class Algorithmically::Neural::Perceptron
Public Class Methods
new(or_problem, inputs, iterations, learning_rate)
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# File lib/Algorithmically/Neural/perceptron.rb, line 6 def initialize(or_problem, inputs, iterations, learning_rate) execute(or_problem, inputs, iterations, learning_rate) end
Public Instance Methods
activate(weights, vector)
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# File lib/Algorithmically/Neural/perceptron.rb, line 28 def activate(weights, vector) sum = weights[weights.size-1] * 1.0 vector.each_with_index do |input, i| sum += weights[i] * input end sum end
execute(domain, num_inputs, iterations, learning_rate)
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# File lib/Algorithmically/Neural/perceptron.rb, line 70 def execute(domain, num_inputs, iterations, learning_rate) weights = initialize_weights(num_inputs) train_weights(weights, domain, num_inputs, iterations, learning_rate) test_weights(weights, domain, num_inputs) weights end
get_output(weights, vector)
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# File lib/Algorithmically/Neural/perceptron.rb, line 40 def get_output(weights, vector) activation = activate(weights, vector) transfer(activation) end
initialize_weights(problem_size)
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# File lib/Algorithmically/Neural/perceptron.rb, line 16 def initialize_weights(problem_size) minmax = Array.new(problem_size + 1) { [-1.0, 1.0] } random_vector(minmax) end
random_vector(minmax)
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# File lib/Algorithmically/Neural/perceptron.rb, line 10 def random_vector(minmax) Array.new(minmax.size) do |i| minmax[i][0] + ((minmax[i][1] - minmax[i][0]) * rand()) end end
test_weights(weights, domain, num_inputs)
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# File lib/Algorithmically/Neural/perceptron.rb, line 59 def test_weights(weights, domain, num_inputs) correct = 0 domain.each do |pattern| input_vector = Array.new(num_inputs) { |k| pattern[k].to_f } output = get_output(weights, input_vector) correct += 1 if output.round == pattern.last end puts "Finished test with a score of #{correct}/#{domain.size}" correct end
train_weights(weights, domain, num_inputs, iterations, lrate)
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# File lib/Algorithmically/Neural/perceptron.rb, line 45 def train_weights(weights, domain, num_inputs, iterations, lrate) iterations.times do |epoch| error = 0.0 domain.each do |pattern| input = Array.new(num_inputs) { |k| pattern[k].to_f } output = get_output(weights, input) expected = pattern.last.to_f error += (output - expected).abs update_weights(num_inputs, weights, input, expected, output, lrate) end puts "> epoch=#{epoch}, error=#{error}" end end
transfer(activation)
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# File lib/Algorithmically/Neural/perceptron.rb, line 36 def transfer(activation) (activation >= 0) ? 1.0 : 0.0 end
update_weights(num_inputs, weights, input, out_exp, out_act, l_rate)
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# File lib/Algorithmically/Neural/perceptron.rb, line 21 def update_weights(num_inputs, weights, input, out_exp, out_act, l_rate) num_inputs.times do |i| weights[i] += l_rate * (out_exp - out_act) * input[i] end weights[num_inputs] += l_rate * (out_exp - out_act) * 1.0 end