用ruby寫的wikipedia上的維特比演算法

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維特比演算法可以解決隱馬爾科夫模型的最可能狀態序列問題。

wikipedia上關於維特比演算法，提供了一個python的例子,原文地址如下

http://zh.wikipedia.org/wiki/%E7%BB%B4%E7%89%B9%E6%AF%94%E7%AE%97%E6%B3%95

鑒於最近正在學習ruby，就把這個演算法從python遷移到ruby，這兩個語言的文法很接近，所以，前移過去沒有什麼難度，希望使用代碼之前先瞭解一下維特比演算法的基礎理論。

 

 

 ##encoding:utf-8puts ‘This is Viterbi‘$states = [:Healthy, :Fever]#puts "length: #{$states.length}"$obervastions = [:normal, :cold, :dizzy]$start_probability = {Healthy: 0.6, Fever: 0.4}# puts $start_probability[$states[0]]$transition_probability = {    Healthy: {Healthy: 0.7, Fever: 0.3},    Fever: {Healthy: 0.4, Fever: 0.6},}$emission_probability = {    Healthy: {normal: 0.5, cold: 0.4, dizzy: 0.1},    Fever: {normal: 0.1, cold: 0.3, dizzy: 0.6}}def print_dptable(v)  puts ‘    ‘  for i in 0..v.length    puts "%7d" % i  end  for y in v[0].keys    puts "%5s" % y    for t in 0...v.length      puts "%.7s" % ("%f" % v[t][y])    end  endenddef viterbi(obs, states, start_p, trans_p, emit_p)  v = [{}]  path = {}# Initialize base cases (t == 0)  states.each { |y|    v[0][y] = start_p[y] * emit_p[y][obs[0]]    path[y] = [y]  }# Run Viterbi for t > 0  for t in 1...obs.length    v << {}    newpath = {}    for y in states            prob, state = states.map { |y0|      [v[t-1][y0] * trans_p[y0][y] * emit_p[y][obs[t]], y0] }.max      v[t][y] = prob      newpath[y] = path[state] + [y]    end    # Don‘t need to remember the old paths    path = newpath  end  print_dptable v  prob, state = states.map { |y| [v[obs.size - 1][y], y] }.max  return prob, path[state]enddef example  viterbi $obervastions,          $states,          $start_probability,          $transition_probability,          $emission_probabilityendputs example