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該代碼是嘗試使用簡單的相異性度量（即0-0匹配完美匹配的地方）編寫二進制變量的聚類代碼。爲了確保它不會以局部最小值結束，我需要運行幾次。但偶爾，我會得到與引入NA值有關的錯誤消息。代碼中沒有使用強制。我不知道NA價值如何增長。爲什麼在集羣代碼中有一個NA介紹？
dissim<-function(a,b){
match<-sum(a==b)
unmatch<-sum(a!=b)
sim<-match/(match+unmatch)
dissim<-1-sim
return(dissim)
}
findmode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
pleasecluster<-function(df){
##create will create the first prototypes i.e. initial cluster centres. First prototype is random, second is the centre farthest from it.
create<-function(dataframe){
proto1<-NULL
for(i in 1:length(dataframe[1,])){
proto1<-c(proto1, sample(c(0,1), 1))
}
proto2<-as.numeric(proto1==0)
return(list(proto1, proto2))
}
##This function will assign a cluster index to each entry in the original data frame.
clusterassign<-function(proto1, proto2, dataframe){
clustervector<-NULL
for(i in 1:length(dataframe[,1])){
dis1<-dissim(dataframe[i,], proto1)
dis2<-dissim(dataframe[i,], proto2)
clusterindex<-which.min(c(dis1, dis2))
clustervector<-c(clustervector, clusterindex)
}
return(clustervector)
}
##Based on the created clusters, this will then find the centres of those clusters
updproto<-function(clvec, dataframe){
cluster1<-(dataframe[clvec==1,])
cluster2<-(dataframe[clvec==2,])
newproto1<-NULL
newproto2<-NULL
for(i in 1:length(dataframe[1,])){
mode1<-findmode(cluster1[,i])
newproto1<-c(newproto1, mode1)
mode2<-findmode(cluster2[,i])
newproto2<-c(newproto2, mode2)
}
return(list(newproto1, newproto2))
}
##This will match the centres found of the current clusters and the initial centres used
checkproto<-function(oldproto1, olproto2, newproto1, newproto2){
if (sum(oldproto1!=newproto1)>0){a1<-FALSE}
else{a1<-TRUE}
if (sum(oldproto2!=newproto2)>0){a2<-FALSE}
else{a2<-T}
return(c(a1,a2))
}
##The main function
starter<-create(df)
proto1<-starter[[1]]
proto2<-starter[[2]]
count<-1
repeat{
clvec<-clusterassign(proto1, proto2, df)
oldproto1<-proto1
oldproto2<-proto2
upd<-updproto(clvec, df)
proto1<-upd[[1]]
proto2<-upd[[2]]
check<-checkproto(oldproto1, oldproto2, proto1, proto2)
count<-count+1
#calc total dissimilarity
totdiss1<-NULL
totdiss2<-NULL
cluster1<-df[clvec==1,]
for(i in 1:sum(clvec==1)){
dissi1<-dissim(cluster1[i,],proto1)
totdiss1<-sum(totdiss1, dissi1)
}
cluster2<-df[clvec==2,]
for(i in 1:sum(clvec==2)){
dissi2<-dissim(cluster2[i,],proto2)
totdiss2<-sum(totdiss2, dissi2)
}
totdiss<-totdiss1+totdiss2
if((all(check))|count>50){break}
}
return(list(oldproto1, oldproto2, clvec, count, totdiss, totdiss1, totdiss2))
}
對於測試數據集，我得到了正確的值和錯誤信息。
a
c.1..1. c.1..0. c.1..1..1 c.0..0. c.0..0..1 c.0..0..2 c.1..1..2 c.1..1..3
1 1 1 1 0 0 0 1 1
2 1 0 1 0 0 0 1 1
3 1 1 1 1 0 0 1 1
4 1 1 1 0 0 0 1 1
5 1 1 0 0 0 0 1 1
6 0 0 0 1 1 1 1 1
7 0 1 0 1 1 1 1 1
8 0 0 0 1 1 1 1 1
9 0 0 0 1 0 1 1 1
pleasecluster(a)
[[1]]
[1] 1 1 1 0 0 0 1 1
[[2]]
[1] 0 0 0 1 1 1 1 1
[[3]]
[1] 1 1 1 1 1 2 2 2 2
[[4]]
[1] 4
[[5]]
[1] 0.625
[[6]]
[1] 0.375
[[7]]
[1] 0.25
pleasecluster(a)
Error in if (sum(oldproto2 != newproto2) > 0) { :
missing value where TRUE/FALSE needed
請讓我知道這是否更適合代碼審查或數據科學SE。
附錄
考慮，可能出現錯誤，由於情況下集羣之一將是空的（導致NA每當行稱爲值），我修改的代碼。 （我也修改了所有for循環使用ncol和nrow運行的長度）。現在我得到一個不同的錯誤。
findmode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
pleasecluster<-function(df){
##create will create the first prototypes i.e. initial cluster centres. First prototype is random, second is the centre farthest from it.
