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version 1.1.1
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and beginning of version 1.1.2(bugfix RobustNormalization.R for data.frames)
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@Mthrun
Mthrun committed Jan 30, 2019
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351 changes: 184 additions & 167 deletions .Rhistory
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@@ -1,170 +1,3 @@
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/pswarmCpp.R')
updateDBT()
updateDBT()
updateDBT()
pswarmCpp(FCPS$Hepta$Data,PlotIt = T,Silent = F,Debug = T)
updateDBT()
pswarmCpp(FCPS$Hepta$Data,PlotIt = T,Silent = F,Debug = T)
?floor
updateDBT()
pswarmCpp(FCPS$Hepta$Data,PlotIt = T,Silent = F,Debug = T)
pswarmCpp(FCPS$Hepta$Data,PlotIt = T,Silent = F,Debug = T)
updateDBT()
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/pswarmCpp.R')
updateDBT()
pswarmCpp(FCPS$Hepta$Data,PlotIt = T,Silent = F,Debug = T)
pswarmCpp(FCPS$Hepta$Data,PlotIt = T,Silent = F,Debug = T)
pswarmCpp(FCPS$Hepta$Data,PlotIt = T,Silent = F,Debug = T)
resU=GeneratePswarmVisualization(Data,res$ProjectedPoints,res$LC)
#Wisconsin Breast Cancer Database
requireRpackage('mlbench')
data(BreastCancer)
BreastCancerD=BreastCancer[,2:10]
Cls=as.numeric(BreastCancer[,11])
requireNamespace('stats')
Dist1=stats::dist(BreastCancerD)
res=pswarmCpp(as.matrix(Dist1),PlotIt = T,Cls = Cls)
Data=as.matrix(BreastCancerD)
Cls2=DBSclustering(2,Data,res$ProjectedPoints,res$LC,T)
resU=GeneratePswarmVisualization(Data,res$ProjectedPoints,res$LC)
Data2=MDS(as.matrix(Dist1),OutputDimension = 9)$ProjectedPoints
resU=GeneratePswarmVisualization(Data2,res$ProjectedPoints,res$LC)
plotUmatrix(resU$Umatrix,resU$Bestmatches,Cls)
updateDBT()
updateDBT()
updateDBT()
# 03GeneralizedUnatrix.R
# erzeugung einer generalisierten U-matrix
#########################################################################
# Sammons Mapping of Chainlink
# einlesen
LrnDirectory = ReDi('GeneralizedUmatrix2017/09Originale')
LrnFileName = 'Chainlink.lrn';
V<- ReadLRN(LrnFileName,LrnDirectory)
Data= V$Data
Key = V$Key
Names = V$Header
# Feststellen der Groesse
V<- size(Data)
AnzDaten = V[1]; AnzDaten
AnzVariablen= V[2]; AnzVariablen
# Cls einlesen
V <- ReadCLS('Chainlink.cls',LrnDirectory);
ClsKey = V$ClsKey
Cls = V$Cls
TheSameKey(ClsKey,Key)
# projektionen einlesen
LrnDirectory = ReDi('GeneralizedUmatrix2017/01Transformierte')
LrnFileName = '01ChanlinkSammonMappingPlanar.lrn';
V<- ReadLRN(LrnFileName,LrnDirectory)
ProjectedPoints= V$Data
Key = V$Key
Names = V$Header
V<- size(ProjectedPoints)
AnzDaten = V[1]; AnzDaten
AnzVariablen= V[2]; AnzVariablen
PlotIt=TRUE
toroid=F
Tiled=F
ComputeInR=F
getUmatrix4Projection(Data,ProjectedPoints,PlotIt,c(),toroid,Tiled,ComputeInR)
# 03GeneralizedUnatrix.R
# erzeugung einer generalisierten U-matrix
#########################################################################
# Sammons Mapping of Chainlink
# einlesen
LrnDirectory = ReDi('GeneralizedUmatrix2017/09Originale')
LrnFileName = 'Chainlink.lrn';
V<- ReadLRN(LrnFileName,LrnDirectory)
Data= V$Data
Key = V$Key
Names = V$Header
# Feststellen der Groesse
V<- size(Data)
AnzDaten = V[1]; AnzDaten
AnzVariablen= V[2]; AnzVariablen
# Cls einlesen
V <- ReadCLS('Chainlink.cls',LrnDirectory);
ClsKey = V$ClsKey
Cls = V$Cls
TheSameKey(ClsKey,Key)
