标签:style blog http color 使用 os
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
用ruby写的wikipedia上的维特比算法,布布扣,bubuko.com
标签:style blog http color 使用 os
原文地址:http://www.cnblogs.com/daoyou/p/3844890.html