本文整理汇总了Python中hmm.HMM.forward方法的典型用法代码示例。如果您正苦于以下问题：Python HMM.forward方法的具体用法？Python HMM.forward怎么用？Python HMM.forward使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类hmm.HMM的用法示例。
在下文中一共展示了HMM.forward方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from hmm import HMM [as 别名]
# 或者: from hmm.HMM import forward [as 别名]
def main():
hmm = HMM(3, ('up', 'down', 'unchanged'),
initial_probability=[0.5, 0.2, 0.3],
transition_probability=[[0.6, 0.2, 0.2],
[0.5, 0.3, 0.2],
[0.4, 0.1, 0.5]],
observation_probability=[[0.7, 0.1, 0.2],
[0.1, 0.6, 0.3],
[0.3, 0.3, 0.4]])
observation = ("up", "up", "unchanged", "down", "unchanged", "down", "up")
ob_length = len(observation)
p, _ = hmm.forward(observation, ob_length)
path = hmm.decode(observation, ob_length)
print("P{} = {:.13f}".format(tuple(observation), p))
print("Observation sequence =", tuple(i+1 for i in path))示例2: read_hmm
# 需要导入模块: from hmm import HMM [as 别名]
# 或者: from hmm.HMM import forward [as 别名]
import numpy as np
from hmm import HMM, read_hmm, read_sequence
hmmfile = "test.hmm"
seqfile = "test.seq"
M, N, pi, A, B = read_hmm(hmmfile)
T, obs = read_sequence(seqfile)
hmm_object = HMM(pi, A, B)
#test forward algorithm
prob, alpha = hmm_object.forward(obs)
print "forward probability is %f" % np.log(prob)
prob, alpha, scale = hmm_object.forward_with_scale(obs)
print "forward probability with scale is %f" % prob
# test backward algorithm
prob, beta = hmm_object.backward(obs)
print "backward probability is %f" % prob
beta = hmm_object.backward_with_scale(obs, scale)
# test baum-welch algorithm
logprobinit, logprobfinal = hmm_object.baum_welch(obs)
print "------------------------------------------------"
print "estimated parameters are: "
print "pi is:"
print hmm_object.pi
print "A is:"