# projektionen einlesen
LrnDirectory = ReDi('GeneralizedUmatrix2017/01Transformierte')
LrnFileName = '01ChanlinkSammonMappingPlanar.lrn';
V<- ReadLRN(LrnFileName,LrnDirectory)
ProjectedPoints= V$Data
Key = V$Key
Names = V$Header
V<- size(ProjectedPoints)
AnzDaten = V[1]; AnzDaten
AnzVariablen= V[2]; AnzVariablen
PlotIt=TRUE
toroid=F
Tiled=F
ComputeInR=F
getUmatrix4Projection(Data,ProjectedPoints,PlotIt,c(),toroid,Tiled,ComputeInR)
# getUmatrix4Projection=function(Data,ProjectedPoints,PlotIt=TRUE,Cls=NULL,toroid=T,Tiled=F,ComputeInR=F){
R.version
updateDBT()
updateDBT()
data("Lsun3D")
plot3
library(rgl)
plot3d(Lsun3D$Data[,1],Lsun3D$Data[,2].Lsun3D$Data[,3],col=Lsun3D$Cls)
plot3d(Lsun3D$Data[,1],Lsun3D$Data[,2],Lsun3D$Data[,3],col=Lsun3D$Cls)
plot3d(Lsun3D$Data[,1],Lsun3D$Data[,2],Lsun3D$Data[,3],col=Lsun3D$Cls,xlab = 'X',ylab = 'Y',zlab = 'Z')
rgl.snapshot('LSUN3D','png')
getwd()
setwd(ReDi('WissenAusDaten2014/15Clusterung/04ESOMUmatrix/Leuk'))
load('Leukaemien.rda')
PlotProjectedPoints(res$ProjectedPoints,Cls)
path=ReDi('WissenAusDaten2014/15Clusterung/04ESOMUmatrix/Leuk')
setwd(path)
load('LeukaemienDBS2.rda')
plotUmatrix(ustar,DBSres$BestMatches,Cls)
ustar=calcUstarmatrix(Umatrix = DBSres$Umatrix,DBSres$Pmatrix)
plotUmatrix(ustar,DBSres$BestMatches,Cls)
Silhouette(Data2,Cls)
Cls=ReadCLS('LeukaemienDBS',path)$Cls
Silhouette(Data2,Cls)
path=ReDi('WissenAusDaten2014/15Clusterung/04ESOMUmatrix/Leuk')
setwd(path)
load('LeukaemienDBS2.rda')
Cls=ReadCLS('LeukaemienDBS',path)$Cls
Silhouette(Data,Cls)
Silhouette(Data2,Cls)
load('LeukaemienDBS2.rda')
path=ReDi('WissenAusDaten2014/15Clusterung/04ESOMUmatrix/Leuk')
setwd(path)
load('LeukaemienDBS2.rda')
Data=ReadLRN('Leukaemien',ReDi('WissenAusDaten2014/15Clusterung/01Transformierte/Leuk'))$Data
Silhouette(Data,Cls)
ClassCount(Cls)
Cls=ReadCLS('LeukaemienDBS',path)$Cls
ClassCount(Cls)
Silhouette(Data,Cls)
HeatMap(Data,Cls)
getwd
getwd()
Cls2=RenameDescendingClassSize(Cls)
Silhouette(Data,Cls2)
setwd(ReDi("WissenAusDaten2014/19Conferences/04Umatrix"))
load(file='NerV_TetrangulaClustering.rda')
Silhouette(Data,ClsNerv)
HeatMap(Data,ClsNerv)
Silhouette(Data,RenameDescendingClassSize(ClsNerv))
HeatMap(Data,RenameDescendingClassSize(ClsNerv))
imx=ReadIMX('NervTetra',getwd())
showUmatrix3D(um$Umatrix,um$Bestmatches,Cls = ClsNerv2,Imx = imx$Imx,ClsColors=c("green","blue","magenta","cyan","black","yellow","red"),BmSize = 1.2)
ClsNerv2=RenameDescendingClassSize(ClsNerv)
plotTopographicMap(um$Umatrix,um$Bestmatches,Cls = ClsNerv2,Imx = imx)
showUmatrix3D(um$Umatrix,um$Bestmatches,Cls = ClsNerv2,Imx = imx$Imx,ClsColors=c("green","blue","magenta","cyan","black","yellow","red"),BmSize = 1.2)
showUmatrix3D(um$Umatrix,um$Bestmatches,Cls = ClsNerv2,Imx = imx,ClsColors=c("green","blue","magenta","cyan","black","yellow","red"),BmSize = 1.2)
library(rgl)
rgl.snapshot('umatrix','png')
getwd()
showUmatrix3D(um$Umatrix,um$Bestmatches,Cls = ClsNerv2,Imx = imx,ClsColors=c("green","blue","magenta","cyan","black","yellow","red"),BmSize = 1.5)
showUmatrix3D(um$Umatrix,um$Bestmatches,Cls = ClsNerv2,Imx = imx,ClsColors=c("green","blue","magenta","cyan","black","yellow","red"),BmSize = 2)
rgl.snapshot('umatrix','png')
R.Version()
library(DatabionicSwarm)
Expand Down Expand Up @@ -493,3 +326,187 @@ updateDBT()
?Pswarm
updateDBT()
?`DatabionicSwarm-package`
Data=c()
Data=cbind(Data,c(2,2,3))
Data=cbind(Data,c(2,2,3))
Data
