library(mva)
library(cluster)

p1<-read.table("pah1-ir.txt.out")
p2<-read.table("pah2-ir.txt.out")
p3<-read.table("pah3-ir.txt.out")
p4<-read.table("pah4-ir.txt.out")
p5<-read.table("pah5-ir.txt.out")
p6<-read.table("pah6-ir.txt.out")

f1<-read.table("fa1-ir.txt.out")
f2<-read.table("fa2-ir.txt.out")
f3<-read.table("fa3-ir.txt.out")
f4<-read.table("fa4-ir.txt.out")
f5<-read.table("fa5-ir.txt.out")
f6<-read.table("fa6-ir.txt.out")
f7<-read.table("fa7-ir.txt.out")
f8<-read.table("fa8-ir.txt.out")
f9<-read.table("fa9-ir.txt.out")
f10<-read.table("fa10-ir.txt.out")

names(p1)<-c("wn","a")
names(p2)<-c("wn","a")
names(p3)<-c("wn","a")
names(p4)<-c("wn","a")
names(p5)<-c("wn","a")
names(p6)<-c("wn","a")

names(f1)<-c("wn","a")
names(f2)<-c("wn","a")
names(f3)<-c("wn","a")
names(f4)<-c("wn","a")
names(f5)<-c("wn","a")
names(f6)<-c("wn","a")
names(f7)<-c("wn","a")
names(f8)<-c("wn","a")
names(f9)<-c("wn","a")
names(f10)<-c("wn","a")

spec<-data.frame(p1$a,p2$a,p3$a,p4$a,p5$a,p6$a,
					f1$a,f2$a,f3$a,f4$a,f5$a,f6$a,f7$a,f8$a,f9$a,f10$a)

names(spec)<-c("pah1","pah2","pah3","pah4","pah5","pah6",
				"fa1","fa2","fa3","fa4","fa5","fa6","fa7","fa8","fa9","fa10")

spec.t <- t(spec)
hc<-hclust(dist(spec.t),"ave")
#plot(hc)

initial <- tapply( spec.t, list(rep(cutree(hc,2),ncol(spec.t)),col(spec.t)), mean)

dimnames(initial)<-list(NULL,dimnames(spec.t)[[2]])
km<- kmeans(spec.t, initial)

spec.pca<-princomp(spec.t)
spec.px<-predict(spec.pca)
dimnames(km$centers)[[2]]<-dimnames(spec.t)[[2]]
spec.centers<-predict(spec.pca,km$centers)
plot(spec.px[,1:2],type="n", xlab="PC1", ylab="PC2")
text(spec.px[,1:2], cex=1, labels=km$cluster)
points(spec.centers[,1:2], pch=3, cex=3)
identify(spec.px[,1:2],cex=0.2,col="red")