Perceptron Learning with 3 inputs

output.txt

1	x0: 1	x1: 0	x2: 1	desired: 1	current: 0	w0: -0.4 	w1: 0.5 	w2: -0.4 	sum: -0.8
 	 	 	 	 	 	 	 	w0: -0.1 			w2: -0.1 	
2	x0: 0	x1: 1	x2: 1	desired: 1	current: 1	w0: -0.1 	w1: 0.5 	w2: -0.1 	sum: 0.4
3	x0: 1	x1: 1	x2: 1	desired: 1	current: 1	w0: -0.1 	w1: 0.5 	w2: -0.1 	sum: 0.3
4	x0: 0	x1: 0	x2: 1	desired: 0	current: 0	w0: -0.1 	w1: 0.5 	w2: -0.1 	sum: -0.1
5	x0: 1	x1: 0	x2: 1	desired: 1	current: 0	w0: -0.1 	w1: 0.5 	w2: -0.1 	sum: -0.2
 	 	 	 	 	 	 	 	w0: 0.2 			w2: -0.4 	
6	x0: 0	x1: 1	x2: 1	desired: 1	current: 1	w0: 0.2 	w1: 0.5 	w2: -0.4 	sum: 0.1
7	x0: 1	x1: 1	x2: 1	desired: 1	current: 1	w0: 0.2 	w1: 0.5 	w2: -0.4 	sum: 0.3
8	x0: 0	x1: 0	x2: 1	desired: 0	current: 0	w0: 0.2 	w1: 0.5 	w2: -0.4 	sum: -0.4
9	x0: 1	x1: 0	x2: 1	desired: 1	current: 0	w0: 0.2 	w1: 0.5 	w2: -0.4 	sum: -0.2
 	 	 	 	 	 	 	 	w0: 0.5 			w2: -0.7 	
10	x0: 0	x1: 1	x2: 1	desired: 1	current: 0	w0: 0.5 	w1: 0.5 	w2: -0.7 	sum: -0.2
 	 	 	 	 	 	 	 			w1: 0.8 	w2: -1.0 	
11	x0: 1	x1: 1	x2: 1	desired: 1	current: 1	w0: 0.5 	w1: 0.8 	w2: -1.0 	sum: 0.3
12	x0: 0	x1: 0	x2: 1	desired: 0	current: 0	w0: 0.5 	w1: 0.8 	w2: -1.0 	sum: -1.0
13	x0: 1	x1: 0	x2: 1	desired: 1	current: 0	w0: 0.5 	w1: 0.8 	w2: -1.0 	sum: -0.5
 	 	 	 	 	 	 	 	w0: 0.8 			w2: -0.7 	
14	x0: 0	x1: 1	x2: 1	desired: 1	current: 1	w0: 0.8 	w1: 0.8 	w2: -0.7 	sum: 0.1
15	x0: 1	x1: 1	x2: 1	desired: 1	current: 1	w0: 0.8 	w1: 0.8 	w2: -0.7 	sum: 0.9
16	x0: 0	x1: 0	x2: 1	desired: 0	current: 0	w0: 0.8 	w1: 0.8 	w2: -0.7 	sum: -0.7
17	x0: 1	x1: 0	x2: 1	desired: 1	current: 1	w0: 0.8 	w1: 0.8 	w2: -0.7 	sum: 0.1
18	x0: 0	x1: 1	x2: 1	desired: 1	current: 1	w0: 0.8 	w1: 0.8 	w2: -0.7 	sum: 0.1
19	x0: 1	x1: 1	x2: 1	desired: 1	current: 1	w0: 0.8 	w1: 0.8 	w2: -0.7 	sum: 0.9

perceptron.rb

require 'colorize'

class Perceptron
  def initialize(weights, learningrate, threshold)
    @weights, @learningrate, @threshold = weights, learningrate, threshold
    @step = 1
  end

  def learn(inputs, desired_output)
      output = "#{@step}\t"
      inputs.each_with_index do |value, index|
        output += "x#{index}: #{value}\t"
      end

      output += "desired: #{desired_output}\t"
      output += "current: #{current(inputs)}\t"

      @weights.each_with_index do |value, index|
        output += "w#{index}: #{value.round 4} \t"
      end

      output += "sum: #{sum(inputs).round 4}"

      if correct?(inputs, desired_output)
        puts output.green
      else
        puts output.red
        adjust_weights(inputs, desired_output)
      end

      @step += 1
  end

  def adjust_weights(inputs, desired_output)
    output = ""
    (inputs.size + 5).times do
      output += " \t"
    end

    inputs.each_with_index do |value, index|
      if value == 1
        adjustment = value * @learningrate
        current(inputs) < desired_output ? @weights[index] += adjustment : @weights[index] -= adjustment
        output += "w#{index}: #{@weights[index].round 4} \t"
      else
        output += "\t\t"
      end
    end
    puts output
  end

  def sum(inputs)
    sum = 0
    inputs.each_with_index do |value, index|
      sum += value * @weights[index]
    end
    sum
  end

  def current(inputs)
    sum(inputs) >= @threshold ? 1 : 0
  end

  def correct?(inputs, desired_output)
    current(inputs) == desired_output
  end
end

neuron = Perceptron.new [-0.4, 0.5, -0.4], 0.3, 0

10.times do
  neuron.learn [1, 0, 1], 1
  neuron.learn [0, 1, 1], 1
  neuron.learn [1, 1, 1], 1
  neuron.learn [0, 0, 1], 0
end