at 14:50

The BucketedRandomProjectionLSH do exactly what you need. The result hash for each point could be a group value. The only problem is to select proper radius, that will set the size of each bucket. Use .setBucketLength(0.02) to set the radius. The other small problem is extract the hash from the vector to the column. I use this method: Spark Scala: How to convert Dataframe[vector] to DataFrame[f1:Double, ..., fn: Double)]

Example with your Data

Source https://stackoverflow.com/questions/60038033

QUESTION

Matching millions of people: k-d tree or locality-sensitive hashing?

Asked 2018-Jul-11 at 20:32

I am looking for a performant algorithm to match a large number of people by location, gender and age according to this data structure:

Longitude (denotes the persons location)
Latitude (denotes the persons location)
Gender (denotes the persons gender)
Birthdate (denotes the persons birthdate)
LookingForGender (denotes what the gender the person is looking for)
LookingForMinAge (denotes what minimum age the person is looking for)
LookingForMaxAge (denotes what maximum age the person is looking for)
LookingForRadius (denotes what maximum distance the person is looking for)
Processed (denotes which other persons this person has already processed)

For any person P the algorithm should return candidates C for which applies:

Gender of C must be equal P.LookingForGender
Gender of P must be equal C.LookingForGender
Birthdate of C must be between P.LookingForMinAge and P.LookingForMaxAge
Birthdate of P must be between C.LookingForMinAge and C.LookingForMaxAge
Lat/Long distance between P and C must be smaller or equal P.LookingForRadius
Lat/Long distance between P and C must be smaller or equal C.LookingForRadius
Processed of P must not contain C

The algorithm should return the first 100 candidates C in order of distance (Lat/Long). The algorithm should be optimized for both search and updates because people may change their location often.

My current thinking is that k-d tree could be more suitable than locality-sensitive-hashing for these needs and that I should go into this direction.

What would be your advise for me? What should I look for? What risks do you see?

Thanks!

Update:

Do I prefer to sacrifice space complexity for better time complexity? Yes I prefer to sacrifice space complexity. However I prefer to have a O(log n) solution that I actually understand and can maintain rather than a O(1) solution that I cannot grasp :)
Does the data fit into main memory? No it does not. The data will be distributed across different nodes of a distributed document database (Azure Cosmos DB SQL API).
Do you want exact results or are approximate results? Approximate results are OK however age/gender should be filtered exact.
Added "Processed" to the algorithm, sorry for having missed that!
How often do people change their location? Users will change their location whenever they start the app and look for candidates. Daily active users will therefore change their location one or multiple times a day. A location change may however be minor so just a few kilometers. From 100 app downloads, 15 users will use the app once or more a a month, and 3 users will use it once or more daily.

...

ANSWER

Answered 2018-Jul-11 at 20:32

Here is some info by Microsoft how to use their spatial indexing ('spatial' is the keyword you want to search for).

The query you are looking for is a k-nearest neighbor query (kNN Search) with k=100.

If you want to serialize the index yourself, have a look at R+tree or R*trees, they are quite good for page based serialization. There are lots of open source example for these trees. Here is my own implementation in Java, unfortunately it does not support Serialization.

About the other indexes:

I have no experience with LHS, so it can't say much about it. One thing I know though, since it's internally a HashMap, you need to take special care to make it scalable with large amounts of data. This definitely increases complexity. Another problem, I'm not sure LSH is good for kNN search, you will have to look that up.
KD-trees are very simply and should to the job, but are bad for serialization and can have large memory overhead unless you implement a version that can have more than one entry in each node. KD-Trees can also degenerate when updated often, so they may need rebalancing.
Otherwise I would suggest quadtrees, for example the qthypercube2. They are also quite simple, very fast in memory, and very well suited for frequent updates, especially if the entries move only a small distance.

Source https://stackoverflow.com/questions/51280479

QUESTION

Apache spark text similarity

Asked 2018-Feb-10 at 15:59

I am trying the below example in java

Efficient string matching in Apache Spark

This is my code

...

ANSWER

Answered 2017-Dec-06 at 11:00

I have a few suggestions:

If you use NGrams consider more granular tokenizer. The goal here is to correct for misspellings:

Source https://stackoverflow.com/questions/47618661

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Locality-sensitive-hashing

You can download it from GitHub.
You can use Locality-sensitive-hashing like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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