# frozen_string_literal: true
# This test was taken from ai4r gem

# Author::    Sergio Fierens
# License::   MPL 1.1
# Project::   ai4r
# Url::       http://ai4r.rubyforge.org/
#
# You can redistribute it and/or modify it under the terms of
# the Mozilla Public License version 1.1  as published by the
# Mozilla Foundation at http://www.mozilla.org/MPL/MPL-1.1.txt

require File.dirname(__FILE__) + '/training_patterns'
require File.dirname(__FILE__) + '/patterns_with_noise'
require File.dirname(__FILE__) + '/patterns_with_base_noise'
require File.dirname(__FILE__) + '/../lib/mlp'
require 'benchmark'

times = Benchmark.measure do
  srand 1

  net = MLP::Network.new(hidden_layers: [2], output_nodes: 3, inputs: 256)

  tr_with_noise = TRIANGLE_WITH_NOISE.flatten.collect { |input| input.to_f / 5.0 }
  sq_with_noise = SQUARE_WITH_NOISE.flatten.collect { |input| input.to_f / 5.0 }
  cr_with_noise = CROSS_WITH_NOISE.flatten.collect { |input| input.to_f / 5.0 }

  tr_with_base_noise = TRIANGLE_WITH_BASE_NOISE.flatten.collect { |input| input.to_f / 5.0 }
  sq_with_base_noise = SQUARE_WITH_BASE_NOISE.flatten.collect { |input| input.to_f / 5.0 }
  cr_with_base_noise = CROSS_WITH_BASE_NOISE.flatten.collect { |input| input.to_f / 5.0 }

  puts 'Training the network, please wait.'
  101.times do |i|
    tr_input = TRIANGLE.flatten.collect { |input| input.to_f / 5.0 }
    sq_input = SQUARE.flatten.collect { |input| input.to_f / 5.0 }
    cr_input = CROSS.flatten.collect { |input| input.to_f / 5.0 }

    error1 = net.train(tr_input, [1, 0, 0])
    error2 = net.train(sq_input, [0, 1, 0])
    error3 = net.train(cr_input, [0, 0, 1])
    puts "Error after iteration #{i}:\t#{error1} - #{error2} - #{error3}" if i % 20 == 0
  end

  def result_label(result)
    if result[0] > result[1] && result[0] > result[2]
      'TRIANGLE'
    elsif result[1] > result[2]
      'SQUARE'
    else
      'CROSS'
    end
  end

  tr_input = TRIANGLE.flatten.collect { |input| input.to_f / 5.0 }
  sq_input = SQUARE.flatten.collect { |input| input.to_f / 5.0 }
  cr_input = CROSS.flatten.collect { |input| input.to_f / 5.0 }

  puts 'Training Examples'
  puts "#{net.feed_forward(tr_input).inspect} => #{result_label(net.feed_forward(tr_input))}"
  puts "#{net.feed_forward(sq_input).inspect} => #{result_label(net.feed_forward(sq_input))}"
  puts "#{net.feed_forward(cr_input).inspect} => #{result_label(net.feed_forward(cr_input))}"
  puts 'Examples with noise'
  puts "#{net.feed_forward(tr_with_noise).inspect} => #{result_label(net.feed_forward(tr_with_noise))}"
  puts "#{net.feed_forward(sq_with_noise).inspect} => #{result_label(net.feed_forward(sq_with_noise))}"
  puts "#{net.feed_forward(cr_with_noise).inspect} => #{result_label(net.feed_forward(cr_with_noise))}"
  puts 'Examples with base noise'
  puts "#{net.feed_forward(tr_with_base_noise).inspect} => #{result_label(net.feed_forward(tr_with_base_noise))}"
  puts "#{net.feed_forward(sq_with_base_noise).inspect} => #{result_label(net.feed_forward(sq_with_base_noise))}"
  puts "#{net.feed_forward(cr_with_base_noise).inspect} => #{result_label(net.feed_forward(cr_with_base_noise))}"
end

puts "Elapsed time: #{times}"