{"id":55,"date":"2010-08-30T09:39:40","date_gmt":"2010-08-30T07:39:40","guid":{"rendered":"http:\/\/lwibs01.gm.fh-koeln.de\/blogs\/ciop\/?page_id=55"},"modified":"2025-06-02T15:52:35","modified_gmt":"2025-06-02T14:52:35","slug":"downloads","status":"publish","type":"page","link":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/downloads\/","title":{"rendered":"Downloads"},"content":{"rendered":"<h3><strong><a id=\"SACOBRA_Py\" name=\"SACOBRA_Py\"><\/a>SACOBRA_Py: Self-adjusting Constrained Optimization By RBF<\/strong><\/h3>\n<p class=\"lead\"><a class=\"thickbox\" href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/output_Op3uU6.gif\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft wp-image-790 size-full\" style=\"width: 100px;height: 87px;float: right\" src=\"https:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/output_Op3uU6.gif\" alt=\"output_Op3uU6\" width=\"412\" height=\"358\" \/><\/a><\/p>\n<div>\n<p>will be available soon!<\/p>\n<h3><strong><a id=\"SACOBRA\" name=\"SACOBRA\"><\/a>SACOBRA: Self-adjusting Constrained Optimization By RBF<\/strong><\/h3>\n<a class=\"thickbox\" href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/output_Op3uU6.gif\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft wp-image-790 size-full\" style=\"width: 100px;height: 87px;float: right\" src=\"https:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/output_Op3uU6.gif\" alt=\"output_Op3uU6\" width=\"412\" height=\"358\" \/><\/a>\n<div>\n<p>Package <strong>SACOBRA\u00a0<\/strong>can be downloaded from <strong><a href=\"https:\/\/github.com\/WolfgangKonen\/SACOBRA\">this GitHub-URL<\/a><\/strong>\u00a0or from <a href=\"https:\/\/cran.r-project.org\/web\/packages\/SACOBRA\/index.html\">this CRAN-URL<\/a>.\u00a0\u00a0<strong>SACOBRA <\/strong>is a derivative-free optimizer which is able to solve constrained expensive problems with very few function evalutions. This package works based on surrogate assisted techniques and utilizes RBF interpolation to model fitness function and constraint functions.<\/p>\n<p>Read more:<\/p>\n<ul>\n<li><a href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/sacobra\/\">SACOBRA project page<\/a><\/li>\n<\/ul>\n<hr \/>\n<h3><strong><a id=\"rCMA\" name=\"rCMA\"><\/a>An R-interface to the JAVA version of CMA-ES by Niko Hansen (rCMA)<\/strong><\/h3>\n<div>\n<div>\n<p>Package <strong>rCMA <\/strong>can be downloaded from <a href=\"http:\/\/cran.r-project.org\/web\/packages\/rCMA\/index.html\">this CRAN-URL<\/a>.<a style=\"line-height: 18.9090900421143px\" href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/CMA_cartoon.gif\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-722 size-full\" style=\"float: right;width: 120px;height: 108px\" src=\"https:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/CMA_cartoon.gif\" alt=\"CMA_cartoon\" width=\"205\" height=\"184\" \/><\/a><\/p>\n<p><strong>rCMA <\/strong>is a package to perform CMA-ES optimization, using the Java implementation by Niko<br \/>\nHansen [Hansen,2009]. CMA-ES [Hansen and Ostermeier,1996], [Hansen,2013] is<br \/>\nthe Covariance Matrix Adapting Evolutionary Strategy for numeric black box optimization.<\/p>\n<p>Read more:<\/p>\n<ul>\n<li><a href=\"http:\/\/www.gm.fh-koeln.de\/ciopwebpub\/Kone15b.d\/rCMA-tutorial.pdf\">The rCMA Tutorial: Examples for using CMA-ES in R<\/a> (2015)<\/li>\n<li><a href=\"https:\/\/cran.r-project.org\/web\/packages\/rCMA\/rCMA.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">The rCMA Reference Manual<\/a><\/li>\n<\/ul>\n<\/div>\n<div>\n<hr \/>\n<div class=\"post-headline\">\n<h3 style=\"margin-top: 10px;margin-bottom: 10px;padding-top: 0px;padding-bottom: 0px\"><strong>R-Package for Sequential Parameter Optimization Toolbox (<a href=\"https:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/spot\/\">SPOT<\/a>)<\/strong><\/h3>\n<table border=\"0\" cellspacing=\"1\" cellpadding=\"1\" align=\"left\">\n<tbody>\n<tr>\n<td>An R version of this toolbox for interactive and automatic optimization of algorithms can be downloaded from\u00a0<a href=\"http:\/\/cran.r-project.org\/web\/packages\/SPOT\/index.html\">this CRAN-URL<\/a>.