Nothing
library(amap)
set.seed(1234)
data(USArrests)
METHODS <- c("euclidean", "maximum", "manhattan", "canberra",
"binary","pearson","correlation","spearman","kendall",
"abspearson","abscorrelation")
METHODSLINKS <- c("ward", "single", "complete", "average", "mcquitty",
"median", "centroid","centroid2","ward.D2")
for (mymethod in METHODS) {
d = Dist(USArrests, method = mymethod)
k = Kmeans(USArrests, centers = 4, method = mymethod)
print(k)
for (mylink in METHODSLINKS)
{
cat(mylink)
cat(mymethod)
hc <- hcluster(USArrests,link = mylink, method = mymethod, nbproc=4)
print(hc)
}
}
COMMONDIST <- c("euclidean", "maximum", "manhattan", "canberra",
"binary")
COMMONLINKS <- c( "single", "complete", "average", "mcquitty",
"median", "centroid","ward.D2")
for (mymethod in COMMONDIST) {
d = dist(USArrests,method = mymethod)
d2 = Dist(USArrests,method = mymethod)
cat("test",mymethod)
stopifnot(floor(d * 1000) == floor(d2*1000))
}
d = dist(USArrests)
for(mylink in COMMONLINKS){
cat("test",mylink)
h = hclust(d, method = mylink)
hc = hcluster(USArrests,link = mylink)
stopifnot(h$order == hc$order)
stopifnot(floor(h$height * 1000) == floor(hc$height*1000))
}
hc <- hcluster(USArrests, nbproc=1)
print(hc)
KERNELS = c("gaussien", "quartic", "triweight", "epanechikov" ,
"cosinus", "uniform")
for(myKernel in KERNELS) {
myacp = acprob(USArrests, kernel = myKernel);
print(myacp)
}
d <-2 * matrix(c(9, 8, 5, 7, 7, 2
, 8, 9, 2, 5, 1, 7
, 5, 2, 9, 8, 7, 1
, 7, 5, 8, 9, 3, 2
, 7, 1, 7, 3, 9, 6
, 2, 7, 1, 2, 6, 9),ncol=6,byrow=TRUE) - 9
pop(d)
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