SOM¶
In [14]:
library(kohonen)
library(RColorBrewer) # to use brewer.pal
library(fields) # to use designer.colors
library(ggplot2)
In [17]:
set.seed(1)
sm <- som(data = cdata.n, grid = somgrid(10, 10, "rectangular"))
str(sm)
In [18]:
sm$codes # 10x10 Grid에 각각에 해당하는 Weight, Node수만큼의 Weight
In [19]:
options(repr.plot.width=6,repr.plot.height=5)
plot(sm, main = "feature distribution")
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plot(sm, type="counts", main = "cluster size")
In [21]:
plot(sm, type="quality", main = "mapping quality")
In [22]:
coolBlueHotRed <- function(n, alpha = 1) {
rainbow(n, end=4/6, alpha=alpha)[n:1]
}
for (i in 1:ncol(sm$data))
plot(sm, type="property", property=sm$codes[,i], main=dimnames(sm$data)[[2]][i], palette.name=coolBlueHotRed)
ggplot2 패키지를 이용하여 SPSS Modeler와 유사한 Grid 도식¶
In [23]:
cdata$clusterX <- sm$grid$pts[sm$unit.classif,"x"]
cdata$clusterY <- sm$grid$pts[sm$unit.classif,"y"]
p <- ggplot(cdata, aes(clusterX, clusterY))
p + geom_jitter(position = position_jitter(width=.2, height=.2))
In [24]:
k = 6
somClusters <- kmeans(sm$codes, centers = k)
plot(0, 0, type = "n", axes = FALSE, xlim = c(0,sm$grid$xdim), ylim = c(0,sm$grid$ydim), xlab = "", ylab = "", asp = 1)
ColRamp <- rev(designer.colors(n=k, col=brewer.pal(k,"Set1")))
ColorCode <- rep("#FFFFFF", length(somClusters$cluster))
for (i in 1:length(somClusters$cluster))
ColorCode[i] <- ColRamp[somClusters$cluster[i]]
offset <- 0.5
for (row in 1:sm$grid$ydim) {
for (column in 0:(sm$grid$xdim-1))
Hexagon(column + offset, row - 1, col = ColorCode[row + sm$grid$ydim * column])
offset <- ifelse(offset, 0, 0.5)
}
