#------------------
# Data Preparation
#------------------

#Read datasets
#Download the data from http://www.saedsayad.com/datasets/BikeRental.zip
train <- read.csv("bike_rental_train.csv")
test <- read.csv("bike_rental_test.csv")

#Rows and Cols
dim(train)
dim(test)

#Columns name
colnames(train)
colnames(test)

#Show  
head(train)
head(test)


#Rows and Cols
dim(train)
dim(test)

#Columns name
colnames(train)
colnames(test)

#Show  
head(train)
head(test)


#---------------------------
# Self-Organizing Map (SOM)
#---------------------------
library(kohonen)

#Data matrix
train.matrix <- as.matrix(scale(train))

#Create the SOM Grid
som_grid <- somgrid(xdim = 20, ydim=20, topo="hexagonal")

#Train
model.SOM <- som(train.matrix, 
		grid=som_grid, 
		rlen=100, 
		alpha=c(0.05,0.01), 
		keep.data = TRUE,
		n.hood='circular')

#Plot training progress
plot(model.SOM, type="changes")

#Plot node counts
plot(model.SOM, type="count")

#Plot neighbors distance
plot(model.SOM, type="dist.neighbours")

#Heatmap
plot(model.SOM, type = "property", property = model.SOM$codes[,4], main=names(model.SOM$data)[4])