Self Organizing Maps in R

# Unsupervised Self-Organizing Maps
# Video: https://youtu.be/wLu213JKfnQ
library(kohonen)

# Data
data <- read.csv(file.choose(), header = T)
str(data)
X <- scale(data[,-1])
summary(X)

# SOM
set.seed(222)
g <- somgrid(xdim = 4, ydim = 4, topo = "rectangular" )
map <- som(X,
           grid = g,
           alpha = c(0.05, 0.01),
           radius = 1)
plot(map)

# Supervised Self-Organizing Maps
# Data Split
set.seed(123)
ind <- sample(2, nrow(data), replace = T, prob = c(0.7, 0.3))
train <- data[ind == 1,]
test <- data[ind == 2,]

# Normalization
trainX <- scale(train[,-1])
testX <- scale(test[,-1],
               center = attr(trainX, "scaled:center"),
               scale = attr(trainX, "scaled:scale"))
trainY <- factor(train[,1])
Y <- factor(test[,1])
test[,1] <- 0
testXY <- list(independent = testX, dependent = test[,1])

# Classification & Prediction Model
set.seed(222)
map1 <- xyf(trainX,
            classvec2classmat(factor(trainY)),
            grid = somgrid(5, 5, "hexagonal"),
            rlen = 100)
plot(map1)

# Prediction
pred <- predict(map1, newdata = testXY)
table(Predicted = pred$predictions[[2]], Actual = Y)

# Cluster Boundaries
par(mfrow = c(1,2))
plot(map1, 
     type = 'codes',
     main = c("Codes X", "Codes Y"))
map1.hc <- cutree(hclust(dist(map1$codes[[2]])), 2)
add.cluster.boundaries(map1, map1.hc)
par(mfrow = c(1,1))