For now, I've been able to think of a temporary solution... until I can
think of a better teaching solution:
The same function f():
#####
f <- function(xmd) {
rs <- NULL
xmd1 <- 999999999
xmd2 <- 999999999
if (xmd[1] > 0) {
xmd1 <- f(r$merge[ xmd[1], ])
} else {
xmd1 <- xmd[1]
}
if (xmd[2] > 0) {
xmd2 <- f(r$merge[ xmd[2], ])
} else {
xmd2 <- xmd[2]
}
return( rbind( rs, rbind( xmd1, xmd2 ) ) )
}
####
Didactic example to illustrate the chain behavior of the "Single Link"
Algorithm
n <- 100
d <- matrix( runif( n * 2 ), n, 2 )
r <- hclust( dist( d ), met='single' )
plot( d, cex=1, col=1 )
for( i in 1:( nrow( d ) - 1 ) ){
p <- as.numeric( abs( f( r$merge[ i, ] ) ) )
points( d[ p,], col=i, cex=2, pch=19 )
cat(' Step: ', i, '\n' )
flush.console()
Sys.sleep( .2 )
}
Em 26/10/2024 16:14, Cleber Borges escreveu:
Hello everybody,
I'm trying to build a function to illustrate, in 2D, the sequence of
the "Single Link" algorithm (the purpose is merely didactic).
The idea is to have the scatter of points on a graph.
Iteratively, build the segments (with the "segments()" function, for
each step).
I simulated a data set "d", and created an object "r" using the command:
r <- hclust( dist( d ), met='single' )
My problem:
I created a recursive function, "f()", to find the shortest distances.
It is defined below.
But the return has only one column, while I thought there would be two
columns.
What I get:
###############
# Testing f()
f( c(2, 3) ) #### is the fourth step of hclust
[,1]
xmd1 -6
xmd2 -10
xmd1 -3
xmd1 -4
xmd2 -5
What I expected:
##################
# xmd1 xmd2
# -6 -10
# -3 -5
# -4 -5
If anyone can help me with this recursive function, I would appreciate
it in advance.
I've never used recursion before, so I don't have a good grasp of how
it works.
Thank you very much in advance,
Cleber.
All details are below:
########################
d <- scan()
0.986 0.900
-1.331 1.503
-0.220 -0.752
0.102 -0.071
-0.171 0.018
-0.782 0.490
1.154 -0.074
-0.768 -1.529
1.761 -1.396
-0.730 0.910
d <- matrix( d, 10, byrow=T )
r <- hclust( dist( d ), met='single' )
r$merge
# [,1] [,2]
# [1,] -4 -5
# [2,] -6 -10
# [3,] -3 1
# [4,] 2 3
# [5,] -2 4
# [6,] -8 5
# [7,] -1 -7
# [8,] 6 7
# [9,] -9 8
f <- function(xmd) {
rs <- NULL
xmd1 <- 999999999
xmd2 <- 999999999
if (xmd[1] > 0) {
xmd1 <- f(r$merge[ xmd[1], ])
} else {
xmd1 <- xmd[1]
}
if (xmd[2] > 0) {
xmd2 <- f(r$merge[ xmd[2], ])
} else {
xmd2 <- xmd[2]
}
return( rbind( rs, rbind( xmd1, xmd2 ) ) )
}
# Testing f()
f( c(2, 3) )
# result of f()
# [,1]
# xmd1 -6
# xmd2 -10
# xmd1 -3
# xmd1 -4
# xmd2 -5
# My expectative:
# xmd1 xmd2
# -6 -10
# -3 -5
# -4 -5
# Testing f()
f( c( 6, 7) )
# result of f()
# [,1]
# xmd1 -8
# xmd1 -2
# xmd1 -6
# xmd2 -10
# xmd1 -3
# xmd1 -4
# xmd2 -5
# xmd1 -1
# xmd2 -7
# My expectative:
# xmd1 xmd2
# -8 -10
# -2 -10
# -6 -10
# -3 -5
# -4 -5
# -1 -7
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