version 1.0.1

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: DatabionicSwarm
 Type: Package
 Title: Swarm Intelligence for Self-Organized Clustering
-Version: 1.0.0
-Date: 2018-01-28
+Version: 1.0.1
+Date: 2018-03-07
 Authors@R: person("Michael", "Thrun", email= "m.thrun@gmx.net",role=c("aut","cre","cph"))
 Maintainer: Michael Thrun <m.thrun@gmx.net>
 Description: Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called databionic swarm (DBS) is introduced. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The package is based on the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.
@@ -17,9 +17,9 @@ NeedsCompilation: yes
 SystemRequirements: C++11
 LazyLoad: yes
 LazyData: TRUE
-URL: https://www.uni-marburg.de/fb12/datenbionik/software-en
+URL: www.deepbionics.org
 Encoding: UTF-8
-Packaged: 2018-01-31 16:02:28 UTC; MT
+Packaged: 2018-03-07 07:37:51 UTC; MCT
 Author: Michael Thrun [aut, cre, cph]
 Repository: CRAN
-Date/Publication: 2018-01-31 18:00:21 UTC
+Date/Publication: 2018-03-07 07:51:02 UTC

MD5

@@ -1,8 +1,8 @@
-b4941f6684af14dc066d625e3ccf319a *DESCRIPTION
+3c2bbd5e8a1774e912070a6ee125df35 *DESCRIPTION
 be6657950a14436ce009a8d2cbc6e38a *NAMESPACE
 987388c4b3e8a417141cd71497e33943 *R/DBSclustering.R
 1cd8e4131e8592a0ff1abc4c1c3adbad *R/Delaunay4Points.R
-81bc30eb03b4c3b923a6c62f9380d174 *R/GeneratePswarmVisualization.R
+06ce8210faba36cdcbfc0cf6a7f2f097 *R/GeneratePswarmVisualization.R
 e7ba915a33c123b23c017d42f8e50868 *R/MultipleSwarms.R
 171f342bae806da52705688ddac07c4f *R/ProjectedPoints2Grid.R
 0e60dd3651f24e9c535e617ee87391d6 *R/Pswarm.R
@@ -21,7 +21,7 @@ cb72bf49fd4a549adda46d116099b714 *R/setRmin.R
 8c0bfb659862b8c66ea693384748491a *data/DefaultColorSequence.rda
 8f20ce6f6321f43d6a6ce858198f2dc8 *data/Lsun3D.rda
 9f0ef3f10d1b5f60739358f06eb665ab *man/DBSclustering.Rd
-6105a55f8d03abd831cf8a8dd8249f29 *man/DatabionicSwarm-package.Rd
+f9d36dcf843beb7ef6ca790d6c9adc0b *man/DatabionicSwarm-package.Rd
 38b5beab7724e4d05d00f795724b9e14 *man/DefaultColorSequence.Rd
 03e0de4e63019e005aaa5cb35d5ae881 *man/Delaunay4Points.Rd
 54ebb21c9ea1ebf58b20c875dc0e4440 *man/Delta3DWeightsC.Rd

R/GeneratePswarmVisualization.R

@@ -118,10 +118,10 @@ calcUmatrixToroid <- function(EsomNeurons){
 #########################################################################
 ##end calcUmatrixToroid
 #########################################################################
-rr=round(Columns/10,0)
-toroid=T
-if(rr<10){
-  HeuristischerParameter=8
+rr=round(max(c(Columns,Lines))/10,0)
+
+if(rr<12){
+  HeuristischerParameter=12
 }else{
   HeuristischerParameter=rr
 }
@@ -160,7 +160,7 @@ vec=pmax(seq(from=AnfangsRadius-1,by=-1,length.out = HeuristischerParameter),1)
   for (i in vec){
     CurrentRadius =  i#max(AnfangsRadius-i,1) #Endradius=1
     #Algorithmus
-      wts=sESOM4BMUs(BMUs,Data, wts, toroid, CurrentRadius,ComputeInR)
+      wts=sESOM4BMUs(BMUs,Data, wts, toroid=T, CurrentRadius,ComputeInR)
     print(paste0('Operator: getUmatrix4BMUs() at ',round(1-i/HeuristischerParameter,2)*100,'%'))
   } # end 1:epochs
 

man/DatabionicSwarm-package.Rd

@@ -45,7 +45,7 @@ DBS is a flexible and robust clustering framework that consists
  sensitive parameters. The clustering can be verified by the visualization and vice versa. 
  The term DBS refers to the method as a whole. 
 
-For further details, see Databionic swarm in [Thrun, 2018], chapter 8.
+For further details, see Databionic swarm in [Thrun, 2018], chapter 8. Further examples will be provided in \url{www.deepbionics.org}.
 
  If you want to verifiy your clustering result externally, you can use \code{Heatmap} or \code{SilhouettePlot} of the CRAN package \code{DataVisualizations}.