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crumbjpcrumbjp
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Clean up
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‎study/R/basic.r

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#!/usr/bin/evn Rscript
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# Vector
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message("== Vector ==")
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A<-c(1,2,3,4,5)
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print(A)
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A<-1:5
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print(A)
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print(length(A))
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# Label
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message("== Labeling ==")
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names(A)<-c('aa','bb','cc','dd','ee')
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print(A)
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# Matrix
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message("== Matrix ==")
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B<-matrix(0:9,2,5)
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message("== dim(B) ==")
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print(dim(B))
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message("== B ==")
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print(B)
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B[1,2]<-99
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message("== B[1,] ==")
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B[1,]
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message("== B[,3] ==")
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B[,3]
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print(B)
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# Matrix label
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message("== Matrix labeling ==")
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colnames(B)<-c('aa','bb','cc','dd','ee')
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rownames(B)<-c('XX','YY')
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message("== B(labeled) ==")
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print(B)
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# Array
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message("== Array ==")
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C<-array(1:30,c(2,5,3))
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print(C)
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print(C[1,4,2])
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# List
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message("== List ==")
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D<-list(A,B,C)
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print(D)
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print(D[[2]])
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print(D[[3]][2,5,3])
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# Write
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message("== Write ==")
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write(A,"A.txt")
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write.table(t(A),"At.txt")
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write(B,"B.txt")
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write.table(B,"Bt.txt")
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write(C,"C.txt")
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for(i in 1:dim(C)[3]){
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write(t(C[,,i]),paste("Ct",i,".txt",sep=""))
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}
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save(D,file="Ds.txt")
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# Read
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message("== Read ==")
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read.table("At.txt")
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read.table("Bt.txt")
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matrix(scan("B.txt"),2,5,byrow=T)
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data.frame(scan("B.txt",what=list(aa=0,bb=0,cc=0,dd=0,ee=0)))
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Cr<-array(0,c(2,5,3))
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Cr[,,1]=matrix(scan("Ct1.txt"),2,5,byrow=T)
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Cr[,,2]=matrix(scan("Ct2.txt"),2,5,byrow=T)
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Cr[,,3]=matrix(scan("Ct3.txt"),2,5,byrow=T)
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print(Cr)
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rm(D)
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load("Ds.txt")
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print(D)

‎study/R/calc.r

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#!/usr/bin/evn Rscript
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message("== Data ==")
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B<-matrix(0:24,5,5)
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colnames(B)<-c('C1','C2','C3','C4','C5')
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rownames(B)<-c('R1','R2','R3','R4','R5')
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B[1,2]<-99
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message("== B ==")
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B
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message("== B*2 ==")
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B*2
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message("== B+matrix(100:129,5,5) ==")
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B+matrix(100:129,5,5)
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message("== matrix(c(10,20,30,40,50),5,1)*c(1,2,3,4,5) ==")
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matrix(c(10,20,30,40,50),5,1)*c(1,2,3,4,5)
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message("== B*c(1,2,3,4,5) ==")
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B*c(1,2,3,4,5)
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message("== SUM col ==")
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apply(B,1,sum)
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message("== MEAN col ==")
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apply(B,1,mean)
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message("== SUM row ==")
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apply(B,2,sum)
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message("== MY row ==")
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my<-function(x){
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x-mean(x)
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}
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apply(B,2,my)

