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 = 100

def recall_hamming(pattern_vectors, input_vec)
  eps = 0.9 / pattern_vectors.length
  outputs = hamming_layer(pattern_vectors, input_vec)
  iter = 0
  while outputs.count { |v| v > 1e-9 } > 1 && iter < MAX_MAXNET_ITER
    outputs = maxnet_step(outputs, eps)
    iter += 1
  end
  outputs.each_with_index.max_by { |v, _| v }[1]
end