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# Measures used as manipulation targets.
# NOTE: This file is only a library, see run.R for more details.
NP <- function(Dx, Dy, k = 9) {
if (any(Dx != t(Dx)) || any(Dy != t(Dy))) {
stop("Dx and Dy must be symmetric")
}
Dx <- as.matrix(Dx)
Dy <- as.matrix(Dy)
if (nrow(Dx) != nrow(Dy)) {
stop("Dx and Dy must have the same number of elements")
}
n <- nrow(Dx)
if (k >= n) {
stop("k must be smaller than the number of elements")
}
preservation <- vector("numeric", n)
for (i in 1:n) {
nx <- order(Dx[i, ])[1 + 1:k]
ny <- order(Dy[i, ])[1 + 1:k]
diff <- setdiff(nx, ny)
preservation[i] <- (k - length(diff)) / k
}
preservation
}
#silhouette <- function(Dy, labels) {
# if (any(t(Dy) != Dy)) {
# stop("Dy must be symmetric")
# }
#
# Dy <- as.matrix(Dy)
# n <- nrow(Dy)
# cohesion <- vector("numeric", n)
# separation <- vector("numeric", n)
#
# for (i in 1:n) {
# label <- labels[i]
# separation[i] <- min(Dy[i, labels != label])
# cohesion[i] <- mean(Dy[i, labels[-i] == label])
# }
#
# (separation - cohesion) / max(separation, cohesion)
#}
silhouette <- function(Dy, labels) {
n <- nrow(Dy)
if (n != length(labels)) {
stop("Number of labels doesn't match number of points")
}
A_labels <- list()
B_labels <- list()
unique.labels <- unique(labels)
for (l in unique.labels) {
A_labels[[l]] <- labels == l
B_labels[[l]] <- labels != l
}
# This factor excludes self comparisons when computing cohesion
m.factor <- n / (n-1)
s <- vector("numeric", n)
for (i in 1:n) {
label_i <- labels[i]
a <- m.factor * mean(Dy[i, A_labels[[label_i]]]) # cohesion
b <- Inf
for (l in unique(labels)) {
b <- min(mean(Dy[i, B_labels[[l]]]), b) # separation
}
s[i] <- (b - a) / max(b, a)
}
s
}
#stress <- function(Dx, Dy) {
# if (any(Dx != t(Dx)) || any(Dy != t(Dy))) {
# stop("Dx and Dy must be symmetric")
# }
#
# Dx <- as.matrix(Dx)
# Dy <- as.matrix(Dy)
# if (nrow(Dx) != nrow(Dy)) {
# stop("Dx and Dy must have the same number of elements")
# }
#
# n <- nrow(Dx)
# s <- vector("numeric", n)
# for (i in 1:n) {
# s[i] <- 0
# for (j in 1:n) {
# if (i == j) {
# next
# }
#
# s[i] = s[i] + (Dx[i, j] - Dy[i, j])^2 / Dx[i, j]
# }
# s[i] = s[i] / sum(D[i, ])
# }
#
# s
#}
stress <- function(Dx, Dy) {
n <- nrow(Dx)
C <- 0
D <- 0
for (i in 1:(n-1)) {
for (j in (i + 1):n) {
C <- C + Dx[i, j]
D <- D + (Dx[i, j] - Dy[i, j])^2 / Dx[i, j]
if (is.nan(D)) {
loginfo("%d, %d", i, j)
loginfo("%f", Dx[i, j])
stop("NaN")
}
}
}
D / C
}
# NOTE: This function requires the 'klmeasure' binary from:
# http://research.cs.aalto.fi/pml/software/dredviz/
smoothed.pr <- function(Dx, Dy, k) {
# Create SOM_PAK file for Dx
Dx.fname <- tempfile()
Dx.f <- file(Dx.fname, "w")
cat(sprintf("%d\n", ncol(Dx)), file=Dx.f)
write.table(Dx, Dx.f, col.names=F, row.names=F)
close(Dx.f)
# Create SOM_PAK file for Dy
Dy.fname <- tempfile()
Dy.f <- file(Dy.fname, "w")
cat(sprintf("%d\n", ncol(Dy)), file=Dy.f)
write.table(Dy, Dy.f, col.names=F, row.names=F)
close(Dy.f)
output <- system2("./klmeasure",
stdout=T,
args=c("--datadist", Dx.fname,
"--projdist", Dy.fname,
"--neighbors", sprintf("%d", k)))
output <- strsplit(output[2], " | ", fixed=T)
output <- unlist(output)
file.remove(Dx.fname, Dy.fname)
list(s.precision=as.double(output[1]), s.recall=as.double(output[2]))
}
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