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data#)
RobustNormalization(Data)
RobustNormalization(Data,WithBackTransformation = T)
RobustNormalization(Data[,1],WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data,WithBackTransformation = T)
RobustNormalization(Data=Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data=Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data=Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data=Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data=Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data=Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data=Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data=Data,WithBackTransformation = T)
debugSource('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data=Data,WithBackTransformation = T)
RobustNormalization(Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
RobustNormalization(Data,WithBackTransformation = T)
aa=RobustNormalization(Data,WithBackTransformation = T)
Data=cbind(c(1,2,3),c(3,4,5))
aa=RobustNormalization(Data,WithBackTransformation = T)
Data=cbind(c(1,2,3),c(3,43,5))
aa=RobustNormalization(Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
aa=RobustNormalization(Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
aa=RobustNormalization(Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
aa=RobustNormalization(Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
aa=RobustNormalization(Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
aa=RobustNormalization(Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
aa=RobustNormalization(Data,WithBackTransformation = T)
source('D:/Subversion/PUB/dbt/DataBionicSwarm/R/RobustNormalization.R')
aa=RobustNormalization(Data,WithBackTransformation = T)
library(DatabionicSwarm)
?RobustNormalization
m=cbind(c(1,2,3),c(2,6,4))
List=RobustNormalization(m,T,F,F,T)
TransformedData=List$TransformedData
mback=List$Denom*(TransformedData+List$MinX)
List$Denom*
List$Denom
TransformedData+List$MinX
List$MinX
TransformedData+List$MinX
TransformedData+t(List$MinX)
TransformedData
m*c(2,3)
m
m%*%c(2,3)
TransformedData=List$TransformedData
for(i in 1:ncol(TransformedData))
mback[,i]=(TransformedData[,i]+List$MinX[i])*List$Denom[i]
mback=c()
TransformedData=List$TransformedData
for(i in 1:ncol(TransformedData))
mback[,i]=(TransformedData[,i]+List$MinX[i])*List$Denom[i]
mback=TransformedData
for(i in 1:ncol(TransformedData))
mback[,i]=(TransformedData[,i]+List$MinX[i])*List$Denom[i]
}
mback=TransformedData
for(i in 1:ncol(TransformedData)){
mback[,i]=(TransformedData[,i]+List$MinX[i])*List$Denom[i]
}
mback
m
m=cbind(c(1,2,3),c(2,6,4))
List=RobustNormalization(m,T,F,F,T)
TransformedData=List$TransformedData
mback=TransformedData
for(i in 1:ncol(TransformedData))
mback[,i]=(TransformedData[,i]+List$MinX[i])*List$Denom[i]
sum(m-mback)
m=cbind(c(1,2,3),c(2,6,4))
List=RobustNormalization(m,T,F,F,T)
TransformedData=List$TransformedData
mback=TransformedData
for(i in 1:ncol(TransformedData))
mback[,i]=(TransformedData[,i]+List$MinX[i])*List$Denom[i]
m-mback
List$MinX[i]
List$MinX
List$Denom
i=1
(TransformedData[,i]+List$MinX[i])*List$Denom[i]
i=2
(TransformedData[,i]+List$MinX[i])*List$Denom[i]
mback
m
RobustNormalization(m[,2],T,F,F,T)
m=cbind(c(1,2,3),c(2,6,4))
List=RobustNormalization(m,F,F,F,T)
TransformedData=List$TransformedData
mback=TransformedData
for(i in 1:ncol(TransformedData))
