R/RcppExports.R

In matrixprofiler: Matrix Profile for R

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

contrast_profile_rcpp <- function(negative_data, positive_data, window_size, positive_matrix, ez, s_size, n_workers, euclidean, progress) {
    .Call(`_matrixprofiler_contrast_profile_rcpp`, negative_data, positive_data, window_size, positive_matrix, ez, s_size, n_workers, euclidean, progress)
}

mass_weighted_rcpp <- function(data_fft, query_window, data_size, window_size, data_mean, data_sd, query_mean, query_sd, data_pre, weight, normalized, n_workers = 1L) {
    .Call(`_matrixprofiler_mass_weighted_rcpp`, data_fft, query_window, data_size, window_size, data_mean, data_sd, query_mean, query_sd, data_pre, weight, normalized, n_workers)
}

mass_absolute_rcpp <- function(data_fft, query_window, data_size, window_size, sumx2, sumy2, n_workers = 1L) {
    .Call(`_matrixprofiler_mass_absolute_rcpp`, data_fft, query_window, data_size, window_size, sumx2, sumy2, n_workers)
}

mass2_rcpp <- function(data_fft, query_window, data_size, window_size, data_mean, data_sd, query_mean, query_sd, n_workers = 1L) {
    .Call(`_matrixprofiler_mass2_rcpp`, data_fft, query_window, data_size, window_size, data_mean, data_sd, query_mean, query_sd, n_workers)
}

mass3_rcpp <- function(query_window, data_ref, data_size, window_size, data_mean, data_sd, query_mean, query_sd, grain) {
    .Call(`_matrixprofiler_mass3_rcpp`, query_window, data_ref, data_size, window_size, data_mean, data_sd, query_mean, query_sd, grain)
}

mass3_rcpp_parallel <- function(query_window, data_ref, data_size, window_size, data_mean, data_sd, query_mean, query_sd, grain, n_workers) {
    .Call(`_matrixprofiler_mass3_rcpp_parallel`, query_window, data_ref, data_size, window_size, data_mean, data_sd, query_mean, query_sd, grain, n_workers)
}

set_k_rcpp <- function(grain, data_size, window_size) {
    .Call(`_matrixprofiler_set_k_rcpp`, grain, data_size, window_size)
}

find_best_k_rcpp <- function(data_ref, query_ref, window_size) {
    .Call(`_matrixprofiler_find_best_k_rcpp`, data_ref, query_ref, window_size)
}

mass_pre_rcpp <- function(data_ref, query_ref, window_size) {
    .Call(`_matrixprofiler_mass_pre_rcpp`, data_ref, query_ref, window_size)
}

mass_pre_abs_rcpp <- function(data_ref, query_ref, window_size) {
    .Call(`_matrixprofiler_mass_pre_abs_rcpp`, data_ref, query_ref, window_size)
}

mass_pre_weighted_rcpp <- function(data_ref, query_ref, window_size, weight) {
    .Call(`_matrixprofiler_mass_pre_weighted_rcpp`, data_ref, query_ref, window_size, weight)
}

std_rcpp <- function(data, na_rm = FALSE) {
    .Call(`_matrixprofiler_std_rcpp`, data, na_rm)
}

list_to_matrix <- function(x) {
    .Call(`_matrixprofiler_list_to_matrix`, x)
}

mode_rcpp <- function(x) {
    .Call(`_matrixprofiler_mode_rcpp`, x)
}

znorm_rcpp <- function(data) {
    .Call(`_matrixprofiler_znorm_rcpp`, data)
}

normalize_rcpp <- function(data, min, max) {
    .Call(`_matrixprofiler_normalize_rcpp`, data, min, max)
}

binary_split_rcpp <- function(n) {
    .Call(`_matrixprofiler_binary_split_rcpp`, n)
}

ed_corr_rcpp <- function(data, window_size) {
    .Call(`_matrixprofiler_ed_corr_rcpp`, data, window_size)
}

corr_ed_rcpp <- function(data, window_size) {
    .Call(`_matrixprofiler_corr_ed_rcpp`, data, window_size)
}

inner_product <- function(a, b) {
    .Call(`_matrixprofiler_inner_product`, a, b)
}

sum_of_squares <- function(a) {
    .Call(`_matrixprofiler_sum_of_squares`, a)
}

fft_rcpp <- function(z, invert) {
    .Call(`_matrixprofiler_fft_rcpp`, z, invert)
}

