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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.


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.

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

News

KernelKnn 1.0.9

I added a test case to check equality of the results between KernelKnnCV and distMat.KernelKnnCV functions

KernelKnn 1.0.8

I added the DARMA_64BIT_WORD flag in the Makevars file to allow the package processing big datasets

KernelKnn 1.0.7

I modified the input_dist_mat function of the distance_metrics.cpp file due to a bug. I modified the distMat.KernelKnn function so that it does not return an error if the rows of the DIST_mat distance matrix is not equal to the length of y (added comments in the function documentation).

KernelKnn 1.0.6

In this version the following functions/parameters were added:

  • seed_num : parameter in KernelKnnCV and distMat.KernelKnnCV cross-validation functions, which specifies the seed of R's random number generator
  • distMat.KernelKnn : this function performs kernel k-nearest-neighbor search by using a distance matrix as input
  • distMat.knn.index.dist : this function returns the indices and distances for k-nearest neighbors using a distance matrix
  • distMat.KernelKnnCV : this function performs cross-validated kernel k-nearest-neighbor search using a distance matrix as input

I also modified the OpenMP clauses of the .cpp file to address the ASAN errors.

KernelKnn 1.0.5

I removed OpenImageR and irlba as package dependencies. I also added an init.c file in the src folder due to a change in CRAN submissions for compiled code [ references : http://stackoverflow.com/questions/42313373/r-cmd-check-note-found-no-calls-to-r-registerroutines-r-usedynamicsymbols, https://github.com/RcppCore/Rcpp/issues/636 ]

KernelKnn 1.0.4

I added a try-catch Rcpp function to make possible the calculation of singular covariance matrices as sugggested in https://github.com/mlampros/KernelKnn/issues/1

KernelKnn 1.0.3

Reimplementation of the Rcpp function due to ASAN-memory-errors

KernelKnn 1.0.2

I updated the Description file with a URL and a BugReports web-address.

KernelKnn 1.0.1

Currently, Software platforms like OSX do not support openMP, thus I've made openMP optional for all cpp functions.

KernelKnn 1.0.0

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.
install.packages("KernelKnn")

1.1.4 by Lampros Mouselimis, 3 months ago

https://github.com/mlampros/KernelKnn

Report a bug at https://github.com/mlampros/KernelKnn/issues

Browse source code at https://github.com/cran/KernelKnn

Authors: Lampros Mouselimis [aut, cre] , Matthew Parks [ctb] (Github Contributor)

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports Rcpp

Suggests testthat, covr, knitr, rmarkdown

Linking to Rcpp, RcppArmadillo

System requirements: libarmadillo: apt-get install -y libarmadillo-dev (deb), libblas: apt-get install -y libblas-dev (deb), liblapack: apt-get install -y liblapack-dev (deb), libarpack++2: apt-get install -y libarpack++2-dev (deb), gfortran: apt-get install -y gfortran (deb)

Imported by RaSEn, imbalance, nmslibR.

Depended on by elmNNRcpp.

Suggested by SuperLearner.

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