bneighbors | Nearest Neighbor Search in High Dimensional Spaces | Machine Learning library

by waylonflinn Python Version: Current License: MIT

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kandi X-RAY | bneighbors Summary

bneighbors is a Python library typically used in Artificial Intelligence, Machine Learning applications. bneighbors has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However bneighbors build file is not available. You can download it from GitHub.

Find exact nearest neighbors in relatively high dimensional spaces. Supports in-memory and out-of-core data sets (via bcolz and bvec). Gives realtime performance in 20-100 dimensional feature spaces, over hundreds of thousands of items. Includes the following similarity measures. The generalized similarity measure is based on an alternate normalization of cosine similarity, and includes both cosine similarity and lift as special cases.

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bneighbors has a low active ecosystem.

It has 12 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

bneighbors has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of bneighbors is current.

Quality

bneighbors has no bugs reported.

Security

bneighbors has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

bneighbors is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

bneighbors releases are not available. You will need to build from source code and install.

bneighbors has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Return the neighbors of source_id
Calculate the similarity between two vectors
Computes the similarity between the given dots
Calculates the jaccard similarity between dots
Calculate the cosine similarity between the source norm

Get all kandi verified functions for this library.

bneighbors Key Features

No Key Features are available at this moment for bneighbors.

bneighbors Examples and Code Snippets

No Code Snippets are available at this moment for bneighbors.

Community Discussions

Trending Discussions on bneighbors

Spark,Graphx program does not utilize cpu and memory

QUESTION

Spark,Graphx program does not utilize cpu and memory

Asked 2017-Jan-30 at 16:42

I have a function that takes the neighbors of a node ,for the neighbors i use broadcast variable and the id of the node itself and it calculates the closeness centrality for that node.I map each node of the graph with the result of that function.When i open the task manager the cpu is not utilized at all as if it is not working in parallel , the same goes for memory , but the every node executes the function in parallel and also the data is large and it takes time to complete ,its not like it does not need the resources.Every help is truly appreciated , thank you. For loading the graph i use val graph = GraphLoader.edgeListFile(sc, path).cache

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ANSWER

Answered 2017-Jan-30 at 16:42

To provide somewhat of an answer to your original question, I suspect that your RDD only has a single partition, thus using a single core to process.

The edgeListFile method has an argument to specify the minimum number of partitions you want. Also, you can use repartition to get more partitions.

You mentionned coalesce but that only reduces the number of partitions by default, see this question : Spark Coalesce More Partitions

Source https://stackoverflow.com/questions/41940394

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

Vulnerabilities

No vulnerabilities reported

Install bneighbors

You can download it from GitHub.
You can use bneighbors 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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