NEWS.md

In KernelKnn: Kernel k Nearest Neighbors

KernelKnn 1.1.5

• I added the order 'p' of the "minkowski" method as a new parameter to the 'KernelKnn()', 'KernelKnnCV()' and 'knn.index.dist()' functions. It defaults to 'k' (see https://github.com/mlampros/KernelKnn/issues/9)
• I added test cases for the 'KernelKnn()' and 'knn.index.dist()' functions

KernelKnn 1.1.4

• The pull request 7 fixed a bug in the checking of the Levels argument (see https://github.com/mlampros/KernelKnn/pull/7)
• I fixed an omission of the column names in case of classification in the KernelKnn() and distMat.KernelKnn() functions (see https://github.com/mlampros/KernelKnn/issues/8)

KernelKnn 1.1.3

• I updated the References section of the switch.ops() function in the utils.R file which explain how the combination of the kernels work
• I added an error case in all functions that make usage of the 'Levels' parameter if the 'Levels' do not match the unique 'y' labels
• I removed the distMat.KernelKnnCV() function (and the tests/test-dist_kernelknnCV.R file) because based on the current implementation of the distMat.KernelKnn() function the TEST_indices parameter must consist of the last indices of the input DIST_mat distance matrix and this is not the case if we run cross-validation (see issue 5)

KernelKnn 1.1.2

• I've fixed an error in the CITATION file

KernelKnn 1.1.1

• I've added the CITATION file in the inst directory

KernelKnn 1.1.0

• I fixed the "failure: the condition has length > 1" CRAN error which appeared mainly due to the misuse of the base class() function in multiple code snippets in the package (for more info on this matter see: https://developer.r-project.org/Blog/public/2019/11/09/when-you-think-class.-think-again/index.html)

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

KernelKnn documentation built on Jan. 7, 2023, 1:18 a.m.