version 1.1.9

cran · Apr 14, 2019 · c95d1fc · c95d1fc
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,14 +2,15 @@ Package: ClusterR
 Type: Package
 Title: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids
         and Affinity Propagation Clustering
-Version: 1.1.8
-Date: 2019-01-11
-Authors@R: c( person("Lampros", "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre")), person("Conrad", "Sanderson", role = "cph", comment = "Author of the C++ Armadillo library"), person("Ryan", "Curtin", role = "cph", comment = "Author of the C++ Armadillo library"), person("Siddharth", "Agrawal", role = "cph", comment = "Author of the C code of the Mini-Batch-Kmeans algorithm (https://github.com/siddharth-agrawal/Mini-Batch-K-Means)"), person("Brendan", "Frey", email = "frey@psi.toronto.edu", role = "cph", comment = "Author of the matlab code of the Affinity propagation algorithm (for commercial use please contact the author)"), person("Delbert", "Dueck", role = "cph", comment = "Author of the matlab code of the Affinity propagation algorithm") )  
+Version: 1.1.9
+Date: 2019-04-14
+Authors@R: c( person("Lampros", "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre")), person("Conrad", "Sanderson", role = "cph", comment = "Author of the C++ Armadillo library"), person("Ryan", "Curtin", role = "cph", comment = "Author of the C++ Armadillo library"), person("Siddharth", "Agrawal", role = "cph", comment = "Author of the C code of the Mini-Batch-Kmeans algorithm (https://github.com/siddharth-agrawal/Mini-Batch-K-Means)"), person("Brendan", "Frey", email = "frey@psi.toronto.edu", role = "cph", comment = "Author of the matlab code of the Affinity propagation algorithm (for commercial use please contact the author of the matlab code)"), person("Delbert", "Dueck", role = "cph", comment = "Author of the matlab code of the Affinity propagation algorithm") )  
 Maintainer: Lampros Mouselimis <mouselimislampros@gmail.com>
 BugReports: https://github.com/mlampros/ClusterR/issues
 URL: https://github.com/mlampros/ClusterR
 Description: Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.
 License: GPL-3
+Encoding: UTF-8
 LazyData: TRUE
 Depends: R(>= 3.2), gtools
 Imports: Rcpp (>= 0.12.5), graphics, grDevices, utils, gmp, FD, stats,
@@ -19,17 +20,17 @@ Suggests: OpenImageR, testthat, covr, knitr, rmarkdown
 VignetteBuilder: knitr
 RoxygenNote: 6.1.0
 NeedsCompilation: yes
-Packaged: 2019-01-11 06:50:53 UTC; lampros
+Packaged: 2019-04-14 05:57:38 UTC; lampros
 Author: Lampros Mouselimis [aut, cre],
   Conrad Sanderson [cph] (Author of the C++ Armadillo library),
   Ryan Curtin [cph] (Author of the C++ Armadillo library),
   Siddharth Agrawal [cph] (Author of the C code of the Mini-Batch-Kmeans
     algorithm
     (https://github.com/siddharth-agrawal/Mini-Batch-K-Means)),
   Brendan Frey [cph] (Author of the matlab code of the Affinity
-    propagation algorithm (for commercial use please contact the
-    author)),
+    propagation algorithm (for commercial use please contact the author
+    of the matlab code)),
   Delbert Dueck [cph] (Author of the matlab code of the Affinity
     propagation algorithm)
 Repository: CRAN
-Date/Publication: 2019-01-11 12:40:03 UTC
+Date/Publication: 2019-04-14 06:42:41 UTC
diff --git a/MD5 b/MD5
@@ -1,29 +1,29 @@
-71b1fd417dfa418d0de123fd457daa40 *DESCRIPTION
+904147ee0b4c54aa58d785626bc4527b *DESCRIPTION
 0813b7c94e3ed7ab12410151f22c30b2 *NAMESPACE