create<-function(dataframe){
repeat{proto1<-NULL
for(i in 1:ncol(dataframe)){
proto1<-c(proto1, sample(c(0,1), 1))
}
proto2<-as.numeric(proto1==0)
if (length(unique(proto1))>1){break}
}
return(list(proto1, proto2))
}
##This function will assign a cluster index to each entry in the original data frame.
clusterassign<-function(proto1, proto2, dataframe){
clustervector<-NULL
for(i in 1:nrow(dataframe)){
dis1<-dissim(dataframe[i,], proto1)
dis2<-dissim(dataframe[i,], proto2)
clusterindex<-which.min(c(dis1, dis2))
clustervector<-c(clustervector, clusterindex)
}
return(clustervector)
}
##Based on the created clusters, this will then find the centres of those clusters
updproto<-function(clvec, dataframe){
cluster1<-(dataframe[clvec==1,])
cluster2<-(dataframe[clvec==2,])
newproto1<-NULL
newproto2<-NULL
if (nrow(cluster2)>0&nrow(cluster1)>0) {for(i in 1:ncol(dataframe)){
mode1<-findmode(cluster1[,i])
newproto1<-c(newproto1, mode1)
mode2<-findmode(cluster2[,i])
newproto2<-c(newproto2, mode2)
}}
else {starter<-create(dataframe)
newproto1<-starter[[1]]
newproto2<-starter[[2]]}
return(list(newproto1, newproto2))
}
##This will match the centres found of the current clusters and the initial centres used
checkproto<-function(oldproto1, olproto2, newproto1, newproto2){
if (sum(oldproto1!=newproto1)>0){a1<-FALSE}
else{a1<-TRUE}
if (sum(oldproto2!=newproto2)>0){a2<-FALSE}
else{a2<-T}
return(c(a1,a2))
}
##The main function
starter<-create(df)
proto1<-starter[[1]]
proto2<-starter[[2]]
count<-1
repeat{
clvec<-clusterassign(proto1, proto2, df)
oldproto1<-proto1
oldproto2<-proto2
upd<-updproto(clvec, df)
proto1<-upd[[1]]
proto2<-upd[[2]]
check<-checkproto(oldproto1, oldproto2, proto1, proto2)
count<-count+1
#calc total dissimilarity
totdiss1<-NULL
totdiss2<-NULL
cluster1<-df[clvec==1,]
for(i in 1:nrow(cluster1)){
dissi1<-dissim(cluster1[i,],proto1)
totdiss1<-sum(totdiss1, dissi1)
}
cluster2<-df[clvec==2,]
for(i in 1:nrow(cluster2)){
dissi2<-dissim(cluster2[i,],proto2)
totdiss2<-sum(totdiss2, dissi2)
}
totdiss<-totdiss1+totdiss2
if((all(check))|count>50){break}
}
return(list(oldproto1, oldproto2, clvec, count, totdiss, totdiss1, totdiss2))
}
pleasecluster(a)
Hide Traceback
Rerun with Debug
Error in e2[[j]] : subscript out of bounds
3 Ops.data.frame(a, b)
2 dissim(cluster2[i, ], proto2)
1 pleasecluster(a)
當您嘗試使用'create'函數時，您應該收到錯誤消息。它不會在'globalenv（）'中。 –
@ 42-這個錯誤信息在腳本的罕見演繹中出現。那怎麼會是由於環境分配不當造成的？我沒有得到如何不能在.glodalenv（）導致問題。 –
@ 42-對不起，但我自己學習R。因此，我可能不知道什麼會是相當常見的知識！ –