用ruby写的wikipedia上的维特比算法

维特比算法可以解决隐马尔科夫模型的最可能状态序列问题。

wikipedia上关于维特比算法，提供了一个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

鉴于最近正在学习ruby，就把这个算法从python迁移到ruby，这两个语言的语法很接近，所以，前移过去没有什么难度，希望使用代码之前先了解一下维特比算法的基础理论。

 

 

 #
#encoding:utf-8
puts '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
  end
end


def 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]
end

def example
  viterbi $obervastions,
          $states,
          $start_probability,
          $transition_probability,
          $emission_probability
end

puts example