‎study/R/cluster.r

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#!/usr/bin/evn Rscript
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library(rpart)
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library(randomForest)
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library(e1071)
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data(iris)
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d<-iris
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plot(d[,1], d[,3], col=c(2,3,4)[unclass(d$Species)],main="Origin")
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message("=== RandomForest ===")
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forest <- randomForest(Species~.,data=d,ntree=500,importance=T)
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predforest <- predict(forest,d,type="class")
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table(d$Species,predforest)
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plot(d[,1], d[,3], col=c("red","blue","green")[unclass(predforest)],main="Forest")
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message("=== Rpart ===")
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tree <- rpart(Species~.,data=d,method="class")
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predtree <- predict(tree,d,type="class")
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table(d$Species,predtree)
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plot(d[,1], d[,3], col=c("red","blue","green")[unclass(predtree)],main="Rpart")
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message("=== Cmeans ===")
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r<-cmeans(d[,-5],3,150,verbose=F,method="cmeans",m=2)
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c<-apply(r$membership,1,function(x){if ( max(x) < 0.6 ) { which.max(x)+10 }else{ which.max(x)}} )
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table(d$Species,c)
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c<-apply(r$membership,1,function(x){rgb(x[1],x[2],x[3])})
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plot(d[,1], d[,3], col=c,main="Cmeans")
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message("=== Ufcl ===")
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r<-cmeans(d[,-5],3,150,verbose=F,method="ufcl",m=2)
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c<-apply(r$membership,1,function(x){if ( max(x) < 0.6 ) { which.max(x)+10 }else{ which.max(x)}} )
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table(d$Species,c)
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c<-apply(r$membership,1,function(x){rgb(x[1],x[2],x[3])})
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plot(d[,1], d[,3], col=c,main="Ufcl")
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message("=== Kmeans hartigan-wong ===")
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k<-kmeans(d[,-5],3,iter.max=10,nstart=1,algorithm="Hartigan-Wong")
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table(d$Species,k$cluster)
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plot(d[,1], d[,3], col=k$cluster,main="Kmeans hartigan-wong")
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message("=== average ===")
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h<-hclust(dist(d[,-5]),method = "average")
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table(d$Species,cutree(h,3))
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plot(d[,1], d[,3], col=cutree(h,3),main="average")
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h<-hclust(dist(d[,-5])^2,method = "average")
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table(d$Species,cutree(h,3))
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plot(d[,1], d[,3], col=cutree(h,3),main="average^2")
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message("=== complete ===")
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h<-hclust(dist(d[,-5]),method = "complete")
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table(d$Species,cutree(h,3))
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plot(d[,1], d[,3], col=cutree(h,3),main="complete")
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h<-hclust(dist(d[,-5])^2,method = "complete")
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table(d$Species,cutree(h,3))
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plot(d[,1], d[,3], col=cutree(h,3),main="complete^2")
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message("=== median ===")
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h<-hclust(dist(d[,-5]),method = "median")
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table(d$Species,cutree(h,3))
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plot(d[,1], d[,3], col=cutree(h,3),main="median")
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message("=== ward ===")
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h<-hclust(dist(d[,-5]),method = "ward")
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table(d$Species,cutree(h,3))
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plot(d[,1], d[,3], col=cutree(h,3),main="ward")
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h<-hclust(dist(d[,-5])^2,method = "ward")
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table(d$Species,cutree(h,3))
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plot(d[,1], d[,3], col=cutree(h,3),main="ward^2")
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‎study/R/graph.r

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#!/usr/bin/evn Rscript
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data(iris)
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# PLOT
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plot(iris[,1],iris[,3])
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# PLOT VAL
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plot(iris[,1],iris[,3],type='n')
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text(iris[,1],iris[,3])
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# PLOT TXT
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plot(iris[,1],iris[,3],type='n')
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text(iris[,1],iris[,3],rep(c("S","C","V"),rep(50,3)))
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# BIG
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plot(iris[,1],iris[,3],type="p",xlab="Length of Sepal", ylab="Length of Petal",cex=2,col="red")
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# Color
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plot(iris[,1],iris[,3],pch = 21, cex=0.5,bg = c(2, 3, 4)[unclass(iris$Species)],col=c(2,3,4)[unclass(iris$Species)])
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# Cloud
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library(lattice)
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cloud(Sepal.Length ~ Petal.Length * Petal.Width, data = iris,groups=Species,pch=c(16,17,18),cex=0.5,bg = c(2, 3, 4),col=c(2,3,4))
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cloud(Sepal.Length ~ Petal.Length * Petal.Width, data = iris,groups=Species,pch=c(16,17,18),cex=0.5,bg = c(2, 3, 4),col=c(2,3,4),screen=list(z=0,x=10,y=-15))
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‎study/R/predict.r

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#!/usr/bin/evn Rscript
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library(rpart)
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library(rpart.plot)
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data(iris)
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d<-iris
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s <- sample(nrow(d),nrow(d)*0.8)
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d.sample <- d[s,]
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d.test <- d[-s,]
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tree <- rpart(Species~.,data=d.sample,method="class")
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predtree <- predict(tree,d.test,type="class")
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table(predtree,d.test$Species)
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rpart.plot(tree,yesno=F,extra=101,type=4,faclen=15,varlen=15,fallen.leaves=T,snip=T)

‎study/R/randomForest.r

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#!/usr/bin/evn Rscript
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library(randomForest)
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data(iris)
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d<-iris
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s <- sample(nrow(d),nrow(d)*0.5)
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d.sample <- d[s,]
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d.test <- d[-s,]
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forest <- randomForest(Species~.,data=d.sample,ntree=500,importance=T)
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forest
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plot(forest)
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varImpPlot(forest)
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importance(forest)
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predforest <- predict(forest,d.test,type="class")
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table(predforest,d.test$Species)

‎study/R/rpart.r

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#!/usr/bin/evn Rscript
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library(rpart.plot)
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data(iris)
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tree <- rpart(Species~.,data=iris,method="class")
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rpart.plot(tree,yesno=F,extra=101,type=4,faclen=15,varlen=15)
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rpart.plot(tree,yesno=F,extra=101,type=4,faclen=15,varlen=15,fallen.leaves=T,snip=T)
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rpart.plot(tree,branch.type=4,yesno=F,faclen=15,varlen=15,extra=101)
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