Package: KernelKnn 1.1.5

Lampros Mouselimis

KernelKnn: Kernel k Nearest Neighbors

Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.

Authors:Lampros Mouselimis [aut, cre], Matthew Parks [ctb]

KernelKnn_1.1.5.tar.gz
KernelKnn_1.1.5.tar.gz(r-4.5-noble)KernelKnn_1.1.5.tar.gz(r-4.4-noble)
KernelKnn_1.1.5.tgz(r-4.4-emscripten)KernelKnn_1.1.5.tgz(r-4.3-emscripten)
KernelKnn.pdf |KernelKnn.html✨
KernelKnn/json (API)
NEWS

# Install 'KernelKnn' in R:
install.packages('KernelKnn', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/mlampros/kernelknn/issues0 issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • Boston - Boston Housing Data
  • ionosphere - Johns Hopkins University Ionosphere database

On CRAN:

Conda:r-kernelknn-1.1.5(2025-03-25)

openblascppopenmp

6.22 score 13 packages 9.4k downloads 3 mentions 5 exports 2 dependencies

Last updated 2 years agofrom:1ec0ddd0c1. Checks:1 OK, 2 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 27 2025
R-4.5-linux-x86_64NOTEMar 27 2025
R-4.4-linux-x86_64NOTEMar 27 2025

Exports:distMat.KernelKnndistMat.knn.index.distKernelKnnKernelKnnCVknn.index.dist

Dependencies:RcppRcppArmadillo

binary classification using the ionosphere data

Lampros Mouselimis

Rendered frombinary_classification_using_the_ionosphere_data.Rmdusingknitr::rmarkdownon Mar 27 2025.

Last update: 2017-10-30
Started: 2016-07-09

Image classification of the MNIST and CIFAR-10 data using KernelKnn and HOG (histogram of oriented gradients)

Lampros Mouselimis

Rendered fromimage_classification_using_MNIST_CIFAR_data.Rmdusingknitr::rmarkdownon Mar 27 2025.

Last update: 2016-09-08
Started: 2016-07-09

Regression using the Housing data

Lampros Mouselimis

Rendered fromregression_using_the_housing_data.Rmdusingknitr::rmarkdownon Mar 27 2025.

Last update: 2017-10-30
Started: 2016-07-09

Citation

Please cite the package in your publications:

Mouselimis L (2023). KernelKnn: Kernel k Nearest Neighbors. R package version 1.1.5, https://CRAN.R-project.org/package=KernelKnn.

Corresponding BibTeX entry: 

  @Manual{,
    title = {{KernelKnn}: Kernel k Nearest Neighbors},
    author = {Lampros Mouselimis},
    year = {2023},
    note = {R package version 1.1.5},
    url = {https://CRAN.R-project.org/package=KernelKnn},
  }

Readme and manuals

KernelKnn


The KernelKnn package extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations. More details on the functionality of KernelKnn can be found in the blog-post and in the package Vignettes ( scroll down for information on how to use the docker image ).

To install the package from CRAN use,


install.packages("KernelKnn")

and to download the latest version from Github use the install_github function of the devtools package, 


devtools::install_github('mlampros/KernelKnn')

Use the following link to report bugs/issues,

https://github.com/mlampros/KernelKnn/issues


UPDATE 29-11-2019


Docker images of the KernelKnn package are available to download from my dockerhub account. The images come with Rstudio and the R-development version (latest) installed. The whole process was tested on Ubuntu 18.04. To pull & run the image do the following,



docker pull mlampros/kernelknn:rstudiodev

docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 mlampros/kernelknn:rstudiodev

The user can also bind a home directory / folder to the image to use its files by specifying the -v command,



docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 -v /home/YOUR_DIR:/home/rstudio/YOUR_DIR mlampros/kernelknn:rstudiodev

In the latter case you might have first give permission privileges for write access to YOUR_DIR directory (not necessarily) using,



chmod -R 777 /home/YOUR_DIR

The USER defaults to rstudio but you have to give your PASSWORD of preference (see https://rocker-project.org for more information).


Open your web-browser and depending where the docker image was build / run give,


1st. Option on your personal computer,


http://0.0.0.0:8787

2nd. Option on a cloud instance,


http://Public DNS:8787

to access the Rstudio console in order to give your username and password.


Citation:Citation:

If you use the KernelKnn R package in your paper or research please cite https://CRAN.R-project.org/package=KernelKnn/citation.html:


@Manual{,
  title = {{KernelKnn}: Kernel k Nearest Neighbors},
  author = {Lampros Mouselimis},
  year = {2021},
  note = {R package version 1.1.5},
  url = {https://CRAN.R-project.org/package=KernelKnn},
}