svm.rb

require 'csv'
require 'libsvm'

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] )
  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 SVM parameters
parameter = Libsvm::SvmParameter.new
parameter.cache_size = 1 # in megabytes
parameter.eps = 0.001
parameter.c = 1
parameter.gamma = 0.01
parameter.kernel_type = Libsvm::KernelType::RBF

# Convert into proper feature arrays for Libsvm
test_x_data = test_x_data.map {|feature_row| Libsvm::Node.features(feature_row) }
training_x_data = training_x_data.map {|feature_row| Libsvm::Node.features(feature_row) }

# Define our problem using the training dat
problem = Libsvm::Problem.new
problem.set_examples(training_y_data, training_x_data)

# Train our model
model = Libsvm::Model.train(problem, parameter)

# Predict single class
prediction = model.predict( Libsvm::Node.features([45, 85]) )
# Round the output to get the prediction
puts "Algorithm predicted class: #{prediction.inspect}"

predicted = []
test_x_data.each do |params|
  predicted.push( model.predict(params) )
end
correct = predicted.collect.with_index { |e,i| (e == test_y_data[i]) ? 1 : 0 }.inject{ |sum,e| sum+e }

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