Dmitry
30 lines
785 B
Ruby
30 lines
785 B
Ruby
def hamming_layer(pattern_vectors, input_vec)
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n = input_vec.length
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pattern_vectors.map do |pv|
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dot = pv.zip(input_vec).sum { |a, b| a * b }
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(dot.to_f + n) / 2.0
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end
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end
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def maxnet_step(outputs, eps)
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outputs.map.with_index do |val, i|
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inhibition = eps * outputs.each_with_index.sum { |v, j| j == i ? 0.0 : v }
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[val - inhibition, 0.0].max
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end
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end
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MAX_MAXNET_ITER = 100
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def recall_hamming(pattern_vectors, input_vec)
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eps = 0.9 / pattern_vectors.length
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outputs = hamming_layer(pattern_vectors, input_vec)
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MAX_MAXNET_ITER.times do
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next_out = maxnet_step(outputs, eps)
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return next_out.each_with_index.max_by { |v, _| v }[1] if next_out.count { |v| v > 1e-9 } <= 1
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outputs = next_out
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end
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outputs.each_with_index.max_by { |v, _| v }[1]
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end
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