# Machine Learning Meetup Notes Ruby Zhao 2

From Noisebridge

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