R/RcppExports.R

In RJcluster: A Fast Clustering Algorithm for High Dimensional Data Based on the Gram Matrix Decomposition

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

getIndividualList <- function(classification, temp_index, i) {
    .Call('_RJcluster_getIndividualList', PACKAGE = 'RJcluster', classification, temp_index, i)
}

getCCmatrix_c <- function(classification, temp_index, G) {
    .Call('_RJcluster_getCCmatrix_c', PACKAGE = 'RJcluster', classification, temp_index, G)
}

getMatrixMeans_c <- function(CC, X, d) {
    .Call('_RJcluster_getMatrixMeans_c', PACKAGE = 'RJcluster', CC, X, d)
}

assignGroups_c <- function(N, G, classification, CC) {
    .Call('_RJcluster_assignGroups_c', PACKAGE = 'RJcluster', N, G, classification, CC)
}

getFinalMeans_c <- function(G, Group, X) {
    .Call('_RJcluster_getFinalMeans_c', PACKAGE = 'RJcluster', G, Group, X)
}

get_gamma_labels_c <- function(z, C) {
    .Call('_RJcluster_get_gamma_labels_c', PACKAGE = 'RJcluster', z, C)
}

step_two_c <- function(C, gamma, GG) {
    .Call('_RJcluster_step_two_c', PACKAGE = 'RJcluster', C, gamma, GG)
}

step_three_c <- function(C, gamma, GG, mean_off, mean_diag) {
    .Call('_RJcluster_step_three_c', PACKAGE = 'RJcluster', C, gamma, GG, mean_off, mean_diag)
}

step_four_c <- function(C, gamma, GG, mean_off, mean_diag) {
    .Call('_RJcluster_step_four_c', PACKAGE = 'RJcluster', C, gamma, GG, mean_off, mean_diag)
}

step_five_c <- function(C, gamma, ss_off, ss_diag, labels, sss, ss_bound, N) {
    .Call('_RJcluster_step_five_c', PACKAGE = 'RJcluster', C, gamma, ss_off, ss_diag, labels, sss, ss_bound, N)
}

get_gamma_c <- function(class_values, K) {
    .Call('_RJcluster_get_gamma_c', PACKAGE = 'RJcluster', class_values, K)
}

get_mean_values_c <- function(K, gamma, GG) {
    .Call('_RJcluster_get_mean_values_c', PACKAGE = 'RJcluster', K, gamma, GG)
}

get_mu_c <- function(labels, gamma, GG_new, N, K, mean_off, mean_diag) {
    .Call('_RJcluster_get_mu_c', PACKAGE = 'RJcluster', labels, gamma, GG_new, N, K, mean_off, mean_diag)
}

dmvnrm_arma_old <- function(x, mean, sigma, logd = FALSE) {
    .Call('_RJcluster_dmvnrm_arma_old', PACKAGE = 'RJcluster', x, mean, sigma, logd)
}

dmvnrm_arma <- function(x, mean, sigma, logd = FALSE) {
    .Call('_RJcluster_dmvnrm_arma', PACKAGE = 'RJcluster', x, mean, sigma, logd)
}

dmvnrm_arma_fast <- function(x, mean, sigma, logd = FALSE) {
    .Call('_RJcluster_dmvnrm_arma_fast', PACKAGE = 'RJcluster', x, mean, sigma, logd)
}

loglik_G_c <- function(y, prob, mu, Sigma, C, N) {
    .Call('_RJcluster_loglik_G_c', PACKAGE = 'RJcluster', y, prob, mu, Sigma, C, N)
}

weights_multi_c <- function(x, prob, mu, Sigma, C) {
    .Call('_RJcluster_weights_multi_c', PACKAGE = 'RJcluster', x, prob, mu, Sigma, C)
}

weights_multi_matrix_c <- function(y, prob, mu, Sigma, C) {
    .Call('_RJcluster_weights_multi_matrix_c', PACKAGE = 'RJcluster', y, prob, mu, Sigma, C)
}

tcrossprod_c <- function(X, Y) {
    .Call('_RJcluster_tcrossprod_c', PACKAGE = 'RJcluster', X, Y)
}

scale_c <- function(X, medians = FALSE) {
    .Call('_RJcluster_scale_c', PACKAGE = 'RJcluster', X, medians)
}

rcpparma_hello_world <- function() {
    .Call('_RJcluster_rcpparma_hello_world', PACKAGE = 'RJcluster')
}

rcpparma_outerproduct <- function(x) {
    .Call('_RJcluster_rcpparma_outerproduct', PACKAGE = 'RJcluster', x)
}

rcpparma_innerproduct <- function(x) {
    .Call('_RJcluster_rcpparma_innerproduct', PACKAGE = 'RJcluster', x)
}

rcpparma_bothproducts <- function(x) {
    .Call('_RJcluster_rcpparma_bothproducts', PACKAGE = 'RJcluster', x)
}