Python Markov_model.generate_data 예제들

예제 #1

파일: Test_gammas.py 프로젝트: bellettif/Grammar_TS

import matplotlib.cm as cm
   
from surrogate.Markov_model import Markov_model
from HMM_algos import Proba_computer

initial = [0.1, 0.1, 0.1]
A = [[0.1, 0.5, 0.1], [0.1, 0.1, 0.5], [0.5, 0.1, 0.1]]
alphabet = ['a', 'b', 'c']
B = [[0.8, 0.1, 0.1], [0.1, 0.8, 0.1], [0.1, 0.1, 0.8]]

my_markov_model = Markov_model(initial,
                               A,
                               B,
                               alphabet)

data = my_markov_model.generate_data(10)
    
my_proba_computer = Proba_computer(initial,
                                 A,
                                 B,
                                 alphabet)

print [x['state'] for x in data]
print [x['obs'] for x in data]

forwards = my_proba_computer.compute_forward_probas([x['obs'] for x in data])

plt.subplot(311)
plt.imshow(forwards, cmap = cm.gray)

backwards = my_proba_computer.compute_backward_probas([x['obs'] for x in data])

예제 #2

파일: Test_Viterbi.py 프로젝트: Jay-Zeng/MultimodePIF

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm

from surrogate.Markov_model import Markov_model
from HMM_algos import Proba_computer

initial = [0.1, 0.1, 0.1]
A = [[0.3, 0.5, 0.3], [0.3, 0.3, 0.5], [0.5, 0.3, 0.3]]
alphabet = ['o', '*', 'p', 'h']
B = [[1.0, 0.5, 0.5, 0.5], [0.5, 1.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.5]]

my_markov_model = Markov_model(initial, A, B, alphabet)

data = my_markov_model.generate_data(20)

my_proba_computer = Proba_computer(initial, A, B, alphabet)

states = np.asarray([x['state'] for x in data])
observations = [x['obs'] for x in data]

gammas = my_proba_computer.compute_probas(observations)

epsilons = my_proba_computer.compute_epsilons(observations)

print epsilons

gammas_eps = np.zeros((epsilons.shape[0], epsilons.shape[2]))

for t in xrange(gammas_eps.shape[1]):

예제 #3

파일: Test_EM.py 프로젝트: Jay-Zeng/MultimodePIF

import matplotlib.cm as cm
   
from surrogate.Markov_model import Markov_model
from HMM_algos import Proba_computer

initial = [0.1, 0.1, 0.1]
A = [[0.3, 0.5, 0.3], [0.3, 0.3, 0.5], [0.5, 0.3, 0.3]]
alphabet = ['o', '*', 'p', 'h']
B = [[1.0, 0.5, 0.5, 0.5], [0.5, 1.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.5]]

my_markov_model = Markov_model(initial,
                               A,
                               B,
                               alphabet)

datas = [my_markov_model.generate_data(100) for i in xrange(100)]
      
proba_cpter = Proba_computer(initial,
                             A,
                             B,
                             alphabet)
observations = [[x['obs'] for x in y] for y in datas]

initial = proba_cpter.initial
A = proba_cpter.A
B = proba_cpter.B

new_initial, new_A, new_B = proba_cpter.estimate_new_model_multi(observations)

print 'Initial'
print 'Model:'

예제 #4

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm

from surrogate.Markov_model import Markov_model
from HMM_algos import Proba_computer

initial = [0.1, 0.1, 0.1]
A = [[0.3, 0.5, 0.3], [0.3, 0.3, 0.5], [0.5, 0.3, 0.3]]
alphabet = ['o', '*', 'p', 'h']
B = [[1.0, 0.5, 0.5, 0.5], [0.5, 1.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.5]]

my_markov_model = Markov_model(initial, A, B, alphabet)

datas = [my_markov_model.generate_data(100) for i in xrange(100)]

proba_cpter = Proba_computer(initial, A, B, alphabet)
observations = [[x['obs'] for x in y] for y in datas]

initial = proba_cpter.initial
A = proba_cpter.A
B = proba_cpter.B

new_initial, new_A, new_B = proba_cpter.estimate_new_model_multi(observations)

print 'Initial'
print 'Model:'
print initial
print 'Estimated'
print new_initial