mback[,i]=(TransformedData[,i]+List$MinX[i])*List$Denom[i]
sum(m-mback)
mback
m
RobustNormalization(m[,2],F,F,F,T)
TransformedData[,2]
3.92*(TransformedData[,2]+2.04)
m=cbind(c(1,2,3),c(1,2,3))
List=RobustNormalization(m,F,F,F,T)
TransformedData=List$TransformedData
mback=TransformedData
for(i in 1:ncol(TransformedData))
mback[,i]=(TransformedData[,i]+List$MinX[i])*List$Denom[i]
sum(m-mback)
m
mback
m=cbind(c(1,2,3),c(1,2,3))
List=RobustNormalization(m,F,F,F,T)
TransformedData=List$TransformedData
mback=TransformedData
for(i in 1:ncol(TransformedData))
mback[,i]=(TransformedData[,i]*List$Denom[i]+List$MinX[i])
sum(m-mback)
m
mback
m=cbind(c(1,2,3),c(2,6,4))
List=RobustNormalization(m,F,F,F,T)
TransformedData=List$TransformedData
mback=TransformedData
for(i in 1:ncol(TransformedData))
mback[,i]=TransformedData[,i]*List$Denom[i]+List$MinX[i]
sum(m-mback)
rnorm(100,2,100)
Scaled=RobustNormalization(rnorm(100,2,100))
range(Scaled)
hist(Scaled)
Scaled=RobustNormalization(rnorm(100,2,100),Capped=T)
hist(Scaled)
Scaled=RobustNormalization(rnorm(100,2,100),Capped=T)
histopt(Scaled)
Scaled=RobustNormalization(rnorm(1000,2,100),Capped=T)
histopt(Scaled)
Scaled=RobustNormalization(rnorm(1000,2,100),Capped=T)
hist(Scaled)
Scaled=RobustNormalization(rnorm(1000,2,100),Capped=F)
hist(Scaled)
hist(rnorm(1000,2,100))
library(DatabionicSwarm)
updateDBT()
updateDBT()
install.packages("D:/Subversion/PUB/dbt/DatabionicSwarm_1.1.1.zip", repos = NULL, type = "win.binary")
updateDBT()
blockDBT()
updateDBT()
updateDBT()
install.packages("D:/Subversion/PUB/dbt/DatabionicSwarm_1.1.1.tar.gz", repos = NULL, type = "source")
install.packages("D:/Subversion/PUB/dbt/DatabionicSwarm_1.1.1.zip", repos = NULL, type = "win.binary")
install.packages("D:/Subversion/PUB/dbt/DatabionicSwarm_1.1.1.zip", repos = NULL, type = "win.binary")
unblockDBT()
6 changes: 3 additions & 3 deletions DESCRIPTION
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@@ -1,14 +1,14 @@
Package: DatabionicSwarm
Type: Package
Title: Swarm Intelligence for Self-Organized Clustering
Version: 1.1.1
Date: 2018-08-25
Version: 1.1.2
Date: 2019-01-29
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>. A comparison to 26 common clustering algorithms on 15 datasets is presented on the website.
License: GPL-3
Imports: Rcpp, deldir, GeneralizedUmatrix
Suggests: DataVisualizations, knitr (>= 1.12), rmarkdown (>= 0.9),rglwidget, plotrix, geometry, sp, spdep, AdaptGauss, ABCanalysis, parallel, matrixStats, rgl, png, ProjectionBasedClustering, parallelDist, pracma
Suggests: DataVisualizations, knitr (>= 1.12), rmarkdown (>= 0.9), plotrix, geometry, sp, spdep, AdaptGauss, ABCanalysis, parallel, matrixStats, rgl, png, ProjectionBasedClustering, parallelDist, pracma
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 3.0)
NeedsCompilation: yes
Expand Down
2 changes: 1 addition & 1 deletion R/GeneratePswarmVisualization.R
View file
Expand Up @@ -176,7 +176,7 @@ vec=pmax(seq(from=AnfangsRadius-1,by=-1,length.out = HeuristischerParameter),1)
LCnew=c(dim(wts)[1],dim(wts)[2])
if(PlotIt){
requireNamespace("GeneralizedUmatrix")
GeneralizedUmatrix::plotTopographicMap(Umap,BMUs)
GeneralizedUmatrix::plotTopographicMap(Umap,BMUs,NoLevels=10)
}
return(list(Bestmatches=BMUs,Umatrix=Umap,WeightsOfNeurons=wts,GridPoints=Points,LC=LCnew))
}

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