mpdist_simple_rcpp <- function(ref_data, query_data, window_size, thr = 0.05, n_workers = 1L) {
    .Call(`_matrixprofiler_mpdist_simple_rcpp`, ref_data, query_data, window_size, thr, n_workers)
}

mpdist_vect_rcpp <- function(data, query, window_size, thr = 0.05, n_workers = 1L) {
    .Call(`_matrixprofiler_mpdist_vect_rcpp`, data, query, window_size, thr, n_workers)
}

mpxis_rcpp <- function(data_ref, batch_size, object, stats, history, mp_time_constraint, progress, threshold) {
    .Call(`_matrixprofiler_mpxis_rcpp`, data_ref, batch_size, object, stats, history, mp_time_constraint, progress, threshold)
}

mpxi_rcpp <- function(new_data, object, history, mp_time_constraint, progress) {
    .Call(`_matrixprofiler_mpxi_rcpp`, new_data, object, history, mp_time_constraint, progress)
}

mpx_na_rcpp <- function(data_ref, window_size, ez, s_size, idxs, euclidean, progress) {
    .Call(`_matrixprofiler_mpx_na_rcpp`, data_ref, window_size, ez, s_size, idxs, euclidean, progress)
}

mpx_rcpp <- function(data_ref, window_size, ez, s_size, idxs, euclidean, progress, wild_sigma_threshold) {
    .Call(`_matrixprofiler_mpx_rcpp`, data_ref, window_size, ez, s_size, idxs, euclidean, progress, wild_sigma_threshold)
}

mpxab_na_rcpp <- function(data_ref, query_ref, window_size, s_size, idxs, euclidean, progress) {
    .Call(`_matrixprofiler_mpxab_na_rcpp`, data_ref, query_ref, window_size, s_size, idxs, euclidean, progress)
}

mpxab_rcpp <- function(data_ref, query_ref, window_size, s_size, idxs, euclidean, progress) {
    .Call(`_matrixprofiler_mpxab_rcpp`, data_ref, query_ref, window_size, s_size, idxs, euclidean, progress)
}

mpx_gpu_rcpp <- function(data_ref, window_size, ez, idxs, euclidean) {
    .Call(`_matrixprofiler_mpx_gpu_rcpp`, data_ref, window_size, ez, idxs, euclidean)
}

mpx_na_gpu_rcpp <- function(data_ref, window_size, ez, idxs, euclidean) {
    .Call(`_matrixprofiler_mpx_na_gpu_rcpp`, data_ref, window_size, ez, idxs, euclidean)
}

mpxab_na_gpu_rcpp <- function(data_ref, query_ref, window_size, idxs, euclidean) {
    .Call(`_matrixprofiler_mpxab_na_gpu_rcpp`, data_ref, query_ref, window_size, idxs, euclidean)
}

mpxab_gpu_rcpp <- function(data_ref, query_ref, window_size, idxs, euclidean) {
    .Call(`_matrixprofiler_mpxab_gpu_rcpp`, data_ref, query_ref, window_size, idxs, euclidean)
}

#' Check if package was compiled with CUDA support
#'
#' @description
#' Queries whether `matrixprofiler` was compiled with NVIDIA CUDA GPU acceleration
#' (`MATRIXPROFILER_ENABLE_CUDA=1`).
#'
#' @details
#' By default, `matrixprofiler` is compiled without CUDA to maintain portability and
#' compliance with CRAN policies, running all algorithms on multi-threaded CPU engines
#' powered by `RcppThread`.
#'
#' If an NVIDIA GPU and the CUDA Toolkit are available on the host system, the package
#' can be recompiled with CUDA acceleration for supported algorithms (such as \code{\link{rfcp}}
#' and \code{\link{mpx}}) using the helper script \code{inst/cuda/build_cuda_rcpp.sh}
#' or by setting the environment variables:
#' \itemize{
#'   \item \code{MATRIXPROFILER_ENABLE_CUDA=1}
#'   \item \code{MATRIXPROFILER_CUDA_OBJECT} pointing to precompiled CUDA object files (\code{.o})
#'   \item \code{MATRIXPROFILER_CUDA_LIBS} pointing to the CUDA runtime library (e.g. \code{-lcudart})
#' }
#'
#' When `has_cuda()` returns `FALSE`, attempting to execute an algorithm with
#' `backend = "cuda"` will stop with an informative error instructing how to enable CUDA.
#'
#' @return A logical value: `TRUE` if CUDA support was enabled at compile time, `FALSE` otherwise.
#' @export
#' @examples
#' has_cuda()
has_cuda <- function() {
    .Call(`_matrixprofiler_has_cuda`)
}

