version 1.1.4

cran · Aug 22, 2018 · fcada89 · fcada89
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,8 +2,8 @@ Package: ClusterR
 Type: Package
 Title: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans and
         K-Medoids Clustering
-Version: 1.1.3
-Date: 2018-07-21
+Version: 1.1.4
+Date: 2018-08-21
 Author: Lampros Mouselimis <mouselimislampros@gmail.com>
 Maintainer: Lampros Mouselimis <mouselimislampros@gmail.com>
 BugReports: https://github.com/mlampros/ClusterR/issues
@@ -20,6 +20,6 @@ Suggests: testthat, covr, knitr, rmarkdown
 VignetteBuilder: knitr
 RoxygenNote: 6.0.1
 NeedsCompilation: yes
-Packaged: 2018-07-21 20:04:35 UTC; lampros
+Packaged: 2018-08-21 15:07:19 UTC; lampros
 Repository: CRAN
-Date/Publication: 2018-07-21 21:00:02 UTC
+Date/Publication: 2018-08-22 16:10:07 UTC
diff --git a/MD5 b/MD5
@@ -1,17 +1,18 @@
-f84fb66ac77dc1559e9c216255c826e5 *DESCRIPTION
+a7c4c5b3735758f55193805cbc1b3913 *DESCRIPTION
 c9c69a326502c31a65128654c3c08cb5 *LICENSE
 1cf3e5d5262851d34692258a58312008 *NAMESPACE
-e5b3eeae0abf7ef205fb46acc4323612 *NEWS.md
-19f3ce55803fd8c849bdfa8fd3cc831b *R/RcppExports.R
+06cd54d8b7a064a60ec1d99b50737459 *NEWS.md
+b36617dd084edbafe873a428ef15d852 *R/RcppExports.R
 7eaf4a58742f7389140945296294ab18 *R/clustering_functions.R
-3a63230b58b121c0df71ee15cb3d8ebb *README.md
+e5277d9130f23f165468de43ced3d67c *README.md
 23337ff64119d9ff1dd1f458a4e93704 *build/vignette.rds
 8b6687f3bb9c58cd74ae67670872215d *data/dietary_survey_IBS.rda
 aa58963ebd13c4c91edf809ca4efc5d4 *data/mushroom.rda
 a9e3dfd8650ed7d2d0a91d3880a67f7b *data/soybean.rda
 128fe5f74b8c6787e9c378b61bd8d423 *inst/doc/the_clusterR_package.R
 86f33097b23d7a5acc0c83647a19d865 *inst/doc/the_clusterR_package.Rmd
-df7cccd53c0f386ee0b693ed2a571c0f *inst/doc/the_clusterR_package.html
+fe04ca3414297b52c8e1831887c82ed1 *inst/doc/the_clusterR_package.html
+f92e5e312e53e5a84410238e97cf7cc6 *inst/include/ClusterRHeader.h
 94cd0d57e5b44e47a3746a7979a4088f *man/Clara_Medoids.Rd
 c4f35a38b1a4271caf0a29ed616c45ba *man/Cluster_Medoids.Rd
 853d94cb9bf0db952fee7ce47788baa5 *man/GMM.Rd
@@ -40,11 +41,9 @@ cd805bfc471c3894870346627bb3b047 *man/predict_Medoids.Rd
 62231fa9f6be487bdbf9f95625d39c0b *man/tryCatch_optimal_clust_GMM.Rd
 c0b0887d0e21dba427791387a69357a4 *src/Makevars
 3924f33984346427452b9a43de77a4c8 *src/Makevars.win
-ba1dafe01a73b757ea17e6c04dee7b39 *src/RcppExports.cpp
-8be2c73e7a0e14a4500c68d9444cc5cd *src/init.c
-02132d8c61a2e864cbe93d557dd70829 *src/kmeans_miniBatchKmeans_GMM_Medoids.cpp
-580cce096456bdd88fc875489a33be99 *src/utils_rcpp.cpp
-76f46da484cec9e5763f21f16c309c50 *src/utils_rcpp.h
+7cb276875ff2c033cf1fbd594e915f64 *src/RcppExports.cpp
+12e44afd14579a0748c99babf1338c03 *src/export_inst_header.cpp
+01549a562eccae0a7c229917b9448c11 *src/init.c
 3243b3e7b85ca7953f191679629429ec *tests/testthat.R
 4b34781ffe49287702dd06d09a4bae60 *tests/testthat/test-dissimilarity_matrices.R
 1ef735012725c45b4a734242fe608e4e *tests/testthat/test-gmm.R

