nearest_neighbour | Golang implementation of k-NN algorithm | Computer Vision library

by amitkgupta Go Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | nearest_neighbour Summary

nearest_neighbour is a Go library typically used in Artificial Intelligence, Computer Vision, Example Codes applications. nearest_neighbour has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Golang implementation of k-NN algorithm (for k = 1)

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nearest_neighbour has a low active ecosystem.

It has 28 star(s) with 12 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of nearest_neighbour is current.

Quality

nearest_neighbour has no bugs reported.

Security

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

License

nearest_neighbour 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.

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

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Run all the valid features
parseCSVFile parses a CSV file
NewLabelWithFeatures creates a LabelWithFeatures from a raw byte slice .
classify returns the classification of features .
Square distance between two features
Returns the square distance between two vectors .
byteSliceTofloat64 converts a byte slice to float64
Converts byte slice to float32

Get all kandi verified functions for this library.

nearest_neighbour Key Features

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nearest_neighbour Examples and Code Snippets

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Community Discussions

Trending Discussions on nearest_neighbour

I can't seem to downscale pixelart to single pixels without antialiasing

calculating average distance of nearest neighbours in pandas dataframe

QUESTION

I can't seem to downscale pixelart to single pixels without antialiasing

Asked 2019-Apr-25 at 20:00

I am trying to downscale an pixelart image (of the game stardew valley) consisting of 4x4 pixels per block of the same color to the same image with 1x1 pixel per block.

Photoshop is doing a great job when I just resize it with the NEAREST_NEIGHBOUR interpolation.

But when I use the technique from: How to scale a BufferedImage but with TYPE_NEAREST_NEIGHBOR instead it gets all destorted.

What is going wrong and how should I go about fixing it?

...

ANSWER

Answered 2019-Apr-25 at 20:00

Cris Luengo found the solution, I should have used .png instead of jpg.

This is the working code:

Source https://stackoverflow.com/questions/55853234

QUESTION

calculating average distance of nearest neighbours in pandas dataframe

Asked 2018-Jul-16 at 17:34

I have a set of objects and their positions over time. I would like to get the distance between each car and their nearest neighbour, and calculate an average of this for each time point. An example dataframe is as follows:

...

ANSWER

Answered 2018-Jul-16 at 17:34

It might be a bit overkill but you could use nearest neighbors from scikit

An example:

Source https://stackoverflow.com/questions/51305370

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

Vulnerabilities

No vulnerabilities reported

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You can download it from GitHub.

Support

Tell me if I should use special compiler flags to improve performance for some of the languages.Tell my why this experiment is invalid.Improve naive implementations without changing the spirit of the algorithm (e.g. use eager evaluation in Haskell).Add optimized implementations of k-NN which improve performance at no cost to accuracy.Add implementations for other languages (with compilation and/or run instructions).

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