library(hclustdemo)
N=1000
x = runif(N/4)
y = runif(N/4)
points = cbind(c(x,x+1.5,x,x+1.5),
c(y,y+1.5,y+1.5,y))
plot(points)
distance.matrix = as.matrix(dist(points))
clusters = my.hclust(distance.matrix, 4)
library(hclustdemo)
N=1000
x = runif(N/4)
y = runif(N/4)
points = cbind(c(x,x+1.5,x,x+1.5),
c(y,y+1.5,y+1.5,y))
plot(points)
distance.matrix = as.matrix(dist(points))
## RUNNING MY.HCLUST
#Rprof()
clusters = my.hclust(distance.matrix, 4)
lapply(clusters, length)
library(hclustdemo)
N=10
x = runif(N/4)
y = runif(N/4)
points = cbind(c(x,x+1.5,x,x+1.5),
c(y,y+1.5,y+1.5,y))
plot(points)
distance.matrix = as.matrix(dist(points))
clusters = my.hclust(distance.matrix, 4)
lapply(clusters, length)
clusters
clusters = my.hclust(distance.matrix, 4)
lapply(clusters, length)
clusters = my.hclust(distance.matrix,4)
clusters = my.hclust(distance.matrix,4)
library(hclustdemo)
clusters = my.hclust(distance.matrix,4)
my.hclust(distance.matrix,4)
library(hclustdemo)
my.hclust(distance.matrix,4)
jmins=c(1,2)
jmins[20]
jmins[50]
jmins[500]
library(hclustdemo)
my.hclust(distance.matrix,4)
library(hclustdemo)
my.hclust(distance.matrix,4)
my.hclust(distance.matrix,4)
library(hclustdemo)
my.hclust(distance.matrix,4)
my.hclust(distance.matrix,4)
v
v=c(1,2)
v[0]
v[0]
my.hclust(distance.matrix,4)
library(hclustdemo)
my.hclust(distance.matrix,4)
View(distance.matrix)
traceback()
traceback()
where
where
library(hclustdemo)
## GENERATING SQUARED DISTANCE MATRIX
N=1000
x = runif(N/4)
y = runif(N/4)
points = cbind(c(x,x+1.5,x,x+1.5),
c(y,y+1.5,y+1.5,y))
plot(points)
distance.matrix = as.matrix(dist(points))
clusters = my.hclust(distance.matrix, 4)
library(hclustdemo)
clusters = my.hclust(distance.matrix, 4)
library(hclustdemo)
library(hclustdemo)
N=100
x = runif(N/4)
y = runif(N/4)
points = cbind(c(x,x+1.5,x,x+1.5),
c(y,y+1.5,y+1.5,y))
plot(points)
distance.matrix = as.matrix(dist(points))
## RUNNING MY.HCLUST
#Rprof()
clusters = my.hclust(distance.matrix, 4)
lapply(clusters, length)
#
clusters = my.hclust(distance.matrix, 4)
