我开始学习隐藏马尔可夫模型,在 wiki 页面以及 github 上有很多示例,但大多数概率已经存在(70% 的降雨变化,30% 的改变状态的机会等) . 拼写检查或句子示例,似乎是在研究书籍,然后对单词的概率进行排名。
那么马尔可夫模型是否包括一种计算概率的方法,或者我们是否假设其他一些模型可以预先计算它?
抱歉,如果此问题已关闭。我认为隐藏马尔可夫模型如何选择可能的序列很简单,但概率部分对我来说有点灰色(因为它经常提供)。示例或任何信息都会很棒。
对于那些不熟悉马尔可夫模型的人,这里有一个例子(来自维基百科)http://en.wikipedia.org/wiki/Viterbi_algorithm和http://en.wikipedia.org/wiki/Hidden_Markov_model
#!/usr/bin/env python
states = ('Rainy', 'Sunny')
observations = ('walk', 'shop', 'clean')
start_probability = {'Rainy': 0.6, 'Sunny': 0.4}
transition_probability = {
'Rainy' : {'Rainy': 0.7, 'Sunny': 0.3},
'Sunny' : {'Rainy': 0.4, 'Sunny': 0.6},
}
emission_probability = {
'Rainy' : {'walk': 0.1, 'shop': 0.4, 'clean': 0.5},
'Sunny' : {'walk': 0.6, 'shop': 0.3, 'clean': 0.1},
}
#application code
# Helps visualize the steps of Viterbi.
def print_dptable(V):
print " ",
for i in range(len(V)): print "%7s" % ("%d" % i),
print
for y in V[0].keys():
print "%.5s: " % y,
for t in range(len(V)):
print "%.7s" % ("%f" % V[t][y]),
print
def viterbi(obs, states, start_p, trans_p, emit_p):
V = [{}]
path = {}
# Initialize base cases (t == 0)
for y in states:
V[0][y] = start_p[y] * emit_p[y][obs[0]]
path[y] = [y]
# Run Viterbi for t > 0
for t in range(1,len(obs)):
V.append({})
newpath = {}
for y in states:
(prob, state) = max([(V[t-1][y0] * trans_p[y0][y] * emit_p[y][obs[t]], y0) for y0 in states])
V[t][y] = prob
newpath[y] = path[state] + [y]
# Don't need to remember the old paths
path = newpath
print_dptable(V)
(prob, state) = max([(V[len(obs) - 1][y], y) for y in states])
return (prob, path[state])
#start trigger
def example():
return viterbi(observations,
states,
start_probability,
transition_probability,
emission_probability)
print example()