-b8fe8974634ae417666b759f8ab1c8b7 *NEWS.md
-b683d50677117bbe70281a2a18bbb9ab *R/RcppExports.R
-ba3119bd629cc5c93f2bdb6d82b2862a *R/clustering_functions.R
-56e73e836b5226507b81572e411733f9 *README.md
+6f6b1bc10349dab8934f19a639f44281 *NEWS.md
+4cfa85ffb931b1d95beb9ae4b8a4b1b6 *R/RcppExports.R
+c9f8f5e010da28463b9d94defff4aa74 *R/clustering_functions.R
+d5d63e6f559b33e78b0748d5c4fe2ada *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
-d9607e9cd63051210a1abd610033a2d7 *inst/doc/the_clusterR_package.html
-157e042d6a7e3ba930c3192f73fcad7e *inst/include/ClusterRHeader.h
-4bfe12409080d29ecad294bd8ff14e4f *inst/include/affinity_propagation.h
+5de6b744baa6dccdb5fd2e92440faf4b *inst/doc/the_clusterR_package.html
+be09a0e70406b60c0a69e6becdbaf562 *inst/include/ClusterRHeader.h
+a860f3aa0880724f4fbefec5f157a367 *inst/include/affinity_propagation.h
 2b20993e66e638d814eb8fcafb807fe5 *man/AP_affinity_propagation.Rd
-4a48132132a7b147c868e53d6e1c6ef4 *man/AP_preferenceRange.Rd
+a59dac6ccfafa3fd86fb2e126afb4c28 *man/AP_preferenceRange.Rd
 94cd0d57e5b44e47a3746a7979a4088f *man/Clara_Medoids.Rd
 c4f35a38b1a4271caf0a29ed616c45ba *man/Cluster_Medoids.Rd
 853d94cb9bf0db952fee7ce47788baa5 *man/GMM.Rd
 add088ce3a97143c6a7920c2a5d04df4 *man/KMeans_arma.Rd
 f6e6bbf6eefd62a4a574ee1611504202 *man/KMeans_rcpp.Rd
 694d22cfdc6bdbcfe3caba54309cf1f4 *man/MiniBatchKmeans.Rd
-e9423c0880e1bc82130d29c26f60da69 *man/Optimal_Clusters_GMM.Rd
-6c8cbd6ab8d193ed59f0b6762163f00b *man/Optimal_Clusters_KMeans.Rd
-0f984e76dc1348eabd76e16a4281aed5 *man/Optimal_Clusters_Medoids.Rd
+f8648453c0d692fc367810fa920158ff *man/Optimal_Clusters_GMM.Rd
+34fbfb091b663a3d643ec3d0653a793f *man/Optimal_Clusters_KMeans.Rd
+c091a272a57e7e4b4e58adfd4991caf2 *man/Optimal_Clusters_Medoids.Rd
 43ac28a91d5922a30be1d5a2b1a69da2 *man/Silhouette_Dissimilarity_Plot.Rd
 e22a73fe44714937058cc67e21c1c783 *man/center_scale.Rd
 1854ae3f31126641f9d7c10986828865 *man/dietary_survey_IBS.Rd
@@ -43,15 +43,15 @@ cd805bfc471c3894870346627bb3b047 *man/predict_Medoids.Rd
 70d748a4892a8f6f676bbb4010707147 *man/tryCatch_optimal_clust_GMM.Rd
 c0b0887d0e21dba427791387a69357a4 *src/Makevars
 3924f33984346427452b9a43de77a4c8 *src/Makevars.win
-20b4146d7b54017cb38195cf79899b86 *src/RcppExports.cpp
-b510df829e350b9f3c9e3508a7daf84e *src/export_inst_folder_headers.cpp
-097d8cad2528a8c4c2a4bbc25a6d93a4 *src/init.c
+45f47ccbe5f1e94ed3297bd5d76979d2 *src/RcppExports.cpp
+13813a3eed426b88f4f0927baf28f991 *src/export_inst_folder_headers.cpp
+0dc087346f6b225064fcefe9c9a7105d *src/init.c
 3243b3e7b85ca7953f191679629429ec *tests/testthat.R
-98f8ee5579068a5360a6f494064a8e2f *tests/testthat/test-AP.R
+ab5320060871f975a7336f1f81c89c8e *tests/testthat/test-AP.R
 4b34781ffe49287702dd06d09a4bae60 *tests/testthat/test-dissimilarity_matrices.R
-1ef735012725c45b4a734242fe608e4e *tests/testthat/test-gmm.R
-bba205c530ebf494ac6c39e6ed4fc23b *tests/testthat/test-kmeans.R
-b46b9aa6065fafe41bac059c8b8096c0 *tests/testthat/test-medoids.R
+74978eb17fb9aca2fb2ea750804fa3b0 *tests/testthat/test-gmm.R
+dadfec105d1bf5baa6712f50a4b274a4 *tests/testthat/test-kmeans.R
+7a66009bc7d1c8cb943629e76c89cb00 *tests/testthat/test-medoids.R
 d2a8fafb44bc41481ebab950e9946604 *tests/testthat/test-plot2d_silhouette_plot_ext_validation_center_scale_dist_mat.R
 0504caa57b692a3c365ced1e5caf8356 *vignettes/Rplot.png
 82787ff50682cf880f9b85227f28e260 *vignettes/Rplot_2d.png

