A simple R neuralnet for mlbnech::circle

simple.neuralnet.mlbench.circle.R

# Always start with a clean environment to avoid subtle bugs
rm(list = ls())

# To get repeatable results with random numbers (easier to debug multiple runs)
set.seed(123)

# Create a circle data set, change labels to -1/1, convert to data.frame
library(mlbench)
circle <- mlbench.circle(500, 2)
labels <- sign(as.numeric(circle$classes) - 1.5)
circle <- data.frame(cbind(circle$x[, 1:2], labels))

# Split in training and test sets
train_index <- sample(nrow(circle), nrow(circle) * 0.3)
training_set <- circle[train_index,]
test_set <- circle[-train_index,]

# Split test set into features and labels
class_index <- dim(training_set)[2]
test_set_features <- test_set[,-class_index]
test_set_labels <- test_set[,class_index]

# Train a classifier, shows results on the test set
library(neuralnet)
classifier <- neuralnet(labels ~ V1 + V2, training_set, hidden = c(5, 5), rep = 3)

best_rep <- which.min(classifier$result.matrix["error", ])
predicted <- compute(classifier, test_set_features, rep = best_rep)

predicted_label <- sign(predicted$net.result)

cm <- table(predicted_label, test_set_labels)
print(cm)

accuracy <- (sum(diag(cm))) / sum(cm)
print(accuracy)