<\/td>\n<td><a class=\"thickbox\" href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/spot\/\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-347 size-full\" style=\"width: 100px;height: 51px\" src=\"https:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/spotOfficial.jpg\" alt=\"spotOfficial\" width=\"152\" height=\"77\" srcset=\"https:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/spotOfficial.jpg 152w, https:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/spotOfficial-150x77.jpg 150w\" sizes=\"auto, (max-width: 152px) 100vw, 152px\" \/><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"post-bodycopy clearfix\">\n<hr \/>\n<div class=\"post-headline\">\n<h3 style=\"margin-top: 10px;margin-bottom: 10px;padding-top: 0px;padding-bottom: 0px\"><strong><a id=\"downloadTDMR\" name=\"downloadTDMR\"><\/a><\/strong><\/h3>\n<h3 style=\"margin-top: 10px;margin-bottom: 10px;padding-top: 0px;padding-bottom: 0px\"><strong>R-Package for Tuned Data Mining (<a href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/tuned-data-mining\/\">TDMR<\/a>)<\/strong><\/h3>\n<table border=\"0\" cellspacing=\"1\" cellpadding=\"1\" align=\"left\">\n<tbody>\n<tr>\n<td>An R version of this toolbox for interactive, automated and efficient tuning of data mining tasks can be downloaded from<a title=\"TDMR package on CRAN\" href=\"http:\/\/cran.r-project.org\/web\/packages\/TDMR\/index.html\" target=\"_blank\" rel=\"noopener noreferrer\">\u00a0<\/a><a href=\"http:\/\/cran.r-project.org\/web\/packages\/TDMR\/index.html\">this CRAN-URL<\/a>.<\/td>\n<td><a class=\"thickbox\" href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/06\/28921_small_christophe-papke_pixelio.de_.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-341 size-thumbnail\" style=\"width: 100px;height: 100px\" title=\"An early tuning device (c) Christophe Papke@pixelio.de\" src=\"https:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/06\/28921_small_christophe-papke_pixelio.de_-150x150.jpg\" alt=\"28921_small_christophe-papke_pixelio.de_\" width=\"150\" height=\"150\" \/><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"post-bodycopy clearfix\">Read more:<\/div>\n<ul>\n<li><a href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/tuned-data-mining\/\">TDMR project page<\/a><\/li>\n<li><a href=\"http:\/\/www.gm.fh-koeln.de\/ciopwebpub\/Kone12b.d\/Kone12b.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">TDMR tutorial<\/a> (CIOP-Report 03\/2012 [Kone12b], <strong>last update May 2016<\/strong>)<\/li>\n<li><a href=\"http:\/\/www.gm.fh-koeln.de\/ciopwebpub\/Kone12a.d\/Kone12a.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">TDMR-docu.pdf<\/a>\u00a0(CIOP-Report [Kone12a], <strong>last update June 2017<\/strong>):\u00a0User manual, for in-depth information on usage and development of the TDMR package.<\/li>\n<\/ul>\n<\/div>\n<hr \/>\n<div class=\"post-headline\">\n<h3 style=\"margin-top: 10px;margin-bottom: 10px;padding-top: 0px;padding-bottom: 0px\"><strong><a id=\"SFA\" name=\"SFA\"><\/a>Slow Feature Analysis Toolkit SFA-TK<\/strong><\/h3>\n<\/div>\n<div class=\"post-bodycopy clearfix\">\n<p style=\"margin: 1em 0px;padding: 0px\"><a href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/slow-feature-analysis-sfa\/\">Slow Feature Analysis (SFA)<\/a>\u00a0is a technique developed by\u00a0<a href=\"https:\/\/www.ini.rub.de\/PEOPLE\/wiskott\/\">Laurenz Wiskott<\/a>\u00a0to find features in complex timeseries or multivariate datasets. SFA-TK V2.6 \u2013 V2.8 by\u00a0<a title=\"Wolfgang Konen\" href=\"http:\/\/gociop.de\/about\/people\/konen\/\">Wolfgang Konen<\/a>\u00a0are extended versions of the original MATLAB-based SFA-Toolkit (<a href=\"http:\/\/people.brandeis.edu\/~berkes\/software\/sfa-tk\/\" target=\"_blank\" rel=\"noopener noreferrer\">SFA-TK V1.0 by Pietro Berkes<\/a>).