mpx_rcpp_parallel <- function(data_ref, window_size, ez = 0.5, s_size = 1.0, idxs = TRUE, euclidean = TRUE, progress = FALSE, n_workers = 1L) {
    .Call(`_matrixprofiler_mpx_rcpp_parallel`, data_ref, window_size, ez, s_size, idxs, euclidean, progress, n_workers)
}

mpx_na_rcpp_parallel <- function(data_ref, window_size, ez = 0.5, s_size = 1.0, idxs = TRUE, euclidean = TRUE, progress = FALSE, n_workers = 1L) {
    .Call(`_matrixprofiler_mpx_na_rcpp_parallel`, data_ref, window_size, ez, s_size, idxs, euclidean, progress, n_workers)
}

mpxab_rcpp_parallel <- function(data_ref, query_ref, window_size, s_size = 1.0, idxs = TRUE, euclidean = TRUE, progress = FALSE, n_workers = 1L) {
    .Call(`_matrixprofiler_mpxab_rcpp_parallel`, data_ref, query_ref, window_size, s_size, idxs, euclidean, progress, n_workers)
}

mpxab_na_segmented_native_rcpp_parallel <- function(data_ref, query_ref, window_size, s_size = 1.0, idxs = TRUE, euclidean = TRUE, progress = FALSE, n_workers = 1L) {
    .Call(`_matrixprofiler_mpxab_na_segmented_native_rcpp_parallel`, data_ref, query_ref, window_size, s_size, idxs, euclidean, progress, n_workers)
}

mpx_na_segmented_native_rcpp_parallel <- function(data_ref, window_size, ez = 0.5, s_size = 1.0, idxs = TRUE, euclidean = TRUE, progress = FALSE, n_workers = 1L) {
    .Call(`_matrixprofiler_mpx_na_segmented_native_rcpp_parallel`, data_ref, window_size, ez, s_size, idxs, euclidean, progress, n_workers)
}

mpxab_na_segmented_rcpp_parallel <- function(data_ref, query_ref, window_size, s_size = 1.0, idxs = TRUE, euclidean = TRUE, progress = FALSE, n_workers = 1L) {
    .Call(`_matrixprofiler_mpxab_na_segmented_rcpp_parallel`, data_ref, query_ref, window_size, s_size, idxs, euclidean, progress, n_workers)
}

mpxab_na_rcpp_parallel <- function(data_ref, query_ref, window_size, s_size = 1.0, idxs = TRUE, euclidean = TRUE, progress = FALSE, n_workers = 1L) {
    .Call(`_matrixprofiler_mpxab_na_rcpp_parallel`, data_ref, query_ref, window_size, s_size, idxs, euclidean, progress, n_workers)
}

mstomp_rcpp <- function(data_ref, window_size, ez, must_dim, exc_dim, n_workers, progress) {
    .Call(`_matrixprofiler_mstomp_rcpp`, data_ref, window_size, ez, must_dim, exc_dim, n_workers, progress)
}

rfcp_na_segmented_native_rcpp_parallel <- function(positive_ref, negative_ref, window_size, max_freq, exclusion_radius, query_begin, query_end, n_workers, return_profiles, progress) {
    .Call(`_matrixprofiler_rfcp_na_segmented_native_rcpp_parallel`, positive_ref, negative_ref, window_size, max_freq, exclusion_radius, query_begin, query_end, n_workers, return_profiles, progress)
}

rfcp_gpu_rcpp <- function(positive_ref, negative_ref, window_size, max_freq, exclusion_radius, query_begin, query_end, return_profiles) {
    .Call(`_matrixprofiler_rfcp_gpu_rcpp`, positive_ref, negative_ref, window_size, max_freq, exclusion_radius, query_begin, query_end, return_profiles)
}

scrimp_rcpp <- function(data_ref, query_ref, window_size, ez, s_size, pre_scrimp, progress) {
    .Call(`_matrixprofiler_scrimp_rcpp`, data_ref, query_ref, window_size, ez, s_size, pre_scrimp, progress)
}

scrimp_rcpp_parallel <- function(data_ref, query_ref, window_size, ez, s_size, pre_scrimp, progress, n_workers) {
    .Call(`_matrixprofiler_scrimp_rcpp_parallel`, data_ref, query_ref, window_size, ez, s_size, pre_scrimp, progress, n_workers)
}

scrimpab_rcpp <- function(data_ref, query_ref, window_size, s_size, progress) {
    .Call(`_matrixprofiler_scrimpab_rcpp`, data_ref, query_ref, window_size, s_size, progress)
}