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,9 @@
 
+## ClusterR 1.1.4
+
+I modified the ClusterR package to a cpp-header-only package to allow linking of cpp code between Rcpp packages. See the update of the README.md file (16-08-2018) for more information.
+
+
 ## ClusterR 1.1.3
 
 I updated the example section of the documentation by replacing the *optimal_init* with the *kmeans++* initializer

diff --git a/R/RcppExports.R b/R/RcppExports.R
@@ -1,8 +1,16 @@
 # Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
-tot_ss_data <- function(x) {
-    .Call(`_ClusterR_tot_ss_data`, x)
+check_NaN_Inf <- function(x) {
+    .Call(`_ClusterR_check_NaN_Inf`, x)
+}
+
+validate_centroids <- function(data, init_centroids) {
+    .Call(`_ClusterR_validate_centroids`, data, init_centroids)
+}
+
+SCALE <- function(data, mean_center = TRUE, sd_scale = TRUE) {
+    .Call(`_ClusterR_SCALE`, data, mean_center, sd_scale)
 }
 
 KMEANS_rcpp <- function(data, clusters, num_init = 1L, max_iters = 200L, initializer = "kmeans++", fuzzy = FALSE, verbose = FALSE, CENTROIDS = NULL, tol = 1e-4, eps = 1.0e-6, tol_optimal_init = 0.5, seed = 1L) {
@@ -17,18 +25,6 @@ opt_clust_fK <- function(sum_distortion, data_num_cols, threshold = 0.85) {
     .Call(`_ClusterR_opt_clust_fK`, sum_distortion, data_num_cols, threshold)
 }
 
-INTRA_CLUSTER_DISS <- function(data, CLUSTERS) {
-    .Call(`_ClusterR_INTRA_CLUSTER_DISS`, data, CLUSTERS)
-}
-
-Rcpp_2arma_mat <- function(x) {
-    .Call(`_ClusterR_Rcpp_2arma_mat`, x)
-}
-
-SILHOUETTE_metric <- function(data, CLUSTER, tmp_clust, in_cluster_dist) {
-    .Call(`_ClusterR_SILHOUETTE_metric`, data, CLUSTER, tmp_clust, in_cluster_dist)
-}
-
 evaluation_rcpp <- function(data, CLUSTER, silhouette = FALSE) {
     .Call(`_ClusterR_evaluation_rcpp`, data, CLUSTER, silhouette)
 }
@@ -45,10 +41,6 @@ GMM_arma <- function(data, gaussian_comps, dist_mode, seed_mode, km_iter, em_ite
     .Call(`_ClusterR_GMM_arma`, data, gaussian_comps, dist_mode, seed_mode, km_iter, em_iter, verbose, var_floor, seed)
 }
 
-INV_COV <- function(COV_VEC) {
-    .Call(`_ClusterR_INV_COV`, COV_VEC)
-}
-
 predict_MGausDPDF <- function(data, CENTROIDS, COVARIANCE, WEIGHTS, eps = 1.0e-8) {
     .Call(`_ClusterR_predict_MGausDPDF`, data, CENTROIDS, COVARIANCE, WEIGHTS, eps)
 }
@@ -57,34 +49,10 @@ GMM_arma_AIC_BIC <- function(data, max_clusters, dist_mode, seed_mode, km_iter,
     .Call(`_ClusterR_GMM_arma_AIC_BIC`, data, max_clusters, dist_mode, seed_mode, km_iter, em_iter, verbose, var_floor, criterion, seed)
 }
 
-METHODS <- function(data, data1, method, i, j, flag_isfinite, cov_mat, minkowski_p = 1.0, eps = 1.0e-6, exception_nan = TRUE) {
-    .Call(`_ClusterR_METHODS`, data, data1, method, i, j, flag_isfinite, cov_mat, minkowski_p, eps, exception_nan)
-}
-
 dissim_mat <- function(data, method, minkowski_p = 1.0, upper = TRUE, diagonal = TRUE, threads = 1L, eps = 1.0e-6) {
     .Call(`_ClusterR_dissim_mat`, data, method, minkowski_p, upper, diagonal, threads, eps)
 }
 