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,10 @@
 
+## ClusterR 1.1.9
+
+* I added parallelization for the *exact* method of the *AP_preferenceRange* function which is more computationally intensive as the *bound* method
+* I modified the *Optimal_Clusters_KMeans*, *Optimal_Clusters_GMM* and *Optimal_Clusters_Medoids* to accept also a contiguous or non-contiguous vector besides single values as a *max_clusters* parameter. However, the limitation currently is that the user won't be in place to plot the clusters but only to receive the ouput data ( this can be changed in the future however the plotting function for the contiguous and non-contiguous vectors must be a separate plotting function outside of the existing one).  Moreover, the *distortion_fK* criterion can't be computed in the *Optimal_Clusters_KMeans* function if the *max_clusters* parameter is a contiguous or non-continguous vector ( the *distortion_fK* criterion requires consecutive clusters ). The same applies also to the *Adjusted_Rsquared* criterion which returns incorrect output. For this feature request see the following [Github issue](https://github.com/mlampros/ClusterR/issues/15).
+
+
 ## ClusterR 1.1.8
 
 * I moved the *OpenImageR* dependency in the DESCRIPTION file from 'Imports' to 'Suggests', as it appears only in the Vignette file.
@@ -16,7 +22,7 @@
 * I modified the *Predict_mini_batch_kmeans()* function to accept an armadillo matrix rather than an Rcpp Numeric matrix. The function appers both in *ClusterRHeader.h* file ( 'inst' folder ) and in *export_inst_folder_headers.cpp* file ( 'src' folder )
 * I added the *mini_batch_params* parameter to the *Optimal_Clusters_KMeans* function. Now, the optimal number of clusters can be found also based on the min-batch-kmeans algorithm (except for the *variance_explained* criterion)
 * I changed the license from MIT to GPL-3
-* I added the [affinity propagation algorithm](https://www.psi.toronto.edu/index.php?q=affinity%20propagation) (conversion of the matlab files *apcluster.m* and *referenceRange.m*).
+* I added the *affinity propagation algorithm* (<span></span>www.psi.toronto.edu/index.php?q=affinity%20propagation). Especially, I converted the matlab files *apcluster.m* and *referenceRange.m*.
 * I modified the minimum version of RcppArmadillo in the DESCRIPTION file to 0.9.1 because the Affinity Propagation algorithm requires the *.is_symmetric()* function, which was included in version 0.9.1
 
 

diff --git a/R/RcppExports.R b/R/RcppExports.R
@@ -81,7 +81,7 @@ affinity_propagation <- function(s, p, maxits = 1000L, convits = 100L, dampfact
     .Call(`_ClusterR_affinity_propagation`, s, p, maxits, convits, dampfact, details, nonoise, eps, time)
 }
 
-preferenceRange <- function(s, method = "bound") {
-    .Call(`_ClusterR_preferenceRange`, s, method)
+preferenceRange <- function(s, method = "bound", threads = 1L) {
+    .Call(`_ClusterR_preferenceRange`, s, method, threads)
 }