<\/p>\n<a class=\"thickbox\" href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/slow-feature-analysis-sfa\/\"><img loading=\"lazy\" decoding=\"async\" width=\"614\" height=\"288\" class=\"alignright size-full wp-image-357\" style=\"width: 300px;height: 120px;float: left\" src=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/SFA-TK-V1.0.png\" alt=\"SFA-TK-V1.0\" srcset=\"https:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/SFA-TK-V1.0.png 614w, https:\/\/blogs.gm.fh-koeln.de\/ciop\/files\/2010\/08\/SFA-TK-V1.0-300x141.png 300w\" sizes=\"auto, (max-width: 614px) 100vw, 614px\" \/><\/a>\n<\/div>\n<div class=\"post-bodycopy clearfix\">\n<p style=\"margin: 1em 0px;padding: 0px\">SFA-TK V2.8 has in addition to V2.7: parametric bootstrap, regularization of Gaussian classifier, nearest neigbor classifier<\/p>\n<p>SFA-TK V2.7 has in addition to V2.6:\u00a0 storable and loadable classifier models, demo dataset descriptions.<\/p>\n<p>SFA-TK V2.6: The extensions are: SVD-based SFA, support for classification, Gaussian classifier, some classification demo datasets (UCI, gesture) with or w\/o cross validation.<\/p>\n<p style=\"margin: 1em 0px;padding: 0px\">Download SFA-TK from the following URLs:<\/p>\n<ul style=\"margin-top: 10px;margin-bottom: 10px\">\n<li><a href=\"http:\/\/cran.r-project.org\/web\/packages\/rSFA\/index.html\">rSFA for R from CRAN<\/a>\u00a0(based on SFA-TK V2.8)<\/li>\n<li><a title=\"SFA-TK V2.8 zip\" href=\"http:\/\/www.gm.fh-koeln.de\/ciopwebpub\/Downloads\/sfa-tk\/sfa-tk-V2.8.zip\">SFA-TK V2.8 for MATLAB<\/a><\/li>\n<li><a title=\"SFA-TK V2.7 zip\" href=\"http:\/\/www.gm.fh-koeln.de\/ciopwebpub\/Downloads\/sfa-tk\/sfa-tk-V2.7.zip\">SFA-TK V2.7 for MATLAB<\/a><\/li>\n<li><a title=\"SFA-TK V2.6 zip\" href=\"http:\/\/www.gm.fh-koeln.de\/ciopwebpub\/Downloads\/sfa-tk\/sfa-tk-V2.6.zip\">SFA-TK V2.6 for MATLAB<\/a><\/li>\n<\/ul>\n<p style=\"margin: 1em 0px;padding: 0px\">For developers: An algorithmic-mathematical summary of the SFA procedure for classification is available\u00a0<a href=\"http:\/\/www.gm.fh-koeln.de\/ciopwebpub\/Downloads\/sfa-tk\/SFA.pdf\">here<\/a>\u00a0(in German only). For more literature on SFA see our\u00a0<a href=\"http:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/slow-feature-analysis-sfa\/\">SFA page<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>SACOBRA_Py: Self-adjusting Constrained Optimization By RBF will be available soon! SACOBRA: Self-adjusting Constrained Optimization By RBF Package SACOBRA\u00a0can be downloaded from this GitHub-URL\u00a0or from this CRAN-URL.\u00a0\u00a0SACOBRA is a derivative-free optimizer which is able to solve constrained expensive problems with very few function evalutions. This package works based on surrogate assisted techniques and utilizes RBF interpolation...  <a href=\"https:\/\/blogs.gm.fh-koeln.de\/ciop\/downloads\/\" class=\"more-link\" title=\"Read Downloads\"><?php _e(\"Read more &raquo;\",\"wpbootstrap\"); ?><\/a><\/p>\n","protected":false},"author":41,"featured_media":0,"parent":0,"menu_order":5,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-55","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/pages\/55","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/users\/41"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/comments?post=55"}],"version-history":[{"count":69,"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/pages\/55\/revisions"}],"predecessor-version":[{"id":1978,"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/pages\/55\/revisions\/1978"}],"wp:attachment":[{"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/media?parent=55"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}