-boolean_function <- function(x, y) {
-    .Call(`_ClusterR_boolean_function`, x, y)
-}
-
-inner_field_func <- function(f, sorted_medoids_elem, END_IDX_nelem, end_indices_vec, data, sorted_medoids, sorted_medoids_increment) {
-    .Call(`_ClusterR_inner_field_func`, f, sorted_medoids_elem, END_IDX_nelem, end_indices_vec, data, sorted_medoids, sorted_medoids_increment)
-}
-
-silhouette_matrix <- function(data, end_indices_vec, end_cost_vec, threads = 1L) {
-    .Call(`_ClusterR_silhouette_matrix`, data, end_indices_vec, end_cost_vec, threads)
-}
-
-subset_vec <- function(x, y) {
-    .Call(`_ClusterR_subset_vec`, x, y)
-}
-
-field_cm_inner <- function(copy_medoids, non_medoids, data, i, j) {
-    .Call(`_ClusterR_field_cm_inner`, copy_medoids, non_medoids, data, i, j)
-}
-
 ClusterMedoids <- function(data, clusters, method, minkowski_p = 1.0, threads = 1L, verbose = FALSE, swap_phase = FALSE, fuzzy = FALSE, seed = 1L) {
     .Call(`_ClusterR_ClusterMedoids`, data, clusters, method, minkowski_p, threads, verbose, swap_phase, fuzzy, seed)
 }
@@ -93,14 +61,6 @@ dissim_MEDOIDS <- function(data, method, MEDOIDS, minkowski_p = 1.0, threads = 1
     .Call(`_ClusterR_dissim_MEDOIDS`, data, method, MEDOIDS, minkowski_p, threads, eps)
 }
 
-fuzzy_and_stats <- function(data, eps = 1.0e-6, fuzzy = FALSE) {
-    .Call(`_ClusterR_fuzzy_and_stats`, data, eps, fuzzy)
-}
-
-isolation <- function(dissim_mat_subset, x) {
-    .Call(`_ClusterR_isolation`, dissim_mat_subset, x)
-}
-
 ClaraMedoids <- function(data, clusters, method, samples, sample_size, minkowski_p = 1.0, threads = 1L, verbose = FALSE, swap_phase = FALSE, fuzzy = FALSE, seed = 1L) {
     .Call(`_ClusterR_ClaraMedoids`, data, clusters, method, samples, sample_size, minkowski_p, threads, verbose, swap_phase, fuzzy, seed)
 }
@@ -117,71 +77,3 @@ OptClust <- function(data, iter_clust, method, clara = FALSE, samples = 5L, samp
     .Call(`_ClusterR_OptClust`, data, iter_clust, method, clara, samples, sample_size, minkowski_p, criterion, threads, swap_phase, verbose, seed)
 }
 
-set_seed <- function(seed) {
-    invisible(.Call(`_ClusterR_set_seed`, seed))
-}
-
-cluster_indices <- function(CLUSTER) {
-    .Call(`_ClusterR_cluster_indices`, CLUSTER)
-}
-
-check_NaN_Inf <- function(x) {
-    .Call(`_ClusterR_check_NaN_Inf`, x)
-}
-
-calc_silhouette <- function(intra, outer) {
-    .Call(`_ClusterR_calc_silhouette`, intra, outer)
-}
-
-sample_vec <- function(num_elem, start, end, replace) {
-    .Call(`_ClusterR_sample_vec`, num_elem, start, end, replace)
-}
-
-squared_norm <- function(x) {
-    .Call(`_ClusterR_squared_norm`, x)
-}
-
-MinMat <- function(x) {
-    .Call(`_ClusterR_MinMat`, x)
-}
-
-WCSS <- function(vec, centroids) {
-    .Call(`_ClusterR_WCSS`, vec, centroids)
-}
-
-validate_centroids <- function(data, init_centroids) {
-    .Call(`_ClusterR_validate_centroids`, data, init_centroids)
-}
-
-kmeans_pp_dist <- function(vec, centroid) {
-    .Call(`_ClusterR_kmeans_pp_dist`, vec, centroid)
-}
-
-kmeans_pp_init <- function(data, clusters, medoids = FALSE) {
-    .Call(`_ClusterR_kmeans_pp_init`, data, clusters, medoids)
-}
-
-norm_fuzzy <- function(vec, eps) {
-    .Call(`_ClusterR_norm_fuzzy`, vec, eps)
-}
-
-quantile_value <- function(x, clusters) {
-    .Call(`_ClusterR_quantile_value`, x, clusters)
-}
-
-duplicated_flag <- function(x) {
-    .Call(`_ClusterR_duplicated_flag`, x)
-}
-
-quantile_init_rcpp <- function(data, sample_rows, clusters) {
-    .Call(`_ClusterR_quantile_init_rcpp`, data, sample_rows, clusters)
-}
-
-check_medoids <- function(data, clust, tol = 0.5) {
-    .Call(`_ClusterR_check_medoids`, data, clust, tol)
-}
-
-SCALE <- function(data, mean_center = TRUE, sd_scale = TRUE) {
-    .Call(`_ClusterR_SCALE`, data, mean_center, sd_scale)
-}
-