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HMM
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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}, | |
} | |
from hmmlearn import hmm | |
import numpy as np | |
model = MultinomialHMM(n_components=2) | |
model.startprob_ = np.array([0.6, 0.4]) | |
model.transmat_ = np.array([[0.7, 0.3], | |
[0.4, 0.6]]) | |
model.emissionprob_ = np.array([[0.1, 0.4, 0.5], | |
[0.6, 0.3, 0.1]]) |
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