机器学习学习笔记--隐式马尔可夫算法

隐式马尔可夫算法,简称HMM，马尔可夫认为将来发生的事情与昨天与前一段时间发生的事情有关。它用上一次发生的事情来预测未来。那么这个算法常常用于处理时序数据，比如实时的翻译，语音识别与自然语言处理。

print(__doc__)

import numpy as np

import matplotlib.pyplot as plt

from hmmlearn import hmm

startprob = np.array([0.6,0.3,0.1,0.0])

transmat = np.array([[0.7, 0.2, 0.0, 0.1],

[0.3, 0.5, 0.2, 0.0],

[0.0, 0.3, 0.5, 0.2],

[0.2, 0.0, 0.2, 0.6]])

means = np.array([[0.0,  0.0],

[0.0, 11.0],

[9.0, 10.0],

[11.0, -1.0]])

covars = .5*np.tile(np.identity(2),(4,1,1))

model = hmm.GaussianHMM(n_components=4,covariance_type="full")

model.startprob_ = startprob

model.transmat_ = transmat

model.means_ = means

model.covars_ = covars

x,z=model.sample(500)

plt.plot(x[:,0],x[:,1],".-",label="observations",ms=6,mfc="orange",alpha=0.7)

for i,m in enumerate(means):

plt.text(m[0], m[1], 'Component %i' % (i + 1),

size=17, horizontalalignment='center',

bbox=dict(alpha=.7, facecolor='w'))

plt.legend(loc='best')

plt.show()