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nominal_to_binary <- function( data )
{
result = NULL
for (i in 1:ncol(data))
{
#print(i)
if (is.numeric( data[,i] ) )
{
if (is.null(result))
result = data.frame(data[,i])
else
result = data.frame(result, data[,i])
colnames(result)[ncol(result)] <- colnames(data)[i]
}
else
{
vals = unique(data[,i])
for (j in 1:length(vals))
{
#print(j)
bins = c()
for (k in 1:nrow(data))
{
if(data[,i][k] == vals[j])
bins = c(bins,1)
else
bins = c(bins,0)
}
#print(bins)
if (is.null(result))
result = data.frame(bins)
else
result = data.frame(result, bins)
colnames(result)[ncol(result)] <- paste(colnames(data)[i],"is",vals[j])
if (length(vals)==2) break
}
}
}
#print(head(result))
result
}
process_data <- function( data )
{
data.num <- as.data.frame(data)
if (!is.numeric(data.num))
{
data.num = nominal_to_binary(data.num)
}
if(any(is.na(data.num)))
{
require("gam")
data.repl = na.gam.replace(data.num)
}
else
data.repl = data.num
data.repl
}
cluster <- function( data, min=10, max=15 )
{
require("vegan")
max <- min(max,nrow(unique(data)))
max <- min(max,nrow(data)-1)
if (min>max)
min=max
print(paste("cascade k-means ",min," - ",max))
s = cascadeKM(data,min,max,iter=30)
m = max.col(s$results)[2]
print(paste("best k-means clustering result: ",((m-1)+min)," num clusters"))
cbind(s$partition[,m])
}
stratified_split <- function( data, ratio=0.3, method="cluster" )
{
data.processed = as.matrix(process_data( data ))
if (method == "samplecube")
{
require("sampling")
# adjust ratio to make samplecube return exact number of samples
ratio = round(nrow(data.processed)*ratio)/nrow(data.processed)
pik = rep(ratio,times=nrow(data.processed))
data.strat = cbind(pik,data.processed)
samplecube(data.strat,pik,order=2,comment=F)
}
else if (method == "cluster")
{
cl = cluster(data.processed)
# require("caret")
# res = createDataPartition(cl,p=ratio)
# split = rep(1, times=nrow(data))
# for (j in 1:nrow(data))
# if ( is.na(match(j,res$Resample1)) )
# split[j]=0
# split
require("sampling")
stratified_split(cl,ratio,"samplecube")
}
else
stop("unknown method")
}
stratified_k_fold_split <- function( data, num_folds=10, method="cluster" )
{
print(paste(num_folds,"-fold-split, data-size",nrow(data)))
data.processed = as.matrix(process_data( data ))
if (method == "samplecube")
{
folds = rep(0, times=nrow(data))
for (i in 1:(num_folds-1))
{
require("sampling")
prop = 1/(num_folds-(i-1))
print(paste("fold",i,"/",num_folds," prop",prop))
pik = rep(prop,times=nrow(data))
for (j in 1:nrow(data))
if(folds[j]!=0)
pik[j]=0
data.strat = cbind(pik,data.processed)
s<-samplecube(data.strat,pik,order=2,comment=F)
print(paste("fold size: ",sum(s)))
for (j in 1:nrow(data))
if (s[j] == 1)
folds[j]=i
}
for (j in 1:nrow(data))
if (folds[j] == 0)
folds[j]=num_folds
folds
}
else if (method == "cluster")
{
require("TunePareto")
cl = cluster(data.processed)
res = generateCVRuns(cl,ntimes=1,nfold=3)
folds = rep(0, times=nrow(data))
for (i in 1:num_folds)
for(j in 1:length(res[[1]][[i]]))
folds[res[[1]][[i]][j]]=i
folds
}
else
stop("unknown method")
}
plot_pre_process <- function( data, method="pca" )
{
data.processed = process_data( data )
if (method == "pca")
{
data.pca <- prcomp(data.processed, scale=TRUE)
as.data.frame(data.pca$x)[1:2]
}
else if (method == "smacof")
{
require("smacof")
data.emb <- smacofSym(dist(data.processed, method = "euclidean"), ndim=2, verbose=T)
data.emb$conf
}
else
stop("unknown method")
}
plot_split <- function( data, split, names=NULL, ... )
{
if (ncol(data)!=2 || !is.numeric(data[,1]) || !is.numeric(data[,2]))
stop("data not suitable for plotting, plot_pre_process() first")
plot( NULL, xlim = extendrange(data[,1]), ylim = extendrange(data[,2]), ... )
if (is.null(names))
names <- c("split 1","split 2")
colos = as.double(rep(2:(max(split)+2)))
legend("topleft",names,pch=2,col=colos)
for (j in max(split):0)
{
set = c()
for (i in 1:nrow(data))
if (split[i] == j)
set = c(set,i)
points(data[set,], pch = 2, col=(j+2))
}
}
#a<-matrix(rnorm(100, mean=50, sd=4), ncol=5)
#b<-matrix(rnorm(5000, mean=0, sd=10), ncol=5)
#data<-rbind(a,b)
#c<-matrix(rnorm(50, mean=-50, sd=2), ncol=5)
#data<-rbind(data,c)
#data=iris
#split = stratified_k_fold_split(data, num_folds=3)
#split = stratified_split(data, ratio=0.33, method="cluster")
#print(sum(split))
#plot_split(plot_pre_process(data),split,c("training","test"))
#cl = cluster(data)
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