scrimpab_rcpp_parallel <- function(data_ref, query_ref, window_size, s_size, progress, n_workers) {
    .Call(`_matrixprofiler_scrimpab_rcpp_parallel`, data_ref, query_ref, window_size, s_size, progress, n_workers)
}

stamp_rcpp <- function(data_ref, query_ref, window_size, ez, s_size, progress) {
    .Call(`_matrixprofiler_stamp_rcpp`, data_ref, query_ref, window_size, ez, s_size, progress)
}

stamp_rcpp_parallel <- function(data_ref, query_ref, window_size, ez, s_size, n_workers = 1L, progress = FALSE) {
    .Call(`_matrixprofiler_stamp_rcpp_parallel`, data_ref, query_ref, window_size, ez, s_size, n_workers, progress)
}

stomp_rcpp <- function(data_ref, query_ref, window_size, ez, progress, left_right_profile) {
    .Call(`_matrixprofiler_stomp_rcpp`, data_ref, query_ref, window_size, ez, progress, left_right_profile)
}

stomp_rcpp_parallel <- function(data_ref, query_ref, window_size, ez, n_workers = 1L, progress = FALSE, left_right_profile = FALSE) {
    .Call(`_matrixprofiler_stomp_rcpp_parallel`, data_ref, query_ref, window_size, ez, n_workers, progress, left_right_profile)
}

movmean_rcpp <- function(data, window_size, na_mode) {
    .Call(`_matrixprofiler_movmean_rcpp`, data, window_size, na_mode)
}

movsum_rcpp <- function(data, window_size, na_mode) {
    .Call(`_matrixprofiler_movsum_rcpp`, data, window_size, na_mode)
}

movsum_ogita_rcpp <- function(data, window_size, na_mode) {
    .Call(`_matrixprofiler_movsum_ogita_rcpp`, data, window_size, na_mode)
}

movvar2_rcpp <- function(data, window_size, na_mode) {
    .Call(`_matrixprofiler_movvar2_rcpp`, data, window_size, na_mode)
}

movvar_rcpp <- function(data, window_size, na_mode) {
    .Call(`_matrixprofiler_movvar_rcpp`, data, window_size, na_mode)
}

movstd_rcpp <- function(data, window_size, na_mode) {
    .Call(`_matrixprofiler_movstd_rcpp`, data, window_size, na_mode)
}

movmean_std_rcpp <- function(data, window_size, na_mode) {
    .Call(`_matrixprofiler_movmean_std_rcpp`, data, window_size, na_mode)
}

precision_test_rcpp <- function(dd) {
    .Call(`_matrixprofiler_precision_test_rcpp`, dd)
}

movmin_rcpp <- function(data, window_size, na_mode) {
    .Call(`_matrixprofiler_movmin_rcpp`, data, window_size, na_mode)
}

movmax_rcpp <- function(data, window_size, na_mode) {
    .Call(`_matrixprofiler_movmax_rcpp`, data, window_size, na_mode)
}

movmean_weighted_rcpp <- function(data, window_size, eps) {
    .Call(`_matrixprofiler_movmean_weighted_rcpp`, data, window_size, eps)
}

movmean_fading_rcpp <- function(data, window_size, eps) {
    .Call(`_matrixprofiler_movmean_fading_rcpp`, data, window_size, eps)
}

movsum_weighted_rcpp <- function(data, window_size, eps) {
    .Call(`_matrixprofiler_movsum_weighted_rcpp`, data, window_size, eps)
}

movsum_fading_rcpp <- function(data, window_size, eps) {
    .Call(`_matrixprofiler_movsum_fading_rcpp`, data, window_size, eps)
}

movvar_weighted_rcpp <- function(data, window_size, eps) {
    .Call(`_matrixprofiler_movvar_weighted_rcpp`, data, window_size, eps)
}

movvar_fading_rcpp <- function(data, window_size, eps) {
    .Call(`_matrixprofiler_movvar_fading_rcpp`, data, window_size, eps)
}

muinvn_rcpp <- function(data, window_size, na_mode) {
    .Call(`_matrixprofiler_muinvn_rcpp`, data, window_size, na_mode)
}

muinvn_rcpp_parallel <- function(data, window_size, n_workers, na_mode) {
    .Call(`_matrixprofiler_muinvn_rcpp_parallel`, data, window_size, n_workers, na_mode)
}

zero_crossing_rcpp <- function(data, window_size) {
    .Call(`_matrixprofiler_zero_crossing_rcpp`, data, window_size)
}