Basic implementation of a perceptron in Ruby

perceptron.rb

class Perceptron
  def initialize(inputs, bias = 0.0)
    @weights = Array.new(inputs.keys.first.size) { rand }
    @inputs = inputs
    @bias = bias
  end

  def run(inputs)
    z = inputs.zip(@weights).map { |i, w| i * w }.reduce(:+) + @bias
    1.0 / (1.0 + Math.exp(-z))
  end

  def learn(learning_rate)
    @inputs.each do |i, output|
      y = run(i)
      error = y - output
      @weights = @weights.zip(i).map { |w, i| w - learning_rate * error * i }
      @bias -= learning_rate * error
    end
  end

  def train(iterations = 1000, learning_rate = 0.1)
    iterations.times do
      learn(learning_rate)
    end
  end

  def to_s
    @inputs.map { |i, j|
      "#{i} => #{run(i).round}"
    }.join("\n")
  end
end

# Create perceptrons for AND, OR and NOT logic functions

and_perceptron = Perceptron.new(
  [1, 1] => 1,
  [1, 0] => 0,
  [0, 1] => 0,
  [0, 0] => 0
)
and_perceptron.train
puts and_perceptron
puts "-----"

or_perceptron = Perceptron.new(
  [1, 1] => 1,
  [1, 0] => 1,
  [0, 1] => 1,
  [0, 0] => 0
)
or_perceptron.train
puts or_perceptron
puts "-----"

not_perceptron = Perceptron.new(
  [1] => 0,
  [0] => 1
)
not_perceptron.train
puts not_perceptron