Machine Learning Meetup Notes Ruby Zhao 2

class ML

 @@weights_for_binary = [0, 0, 0]
 @@weights_for_linear = [0.0]

 def weights_for_binary
   @@weights_for_binary
 end

 def weights_for_linear
   @@weights_for_linear
 end

 def binary_training_set
   [[[1, 0, 0], 1], [[1, 0, 1], 1], [[1, 1, 0], 1], [[1, 1, 1], 0]]
 end

 def linear_training_set
   [[[1.0], 2.0], [[1.5], 3.0], [[2.0], 4.0]]
 end

 def threshold
   0.5
 end

 def dot_product(input, weights)
   [input, weights].transpose.map{ |e| e = e[0] * e[1] }.inject{ |s, n| s += n }
 end

 def binary_train_all
   train_all(binary_training_set, weights_for_binary, lambda{ |a,b| dot_product(a, b) > threshold ? 1 : 0 })
 end

 def linear_train_all
   train_all(linear_training_set, weights_for_linear, lambda{ |a,b| dot_product(a, b) })
 end

 def sigmoid_train_all

 end

 def stop_condition(training_set, counter, repetition, weights)
   counter == training_set.size || ( repetition > 1500 && rms(weights) < 0.01)
 end

 def rms(w)
   Math.sqrt(w.inject(0){ |s, n| s += n * n } / w.size)
 end

 def train_all(training_set, weights, activation_function)
   counter = 0
   repetition = 0
   learning_rate = 0.9
   while !stop_condition(training_set, counter, repetition, weights)
     counter = 0
     repetition += 1
     training_set.each do |set|
       counter += train(set[0], set[1], learning_rate, weights, activation_function)
     end
     learning_rate *= 0.99
     p learning_rate
     puts "intermediary weights: " + weights.join(":")
   end
   puts "final weights: " + weights.join(":")
 end

 def train(input, expected_output, learning_rate, weights, activation_function)
   actual_output = activation_function.call(input, weights)
   train_step = (expected_output - actual_output) * learning_rate
   return 1 if train_step == 0

   input.each_with_index do |inp, index|
     weights[index] += inp * train_step
   end

   0
 end

end