library(gmum.r)
library(caret) # For ConfusionMatrix

wine <- get.wine.dataset.X(scale=TRUE)

# Train in an offline manner
gng <- GNG(wine, labels=get.wine.dataset.y(), max.nodes=20, 
           max.iter=10000, min.improvement=1e-1)

## [1] "Training offline"
## [1] "Iteration 10000"
## [1] "Iteration 10000"

# Print number of nodes
numberNodes(gng)

## [1] 20

# Convert to igraph directly!
ig = convertToIGraph(gng)

# Print mean degree of the network
mean(degree(ig))

## [1] 5.2

# You can access different attributes of the nodes
V(ig)$error[1]

## [1] 7037.935

# Plot using igraph layout
plot(gng, vertex.color="label", layout=igraph::layout.fruchterman.reingold, 
     vertex.size=9)

# Print summary of trained object
summary(gng)

## [1] "Growing Neural Gas"
## [1] "20 nodes with mean error 3.476649"
## [1] "Trained 10000 iterations"
## [1] "Mean errors[s]: "
##  [1] 3.430407 3.457238 3.505368 3.497008 3.618063 3.425084 3.373586
##  [8] 3.515994 3.477688 3.521820 3.476649

# You can use graph to predict new samples 
# (in a closest neighbour way)
preds <- c()
for(i in 1:nrow(wine)){
  preds <- c(preds,round(node(gng, predict(gng, wine[i,]))$label))
}

# Print prediction statistics
confusionMatrix(table(preds, get.wine.dataset.y()))

## Confusion Matrix and Statistics
## 
##      
## preds  1  2  3
##     1 59  2  0
##     2  0 51  0
##     3  0 18 48
## 
## Overall Statistics
##                                         
##                Accuracy : 0.8876        
##                  95% CI : (0.8318, 0.93)
##     No Information Rate : 0.3989        
##     P-Value [Acc > NIR] : < 2.2e-16     
##                                         
##                   Kappa : 0.8328        
##  Mcnemar's Test P-Value : NA            
## 
## Statistics by Class:
## 
##                      Class: 1 Class: 2 Class: 3
## Sensitivity            1.0000   0.7183   1.0000
## Specificity            0.9832   1.0000   0.8615
## Pos Pred Value         0.9672   1.0000   0.7273
## Neg Pred Value         1.0000   0.8425   1.0000
## Prevalence             0.3315   0.3989   0.2697
## Detection Rate         0.3315   0.2865   0.2697
## Detection Prevalence   0.3427   0.2865   0.3708
## Balanced Accuracy      0.9916   0.8592   0.9308