def hamming_layer(pattern_vectors, input_vec)
  n = input_vec.length
  pattern_vectors.map do |pv|
    dot = pv.zip(input_vec).sum { |a, b| a * b }
    (dot.to_f + n) / 2.0
  end
end

def maxnet_step(outputs, eps)
  outputs.map.with_index do |val, i|
    inhibition = eps * outputs.each_with_index.sum { |v, j| j == i ? 0.0 : v }
    [val - inhibition, 0.0].max
  end
end

MAX_MAXNET_ITER = 500

def recall_hamming(pattern_vectors, input_vec)
  eps = 0.9 / pattern_vectors.length

  outputs = hamming_layer(pattern_vectors, input_vec)
  MAX_MAXNET_ITER.times do
    next_out = maxnet_step(outputs, eps)
    return next_out.each_with_index.max_by { |v, _| v }[1] if next_out.count { |v| v > 0 } <= 1
    outputs = next_out
  end

  outputs.each_with_index.max_by { |v, _| v }[1]
end