R/R_scripts/wine_dataset.R

In gmum.r: GMUM Machine Learning Group Package

library("gmum.r")

data(wine, package="rattle")
scaled.wine <- scale(wine[-1])

# Train in an offline manner
gng <- GNG(scaled.wine, labels=wine$Type, max.nodes=20, 
           training=gng.train.offline(max.iter=10000, min.improvement=1e-1))


# Print number of nodes
numberNodes(gng)

# Print mean degree of the network
ig = convertToGraph(gng)
mean(degree(ig))

# Plot using igraph layout
plot(gng, mode = gng.plot.2d, 
     vertex.color=gng.plot.color.label, layout=igraph::layout.fruchterman.reingold)

# Print summary of trained object
print(summary(gng))

# Print prediction accuracy
labels = as.vector(wine[,c("Type")], mode="double") 
preds <- c()
for(i in 1:nrow(scaled.wine)){
  preds <- c(preds,round(node(gng,  round(predict(gng, as.vector(scaled.wine[i,], mode="double")))+1)$label))
}
print(table(preds, labels))