nn.rb

require 'csv'
require 'ruby-fann'

x_data = []
y_data = []
# Load data from CSV file into two arrays - one for independent variables X and one for the dependent variable Y
CSV.foreach("./data/admission.csv", :headers => false) do |row|
  x_data.push( [row[0].to_f, row[1].to_f] )
  #x_data.push( [row[0].to_f, row[1].to_f] )
  y_data.push( [row[2].to_i] )
end

# Divide data into a training set and test set
test_size_percentange = 20.0 # 20.0%
test_set_size = x_data.size * (test_size_percentange/100.to_f)

test_x_data = x_data[0 .. (test_set_size-1)]
test_y_data = y_data[0 .. (test_set_size-1)]

training_x_data = x_data[test_set_size .. x_data.size]
training_y_data = y_data[test_set_size .. y_data.size]

# Setup training data model
train = RubyFann::TrainData.new( :inputs=> training_x_data, :desired_outputs=>training_y_data );

# Setup model and train using training data
model = RubyFann::Standard.new(
              num_inputs: 2,
              hidden_neurons: [6],
              num_outputs: 1 );

# 5000 max_epochs, 100 errors between reports and 0.01 desired mean-squared-error
model.train_on_data(train, 5000, 500, 0.01)

# Predict single class
prediction = model.run( [45, 85] )
# Round the output to get the prediction
puts "Algorithm predicted class: #{prediction.map{ |e| e.round }}"

predicted = []
test_x_data.each do |params|
  predicted.push( model.run(params).map{ |e| e.round } )
end

correct = predicted.collect.with_index { |e,i| (e == test_y_data[i]) ? 1 : 0 }.inject{ |sum,e| sum+e }

puts "Accuracy: #{((correct.to_f / test_set_size) * 100).round(2)}% - test set of size #{test_size_percentange}%"