R/RcppExports.R

In GMKMcharlie: Unsupervised Gaussian Mixture and Minkowski and Spherical K-Means with Constraints

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

d2s <- function(X, zero = 0, threshold = 1e-16, verbose = TRUE) {
    .Call(`_GMKMcharlie_d2s`, X, zero, threshold, verbose)
}

s2d <- function(X, d, zero = 0, verbose = TRUE) {
    .Call(`_GMKMcharlie_s2d`, X, d, zero, verbose)
}

testGlogdensity <- function(X, mu, sigma, alpha = 1) {
    .Call(`_GMKMcharlie_testGlogdensity`, X, mu, sigma, alpha)
}

findSpreadedMeanWrapper <- function(X, K, maxCore = 7L) {
    .Call(`_GMKMcharlie_findSpreadedMeanWrapper`, X, K, maxCore)
}

makeCovariancesWrapper <- function(X, K) {
    .Call(`_GMKMcharlie_makeCovariancesWrapper`, X, K)
}

paraGmm <- function(X, Xw, G, alpha, mu, sigma, eigenRatioLim, convergenceEPS, alphaEPS, maxIter, tlimit, verbose, maxCore, updateAlpha, updateMean, updateSigma, convergenceTail, embedNoise, checkInitialization) {
    .Call(`_GMKMcharlie_paraGmm`, X, Xw, G, alpha, mu, sigma, eigenRatioLim, convergenceEPS, alphaEPS, maxIter, tlimit, verbose, maxCore, updateAlpha, updateMean, updateSigma, convergenceTail, embedNoise, checkInitialization)
}

paraGmmCW <- function(X, Xw, G, alpha, mu, sigma, eigenRatioLim, convergenceEPS, alphaEPS, maxIter, tlimit, verbose, maxCore) {
    .Call(`_GMKMcharlie_paraGmmCW`, X, Xw, G, alpha, mu, sigma, eigenRatioLim, convergenceEPS, alphaEPS, maxIter, tlimit, verbose, maxCore)
}

paraGmmFJ <- function(X, Xw, G, Gmin, alpha, mu, sigma, eigenRatioLim, convergenceEPS, alphaEPS, maxIter, tlimit, verbose, maxCore) {
    .Call(`_GMKMcharlie_paraGmmFJ`, X, Xw, G, Gmin, alpha, mu, sigma, eigenRatioLim, convergenceEPS, alphaEPS, maxIter, tlimit, verbose, maxCore)
}

KMcpp <- function(X, centroid, Xw, minkP, maxCore, maxIter, verbose) {
    .Call(`_GMKMcharlie_KMcpp`, X, centroid, Xw, minkP, maxCore, maxIter, verbose)
}

KMconstrainedCpp <- function(X, centroids, Xw, clusterWeightUpperBound, minkP, maxCore, convergenceTail, tailConvergedRelaErr, maxIter, paraSortInplaceMerge, verbose) {
    .Call(`_GMKMcharlie_KMconstrainedCpp`, X, centroids, Xw, clusterWeightUpperBound, minkP, maxCore, convergenceTail, tailConvergedRelaErr, maxIter, paraSortInplaceMerge, verbose)
}

KMppIniCpp <- function(X, firstSelection, K, minkP, stochastic, seed, maxCore, verbose) {
    .Call(`_GMKMcharlie_KMppIniCpp`, X, firstSelection, K, minkP, stochastic, seed, maxCore, verbose)
}

KMppIniSparseCpp <- function(X, d, firstSelection, K, minkP, stochastic, seed, maxCore, verbose) {
    .Call(`_GMKMcharlie_KMppIniSparseCpp`, X, d, firstSelection, K, minkP, stochastic, seed, maxCore, verbose)
}

sparseKMcpp <- function(X, d, centroid, Xw, minkP, maxCore, maxIter, verbose) {
    .Call(`_GMKMcharlie_sparseKMcpp`, X, d, centroid, Xw, minkP, maxCore, maxIter, verbose)
}

sparseKMconstrainedCpp <- function(X, d, centroid, Xw, clusterWeightUpperBound, minkP, maxCore, convergenceTail, tailConvergedRelaErr, maxIter, paraSortInplaceMerge, verbose) {
    .Call(`_GMKMcharlie_sparseKMconstrainedCpp`, X, d, centroid, Xw, clusterWeightUpperBound, minkP, maxCore, convergenceTail, tailConvergedRelaErr, maxIter, paraSortInplaceMerge, verbose)
}